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

MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics

Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein-ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein-ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein-ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure-activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.

Insights into the inhibitory potential of novel hydrazinyl thiazole-linked indenoquinoxaline against alpha-amylase: a comprehensive QSAR, pharmacokinetic, and molecular modeling study

The rising prevalence of diabetes necessitates the development of novel drugs, especially given the side effects associated with current medications like Acarbose and Voglibose. A series of 36 Hydrazinyl thiazole-linked indenoquinoxaline derivatives with notable activity against alpha-amylase were studied. To create a molecular model predicting alpha-amylase activity, a QSAR study was performed on these compounds. Molecular descriptors were calculated using Chem3D and Gaussian software and then correlated with their IC50 biological activities to form a dataset. This model data was refined using PCA and modeled with MLR. The model's performance was statistically verified (R2 =0.800; R adj 2 = 0.767; R cv 2 = 0.651) and its applicability domain was defined. It was predicted to possess high predictive power (R test 2   = 0.872). Based on this, new compounds were proposed, and their activities were predicted using the developed model. Additionally, their binding ability to the biological target was studied through molecular docking and dynamics. Their pharmacokinetics were also evaluated using ADMET predictions. Two designed compounds named AE and AB emerged as particularly promising, displaying properties that suggest substantial therapeutic potential and they can form stable complexes into the binding pocket of alpha-amylase enzyme.Communicated by Ramaswamy H. Sarma.

Nirmatrelvir and ritonavir combination: an antiviral therapy for COVID-19

INTRODUCTION

The emergence of the Omicron SARS-CoV-2 variant of concern in late November 2021 presaged yet another stage of the COVID-19 pandemic. Paxlovid, a co-packaged dosage form of two antiviral drugs (nirmatrelvir and ritonavir) developed by Pfizer, received its first FDA Emergency Use Authorization (EUA) and conditional marketing by European Medical Agency in patients at high risk of developing severe COVID-19.

AREAS COVERED

We reviewed the timeline of the drug nirmatrelvir from its discovery to authorization by FDA. After 1 year of its authorization, numerous studies and reports on paxlovid's use and post-use consequences are available. This review summarizes the complete journey of paxlovid from its development, preclinical studies, clinical trials, regulatory approvals, ongoing clinical trials, and safety measures, followed by discussions on recent updates on drug-drug interactions, adverse effects, and relapse of COVID-19.

EXPERT OPINION

Paxlovid, a new oral antiviral therapy for COVID-19, has shown promising results in clinical trials and has the potential to be effective against the pandemic, particularly for individuals at high risk of severe illness. Comorbidity usage and pharmacovigilance will play a significant stake in the future of paxlovid development. Second-generation Mpro inhibitors play an important role in the upcoming problems associated with COVID-19.

Sigma-1 receptor agonist properties that mediate the fast-onset antidepressant effect of hypidone hydrochloride (YL-0919)

The most intriguing characteristic of the sigma-1 receptor is its ability to regulate multiple functional proteins directly via protein-protein interactions, giving the sigma-1 receptor the powerful ability to regulate several survival and metabolic functions in cells, fine tune neuronal excitability, and regulate the transmission of information within brain circuits. This characteristic makes sigma-1 receptors attractive candidates for the development of new drugs. Hypidone hydrochloride (YL-0919), a novel structured antidepressant candidate developed in our laboratory, possess a selective sigma-1 receptor agonist profile, as evidenced by molecular docking, radioligand receptor binding assays, and receptor functional experiments. In vivo studies have revealed that YL-0919 elicits a fast-onset antidepressant activity (within one week) that can be attenuated with pretreatment of the selective sigma-1 receptor antagonist, BD-1047. Taken together, the findings of the current study suggest that YL-0919 activates the sigma-1 receptor to partially mediate the rapid onset antidepressant effects of YL-0919. Thus, YL-0919 is a promising candidate as a fast-onset antidepressant that targets the sigma-1 receptor.

Recent advancements in the structural biology of human telomerase and their implications for improved design of cancer therapeutics

Telomerase is a specialized reverse transcriptase that synthesizes telomeric repeats at the ends of linear chromosomes. Telomerase is transiently expressed in germ and stem cells, but nearly all somatic cells silence it after differentiating. However, the vast majority of cancer cells reactivate and constitutively express telomerase to maintain replicative immortality. Because of this, telomerase has remained a promising broad-spectrum chemotherapeutic target for over 30 years. However, various challenges associated with obtaining high-resolution structural data for telomerase have limited the development of rationally designed structure-based therapeutics. Various techniques and model systems have been utilized to advance our understanding of the structural biology of telomerase. In particular, multiple high-resolution cryogenic electron microscopy (cryo-EM) structures published within the past few years have revealed new components of the telomerase complex with near atomic resolution structural models. Additionally, these structures have provided details for how telomerase is recruited to telomeres and its mechanism of telomere synthesis. With these new pieces of evidence, and the promising outlook for future refinements to our current models, the possibility of telomerase specific chemotherapeutics is becoming more tangible than ever. This review summarizes these recent advancements and outlines outstanding questions in the field.

The exploration of phytocompounds theoretically combats SARS-CoV-2 pandemic against virus entry, viral replication and immune evasion

BACKGROUND

The novel coronavirus disease-2019 (COVID-19) that emerged in China, is an extremely contagious and pathogenic viral infection caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) that has sparked a global pandemic. The few and limited availability of approved therapeutic agents or vaccines is of great concern. Urgently, Remdesivir, Nirmatrelvir, Molnupiravir, and some phytochemicals including polyphenol, flavonoid, alkaloid, and triterpenoid are applied to develop as repurposing drugs against the SARS-CoV-2 invasion.

METHODS

This study was conducted to perform molecular docking and absorption, distribution, metabolism, excretion and toxicity (ADMET) analysis of the potential phytocompounds and repurposing drugs against three targets of SARS-CoV-2 proteins (RNA dependent RNA polymerase, RdRp, Endoribonclease, S-protein of ACE2-RBD).

RESULTS

The docking data illustrated Arachidonic acid, Rutin, Quercetin, and Curcumin were highly bound with coronavirus polyprotein replicase and Ebolavirus envelope protein. Furthermore, anti- Ebolavirus molecule Remedesivir, anti-HIV molecule Chloroquine, and Darunavir were repurposed with coronavirus polyprotein replicase as well as Ebolavirus envelope protein. The strongest binding interaction of each targets are Rutin with RdRp, Endoribonclease with Amentoflavone, and ACE2-RBD with Epigallocatechin gallate.

CONCLUSIONS

Taken altogether, these results shed a light on that phytocompounds have a therapeutic potential for the treatment of anti-SARS-CoV-2 may base on multi-target effects or cocktail formulation for blocking viral infection through invasion/activation, transcription/reproduction, and posttranslational cleavage to battle COVID-19 pandemic.

Pharmacological therapies and drug development targeting SARS-CoV-2 infection

The development of therapies for SARS-CoV-2 infection, based on virus biology and pathology, and of large- and small-scale randomized controlled trials, have brought forward several antiviral and immunomodulatory drugs targeting the disease severity. Casirivimab/Imdevimab monoclonal antibodies and convalescent plasma to prevent virus entry, Remdesivir, Molnupiravir, and Paxlovid nucleotide analogs to prevent viral replication, a variety of repurposed JAK-STAT signaling pathway inhibitors, corticosteroids, and recombinant agonists/antagonists of cytokine and interferons have been found to provide clinical benefits in terms of mortality and hospitalization. However, current treatment options face multiple clinical needs, and therefore, in this review, we provide an update on the challenges of the existing therapeutics and highlight drug development strategies for COVID-19 therapy, based on ongoing clinical trials, meta-analyses, and clinical case reports.

Novel computational pipelines in antiviral structure‑based 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.

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.

Discovery of novel IDH1-R132C inhibitors through structure-based virtual screening

Isocitrate dehydrogenase (IDH) belongs to a family of enzymes involved in glycometabolism. It is found in many living organisms and is one of the most mutated metabolic enzymes. In the current study, we identified novel IDH1-R132C inhibitors using docking-based virtual screening and cellular inhibition assays. A total of 100 molecules with high docking scores were obtained from docking-based virtual screening. The cellular inhibition assay demonstrated five compounds at a concentration of 10 μM could inhibit cancer cells harboring the IDH1-R132C mutation proliferation by > 50%. The compound (T001-0657) showed the most potent effect against cancer cells harboring the IDH1-R132C mutation with a half-maximal inhibitory concentration (IC₅₀) value of 1.311 μM. It also showed a cytotoxic effect against cancer cells with wild-type IDH1 and normal cells with IC₅₀ values of 49.041 μM and >50 μM, respectively. Molecular dynamics simulations were performed to investigate the stability of the kinase structure binding of allosteric inhibitor compound A and the identified compound T001-0657 binds to IDH1-R132C. Root-mean-square deviation, root-mean-square fluctuation, and binding free energy calculations showed that both compounds bind tightly to IDH1-R132C. In conclusion, the compound identified in this study had high selectivity for cancer cells harboring IDH1-R132C mutation and could be considered a promising hit compound for further development of IDH1-R132C inhibitors.

A computational study of cooperative binding to multiple SARS-CoV-2 proteins

Structure-based drug design targeting the SARS-CoV-2 virus has been greatly facilitated by available virus-related protein structures. However, there is an urgent need for effective, safe small-molecule drugs to control the spread of the virus and variants. While many efforts are devoted to searching for compounds that selectively target individual proteins, we investigated the potential interactions between eight proteins related to SARS-CoV-2 and more than 600 compounds from a traditional Chinese medicine which has proven effective at treating the viral infection. Our original ensemble docking and cooperative docking approaches, followed by a total of over 16-micorsecond molecular simulations, have identified at least 9 compounds that may generally bind to key SARS-CoV-2 proteins. Further, we found evidence that some of these compounds can simultaneously bind to the same target, potentially leading to cooperative inhibition to SARS-CoV-2 proteins like the Spike protein and the RNA-dependent RNA polymerase. These results not only present a useful computational methodology to systematically assess the anti-viral potential of small molecules, but also point out a new avenue to seek cooperative compounds toward cocktail therapeutics to target more SARS-CoV-2-related proteins.