Description of various roles of non-structural proteins from
Unfortunately, to date, there is no approved specific antiviral drug treatment against COVID-19. Due to the costly and time-consuming nature of the de novo drug discovery and development process, in recent days, the computational drug repositioning method has been highly regarded for accelerating the drug-discovery process. The selection of drug target molecule(s), preparation of an approved therapeutics agent library, and in silico evaluation of their affinity to the subjected target(s) are the main steps of a molecular docking-based drug repositioning process, which is the most common computational drug re-tasking process. In this chapter, after a review on origin, pathophysiology, molecular biology, and drug development strategies against COVID-19, recent advances, challenges as well as the future perspective of molecular docking-based drug repositioning for COVID-19 are discussed. Furthermore, as a case study, the molecular docking-based drug repurposing process was planned to screen the 3CLpro inhibitor(s) among the nine Food and Drug Administration (FDA)-approved antiviral protease inhibitors. The results demonstrated that Fosamprenavir had the highest binding affinity to 3CLpro and can be considered for more in silico, in vitro, and in vivo evaluations as an effective repurposed anti-COVID-19 drug.
- protein–peptide interactions
- biological targets
- drug development
- 3CLpro inhibitor
- biological computation
- drug design
The Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (
Generally, time is a vital factor in the pandemic condition, so that, rapid detection, vaccination, and treatment methods can significantly reduce mortality. De novo drug discovery and development for lesser-known diseases such as COVID-19 is costly and tedious. Consequently, alternative methods such as the computational drug repurposing approach can accelerate the discovery of new drugs. In this regard, several pipelines have been introduced for in silico drug repositioning against COVID-19. Lately, molecular docking as a popular bioinformatics method has been highly regarded as the core of the most drug repositioning process to achieve effective drug candidates to combat COVID-19 [4, 5, 6]. In this chapter, we discussed new advancements and challenges in drug repositioning by molecular docking of pharmaceutical resources to the identification of potential
2. Origin and pathophysiology aspects of COVID-19
3. Molecular biology of
|Protein name||Length (amino acid)||Role||References|
|NSP1||180||Host translation inhibitor and also degrade host mRNAs|||
|NSP2||638||Binds to prohibitin 1 and prohibitin 2|||
|NSP3||1945||Responsible for release of NSP1, NSP2, and NSP3|||
|NSP5||306||Cleaves at multiple distinct sites to yield mature|||
|NSP6||290||Induces formation of ER-derived autophagosomes|||
|NSP7||83||Forms complex with NSP8 and NSP12 to yield the RNA polymerase activity of NSP8|||
|NSP8||198||Makes heterodimer with NSP8|||
|NSP9||198||bind to helicase|||
|NSP12||932||Replication and methylation|||
|NSP13||932||A helicase core domain|||
|NSP14||527||Exoribonuclease activity a|||
|NSP15||346||Mn(2 +)-dependent endoribonuclease activity|||
4. Antiviral molecular targets and drug development strategies against COVID-19
Generally, a probable antiviral drug target is a molecule (often a protein) with a vital role in the life cycle of the planned virus [14, 15]. Accordingly, to date, several structural and accessory proteins from
4.1 Virus attachment and entry
4.2 Virus genome replication
Generally, the virus replication directly affects the viral burden and symptom severity in viral infections. Therefore, targeting the key molecules in the
5. Current in use anti-COVID-19 treatments
Unfortunately, to date, there is no specific anti-COVID-19 drug. However, the results of some studies suggested that other anti-viral medicines could be repurposed as effective anti-COVID-19 drugs. Remdesivir, an FDA-approved repurposed antiviral drug, is only in used approved anti-viral therapy against COVID-19 . However, other anti-viral and non-antiviral drugs have also been used for studying their anti-COVID-19 activities. Hydroxychloroquine, an anti-malaria drug with polymerase inhibitory activity, was the first repurposed drug against COVID-19, which was supported by some in vitro effectiveness evidence. However, further clinical trials indicate that there is no association between hydroxychloroquine administration and reduction in the death rate due to COVID-19. Kaletra (a brand name of lopinavir/ritonavir complex) is an approved anti-human immunodeficiency virus (HIV) protease inhibitor, which empirically evaluated for 3CLpro inhibitory activities. Despite, promising in vitro results, clinical trials have not confirmed the significant efficacy of Kaletra in individuals hospitalized with COVID-19. Favipiravir, a purine nucleic acid analog, is another anti-viral drug that is repurposed against mild to moderate COVID-19. The results of clinical trials suggest that Favipiravir has no significant beneficial effect on the mortality rate in patients with COVID-19. Additionally, some other drugs such as colchicine, oseltamivir, ivermectin, tocilizumab, nafamostat, camostat, famotidine, umifenovir nitazoxanide are under evaluation for investigating their probable anti-COVID-19 activities [31, 32, 33].
6. Computational drug repositioning
Because of the costly, time-consuming, and complexity of De novo drug discovery, until now all proposed anti-COVID-19 drug candidates are repurposed drugs. Drug repurposing also known as drug re-tasking is a procedure of recognizing new therapeutic application(s) for previously approved, failed, investigational, and or already marketed drugs. Naturally, the drug-repurposing process is based on two fundamental principles including interdependence between different diseases and the confounding nature of drugs. Therefore, drug-repositioning approaches could be categorized into drug-based and disease-based strategies.
The drug-based strategies are vastly based on drug-related data and are used for better understanding the role of pharmacological properties and defining the possibility of defining new pharmaceutical capabilities. Despite the advantages of experimental drug repositioning, the fact that it was time consuming still remained as the main limitation for drug discovery, especially in a pandemic condition. Furthermore, conventional methods use small datasets and biological networks, which may lead to unreliable discoveries.
Nowadays, different computational methods have been introduced that can accelerate the drug-repositioning process . In the next sections, the most common computational approaches for drug repositioning are propounded.
6.1 Molecular target identification and validation in the drug-repositioning process
In a drug discovery project, target identification and validation are key steps that directly affect drug efficacy, as well as probable side effect(s). Theoretically, a drug target molecule can be selected among a wide range of biological entities including proteins, genes, and RNAs. However, an ideal drug target molecule should be drug accessible, efficacious, safe, and meet clinical and commercial requirements . Target identification can be performed by different tools such as analysis of gene modifications, protein overexpression, signaling pathways, protein interactions, and recent bioinformatics evaluations. Regarding antiviral drug discovery, different targets such as envelop proteins, S-adenosyl-L-homocysteine hydrolase, orotidine 5′-phosphate decarboxylase, cytidine triphosphate synthetase, inosine monophosphate dehydrogenase, and DNA/RNA polymerase have been investigated for discovering effective antiviral drugs [34, 35, 36, 37]. The identified target molecules can be validated by knocking in/down/out the genes, monoclonal antibodies, and chemical genomics [4, 38]. As mentioned, recently bioinformatics methods, such as ligand-based interaction fingerprint (LIFt), protein-ligand interaction fingerprints (PLIF), and network-based drug discovery, have successfully been used for drug target identification .
6.2 Data mining
There are now a large number of diseases- and drugs-linked information such as gene sequences, protein–protein interactions, and drug–protein interactions with increasing rapid growth, which needs effective approaches to quick access and analysis of hidden information. Commonly, text mining is the most applicable method in the majority of data mining–related studies. In the field of computational drug repurposing, text mining has been used to find the gene, drug, and diseases-related data and then categorize the relevant entities. Regarding drug repurposing, text mining has successfully been used in several studies [40, 41]. Brown et al. suggested an online text-mining server with the ability to drug clustering based on the similarity of their physicochemical properties . A text mining-based tool was also introduced by Leaman et al. for identifying disease-related information mentioned in the literature . In another study, Papanikolaou et al. used text mining to recognize biological entities in the Drug Bank database. The retrieved data were then clustered by different algorithms and used for obtaining novel drug–drug relations .
6.3 Machine learning (ML)
Machine learning, a crucial subset of artificial intelligence (AI), has been combined into many fields, such as data generation and analytics. Related to drug discovery, ML algorithms may participate in target and lead discovery as well as develop quantitative structure–activity relationships. Briefly, in machine learning-based drug repositioning, different algorithms, such as artificial neural networks (ANNs), support vector machines (SVMs), and random forest (RF), were trained by numerical forms of different features of drugs, diseases, genes, and so on. The trained algorithms can then predict the drug ability of unknown compounds . In this regard, Gottlieb et al. used drug–drug and disease–disease similarity events as grouping features for training a logistic regression classifier and prediction of drug-disease associations . Similarly, Napolitano et al. introduced a SVM model trained by drug-related similarities with the ability to forecast the therapeutic class of United States Food and Drug Administration (FDA)-approved compounds . Aliper et al. introduced a fully connected deep neural network algorithm trained by gene expression signatures for predicting therapeutic potentials and new drug suggestions .
6.4 Network analysis
Biological networks, an outstanding way of modeling biological entities and their interactions, can supply significant insight into the mechanism action of drugs and drug targets and symptoms of diseases. The models can be used to determine informative associations between genes, chemicals, proteins, phenotypes, and any other biological entities by statistical analysis, computational models, and leveraging graph theory concepts. Based on the data sources, network analysis can be classified into metabolic networks, protein–protein interaction networks, drug–drug interaction networks, drug-side effect association networks, disease–disease interaction networks, and gene regulatory networks. Consequently, bionetworks and their analysis can be used to identify potential therapeutic agents and drug repositioning [49, 50, 51].
6.5 Molecular docking
Studying the ligand-protein interactions at the molecular level has a crucial role in pharmaceutical research. Therefore, the scientific community focused on the exploration of the binding phenomenon over the years. Accordingly, some theories, such as lock and key hypothesis, induced-fit theory, and conformational selection were introduced for the interpretation of ligand–protein interactions . Historically, the refinement of a complex structure by optimization of the separation between the partners was the first description of the docking term in 1970. Molecular docking was first being developed in 1980 to predict the best matching binding mode and the molecular interactions of a ligand to a macromolecular partner through the generation of a number of probable orientations of the ligand inside the protein cavity. The method comprises two interrelated steps including orientations sampling and a scoring function, which are responsible for reproducing experimental binding mode and ranking of prepared complexes [52, 53]. Molecular docking can classify into rigid, semi-flexible, and flexible types, according to the degrees of flexibility of the ligand and receptor. In the rigid docking-like to lock-key theory, both ligand and protein are considered rigid entities and hence, there is no internal degree of freedom. Semi-flexible docking is a molecular docking simulation with flexible ligand and rigid receptors. Thus, all degrees of freedom of ligand are explored. Recently, several online and standalone software such as AutoDock, AutoDock Vina, Molegro Virtual Docker, Gold, Surflex-Dock, GLIDE, FlexX, DOCK, FRED, and so on, have been developed for computing different types of molecular docking. Most available software for molecular docking uses flexible ligands and several are trying to model flexible receptor proteins. In recent years, with promising advancements in optimization and the development of new molecular docking algorithms, numerous publications have been planned for comparing the performance of different molecular docking tools. However, it should be stressed that comparison between molecular docking methods is problematic, due to the dependance on docking performance with classes of the subjected targets. The ability of molecular docking methods to reveal the possibility of enzymatic reactions is a compelling reason for various applications related to computational drug design and repurposing, hit identification, lead optimization, binding site prediction, mechanisms of enzymatic reactions, and protein engineering [54, 55, 56]. Since the emergence of COVID-19, several molecular docking-based studies [57, 58, 59, 60, 61, 62] have been planned to introduce effective anti-COVID-19 drugs by means of drug repositioning. In Figure 1, the main steps of a molecular docking-based drug repurposing study are represented.
6.5.1 Recent projects, challenges, and future prospects in molecular docking-based drug repositioning against COVID-19
As a popular bioinformatics method, recently several types of research have been conducted to reposition approved drugs against COVID-19 by means of molecular docking. Despite similar aspects and methodology, the used software, subjected target and ligands can affect the outputs of molecular docking-based drug repositioning [54, 63]. In Table 3, some recently published works associated with molecular docking-based drug repurposing are presented. Based on our best knowledge,
|Subjected target||ligands||Proposed drug or ligand||References|
|Mpro||FDA-approved drugs||binifibrate and bamifylline|||
|Mpro||4384-approved drugs||Daunorubicin and eight other compounds|||
|Mpro||6218-approved drugs||Emodin and blonanserin|||
|RBD, NSP 10, NSP 16, Mpro, and RdRp||Brazilian Public Health System-approved drugs||penciclovir, ribavirin, and zanamivir|||
|Mpro||Drug Bank database||levothyroxine, amobarbital and ABP-700|||
|spike glycoprotein||FDA-approved drugs||Conivaptan and Trosec|||
|spike glycoprotein||Plant secondary metabolites||Dicaffeoylquinic acid|||
|Mpro||FDA-approved antiviral drugs||Lopinavir-Ritonavir, Tipranavir, and Raltegravir|||
|papain like protease||Plant secondary metabolites||I-Asarinin|||
|Mpro||superDRUG2 database||Binifibrate and Bamifylline|||
|Mpro||Plant secondary metabolites||ursolic acid, carvacrol and oleanolic acid|||
|RdRp||FDA-approved anti-viral drugs||remdesivir, ribavirin, sofosbuvir and galidesivir|||
|Mpro||FDA approved drugs||remdesivir and glycyrrhizin|||
|Mpro and RdRp||Plant secondary metabolites||cryptomisrine, cryptospirolepine, cryptoquindoline, and biscryptolepine|||
6.5.2 A case study: repurposing FDA-approved antiviral protease inhibitors as
As mentioned in Section 3.2, due to the important role in the viral life cycle alongside the absence of closely related homologs in humans, the 3CLpro is considered a proper target for discovering effective antiviral drugs against
220.127.116.11 Retrieval and preparation of ligands and receptor
A small molecule–protein molecular docking study is based on the prediction of probable interactions between the ligand and its receptor. Obtaining the three-dimensional structures of both the ligand and receptor is the first vital step for performing a molecular docking process. Therefore, the raw three structures of a set of FDA-approved antiviral protease inhibitors, as well as 3CLpro from
|Approved drug||Chemical formula||Accession number|
18.104.22.168 Primary screening by blind docking method
Despite primary screening done by the blind docking method, several studies have been conducted to introduce effective 3CLpro inhibitors. However, to date, binding pockets and key amino acids in the enzyme catalytic activity are not well known. Therefore, as primary screening, the blind docking processes through Molegro Virtual Docker 6.0 software were performed between the standard drugs and the 3CLpro to determine the key amino acid(s). In blind molecular docking, the whole surface of a subjected receptor is considered for evaluation of probable interactions with the ligand.
22.214.171.124 Targeted molecular docking
After determining the total affinities of the standard drugs to the 3CLpro as well as more reactive amino acids, targeted molecular docking studies were conducted between the receptor the three top-scoring docked ligands in a grid box, which covers the key amino acid(s) by Autodock 4.2.6 software.
The results of the primary screening are presented in Table 5. The results demonstrated that Amprenavir, Tipranavir, and Fosamprenavir had a higher binding affinity to the 3CLpro than the other tested viral protease inhibitors with Moldock scores of −160.384, −158.307, and −146.601 respectively. Furthermore, it was clear that GLN 189 is a key amino acid in the 3CLpro interactions with different proteases. Therefore, a targeted molecular docking between the three top-scoring standard protease inhibitors (Amprenavir, Tipranavir, and Fosamprenavir) were also performed in a grid box with the center of GLN189. As depicted in Figure 2, the subjected standard drugs also showed high affinity to the 3CLpro with binding energies of −5.3, −5.1, and −6.2 kcal/mol respectively. Subsequently, due to the high affinity of Fosamprenavir to the 3CLpro, this antiviral protease inhibitor could be considered for further in silico, in vitro, and in vivo evaluation to develop as a repurposed anti
|Drug||Moldock score (kcal/mol)||Key amino acids|
To date, the only approved anti-COVID-19 treatment is a repurposed antiviral drug (Remdesivir). Hence, drug repurposing might be an effective approach for accelerating drug discovery against COVID-19. Computational drug repositioning offers a noteworthy reduction in time and costs of new drug development and increases success rates in comparison to traditional methods. Therefore, to date, different computational methods such as data mining, machine learning, network analysis, and molecular docking have successfully been used for drug repurposing.
Molecular docking is a popular bioinformatics method that recently has been highly regarded for studying the drug ability of biological entities, protein-ligand interactions, mechanism action of drug candidates, and drug repositioning. Retrieval drug candidates from standard databases or previous reports, lead and target optimization, running the molecular docking process, and results analysis are the main steps in molecular docking-based drug repositioning. The binding affinity of a drug candidate to key amino acid(s) of the identified target molecule can be considered a decision factor in the drug repositioning process.
Despite the advantages of computational drug repositioning, studying drug-target interactions by in silico methods is still far from reality.