Comparison of the fitness value of the initial population using AR alone and AR with C\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tM\n\t\t\t\t\t\t.
Many essential functions for life are performed by proteins and the study of their structures yields the ability to elucidate these functions in terms of a molecular view. (Creighton, 1992; Devlin, 1997) The interest in discovering a methodology for protein structure prediction (PSP) is of great interesti on many fields including drug design and carriers, disease mechanisms, and the food industry. In this context, several in vitro methods have been applied, as X-ray crystallography and nuclear magnetic resonance. Despite their relative success, both methods have their limitations. Conversely, the knowledge of the primary sequence of the amino acids of a protein can be achieved by a relatively simpler experimental measurement. From this information, one can in principle predict the three dimensional arrangement of its atoms, which has motivated the investigation of ab initio methods combining such initial knowledge with effective models (force fields) in order to predict the spatial structure of a protein (Bonneau & Baker, 2001; Hardin et al., 2002).
\n\t\t\tIn fact, several computational methods for PSP are semi ab initio methodologies in the sense that they also use prior knowledge from both the sequence homology and the statistics found on protein databases [see e.g. (Miyazawa & Jernigan, 1985; Poole & Ranganathan, 2006)]. However, the use of these additional information restrict the search of protein structures that could be correctly predicted from the vast universe of proteins.
\n\t\t\tThis chapter focuses on the development of a pure ab initio approach for PSP, not using prior information. In this context, evolutionary algorithms (EAs) have been investigated as a search method due to their flexibility to solve complex optimization problems. Our researches on EAs applied to PSP are twofold: 1) the investigation of more appropriate modeling of the physical and chemical interactions of a protein for the purpose of an optimization algorithm; 2) the development of computationally efficient EAs for PSP. Two important modeling issues have been poorly investigated in the literature related to the optimization techniques for PSP: a) the combined effects of the effective Hamiltonians based on force fields and the solvation free energy contribution (Section 3), and b) the use of multiple criteria to evaluate the predicted molecules since several physical interactions drive the folding process (Section 4). We show how both modeling issues can improve protein prediction quality.
\n\t\t\tWe also present recently developed computational techniques to increase the efficiency of the algorithms for PSP. Algorithms using simple lattice models can work in a relatively small search space, however, they often generate a large number of unfeasible structures (with amino acid collisions). We present in this chapter lattice models that eliminate unfeasible solutions (Section 2). To increase the capacity of an EA to overcome local minimal of the potential energy, we propose an adaptation of the Self-Organizing Random Immigrants Genetic Algorithms for the PSP problem (Section 6). To work with large proteins, we explore computational strategies to enhance the efficiency of the calculi of the more complex energy functions (Section 5). Another strategy is the use of some heuristic about the proteins or its family to create the initial population of the EA, instead of the use of random solutions (Section 7).
\n\t\t\tFinally, this chapter shows how to combine the results from the set of above described researches in order to construct an ab initio PSP approach able to work with large proteins independently from prior information of similar structures (Section 8).
\n\t\tLattice models are simplified models of proteins which represent a conformation as a set of points in a grid. The simplest topologies of lattices are the squared lattice, for two dimensions, or the cubic lattice, for three dimensions. These models were originally employed in order to reduce the computational calculi (Dill, 1985; Unger & Moult, 1993). In this research field, they have been used to quickly evaluate the effect of parameter and operator of EAs, and, thus, motivated the development of new techniques in advanced models.
\n\t\t\tOne of the most studied lattice models for protein folding is the hydrophobic-hydrophilic model (so-called HP model), where each amino-acid is classified in two classes: hydrophobic or non-polar (H), and hydrophilic or polar (P), according to their interaction with water molecules. (Chan & Dill, 1993a, 1993b) Moreover, each pair of amino acids in a conformation can be classified as connected or neighbors. Two amino acids from positions i and j in a sequence are connected if, and only if, j = i + 1 or j = i – 1. Notice that the number of amino acids is fixed. On the other hand, two amino acids in positions i and j are neighbors if the Euclidean distance between i and j is equal to 1. There are common features and assumptions behind such model with the classical Bragg-Willians and Flory-Huggins ones (Jönsson, B. et al, 1998).
\n\t\t\tThe native state of a protein is a low-energy conformation. Thus, each pair of neighbors of H type contributes with a contact free energy -1. Then, the number of HH contacts is maximized in the native state. Despite the apparent simplicity of the model, finding the globally optimal conformation under HP model is an NP-Complete problem (Berger & Leighton, 1997), justifying the use of heuristic-based techniques for solving this problem. In the following, we present EAs developed for the PSP problem.
\n\t\t\tIn the HP model, a protein conformation must be represented in a particular lattice; thus, each individual of the EA represents a conformation. In general, the fold is expressed as a sequence of movements into lattice. The position of the first amino acid is fixed and the other positions are specified by n – 1 movements for a sequence of n amino acids.
\n\t\t\tTwo major schemes for representing the movements can be found in the literature:
\n\t\t\tThe absolute\n\t\t\t\t\t\trepresentation (AR) (Unger & Moult, 1993), where each new position is defined from the previous position. However, this representation allows movements of return, i.e., movements that annul the previous movement generating amino acid collision;
The relative representation (RR) (Patton et al., 1995), where a movement is generated depending on the last movement in a way to avoid amino acid collision.
Since both representation do not avoid unfeasible solutions (with collisions), a penalty function assigns lower fitness values to these solutions during the evaluation stage. However, researches on EA representations for complex problems (Rothlauf, 2006) show that populations with feasible solutions have very slow convergence, since the unfeasible solutions dominated the evolutionary process with the increasing of the problem size. This phenomenon has been also verified in the PSP problem (Krasnogor et al., 1999; Cotta, 2003; Gabriel & Delbem, 2009).
\n\t\t\tIn order to solve the problem of unfeasible solutions, Gabriel & Delbem (2009) propose a new approach using AR and a conformation matrix (CM). This representation uses a matrix to decode AR of conformations. Each position of amino acid from a RR is indexed in a position of the matrix C\n\t\t\t\t\n\t\t\t\t\tM\n\t\t\t\t representing the lattice. If there is already an amino acid in position (x,y,z) of C\n\t\t\t\t\n\t\t\t\t\tM\n\t\t\t\t when decoding amino acid x, a collision is identified, x is replaced for an empty position of C\n\t\t\t\t\n\t\t\t\t\tM\n\t\t\t\t and the corresponding movement in AR is properly updated.
\n\t\t\tTo guarantee the efficiency of the decoding process, an array stores permutations of a set of relative movements (that are encoded using integers numbers) from a grid position. To repair the AR due to a collision, movements from the array are probed in C\n\t\t\t\t\n\t\t\t\t\tM\n\t\t\t\t until finding a movement that avoids collision. If all possibilities in the array have been explored, the collisions are not repairable using local movements. Then, the individual (conformation) is eliminated, i.e., the building process is is canceled out and a new individual starts to be generated. For each collision a new array of permutations is constructed.
\n\t\t\tTo analyzes the effect of C\n\t\t\t\t\n\t\t\t\t\tM\n\t\t\t\t on the reduction of unfeasible solutions, we compare the quality of initial populations generated using C\n\t\t\t\t\n\t\t\t\t\tM\n\t\t\t\t with random initial populations produced by classical EA based on AR. The usual fitness function for
\n\t\t\tSequences | \n\t\t\t\t\t\tAR | \n\t\t\t\t\t\tAR + C\n\t\t\t\t\t\t\t\tM\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t||||
\n\t\t\t\t\t\t | Best | \n\t\t\t\t\t\tAverage | \n\t\t\t\t\t\tWorst | \n\t\t\t\t\t\tBest | \n\t\t\t\t\t\tAverage | \n\t\t\t\t\t\tWorst | \n\t\t\t\t\t
27 amino acids | \n\t\t\t\t\t\t0.50 | \n\t\t\t\t\t\t-7.44 | \n\t\t\t\t\t\t-3.40 | \n\t\t\t\t\t\t3.20 | \n\t\t\t\t\t\t0.57 | \n\t\t\t\t\t\t0.00 | \n\t\t\t\t\t
64 amino acids | \n\t\t\t\t\t\t-7.80 | \n\t\t\t\t\t\t-23.98 | \n\t\t\t\t\t\t-62.80 | \n\t\t\t\t\t\t6.90 | \n\t\t\t\t\t\t2.00 | \n\t\t\t\t\t\t0.00 | \n\t\t\t\t\t
Comparison of the fitness value of the initial population using AR alone and AR with C\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tM\n\t\t\t\t\t\t.
HP models adds -1 for each collision in the conformation and +1 for each HH interaction (Gabriel & Delbem, 2009). Table 1 compares the average value the usual fitness of initial populations generated for 20 sequences (Unger & Moult, 1993; Patton et al., 1995): 10 with 27 amino acids and 10 with 64 amino acids. The percentage of feasible conformations in the initial population generated using AR. On the other hand, all conformations are feasible when AR and C\n\t\t\t\t\n\t\t\t\t\tM\n\t\t\t\t are employed. In some populations, there are conformations very near to the global optimum.
\n\t\tAn aqueous solution is the environment where almost all important (bio)chemical reactions happen. It is central to several chemical, petrochemical, pharmaceutical and food engineering processes too (Eisenberg & Kauzmann, 1969; Ben-Naim, 1994; Devlin, 1997; Loehe & Donohue, 1997; Degrève & da Silva, 1999; Guillot, 2002; Dill et al, 2005; Levy & Onuchic, 2006). Proteins are just a type of solute found in this medium whose behavior is strongly affected by liquid water due to its intrinsic properties as a hydrogen-bonding solvent (Skaf & da Silva, 1994; Dill et al, 2005; Levy & Onuchic, 2006). In physiological conditions, the picture is far more complicated, principally because the presence of electrolytes (Na+,K+,Cl-, etc.) (Jönsson, Lund & da Silva, 2007).
\n\t\t\tWater is well known to be a decisive factor on the protein conformational stability and determinative in processes such as the “protein folding" via the complex interplay especially of the solvent-protein and solvent-solvent interactions (Creighton, 1992; Eisenhaber & Argos, 1996; Devlin, 1997; Dill et al, 2005; Levy & Onuchic, 2006; Chaplin, 2008). In a classic view, these intermolecular interactions are given by electronic repulsion (whose origin is the Pauli exclusion principle), multipole-multipole interactions (often used as electrostatic interactions), induction (multipole-induced multipole interaction) and instantaneous induced multipole-induced multipole interactions (the London dispersion forces) (Israelachvili, 1991; Evans & Wennerström, 1994). The induction and dispersion interactions are known as the van der Walls interactions. In addition to these enthalpic contributions, due to the thermal effect, entropy has an important participation in these processes. Combined with the high affinity that water has for water, the final result is the so-called “hydrophobic effect”, (Creighton, 1992; Garde et al., 1996; Jönsson et al, 1998; Levy & Onuchic, 2006; Chaplin, 2008) which is a key driven force for the protein folding.
\n\t\t\tIn a molecular modeling approach, the initial challenge is to identify the main characteristics of the real physical system (in this case, a protein in solution), and to define how the intermolecular interactions and effects above mentioned may be replaced by suitable averages. This results in a mathematical expression that describes the potential energy of the system as a function of the separation distance between the species and is called an effective Hamiltonian model (EHM), (Friedman, 1977, 1981; da Silva, 1999) or, more commonly, the force field (FF) definition (Levitt, 1995; Ponder & Case, 2003; Mackerell Jr, 2004; Oostenbrink et al, 2004; Guvench & Mackerell Jr, 2008; Stone, 2008). We refer to this model as “effective”, because it is not identical to the reality, but it is supposed to behave as the reality. Besides an atomist description, a vast diversity of coarse-grained models has also been reported (Tozzini, 2005; Monticelli et al, 2008).
\n\t\t\tDepending on the applications and questions to be answered, water may be modeled by different ways, from explicit (or molecular) to continuum (or implicit) solvent models (Friedman, 1981; Jorgensen et al, 1983; da Silva, Jönsson & Penfold, 2001; Guillot, 2002; Chen et al., 2008) (see Fig. 1). The main difference is the variables that explicitly enter the EHM and the computational costs. In the explicit models, the input variables are the coordinates and momenta of the solvent (i.e., a molecular model should be used), while the solvent only enters by an averaging over of its coordinates and momenta in the implicit models (i.e., water is replaced by its bulk static dielectric constant, ε\n\t\t\t\t\n\t\t\t\t\ts\n\t\t\t\t). The latter model has a substantial reduction on cpu time with the price of loosing details that might be relevant for some cases. In this model level, the solvent in the immediate neighborhood of any solute is assumed to behave as the bulk solvent. It is an idealization widely used in Molecular Biology (Garcia-Moreno, 1985; Bashford & Gerwert, 1992; da Silva et al., 2001; Chen et al., 2008) that captures part of the water properties, neglecting its molecular structure that is responsible principally for local, short-range, interactions and might be crucial in some cases (e.g. to study the hydration phenomenon).
\n\t\t\tA representation of a solute in two different water models: (a) A solute in a structure-less continuum model (implicit solvent model). Water only enters in the calculations by means of its bulk dielectric permittivity ε; (b) A solute molecule is surrounded by an explicit solvent model.
A large number of molecular models for water with different number of interaction sites based either on empirical or quantum mechanical methods have been developed during the last forty years, including rigid [e.g., SPC/E (Berendsen et al., 1987), TIP4P (Jorgensen et al, 1983), flexible [e.g., SPCFX (Teleman, O. et al., (1987)], dissociable [e.g., DCF (Halley et al., 1993)] and polarizable [e.g., SWM4-DP (Lamoureux et al., 2003), TIP4P/FQ ( Rick, Stuart & Berne, 1994) models. Most water models assume site-site pair interactions in order to facilitate their application in numerical simulations. A common characteristic of them is to define a water molecule by a set of point sites. Fractions of electric charges are attributed to these sites and used to calculate the electrostatic pair potential energy (E\n\t\t\t\t\n\t\t\t\t\tele\n\t\t\t\t) according to Coulomb\'s law. The van der Waals forces are normally modeled by a particular case of Mie´s interaction potential known as Lennard-Jones (E\n\t\t\t\t\n\t\t\t\t\tLJ\n\t\t\t\t) pair interaction potential (Verlet, 1967; Orea et al., 2008). The combination of these two terms (E\n\t\t\t\t\n\t\t\t\t\tele\n\t\t\t\t+E\n\t\t\t\t\n\t\t\t\t\tLJ\n\t\t\t\t ) is applied to the system constituents and gives the total interaction energy. Often a given model shows good behavior for a few properties and fails to describe others. Several reviews and comparisons between such models are available on the literature (van der Spoel et al., 1998; Wallqvist e Mountain, 1999; Guillot, 2002).
\n\t\t\tA compromise between the model details and the simulation costs is always a challenge task. To describe the hydrophobic effect, retaining the main characteristics of a real physical system (solute-solvent) and simulate it using reasonable cpu time, we need to find an adequate way to replace the exact calculation of the intermolecular interactions and effects by suitable averages, where the use of implicit solvent models is surely appealing. There are two basic models of implicit solvent normally used for this purpose: continuum electrostatic models and approaches based on solvent accessible surface (SAS) Variations of these models and combinations of them have also been proposed. (Garcia-Moreno, 1985; Bashford & Gerwert, 1992; Street & Mayo, 1998; da Silva et al., 2001; Snow et al, 2005, Chen et al., 2008).
\n\t\t\tBiomolecular systems are surrounded by solvent particles (water molecules, cations, and anions). In this environment, ions electrically interact with the molecule, which can have an electric charge due to ionization mechanisms. (Jönsson et al., 2007) pH affects can be taken into account and give peculiar interactions. (da Silva et al, 2006; da Silva & Jönsson, 2009) The Poisson-Boltzmann equation (PBE) describes the electrostatic environment of a solute in solvent containing ions on a mean-field level of treatment. (Warwicker & Watson, 1982; da Silva, Jönsson & Penfold, 2001; Neves-Petersen & Petersen, 2003; Grochowski & Trylska, 2007; de Carvalho, Fenley e da Silva, 2008). It can be written as:
\n\t\t\twhere the right term of the equation is the contribution of the fixed charges in the biomolecule, the other terms are the contribution of mobile positive and negative ions (treated here as point charges whose distribution around the central charged molecule obeys a Boltzmann distribution), ε(r) is a position-dependent dielectric, Φ(r) is the electrostatic potential, ρ(r) is the charge density of the solute, q is the charge of an ion, n+ and n- are densities of ion i at an infinite distance from the solute (bulk), K\n\t\t\t\t\n\t\t\t\t\tB\n\t\t\t\t is the Boltzmann constant, v(r) is 0 for accessible regions and infinite for unaccessible regions, and T is the temperature.
\n\t\t\tAnalytical solutions of the PBE are possible only on ideal cases. One of these special situations is the infinite charged planar surface case. This example is given in details in refs. (Russel et al., 1989; Lyklema, 1991) and is usually described as the Gouy-Chapman case. (Usui, 1984) For biomolecular applications, numerical methods are necessary, and a number of different schemes have been proposed. (Lu et al, 2008) Nevertheless, despite the chosen numerical method, the PBE requires large memory resources and cpu time to be calculated without further approximations (e.g. to linearized it). (da Silva et al., 2001; de Carvalho et al., 2008) Although there are a large number of computational packages available (e.g. Melc, (Juffer, 1992) Delphi, (Sharp et al., 1998) APBS, (Holst et al., 2000) and MEAD (Bashford & Gerwert, 1992), the computational costs can still be a limitation and prohibitive when dealing with large molecules as proteins in applications that would require the systematic repetition of the same calculation for different macromolecular conformations. Critical investigations of the PBE are available elsewhere. (da Silva et al., 2001; de Carvalho et al., 2008)
\n\t\t\tThe methods based on SAS arise experimentally by the linear relation between free energy and the surface area of a solute molecule (Hermann, 1972; Valvani et al., 1976; Amidon et al., 1975; Camilleri et al., 1988; Doucette e Andren, 1987; Dunn III et al., 1987; Snow et al, 2005). In this way, this method can directly provide the free energy of solvation. The continuum representation of solvent significantly reduces the cpu time. The SAS is the locus of points traced out by the inward facing part of the probe sphere, representing the solvent molecule that rotates over the van der Waals surface of the protein (see Fig. 2).
\n\t\t\tIt is important to note that SAS solvent models also have limitations mainly related to:
\n\t\t\tViscosity: SAS and other implicit solvent models lack the viscosity, which affects the motion of solutes;
Hydrogen-bonds with water: the directionality of the hydrogen bonds is missing in an implicit solvent. Moreover, bulk and buried water molecules are assumed to have the same behavior;
Choice of model solvent: different atomic solvation parameters should be applied for modeling of the protein folding problem. These parameters should be derived from experimental coefficients involving organic molecules.
Nowadays, there are computational tools that calculate SAS and the free energy of solvation such as: GROMACS (Lindahl et al., 2001), POPS (Cavallo, 2003), Naccess (Hubbard e Thornton, 1993), STRIDE (Frishman e Argos, 1995), among others. In the following, we present preliminary results using SAS software based on the package STRIDE.
\n\t\t\tA representation of van der Waals surface area and solvent accessible area surfaces (Richards, 1977).
The EA implementation for PSP, called ProtPred (Lima, 2007), was used to perform experiments to evaluate the effects of SAS and internal energies on the PSP. The potential energy functions are based on the CHARMm force fields of and solved by the Tinker package for molecular modeling (Ponder, 2001). The ProtPred uses the full-atom model (Cui et al, 1998; Cutello et al, 2005), representing the backbone and side-chain torsion angles (internal coordinates). The individuals (conformations) of the initial population are randomly generated with the angles in the constraint regions of each amino acid according to the Ramachandran map. The ProtPred code was run with 200 iterations and populations with 200 individuals were generated, using three recombination operators: i) two-point crossover, ii) uniform crossover, and iii) BLX-α (Deb et al., 2002). The algorithm uses three mutation operators: one changes all torsion angles of an amino acid by reselecting the values from the constraint regions; the others perform uniform mutation, but they differ from the use distinct step sizes.
\n\t\t\tThe ProtPred minimizes a weighted objective function composed by energy functions. Fig. 3(a) shows the native protein structure of an acetylcholine receptor, obtained from PDB database (PDB id 1A11). Fig. 3(b) presents the structure obtained by ProtPred with the following weights for the objective function: 1.0 for wan der Waals, 0.5 for electrostatic, and zero for SAS. Fig. 3(c) shows the protein structure obtained changing the weights for SAS from zero to 0.001, indicating that SAS is relevant for PSP.
\n\t\t\tConfigurations of 1A11 protein.
Even though this protein is relatively small (25 aminoacids), the results encourage further works with this approach for modeling of the potential energy as a new criterion for evaluation in EAs to the PSP, considering not only the SAS, but also the internal energy.
\n\t\tThe PSP problem can be seen as a multi-objective problem, since it deals with several criteria involving energy functions estimating different aspects of intermolecular interactions (van der Waals, electrostatic, hydrogen-bond) and solvation free energies. A multi-criteria problem in general possesses the following characteristics:
\n\t\t\tThe determination of weights for an weighted function combining all the criteria is difficult;
The criteria conflicts, i.e. the improvement of one objective in general involves the damage for other objective.
A protein structure with very low electrostatic energy may correspond to relatively high van der Waals energy. This kind of structure is in general inconsistent with the conformation of a real protein. Problems with conflicting objectives generally do not have a unique solution, but a solution set, called Pareto-optimal (Handl et al., 2006). This decade has produced relevant Multi-objective EAs, as the NSGA-II, which has been used to solve complex multi-objective problems. Unfortunately, even the NSGA-II losses performance for more than 3 objectives. For a large number of objectives, alternative solutions ought to be developed as the use of heuristics (Deb et al., 2006).
\n\t\t\tA first multiple criteria approach to PSP, called mo-ProtPred, was proposed in (Lima, 2006). This approach is based on NSGA-II (Deb, 2001), one of the main Multi-objective EA. The mo-ProtPred can work with three objectives without requiring weights for them.
\n\t\t\tThe mo-ProtPred was applied to predict a transducin alpha-1 subunit (PDB id 1AQG). For this test, 500 generations with a population size equals to 400 were used. The objectives were functions corresponding to van de Waals, electrostatic, and hydrogen bond interaction energies. Fig. 4 illustrates polypeptides structures produced by the mo-ProtPred. Several of these structures are very similar to the native protein structure, displayed in Fig. 4(a). Table 2 shows the RMS values for each structure obtained.
\n\t\t\tAdequate structures for 1AQG were also achieved using a weighted function for the three objectives. Nevertheless, 27 different triples of weights were tested, 3 values (1.0, 0.5, 0.0) for each weight, and the ProtPred was executed for each triple. Thus, the ProtPred required much more running time than mo-ProtPred to achieve similar results.
\n\t\t\t\n\t\t\t\t\t\t | Conformations | \n\t\t\t\t\t|||||||||||
\n\t\t\t\t\t\t | a | \n\t\t\t\t\t\tb | \n\t\t\t\t\t\tc | \n\t\t\t\t\t\td | \n\t\t\t\t\t\te | \n\t\t\t\t\t\tf | \n\t\t\t\t\t\tg | \n\t\t\t\t\t\th | \n\t\t\t\t\t\ti | \n\t\t\t\t\t\tj | \n\t\t\t\t\t\tk | \n\t\t\t\t\t\tl | \n\t\t\t\t\t
RMS | \n\t\t\t\t\t\t0.00 | \n\t\t\t\t\t\t3.83 | \n\t\t\t\t\t\t4.09 | \n\t\t\t\t\t\t3.83 | \n\t\t\t\t\t\t3.77 | \n\t\t\t\t\t\t4.09 | \n\t\t\t\t\t\t4.06 | \n\t\t\t\t\t\t3.93 | \n\t\t\t\t\t\t3.77 | \n\t\t\t\t\t\t4.01 | \n\t\t\t\t\t\t4.14 | \n\t\t\t\t\t\t3.83 | \n\t\t\t\t\t
Results with 1AQG using mo-ProtPred.
The van der Waals potential energy models the induced and dispersion attraction among pairs of atoms and the electronic repulsion. It has as parameters the separation distance
\n\t\t\tProtein conformations of 1AQG obtained by mo-ProtPred.
between the atoms and van der Waals radii. At large distances, there is no attraction between pairs of atoms. However, when electron clouds of the atoms are close enough to overlap, there is a strong repulsion and its value exponentially grows. The energy is smaller when a balance point exists between the attractive and repulsive forces; this is known as van der Waals contact (Berg et al., 2002; Nelson, 2004). The Lennard-Jones 12-6 potential is frequently used to represent this interaction, because it models appropriately the attractive and repulsive forces and, from a numerical point of view, it can be efficiently implemented.
\n\t\t\tTogether with the electrostatic interactions, van der Waals has considerable influence on the molecular structure. Studies have shown that the van der Waals forces contribute up to 65% of the total free energy of a protein (Becker, 2001).
\n\t\t\tIn the evaluation of an individual to be used in an EA applied to PSP, there are several functions that contribute to the calculation of the minimum free energy of the protein. However, the computation of the van der Waals and electrostatic energies have time complexity O(n\n\t\t\t\t\n\t\t\t\t\t2\n\t\t\t\t\n\t\t\t\t), where n is the number of atoms. The interaction energy Eij is calculated for each atom pairs (i,j). The interactions in the molecule can be represented by a matrix E. Since Eij = Eji, E is an upper triangular matrix.
\n\t\t\tThe computation time for both interaction energies corresponds to about 99% of the total EA execution time (Jain et al., 2009). It is therefore interesting to elaborate efficient algorithms and to use parallel processing wherever possible to reducing the total execution time.
\n\t\t\tThere are classical methods for the parallelization of computations with upper triangular matrices. An obvious strategy is to distribute each row of the upper triangular matrix as a task for the processes. Therefore, using n processors, each one executing a task in parallel, the energy can be calculated in time O(n), because the largest task would have n inter-atomic interactions to determine. On the other hand, some processors would have just some inter-atomic interactions to compute, generating a non-uniform load balancing among processors. This can be reduced by combining the first row with the last row, the second row with the last but one row, and so on. This process produces n/2 tasks of size n (see Fig. 5).
\n\t\t\tThe new individuals created by reproduction operators of EA have atoms with different coordinates from the parents. At each new generation, those coordinates must be sent to processors that calculate the electrostatics and van der Waals energies. The parallelization of these computations produces good results for large proteins, but not for small ones, as can be seen in Fig. 6. This figure shows the speedup achieved with the use of a given number of processors. The speedup is defined as the sequential execution time divided by the parallel execution time, and is equal to the number of processors in an ideal case (shown as a straight line in this figure). For a smaller protein, the computation time is on the order of the communication time for sending the coordinates to the processors, limiting the achieved speedup. For larger proteins, the computation time grows faster than the communication one, and the speedups are better. This is typical for parallel programs, which have overhead
\n\t\t\ta) Upper triangular matrix of interaction. (b) Cut accomplished in the half of the matrix and the indication of the rotation of 180 of the second half. (c) Alignment of the second half to satisfy the dense matrix criterion. (d) New dense matrix of interactions with n/2 tasks of the same size.
The speedup reached by 5 different sizes of proteins to calculate van der Waals energy using 20 Athlon64-X2 processors.
costs that are independent of problem size (Quinn, 1994), and therefore work better for large instances. This phenomenon is also related to the Amdahl effect (Goodman & Hedetniemi, 1997).
\n\t\t\tSince evaluations of different individuals are independent, rows of two or more dense matrices of interactions (Fig. 5) can be composed in larger tasks. This strategy can be especially adequate for the processing in GPUs (Graphic Processing Units) (Owens et al., 2008; Stone et al., 2007). The last generation of GPUs is capable of processing more than 3,500 matrices of 512x512 per second. This situation makes possible to compute energies of large molecules (hundreds of thousands of atoms) for several different configurations (individuals).
\n\t\t\tThe computation time can also be improved using characteristics of the crossover operator used for the construction of new solutions. Since this operator preserves part of the information of each parent, the matrix of interactions of an offspring from crossover can be partly copied from that of its parents, as the energies depend only on the distances between the atoms, and those are preserved for pair of atoms that come from the same parent. Fig. 7 illustrates the areas of the upper triangular matrices that can be directly copied from the parents. The efficiency of this technique depends on the position of the crossover cut point. The worst case happens when the point is in the middle of the chromosome (see Fig. 8(a)).
\n\t\t\tCalculated values Eij of inter-atomic interactions for the parents are copied to the offspring. Only the pink area needs to be recalculated.
a) Percentage of the required running time when Eij\'s are copied from parent. (b) Required memory to save matrix E of an new individual.
Because both interaction types discussed here decay with the distance, to reduce the amount of computations it is usually defined a cutoff radius of 8Å for van der Waals and 13Å for electrostatic energies (Cui et al., 1998), and the computation of these energies for larger distances is avoided.
\n\t\t\tTaking this into account, we see that the matrix of interactions is in fact a sparse matrix. This helps to overcome a possible limitation for the computation of large proteins, as the interaction matrix would otherwise grow with O(n\n\t\t\t\t\n\t\t\t\t\t2\n\t\t\t\t\n\t\t\t\t). The data structure used is therefore important. Fig. 8(b) shows that the memory required for saving the whole matrix E increases quadratically with the size of the molecule, while using sparse matrix data structure the size increases more slowly.
\n\t\t\tAnother optimization enabled by the use of a cutoff on the calculation of van der Waals and electrostatic energies makes possible to significantly reduce the amount of calculi for large proteins. One technique is the so called “Cell Lists” (Allen & Tildesley, 1987), which splits the space into cubic cells, as Fig. 9 illustrates (for the 2D case). Each cell has edge size equals to or larger than the cutoff radius. Thus, atoms in a cell interact only with other atoms in the same cell or in one of the neighbouring cells. This technique reduces the computation complexity from O(n\n\t\t\t\t\n\t\t\t\t\t2\n\t\t\t\t\n\t\t\t\t) to O(n+m), where m is the number of cells with atoms, because a larger number of atoms in a protein is related with increased size, and therefore increased number of cells, instead of increased number of atoms per cell.
\n\t\t\t\n\t\t\t\tFig. 10(a) shows the running cpu time required by the computation of the interactions without and with the cell-list approach. These result can also be improved by using an off-line procedure that previously compute the interaction energy between each possible pair of types of atoms (C, O, H, N, and S) for inter-atomic distances in a specified range (from a minimal distance to the maximal distance given by the cutoff radius) (Lodish et. at., 2003), see Fig. 10(b).
\n\t\t\tA performance enhancement for the computation of the potential energy has also been achieved using parallel solutions base on hardware like FPGAs (Field Programming Gate Arrays) (Wolf, 2004). The development of a specific solution in hardware is in general a hard task. One of the first researches using FPGAs for protein potential energy calculi did not reach a PC performance for the same problem. (Azizi et. al., 2004) In fact, this hardware solution was four time slower than the PC-base approach. Recent researches have achieved
\n\t\t\tCells, where the neighbour cells of the green cell are in yellow and the atoms are represented as grey balls.
Running cpu time necessary to calculate van der Waals potential: (a) Enhancement produced by the cell-list approach. (b) Improvement of performance combining cell-list and off-line calculi of Eij.
better performances by using faster memory bus and higher clock (Kindratenko & Pointer, 2006; Jain et. al., 2009). For example, Gu (2008) using a Virtex-5 (Xilinx, 2009) reached a speedup of 16.8.
\n\t\t\tThe off-line calculi of inter-atomic energies (Eij \'s) is another aspect that can be explored in FPGA solutions. The values Eij can be stored in lookup tables avoiding calculi with floating-point, which in general require large number of logic gates from FPGAs. Then, the available gates can be used to implement several lookup tables in parallel. The sum of the Eij \'s can also be implemented in a parallel way. The implementation of lookup tables and the parallel sum are relatively simple, motivating more investigation of the use of FPGAs for PSP.
\n\t\tA common issue related to evolutionary computation and PSP is the premature convergence of the population towards local optimal solutions. The PSP problem has extremely complex search space complicating the determination of the global optima. In this case, a fast convergence is not a desired feature, at least on the former generations. In this sense, this section presents a strategy to insert population diversity in an organized way in Genetic Algorithms (GAs) with Random Immigrants (RIs), increasing the replacement rate whenever the population is becoming uniform.
\n\t\t\tThe use of RIs introduces variation in the population [Grefenstette, 1992]. Thus, a GA with RIs can be more efficient in reaching better results than the conventional GA for the PSP problem [Tragante do O & Tinos, 2009]. However, the amount of RIs to be inserted at every generation has an important role in the GA evolution. The insertion of few RIs may not produce sufficient diversity in the population. On the other side, if a large number of individuals is replaced, the number of individuals of the population that explore the current best solutions may decrease, which can result in a smaller convergence rate.
\n\t\t\tIn this way, a dynamic replacement rate, i.e. the algorithm decides the number of RIs for each population, can be useful to control the population diversity of the GA for the PSP problem. This is the objective of the Simplified Self-Organizing Random Immigrants (SSORIGA), which is based on the algorithm SORIGA presented in [Tinos & Yang, 2007] for dynamic optimization problems. In the original SORIGA, the worst individual and its neighbours are replaced by randomly-generated individuals and kept in a subpopulation in order to avoid the competition of RIs with other individuals to compose the new population, since the fitness of an individual from the subpopulation is probably worse than the ones from the remaining of the population.
\n\t\t\tInstead of creating a subpopulation, the SSORIGA inserts RIs automatically in the following generation as part of the normal population. Before generating the individuals for the next generation, the individual from the current population with the worst fitness is determined. If this individual is a RI just inserted, then the amount of RIs for the next generation is increased by 2. Otherwise, the number of RIs is restarted to 2. If the number of RIs reaches 70%, it is reset to 2. Fig. 11 presents the pseudocode of SSORIGA.
\n\t\t\tPseudocode of SSORIGA.
The SSORIGA was adapted for the PSP as follows. All the Dihedral angles φ, ψ and χ\n\t\t\t\t\n\t\t\t\t\ti\n\t\t\t\t (i the number of angles of the side chain) obtained from the protein structures found on the PDB database compose a set T of triples (φ, ψ,\n\t\t\t\tχ\n\t\t\t\t\n\t\t\t\t\ti\n\t\t\t\t). Then, T is sorted according to φ generating a sequence S\n\t\t\t\t\n\t\t\t\t\tT\n\t\t\t\t of triples. The representation of an individual (conformation of a protein of size n) is a sequence of n amino acids and pointers to (indices for) triples from T.
\n\t\t\tThe mutation operator of SSORIGA only changes a pointer a little bit, automatically choosing a new triple in the neighbourhood of the pointer in S\n\t\t\t\t\n\t\t\t\t\tT\n\t\t\t\t. The crossover operator is the usual with the one-point crossing-over. The selection operator is tournament selection, with 75% chance of the best individual being chosen to be one of the parents of the crossover.
\n\t\t\tThe protein sequences Crambin (PDB code 1CRN), Met-Enkephalin (PDB code 1PLW) and DNA-Ligand (PDB code 1ENH) were used to evaluate the SSORIGA for the the PSP problem. The results indicated that SSORIGA was statistically better than the conventional GA approach and than RIGA with a fixed replacement rate of 6% or 10% depending on the protein. For other values of the replacement rate, SSORIGA was better than RIGA, indicating that SSORIGA was capable of finding the best replacement rate for RIs. It is important to note that:
\n\t\t\tThe mean number of replaced individuals in SSORIGA was between 3% and 5%;
The small replacement rates occur in the first generations and the larger replacement rates take place periodically when the diversity is reduced.
In terms of potential energy values, the SSORIGA was able to reach much lower values than the standard GA. Comparing results with statistical tests, such as Student’s T test, there is a probability of under 1.7% for all proteins of sampling error, considering as a final result the lowest energy value after the same number of evaluations of individuals for all algorithms.
\n\t\t\tIn conclusion, the algorithm was suitable for reaching lower energy values than the standard GA. It can be applied to other domains, since the idea may be useful in other problems that require slight or heavy increase of diversity along time, such as Dynamic Optimization Problems.
\n\t\tAlthough the number of sequenced proteins and their 3D structure determined experimentally grow systematically, the number of folds seems to be practically stabilized, since the year 2007, as shown by structural and topological classification of proteins databases, like SCOP and CATH. This scenario suggests that advances on computational methods, combined with the traditional techniques for theoretical prediction protein structure (Comparative modeling, Threading and ab initio), may improve substantially our capability for predicting protein structures. Algorithms that combine biological information from several databases with physical insights have proved to be a promising approach (Zhang, 2008). Each technique lonely has its specific strategies, advantages and limitations, as discussed in (Echenique, 2007) and illustrated in Table 3. Automated protein structure prediction is necessary because the protein folding process is not yet fully understood and experimental method (crystallographic and NMR) are relatively slow and financially expensive.
\n\t\t\t\n\t\t\t\tEchenique (2007) shows a schematic classification of methods for protein structure prediction, and discuss about experimental data and physical principles, emphasizing the need for more computer power and more accurate models. He concludes that, at the present stage, as more and more structural information is available, easier becomes the task for structural prediction. Indeed, in the last years we have seen increasing number and sophistication of biological databases: PDB, NCBI, Entrez, Dali Server (Holm et. al., 2008), SCOP, CATH, among others. These databases deal with experimental structural data reporting them directly (e.g. PDB), or presenting processed data information, such as in CATH.
\n\t\t\tThe EA has been used for protein structure prediction as other sections discussed. The its population initialization is crucial for EA performance (Rahnamayan et al., 2007). Therefore, once considered that physical properties about protein folding, as proposed by Anfinsen (1973), in general lines, suggest that at physiological conditions the protein primary
\n\t\t\tTechnique | \n\t\t\t\t\t\tSpecific strategy | \n\t\t\t\t\t
Comparative Modeling | \n\t\t\t\t\t\tBased on observation which proteins with similar sequences frequently share similar structure (Chotia & Lesk, 1986). | \n\t\t\t\t\t
Threading | \n\t\t\t\t\t\tTry to identify similarities between 3D structure that aren\'t join by any significant sequence similarity. In other words, proteins often adopt similar folds despite no significant similarity sequence. Thus, can be to exist a limited number of protein folds in nature (Orengo et al., 2004). | \n\t\t\t\t\t
Ab initio | \n\t\t\t\t\t\tPredict the protein native conformation from only its sequence aminoacids (Bourne & Weissig, 2003). No more information is needed. | \n\t\t\t\t\t
Technique of 3D structure prediction and their specific strategy.
sequence has all necessary information for its folding, Comparative Modeling (Table 3) may be used as a good method for generating the population initialization (Bourne & Weissig, 2003) because, beyond of being the easiest method for application, it is based in the following arguments:
\n\t\t\tThe protein structure is unique determined by its amino acid sequence (Anfinsen, 1973). Therefore, knowing the sequence it could, in principle, to obtain the corresponding native structure;
Along the evolution process, proteins structures became resistant to changes. Therefore, similar sequences should correspond also to similar structures.
Furthermore considering the three prediction models, Comparative Modeling, has more information and accurate models than that its time computing required is lower (Echenique, 2007).
\n\t\t\tThe next section will describe in more details Comparative Modeling. It is so clear that Comparative Modeling is good way for initial population for EA which will predict the 3D structure unknown. However, the difference between this initialization and the traditional approaches to Comparative Modeling is the latter choose only one and it is the solution. On the other hand, this section proposes that applying Comparative Modeling for to chose good individuals for the population instead of your random starting. The results intended are reducing the search space, number of individuals and generation.
Comparative Modeling\n\t\t\t\tGreer (1981) argues that the central point to the traditional approach to Comparative Modeling is the insertion and deletion issue, which is generally treated by identifying Structurally Conserved Regions and doing local changes on the Structurally Variable Regions. This procedure may be split into eight steps (Orengo et al., 2004), namely,
\n\t\t\tIdentify one or more homologous sequences which have their structures known. These structures will be used as templates or parents;
Align the target sequence to be modeled with the parents obtained in step 1;
Determine the boundaries of the framework or Structurally Conserved Regions and the Structurally Variable Regions. Normally, Structurally Conserved Regions are loops;
Inherit the Structurally Conserved Regions from the parents;
Build the Structurally Variable Regions;
Built the side-chains;
Refine the model; and
Evaluate errors in the model;
which are considered in more details as follows:
\n\t\t\t\n\t\t\t\t\t\tStep 1. The target sequence is searched through PDB database employing specific algorithms, such as Fasta and Blast to identify homologous [1] -structures. Eventually, distant homologous can be identifying by PSI-Blast
\n\t\t\t\t\t\tStep 2. When the sequence identity is high (> 70%) the alignment is trivial. Moreover, when the identity is lower and the number of insertions and deletions is higher, it is very difficult to obtain a correct alignment which is fundamental for a good model (Orengo et al., 2004). Proteins of two (pair-wise) or more (multiple) sequence alignment may be directly compared, and this procedure is called Sequence Alignment. For pair-wise there are two types, global and local. The global alignment tries to align the entire sequences, using all their sequence characters. On other hands, local alignment tries to align sequence stretches and thus is created sub-alignments. Therefore, global alignments are used when sequences have quite similar and approximately the same length. Local alignments are employed when similar sequences have different lengths or share a conservation region or domain (Mount, 2004). Spaces may need to be inserted and there are called GAP (Pal et al., 2006). It is understood as multiple sequence alignment, that alignment of three or more sequences where each sequence columns represents the evolutionary changes in one sequence position (Mount, 2004). Simulated Annealing (Aart & Laarhoven, 1987) and Gibbs sampling (Lawrence et al., 1993), and particularly EA, have been used for multiple sequence alignment (Notredame & Higgins, 1996; Yokoyama et al. 2001; O\'Sullivan et al., 2004). The difference about these EAs is their mutation operators (Pal et al., 2006)).
\n\t\t\t\t\t\tStep 3.\n\t\t\t\t\t\tStructurally Conserved Regions have all the same lengths are called core. The other regions, which differ structurally among parent sequences, are the Structurally Variable Regions (Orengo et al., 2004).
\n\t\t\t\t\t\tStep 4. This step can be divided into two situation:
Single Parent – Structurally Conserved Regions are just copied from this parent and used as the model.
Multiple Parents – distinct approaches may be used and choices depend on the modeler preferences, although the first step is always to fit structurally all multiple parents to one another.
\n\t\t\t\t\t\tStep 5. Normally, Structurally Variable Regions are loop regions. When the lengths of loops of the parent structures are different, they are built with lower accuracy when compared with the rest of the structure. Furthermore, even if the corresponding lengths of these regions are the same, they may adopt different structural conformations.
\n\t\t\t\t\t\tStep 6. There are various protocols to build the side chains: It can be simple like Maximum Overlap Protocol, in which side-chain torsion angles are inherited from their parent\'s side-chain, where possible, and additional atoms are built from a single conformation (Orengo et al., 2004). In the Minimum Perturbation Protocol Shih et. al. (1985) each substitution is guided by a rotation about the sidechain\'s torsion angles to relieve clashes. Another protocol, called Coupled Perturbation Protocol, developed by Mark Snow and Mario Amzel, is similar to the Minimum Perturbation Protocol, albeit the side chain torsion angles of structurally adjacent residues are also rotated.
\n\t\t\t\t\t\tStep 7. Model may be refined by Energy Minimization, a process in which all the atoms, governed by a previously specified force field, move until a conformation that represents a system minimum energy is reached. This issue is related to the Molecular Dynamics (MD) method (Frenkel & Smit, 1996). There are several popular software packages to run MD simulations, like Gromacs (Hess et al., 2008).
\n\t\t\t\t\t\tStep 8. A conventional measure of the modeling quality is done by applying the Root Mean Square Deviation (RMSD). Basically, RMSD shows how similar one structure is to another (Orengo et al., 2004). Usualy, the modeling quality may be tested first against a number of known protein structures.
Therefore, applying Comparative Model for starting the EA population may improve the efficiency of EA when compared by randomized starting population, since relevant initial information about unknown spatial structure of proteins are produced. Therefore, as argued by Zhang & Skolnick (2005), the PDB library is a systematically increasing contributor for the protein structure prediction problem. For example, such initial information can help EA get out of non-native local minimum energy; as well complementary information from specific biological databases may be used to build specific genetic operators.
\n\t\tIt is noteworthy that the modelling, sampling and convergence properties might be a critical issue in the PSP. From a computation perspective based on EAS, different modeling issues for PSP were revised in this Chapter. Although lattice models are relatively simple, they are very appealing for EAS approaches where the computational efficiency can be highly improved, enabling the prediction of better protein structures. In fact, the data structure based on AR + C\n\t\t\t\t\n\t\t\t\t\tM\n\t\t\t\t (Section 2) simplifies the objective function of lattice models since there is no need for an additional function penalizing amino acid collisions. As a consequence, the objective function uses only one criterion, i.e., the evaluation of the number of interactions between hydrophobic amino acids.
\n\t\t\tThe hydrophobicity of protein is a measure of the interplay of the protein and solvent interactions. The objective function of the lattice models based on AR + C\n\t\t\t\t\n\t\t\t\t\tM\n\t\t\t\t estimates this interaction. Thus, the EA using such model may also lead to a computationally efficient process in order to find protein conformations with more plausible solvent interaction.
\n\t\t\tThe solvent effect on PSP is an important issue: for most cases, the solvation energy basically drives the process. Different alternatives on how to model the solvent have been pointed out on Section 3. Despite the fact that some protein structure were successfully obtained with models based on potential energy functions with no hydration free energy contributions, this is not a general rule. For a general protein case, the solvation free energy and interaction potential energy functions are recommended for the proper prediction baring on mind the computational costs and efficiency. In order to use both contributions in PSP by EAs, two main issues should be adequately considered: 1) the computational efficiency of the energy calculi, and 2) an adequate manner to combine them. Section 5 presented strategies that reduce the running time to calculate van der Waals and electrostatic interaction energies from quadratic to linear. The off-line calculi of energies for a range of inter-atomic distances and the use of interaction energies previously calculated for parent solutions by the crossover operator can also enhance the computational efficiency.
\n\t\t\tThe combined effect of different potential energies terms is dependent on the folding stage, i.e., the influence of a different term of the effective Hamiltonian may dominate the initial stage of the folding and another dominates the intermediate or the final stage. The influence of such terms on folding stages also depends on the protein molecule. In this context, the EAs enable us to simulate the effects of different combinations (weight set) of the energy functions involved in the PSP (Section 3). The most adequate weight set would reveal the key contribution of a Hamiltonian on the process contributing to enlighten the quantification of each physical mechanism behind the protein folding process.
\n\t\t\tMoreover, the mo-ProtPred can consider multiple criteria without previous knowledge of weights (Section 4) producing coherent protein structures. As an interesting consequence, the effects of each intermolecular interaction together with the solvation free energy can also be considered in PSP without previous information of the relative contribution of each Hamiltonian term on the folding.
\n\t\t\tThe use of heuristics about proteins to create the initial population can improve significantly the performance EAs for PSP (Section 7). However, it may drive the algorithm for local optima, which may not correspond to an adequate protein structure. The SORIGA (Section 6) can be employed when heuristics population are used, reducing significantly the premature convergence for local optima. Thus, the initial-population heuristics combined with SORIGA should produce gains on convergence or even enable to work with larger proteins maintaining the quality of the prediction.
\n\t\t\tIn short, the presented research attacked fundamental questions of the numerical sampling issues of the PSP. Moreover, it brought up solutions for each of these questions showing preliminary results and directions. In the literature, the questions clustered here have been the focus of several independent studies involving different areas of Science. The present chapter proposes some suggestions on how to combine the knowledge of diverse subjects to solve the PSP problem. There are issues that can be seen as details from on point of view but they are crucial from other perspective. Some of these aspects have been neglected in the development of a global solution for PSP on previous reviews.
\n\t\t\tIn fact, hard problems can be characterized by the presence of several sets of highly interacting variables (Goldberg, 2002). One strategy to deal with such problems is to determine the interactions and adequately treat them. In PSP, the variables involve different research fields. Thus, a PSP modeling based on the integration of knowledge seems a promising path to achieve relevant advances.
\n\t\tSoybean is one of the world’s most economically and nutritionally important crops. In 2018, soybean were 61% of international oilseed production with 397.9 tons harvested worldwide [1]. The United States and Brazil were the largest producers at 4545 and 4299 million bushels, respectively, with China being the largest importer of U.S. whole soybeans valued over $3 billion U.S. [2, 3]. Soybean products, namely meal and oil, are popular in a myriad of industries for their versatility and utility. Soybean oil provides the most versatility with uses in fuel, solvents, candles, cosmetics, construction, and foam. However, soybean meal is the driving factor for 70% of the plant’s value with 97% of all U.S. soybean meal being used for animal feed [4]. As such, an enormous portion of soybean’s importance lies with its nutritional capabilities for livestock and humans.
\nNutritionally speaking, soybeans are a highly valued protein source. Proteins are a crucial macromolecule needed in the diets of human and livestock. However, the true significance of soybean protein is due to its well-balanced amino acid profile that aligns with dietary needs of humans and animals [5]. Amino acids are the functional subunits of proteins that, when linked together in different orders, generate the variety of proteins critical to life. Amino acids are also important intermediates for many biosynthesis pathways [6]. Deficiencies in single or multiple amino acids can negatively impact an individual’s growth and development [7, 8]. Intriguingly, an excess of certain amino acids has also been shown to worsen feed intake, nitrogen efficiency, and growth rate in livestock [9, 10, 11]. The importance of amino acid levels on human health has also been well documented [12, 13].
\nAmino acids are characterized by having amine (−NH2) and carboxyl (−COOH) functional groups as well as a “R-group” that is unique to each amino acid [14]. Amino acids are abundant in both proteinogenic (protein-incorporated) and non-proteinogenic forms [15]. The 20 common, proteinogenic amino acids are generally the focus of research in soybeans as they are the defining nutritive feature. Of those twenty, nine amino acids are essential for humans to consume. Livestock usually require these same amino acids from feed and might require others because of their biological systems [16]. Soybeans contain some level of all nine essential amino acids which creates a suitable nutritional foundation for livestock feed and human food.
\nEssential amino acids are ones that living organisms are unable to biosynthesize themselves and must obtain from their food source [7, 12, 16]. Therefore, in this term, “essential” refers to the amino acid requirements in dietary ingredients. The nine standard essential amino acids for humans present in soybean are: histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine [17]. Arginine is regularly considered an essential amino acid for fish, poultry and sometimes swine due to absent or deficient urea cycles [9, 16]. Poultry and reptiles also require dietary glycine because of differing waste excretion pathways [16]. While crude protein content is normally recognized as the driving nutritional factor for soybean meal, these essential amino acids provide true utility.
\nIt has long been recommended that protein quality is based upon essential amino acid content. However, for many reasons, animal feed and human food markets have only recently begun assessing accordingly [5, 18, 19]. Equipment required for accurate amino acid measurement and the diversity of markets for amino acids makes it difficult for supply chain evaluators like elevator operators to appraise amino acid content on site. To some degree, the well-balanced soybean amino acid profile also devalues the need to measure individual amino acid levels. Since all essential amino acids are present, less attention is paid to deficient amino acids such as methionine and tryptophan [17, 20].
\nDeficiencies in soybean’s essential amino acid profile has led to a large section of the livestock industry focusing on feed mixing and supplementation. Rationing with other feed sources such as cereal grain and synthetic amino acid augmentation can effectively resolve the issue. Although, this comes with economic and environmental problems. Supplementing amino acids adds costs to farmers. For example, the average cost for amino acids supplementation for dairy farmers is 20 cents per head per day [21]. Maximizing crude protein for a growth limiting factor also negatively impacts livestock nitrogen-use-efficiency and environmental nitrogen outputs [8, 22]. Synthetic amino acid production can produce hazardous environmental waste and synthetic methionine, the most limiting soybean amino acid for poultry, has also been banned for organic poultry production [20, 23]. Movement towards sustainable agriculture will pressure the feed industry to alter how soybean meal is enhanced for essential amino acid livestock maximization. Furthermore, the increasing popularity of meat-less diets in humans will create new markets for soybean’s well-balanced amino acid profile.
\nNonessential amino acids should not be misconstrued as unimportant amino acids. Of the 20 proteinogenic amino acids, those considered nonessential are still necessary for living organisms. Healthy organisms are able to biosynthesize them and are not required from food and feed consumption. The 20 standard nonessential amino acids for humans found in soybean are: arginine, alanine, asparagine, aspartic acid, cysteine, glutamine, glutamic acid, glycine, proline, serine, and tyrosine [24, 25, 26]. As previously mentioned, the necessity of amino acids such as arginine and glycine can differ amongst species. Some nonessential amino acids are also affected by the presence and amounts of essential amino acids.
\nCysteine not provided from food consumption is directly biosynthesized from methionine via trans-sulfuration [16, 27, 28, 29, 30]. Consequently, if cysteine is not provided in the diet, then enough methionine must be provided to compensate for both amino acid needs. For that reason, feed research for poultry occasionally measures methionine and cysteine jointly [10, 31, 32]. Tyrosine is also directly formed from phenylalanine via hydroxylation [16, 29, 33]. Other amino acids like arginine, glycine, and proline can be required from the diet when animals are young, old, sick, or otherwise deficient in body protein regulation. As human and livestock diets become more sustainably plant-based, it will become more important to evaluate nonessential amino acids, specifically the ones immediately affected by essential amino acids.
\nThe two proteinogenic sulfur-containing amino acids, methionine and cysteine, are critical to evaluating soybean meal as food and feed. While present in soybean, methionine and cysteine levels are both inadequate for consumer needs [8, 20, 24]. Similar to research determinations, the nutritional requirements for methionine and cysteine intake are often grouped together as overall sulfur consumption from protein. Adult humans are recommended to intake 910–1120 mg methionine and cysteine (based on body weight) per day [13]. For livestock, sulfur amino acids recommendations can vary based on species, age, end-use, and diet formulation. The importance of dietary sulfur amino acids for livestock is greatly emphasized in literature, especially with soybean meal as base feed [8, 20, 34, 35].
\nMethionine and cysteine are vital to biological functions because of the sulfur contained in their R-groups and versatility in macromolecule synthesis. Methionine is well-known for being the typical initiating amino acid for protein synthesis and has hydrophobic properties when incorporated into proteins [36]. These hydrophobic properties usually result in methionine incorporation within the core of proteins. However, certain proteins have surface-exposed methionine susceptible to oxidation that is associated with age-related disease [30, 37, 38]. S-adenosylmethionine, a methionine metabolism intermediate, is widely-used with functions in methylation as well as amine, methylene, and sulfur atom donation [30, 39, 40]. Methionine is especially important for poultry production as birds have exceedingly high sulfur amino acid requirements, and low methionine levels can negatively affect growth rate, carcass yield, fat content, and disease immunity [41, 42, 43]. Cysteine’s ability to form disulfide bonds makes it incredibly important to tertiary protein structure and can occur with and without enzyme interactions [30, 44]. Cysteine is involved with keratin and feather production in poultry and deficiencies have been correlated to poor breast muscle development [45, 46]. Swine also need higher amounts of cysteine as they age to compensate for body maintenance [29, 47].
\nSoybean has an extensive cultivated history dating back thousands of years to its country of origin, China. Glycine max, the contemporary species of soybean, was domesticated from the wild species Glycine soja and has been continually been improved through selective and molecular breeding [48, 49, 50]. Once harvesting traits such as seed shatter and lodging were improved in the late 1930s to make soybean a competitive row crop, other cultivar improvements became a valuable research goal [49]. Current breeding programs tend to focus on traits such as yield, disease resistance, abiotic stress tolerance, and seed composition. Seed composition improvements include protein and oil content, fatty acid levels, anti-nutritional factors, isoflavones, and amino acids profiles. Before 1972, there had been zero reported research for improvement of soybean amino acid profiles, rather with emphasis on overall protein content [51]. Modern breeders are also inclined to concentrate efforts on protein content and consider amino acid levels an afterthought [52]. TN04-5321 is the only released germplasm in the United States that maintains yield and protein content while improving amino acid balance by increasing methionine and cysteine to levels recommended livestock needs [53].
\nThe major soybean storage proteins are 11S (glycinin) and 7S (conglycinin) and provide the bulk of amino acids with limited non-proteinogenic amino acids in seed [54, 55, 56]. By increasing 11S and 7S quantity, more protein can ultimately be present in food and feed. Overemphasis on crude protein content can have negative ramifications on overall protein quality, specifically deficient amino acids. While an increase in protein content would theoretically entail an increase in amino acids including methionine and cysteine [57], the opposite effect has been more notable [58, 59]. Molecular breeding techniques have recently improved the understanding of amino acid genomic regulation. Multiple quantitative trait loci (QTL) studies have been performed to identify genomic regions that control amino acids levels in soybean seed [60, 61, 62, 63, 64]. A myriad of QTL’s were found to create amino acid phenotypic variation. Individual amino acids had reoccurring or proximal QTL’s discovered such as Satt 518 [60], ss107913002 [61], and BARC-048619 [62] for glycine and threonine. QTL’s for methionine and cysteine were also discovered which could lead to valuable improvements for soybean livestock feed [60, 61, 62, 63, 64]. Other genomic studies such as genome-wide association studies (GWAS) and genetic diversity analyses would further improve genetic understanding.
\nGenetic engineering experiments such as genetically modified organisms (GMOs) and gene editing are also promising avenues for improving amino acid profiles of soybeans. Compared to conventionally bred varieties, transgenic soybeans face additional adversity from registration requirements and public opinion. Transgenic efforts generally have one of three targets: magnifying biosynthesis genes, adjusting biosynthesis regulation, and modifying storage proteins. The earliest example would be a Brazil nut gene transfer in 1992. This successfully increased protein content and methionine biosynthesis, however a major food allergen was also transferred making commercialization impossible [65]. Expressing zein proteins from corn has also been shown to increase sulfur-containing amino acids levels in soybean [66, 67]. Altering biosynthesis feedback regulation amplified both non-proteinogenic and proteogenic lysine by circumventing normal enzymatic pathways [68]. Tryptophan in soybean also exhibited increased non-proteinogenic levels when a feedback-insensitive enzyme was transferred [25]. While soybean is not deficient in lysine or tryptophan, corn is deficient in both. By increasing lysine and tryptophan concentrations, soybean becomes an even more useful feed additive to corn rations. Even with limited research on modifying overall amino acids profiles in soybeans, modifying 11S and 7S storage proteins ratios [69] or silencing their expression entirely [70] has displayed increased amino acids levels. Similarly, a study using irradiated mutant soybeans lacking storage proteins as breeding parents demonstrated increased non-proteinogenic amino acids contents [54]. In addition, further research should be conducted to determine the bioavailability and digestibility of increased non-proteinogenic amino acids in soybean.
\nAmino acids concentrations in soybean are not only affected by their genetic potential. Agronomic properties greatly impact the final levels of amino acids. Agronomy encompasses all aspects of crop production including environmental effects, climatic variables, and abiotic factors. Perhaps the most considered agronomic factor is soil nutrient availability. Insufficient soil nutrient levels of nitrogen, potassium, phosphorous, sulfur, calcium, and magnesium create poor amino acid profiles in soybean plants [71]. Increased phosphorous rates have been shown to increase the percentage of methionine and tryptophan in seed but had no effect on protein content percentage [72]. Applications of sulfur, phosphorous, and nitrogen (individual and combined) produced a variety of different methionine and cysteine seed concentrations [73]. Sulfur deficiencies were also shown to inhibit the production of 11S proteins while almost eliminating methionine and cysteine in 7S proteins [74]. It is becoming more popular to also apply biological substances such as amino acids to plants through foliar and seed application. Amino acid uptake by soybean and wheat have been proven, and improved soybean growth rates and antioxidant effects have also occurred [75, 76]. Further research should be conducted to determine if biofortification solutions are possible through amino acid application.
\nAmino acid variation has also been shown to occur across environments [24, 59]. Specific correlations have emerged in response to temperature, solar radiation, and rainfall. One study shows that increased temperature leads to increased concentrations of all proteinogenic amino acids [77], while another concludes that only methionine and cysteine increase alongside temperature [78]. Increased solar radiation and greater available water appeared to have a negative relationship with amino acid content [77]. These favorable conditions would increase yield which has been shown to have a negative correlation with overall protein content [57]. The multitude of agronomic factors that affect amino acid profiles in soybean make it exceedingly important to compensate for variables when researching.
\nThe diversity in food, feed, and industrial used for soybean require the whole seed or seed components to be processed. Processing can affect the nutritional value of soybean protein and presence of amino acids in food and feed. Processing procedures can either separate seed components for different purposes or convert the entire seed into a product (usually human food). Some human soy foods such as edamame and soybean sprouts need little to no processing. Others including soymilk, tofu, natto, and soy sauce involve more processing. Soymilk and tofu processing are interconnected. Soymilk is a water-extract of whole or crushed soybeans that is coagulated and pressed into tofu [48]. While not all seed proteins convert into protein in tofu, 11S/7S storage protein ratios have been shown to be both positively and negatively correlated with tofu hardness [79, 80]. Natto is a soy food created by fermenting whole soybeans with Bacillus subtilis. Fermentation time affects final amino acid concentrations, and proper fermentation length could potentially increase nutritional values [81]. Soy sauce is produced by traditional and commercial methods, but both are based around whole seed or meal fermentation with Aspergillus sp. However, commercial methods have a lower amino acid to nitrogen ratio [48].
\nSoybean meal processing also impacts the level of amino acids in livestock feed. The first step in soybean meal processing is essentially separating protein from oil. A variety of methods exist including solvent extraction, screw pressing, and extruding [48, 82, 83]. All three processes have three final products: oil, meal (usually toasted to lessen anti-nutritional factors), and hulls. Over processing of solvent extracted soymeal has been shown to decrease lysine, cysteine, and arginine levels [84, 85]. Protein solubility and dispersibility measurements may be a useful indicator of over processing [86, 87]. Soybean hulls are sometimes added to livestock feed for additional fiber, however an increase in hull/meal ratios decrease the digestibility of amino acids [88]. While soybean is renowned for its protein and amino acid content, actual nutritional values can be decreased through certain processing methods.
\nAll previously mentioned aspects of soybean production in regard to amino acid levels and human and animal nutrition depend on a single common denominator: amino acid quantification. Amino acids must be reliably, effectively, and accurately identified, measured and evaluated. A Google Scholar search of “amino acid analysis” will display over 1 million results. Several reviews have been published regarding the development of amino acid analysis [89, 90, 91]. In general, contemporary analysis of amino acids from any source will be performed by chromatography or near-infrared reflectance spectroscopy. Chromatography is the common method with specific techniques including ion exchange chromatography (IEC), high-performance liquid chromatography (HPLC), and gas chromatography (GC). HPLC is the more validated method for soybean amino acid analysis. It is more efficient than IEC, and it does not require the transformation into volatiles like GC [91, 92, 93, 94]. Near-infrared reflectance spectroscopy (NIRS) is a more recent addition to amino acid analysis, and it has the potential to drastically improve the efficiency in soybean feed evaluation [19]. The inability to actually measure amino acid levels is main hindrance for NIRS amino acid analysis. NIRS methods must be developed from a calibration set of raw data (often from HPLC) [95, 96, 97]. Nonetheless, efficiency improvements should persuade researchers to continually explore future NIRS amino acid analysis applications.
\nSoybean is a valuable source of protein and amino acids for humans and livestock. Soybean’s well-balanced amino acid profile provides all essential amino acids as well as most nonessential. However, there is much room for nutritional improvement. Proteinogenic sulfur-containing amino acids, methionine and cysteine, are deficient in soybean and are especially needed in livestock rations. Increased levels of these amino acids would augment soybean meal value and lessen the need for synthetic amino acid supplements. Breeding efforts have made little progress in adjusting amino acid profiles thus far, however significant developments in understanding genomic control regions promise future success. Genetic engineering efforts have shown promising amino acid improvements, but regulations and public opinion made commercialization difficult. New gene-editing technology could be the key to unlocking true nutritional improvement.
\nAgronomic properties and processing methods both impact the final quantities of amino acids available to humans and livestock. Understanding these impacts are essential to improve the nutritional quality of soybeans. Amino acid evaluation through HPLC provides reliable and efficient quantification, yet even quicker measurements are possible through NIRS. As the world’s population continues to grow, soybeans will be essential to both human and livestock for amino acid requirements. Wholesome approaches that understand the complexities of amino acids in soybean will be required to maximize overall success and feed the world with balance soy proteins.
\nThe authors would like to acknowledge Virginia Polytechnic Institute and State University’s Open Access Subvention Fund for funding the publishing fee.
\nThe authors declare there is no conflict of interest.
The Edited Volume, also known as the IntechOpen Book, is an IntechOpen pioneered publishing product. Edited Volumes make up the core of our business - and as pioneers and developers of this Open Access book publishing format, we have helped change the way scholars and scientists publish their scientific papers - as scientific chapters.
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\\n\\nOut of all of the publishing options available to researchers, why choose to contribute your research to an IntechOpen Edited Volume? The reasons are simple. IntechOpen has worked exceptionally hard over the past years to fine tune the Open Access book publishing process and we continue to work hard to deliver the best for all of our contributors. The quality of published content is of utmost importance to us, followed closely by speed, and of course, availability and accessibility. To view current Open Access book projects that are Open for Submissions visit us here.
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\\n\\nYOUR WORK, YOUR COPYRIGHT
\\n\\nThe utilization of CC licenses allow researchers to retain copyright to their work. Researchers are free to use, adapt and share all content they publish with us. You will never have to pay permission fees to reuse a part of an experiment that you worked so hard to complete and are free to build upon your own research and the research of others. The Edited Volume helps bring together research from all over the world and compiles that research into one book - accessible for all. The research presented in chapter one can inspire the author of chapter three to take his or her research to the next level. It is about sharing ideas, insights and knowledge.
\\n\\nCan collaboration be inspired by a publishing format? At IntechOpen, the answer is yes. The way the research is published, the way it is accessed, it’s all part of our mission to help academics make a greater impact by giving readers free access to all published work.
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\n\nOut of all of the publishing options available to researchers, why choose to contribute your research to an IntechOpen Edited Volume? The reasons are simple. IntechOpen has worked exceptionally hard over the past years to fine tune the Open Access book publishing process and we continue to work hard to deliver the best for all of our contributors. The quality of published content is of utmost importance to us, followed closely by speed, and of course, availability and accessibility. To view current Open Access book projects that are Open for Submissions visit us here.
\n\nQUALITY CONTENT
\n\nOver the years we have learned what is important. What makes a difference to the researchers that work with us, what they value. Something that is very high not only on their lists, but our own, is the quality of the published content.
\n\nOur books contain scientific content written by two Nobel Prize winners, two Breakthrough Prize winners and 73 authors who are in the top 1% Most Cited.
\n\nWith regular submission for coverage in the single most important database, the Book Citation Index in the Web of Science™ Core Collection (BKCI), and no rejected submissions to date, over 43% of all Open Access books indexed in the BKCI are IntechOpen published books.
\n\nIn addition to BKCI, IntechOpen covers a number of important discipline specific databases as well, such as Thomson Reuters’ BIOSIS Previews.
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\n\nThe need for up to date information available at the click of a mouse is one thing that sets IntechOpen apart. By developing our own technologies in order to streamline the publishing process, we are able to minimize the amount of time from initial submission of a manuscript to its final publication date, without compromising the rigor of the editorial and peer review process. This means that the research published stays relevant, and in this fast paced world, this is very important.
\n\nYOUR WORK, YOUR COPYRIGHT
\n\nThe utilization of CC licenses allow researchers to retain copyright to their work. Researchers are free to use, adapt and share all content they publish with us. You will never have to pay permission fees to reuse a part of an experiment that you worked so hard to complete and are free to build upon your own research and the research of others. The Edited Volume helps bring together research from all over the world and compiles that research into one book - accessible for all. The research presented in chapter one can inspire the author of chapter three to take his or her research to the next level. It is about sharing ideas, insights and knowledge.
\n\nCan collaboration be inspired by a publishing format? At IntechOpen, the answer is yes. The way the research is published, the way it is accessed, it’s all part of our mission to help academics make a greater impact by giving readers free access to all published work.
\n\nOur Open Access book collection includes:
\n\n3,332 OPEN ACCESS BOOKS
\n\n107,564 INTERNATIONAL AUTHORS AND ACADEMIC EDITORS
\n\n113+ MILLION DOWNLOADS
\n\nPUBLISHING PROCESS STEPS
\n\nSee a complete overview of all publishing process steps and descriptions here.
\n\nCURRENT PROJECTS
\n\nTo view current Open Access book projects that are Open for Submissions visit us here.
\n\nNot sure if this is the right publishing option for you? Feel free to contact us at book.department@intechopen.com.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. 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He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). 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Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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