Looking for SARS-CoV-2 therapeutics through computational approaches.

BACKGROUND: In the last few years in silico tools, including drug repurposing coupled with structure-based virtual screening, have been extensively employed to look for anti-COVID-19 agents. OBJECTIVE: The present review aims to provide readers with a portrayal of computational approaches that could conduct more quickly and cheaply to novel anti-viral agents. Particular attention is given to docking-based virtual screening. METHOD: The World Health Organization website was consulted to gain the latest information on SARS-CoV-2, its novel variants and their interplay with COVID-19 severity and treatment options. The Protein Data Bank was explored to look for 3D coordinates of SARS-CoV-2 proteins in their free and bound states, in the wild-types and mutated forms. Recent literature related to in silico studies focused on SARS-CoV-2 proteins was searched through PubMed. RESULTS: A large amount of work has been devoted thus far to computationally targeting viral entry and searching for inhibitors of the S-protein/ACE2 receptor complex. Another large area of investigation is linked to in silico identification of molecules able to block viral proteases -including Mpro- thus avoiding maturation of proteins crucial for virus life cycle. Such computational studies have explored the inhibitory potential of the most diverse molecule databases (including plant extracts, dietary compounds, FDA approved drugs). CONCLUSION: More efforts need to be dedicated in the close future to experimentally validate the therapeutic power of in silico identified compounds in order to catch, among the wide ensemble of computational hits, novel therapeutics to prevent and/or treat COVID-19.

Predictive analysis, diagnosis of COVID-19 through computational screening and validation with spectro photometrical approach

Objective: To develop Favipiravir, based predictive models of coronavirus disease 2019 (COVID-19) from small molecule databases such as PubChem, Drug Bank, Zinc Database, and literature. Method(s): High Throughput Virtual Screening (HTVS) using different computational screening methods is used to identify the target and lead molecules. CoMFA (Comparative Molecular Field Analysis) is a 3D-QSAR procedure depending on information from known dynamic atoms and eventually permits one to plan and anticipate exercises of particles. These two analysis is used to train predictive models. Result(s): The predictive model achieved the highest accuracy score with a relatively small dataset size can be a subject of overfitting. Datasets with over 500 samples demonstrate an accuracy of about 85-95%, that can be considered as very good. Conclusion(s): From the result it is observed that Increasing level of potassium, sodium and nitrogen will lead to burst lipid bilayer membrane of virus which cause RNA replication rapidly. However, low level of sodium, potassium and nitrogen will help in the DNA polymerase inhibition and replication can be stopped. The best developed QSAR model in terms of the druggability and activity relation has been selected over the parent Favipiravir molecule for designing COVID-19 drugs may lead towards pharmaceutical development in future.Copyright © 2023, The Author(s), under exclusive licence to Korean Society of Environmental Risk Assessment and Health Science.

Structural insights into SARS-CoV-2 main protease conformational plasticity.

The spread of different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants underscores the need for insights into the structural properties of its structural and non-structural proteins. The highly conserved homo-dimeric chymotrypsin-like protease (3CL MPRO ), belonging to the class of cysteine hydrolases, plays an indispensable role in processing viral polyproteins that are involved in viral replication and transcription. Studies have successfully demonstrated the role of MPRO as an attractive drug target for designing antiviral treatments because of its importance in the viral life cycle. Herein, we report the structural dynamics of six experimentally solved structures of MPRO (i.e., 6LU7, 6M03, 6WQF, 6Y2E, 6Y84, and 7BUY including both ligand-free and ligand-bound states) at different resolutions. We have employed a structure-based balanced forcefield, CHARMM36m through state-of-the-art all-atoms molecular dynamics simulations at µ-seconds scale at room temperature (303K) and pH 7.0 to explore their structure-function relationship. The helical domain-III responsible for dimerization mostly contributes to the altered conformational states and destabilization of MPRO . A keen observation of the high degree of flexibility in the P5 binding pocket adjoining domain II-III highlights the reason for observation of conformational heterogeneity among the structural ensembles of MPRO . We also observe a differential dynamics of the catalytic pocket residues His41, Cys145, and Asp187, which may lead to catalytic impairment of the monomeric proteases. Among the highly populated conformational states of the six systems, 6LU7 and 7M03 forms the most stable and compact MPRO conformation with intact catalytic site and structural integrity. Altogether, our findings from this extensive study provides a benchmark to identify physiologically relevant structures of such promising drug targets for structure-based drug design and discovery of potent drug-like compounds having clinical potential.

Applications and prospects of cryo-EM in drug discovery.

Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time- and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy (cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence (AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of medium-resolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.

Selection of suitable protein structure from Protein Data Bank: An important step in Structure based Drug Design Studies.

Selection of a protein structure is an important step for the success of the drug discovery process using structure-based design. Selection of the right crystal structure is a critical step as multiple crystal structures are available for the same protein in the protein data bank (PDB). In this communication, we have discussed a systematic approach for selecting the right type of protein structure. Some case studies for the selection of crystal structures of TACE, 11ß-HSD1, DprE1 andSARS-CoV-2 Mpro enzymes have been discussed for the purpose of illustration.

Novel computational pipelines in antiviral structure­based drug design (Review).

Viral infections constitute a fundamental and continuous challenge for the global scientific and medical community, as highlighted by the ongoing COVID-19 pandemic. In combination with prophylactic vaccines, the development of safe and effective antiviral drugs remains a pressing need for the effective management of rare and common pathogenic viruses. The design of potent antivirals can be informed by the study of the three-dimensional structure of viral protein targets. Structure-based design of antivirals in silico provides a solution to the arduous and costly process of conventional drug development pipelines. Furthermore, rapid advances in high-throughput computing, along with the growth of available biomolecular and biochemical data, enable the development of novel computational pipelines in the hunt of antivirals. The incorporation of modern methods, such as deep-learning and artificial intelligence, has the potential to revolutionize the structure-based design and repurposing of antiviral compounds, with minimal side effects and high efficacy. The present review aims to provide an outline of both traditional computational drug design and emerging, high-level computing strategies.

Two Ligand-Binding Sites on SARS-CoV-2 Non-Structural Protein 1 Revealed by Fragment-Based X-ray Screening.

The regular reappearance of coronavirus (CoV) outbreaks over the past 20 years has caused significant health consequences and financial burdens worldwide. The most recent and still ongoing novel CoV pandemic, caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) has brought a range of devastating consequences. Due to the exceptionally fast development of vaccines, the mortality rate of the virus has been curbed to a significant extent. However, the limitations of vaccination efficiency and applicability, coupled with the still high infection rate, emphasise the urgent need for discovering safe and effective antivirals against SARS-CoV-2 by suppressing its replication or attenuating its virulence. Non-structural protein 1 (nsp1), a unique viral and conserved leader protein, is a crucial virulence factor for causing host mRNA degradation, suppressing interferon (IFN) expression and host antiviral signalling pathways. In view of the essential role of nsp1 in the CoV life cycle, it is regarded as an exploitable target for antiviral drug discovery. Here, we report a variety of fragment hits against the N-terminal domain of SARS-CoV-2 nsp1 identified by fragment-based screening via X-ray crystallography. We also determined the structure of nsp1 at atomic resolution (0.99 Å). Binding affinities of hits against nsp1 and potential stabilisation were determined by orthogonal biophysical assays such as microscale thermophoresis and thermal shift assays. We identified two ligand-binding sites on nsp1, one deep and one shallow pocket, which are not conserved between the three medically relevant SARS, SARS-CoV-2 and MERS coronaviruses. Our study provides an excellent starting point for the development of more potent nsp1-targeting inhibitors and functional studies on SARS-CoV-2 nsp1.

Fundamental considerations in drug design

The drug discovery paradigm has been very time-consuming, challenging, and expensive;however, the disease conditions originating from bacteria, virus, protozoa, fungus and other microorganisms are steadily shooting up. For instance, COVID-19 is the latest viral infection that affects millions of people and the world’s economy very severely. Therefore, the quest for discovery of novel and potent drug compounds against deadly pathogens is crucial at the moment. Despite a lot of drawbacks in drug discovery and development and its pertaining technology, the advancement must be taken into account so the time duration and cost would be minimized. In this chapter, basic principles in drug design and discovery have been discussed together with advances in drug development. © 2022 Elsevier Inc. All rights reserved.

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.

From Repurposing to Redesign: Optimization of Boceprevir to Highly Potent Inhibitors of the SARS-CoV-2 Main Protease.

The main protease (Mpro) of the betacoronavirus SARS-CoV-2 is an attractive target for the development of treatments for COVID-19. Structure-based design is a successful approach to discovering new inhibitors of the Mpro. Starting from crystal structures of the Mpro in complexes with the Hepatitis C virus NS3/4A protease inhibitors boceprevir and telaprevir, we optimized the potency of the alpha-ketoamide boceprevir against the Mpro by replacing its P1 cyclobutyl moiety by a γ-lactam as a glutamine surrogate. The resulting compound, MG-78, exhibited an IC50 of 13 nM versus the recombinant Mpro, and similar potency was observed for its P1' N-methyl derivative MG-131. Crystal structures confirmed the validity of our design concept. In addition to SARS-CoV-2 Mpro inhibition, we also explored the activity of MG-78 against the Mpro of the alphacoronavirus HCoV NL63 and against enterovirus 3C proteases. The activities were good (0.33 µM, HCoV-NL63 Mpro), moderate (1.45 µM, Coxsackievirus 3Cpro), and relatively poor (6.7 µM, enterovirus A71 3Cpro), respectively. The structural basis for the differences in activities was revealed by X-ray crystallo-graphy. We conclude that the modified boceprevir scaffold is suitable for obtaining high-potency inhibitors of the coronavirus Mpros but further optimization would be needed to target enterovirus 3Cpros efficiently.

Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal.

The main protease (Mpro) of SARS-Cov-2 is the essential enzyme for maturation of functional proteins implicated in viral replication and transcription. The peculiarity of its specific cleavage site joint with its high degree of conservation among all coronaviruses promote it as an attractive target to develop broad-spectrum inhibitors, with high selectivity and tolerable safety profile. Herein is reported a combination of three-dimensional quantitative structure-activity relationships (3-D QSAR) and comparative molecular binding energy (COMBINE) analysis to build robust and predictive ligand-based and structure-based statistical models, respectively. Models were trained on experimental binding poses of co-crystallized Mpro-inhibitors and validated on available literature data. By means of deep optimization both models' goodness and robustness reached final statistical values of r2/q2 values of 0.97/0.79 and 0.93/0.79 for the 3-D QSAR and COMBINE approaches respectively, and an overall predictiveness values of 0.68 and 0.57 for the SDEPPRED and AAEP metrics after application to a test set of 60 compounds covered by the training set applicability domain. Despite the different nature (ligand-based and structure-based) of the employed methods, their outcome fully converged. Furthermore, joint ligand- and structure-based structure-activity relationships were found in good agreement with nirmatrelvir chemical features properties, a novel oral Mpro-inhibitor that has recently received U.S. FDA emergency use authorization (EUA) for the oral treatment of mild-to-moderate COVID-19 infected patients. The obtained results will guide future rational design and/or virtual screening campaigns with the aim of discovering new potential anti-coronavirus lead candidates, minimizing both time and financial resources. Moreover, as most of calculation were performed through the well-established web portal 3d-qsar.com the results confirm the portal as a useful tool for drug design.

A systematic review of RdRp of SARS-CoV-2 through artificial intelligence and machine learning utilizing structure-based drug design strategy

Since the coronavirus disease has been declared a global pandemic, it had posed a challenge among researchers and raised common awareness and collaborative efforts towards finding the solution. Caused by severe acute respiratory coronavirus syndrome-2 (SARS-CoV-2), coronavirus drug design strategy needs to be optimized. It is understandable that cognizance of the pathobiology of COVID-19 can help scientists in the development and discovery of therapeutically effective antiviral drugs by elucidating the unknown viral pathways and structures. Considering the role of artificial intelligence and machine learning with its advancements in the field of science, it is rational to use these methods which can aid in the discovery of new potent candidates in silico. Our review utilizes similar methodologies and focuses on RNA-dependent RNA polymerase (RdRp), based on its importance as an essential element for virus replication and also a promising target for COVID-19 therapeutics. Artificial neural network technique was used to shortlist articles with the support of PRISMA, from different research platforms including Scopus, PubMed, PubChem, and Web of Science, through a combination of keywords. ???English language???, from the year ???2000??? and ???published articles in journals??? were selected to carry out this research. We summarized that structural details of the RdRp reviewed in this analysis will have the potential to be taken into consideration when developing therapeutic solutions and if further multidisciplinary efforts are taken in this domain then potential clinical candidates for RdRp of SARS-CoV-2 could be successfully delivered for experimental validations.

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design.

The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and beyond. Drug development is a costly and time-consuming business, and only a minority of approved drugs generate returns exceeding the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper reviews the most significant research on artificial intelligence in de novo drug design for COVID-19 pharmaceutical research.

Cryo-EM as a powerful tool for drug discovery: recent structural based studies of SARS-CoV-2.

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has arisen as a global pandemic affecting the respiratory system showing acute respiratory distress syndrome (ARDS). However, there is no targeted therapeutic agent yet and due to the growing cases of infections and the rising death tolls, discovery of the possible drug is the need of the hour. In general, the study for discovering therapeutic agent for SARS-CoV-2 is largely focused on large-scale screening with fragment-based drug discovery (FBDD). With the recent advancement in cryo-electron microscopy (Cryo-EM), it has become one of the widely used tools in structural biology. It is effective in investigating the structure of numerous proteins in high-resolution and also had an intense influence on drug discovery, determining the binding reaction and regulation of known drugs as well as leading the design and development of new drug candidates. Here, we review the application of cryo-EM in a structure-based drug design (SBDD) and in silico screening of the recently acquired FBDD in SARS-CoV-2. Such insights will help deliver better understanding in the procurement of the effective remedial solution for this pandemic.

Drug repurposing in COVID-19: A review with past, present and future.

The coronavirus SARS-CoV-2 which causes the COVID-19 disease is a global public health emergency. Coronavirus are single-stranded positive-sense RNA viruses and their genome size is approximately 30 kb, which encodes some important structural proteins. The interaction between viral Spike protein and ACE2 on the host cell surface is of significant interest since it initiates the infection process. This review will focus on the effectiveness of reuse of currently used drugs against COVID-19, including clinical trials, molecular docking, and computational modelling approach. METHODS: A systematic search in Pubmed, MEDLINE, EMBASE was conducted from from January 2020 to July 2021.Applying computational, clinical and experimental approaches, numerous drugs such as remdesivir, favipiravir, ribavirin, lopinavir, ritonavir, tocilizumab have been repurposed and have shown promising protection against SARS-CoV2 both in vitro and in clinical conditions. Although there is only one repurposed drug approved by the U.S. Food and Drug Administration (FDA) to treat coronavirus disease 2019 (COVID-19), i.e, Remdesivir. However, the FDA withdrew the authorization of the drugs Hydroxychloroquine and chloroquine,that are not effective for COVID-19 and can also cause serious heart problems. Molecular coupling would be the ideal technique to identify such therapeutic agents against COVID19.

Rapid structure-based identification of potential SARS-CoV-2 main protease inhibitors.

The COVID-19 outbreak has thrown the world into an unprecedented crisis. It has posed a challenge to scientists around the globe who are working tirelessly to combat this pandemic. We herein report a set of molecules that may serve as possible inhibitors of the SARS-CoV-2 main protease. To identify these molecules, we followed a combinatorial structure-based strategy, which includes high-throughput virtual screening, molecular docking and WaterMap calculations. The study was carried out using Protein Data Bank structures 5R82 and 6Y2G. DrugBank, Enamine, Natural product and Specs databases, along with a few known antiviral drugs, were used for the screening. WaterMap analysis aided in the recognition of high-potential molecules that can efficiently displace binding-site waters. This study may help the discovery and development of antiviral drugs against SARS-CoV-2.

Molecular modeling of potent novel sulfonamide derivatives as non-peptide small molecule anti-COVID 19 agents.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent for the COVID-19. The Sulfonamides groups have been widely introduced in several drugs, especially for their antibacterial activities and generally prescribed for respiratory infections. On the other hand, imidazole groups have the multipotency to act as drugs, including antiviral activity. We have used a structure-based drug design approach to design some imidazole derivatives of sulfonamide, which can efficiently bind to the active site of SARS-CoV-2 main protease and thus may have the potential to inhibit its proteases activity. We conducted molecular docking and molecular dynamics simulation to observe the stability and flexibility of inhibitor complexes. We have checked ADMET (absorption, distribution, metabolism, excretion and toxicity) and drug-likeness rules to scrutinize toxicity and then designed the most potent compound based on computational chemistry. Our small predicted molecule non-peptide protease inhibitors could provide a useful model in the further search for novel compounds since it has many advantages over peptidic drugs, like lower side effects, toxicity and less chance of drug resistance. Further, we confirmed the stability of our inhibitor-complex and interaction profile through the Molecular dynamics simulation study. Our small predicted moleculeCommunicated by Ramaswamy H. Sarma.

Systematic review on role of structure based drug design (SBDD) in the identification of anti-viral leads against SARS-Cov-2.

The outbreak of existing public health distress is threatening the entire world with emergence and rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The novel coronavirus disease 2019 (COVID-19) is mild in most people. However, in some elderly people with co-morbid conditions, it may progress to pneumonia, acute respiratory distress syndrome (ARDS) and multi organ dysfunction leading to death. COVID-19 has caused global panic in the healthcare sector and has become one of the biggest threats to the global economy. Drug discovery researchers are expected to contribute rapidly than ever before. The complete genome sequence of coronavirus had been reported barely a month after the identification of first patient. Potential drug targets to combat and treat the coronavirus infection have also been explored. The iterative structure-based drug design (SBDD) approach could significantly contribute towards the discovery of new drug like molecules for the treatment of COVID-19. The existing antivirals and experiences gained from SARS and MERS outbreaks may pave way for identification of potential drug molecules using the approach. SBDD has gained momentum as the essential tool for faster and costeffective lead discovery of antivirals in the past. The discovery of FDA approved human immunodeficiency virus type 1 (HIV-1) inhibitors represent the foremost success of SBDD. This systematic review provides an overview of the novel coronavirus, its pathology of replication, role of structure based drug design, available drug targets and recent advances in in-silico drug discovery for the prevention of COVID-19. SARSCoV- 2 main protease, RNA dependent RNA polymerase (RdRp) and spike (S) protein are the potential targets, which are currently explored for the drug development.