Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus.

Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure-activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R2 = 0.991 and Q2 = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R2 = 0.915 and Q2 = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV.

COVID-19: In silico identification of potent α-ketoamide inhibitors targeting the main protease of the SARS-CoV-2.

The COVID-19 has been creating a global crisis, causing countless deaths and unbearable panic. Despite the progress made in the development of the vaccine, there is an urge need for the discovery of antivirals that may better work at different stages of SARS-CoV-2 reproduction. The main protease (Mpro) of the SARS-CoV-2 is a crucial therapeutic target due to its critical function in virus replication. The α-ketoamide derivatives represent an important class of inhibitors against the Mpro of the SARS-CoV. While there is 99% sequence similarity between SARS-CoV and SARS-CoV-2 main proteases, anti-SARS-CoV compounds may have a huge demonstration's prospect of their effectiveness against the SARS-CoV-2. In this study, we applied various computational approaches to investigate the inhibition potency of novel designed α-ketoamide-based compounds. In this regard, a set of 21 α-ketoamides was employed to construct a QSAR model, using the genetic algorithm-multiple linear regression (GA-MLR), as well as a pharmacophore fit model. Based on the GA-MLR model, 713 new designed molecules were reduced to 150 promising hits, which were later subject to the established pharmacophore fit model. Among the 150 compounds, the best selected compounds (3 hits) with greater pharmacophore fit score were further studied via molecular docking, molecular dynamic simulations along with the Absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. Our approach revealed that the three hit compounds could serve as potential inhibitors against the SARS-CoV-2 Mpro target.

Distinguishing the optimal binding mechanism of an E3 ubiquitin ligase: Covalent versus noncovalent inhibition.

The development of covalent drugs, specifically in cancer therapeutics, has recently sparked interest among the pharmaceutical research community. While representing a significant fraction of the drugs in the market, very few have been deliberately designed to interact covalently with their biological target. One of the enzymes that have been both covalently and noncovalently targeted is the Neural Precursor Cell Expressed Developmentally Downregulated gene 4-1 (Nedd4-1). This enzyme has been found to have multiple physiological implications, including its involvement in cancer invasion. A critical gap still remains in the molecular understanding of the structural mechanism upon the covalent and noncovalent binding to Nedd4-1. In this study, we explore the most optimal binding mechanism in the inhibition of the catalytic site of the Nedd4-1. Our results exhibited a greater stability in the covalent complex compared with the noncovalent complex. This was supported by the secondary structure elements that were more dominant in the covalently inhibited complex. This complex disclosed an optimal free binding energy landscape, induced by the catalytic site energy contributions that showed to be more favorable. The insights demonstrating the above binding mechanism of Nedd4-1 establishes covalent inhibition as the preferred method of inhibition of the enzyme. This investigation aids in the understanding of the structural mechanism of Nedd4-1 inhibition and would assist in the design of more potent covalent inhibitors at the catalytic site of Nedd4-1.

Tracing Potential Covalent Inhibitors of an E3 Ubiquitin Ligase through Target-Focused Modelling.

The Nedd4-1 E3 Ubiquitin ligase has been implicated in multiple disease conditions due its overexpression. Although the enzyme may be targeted both covalently and non-covalently, minimal studies provide effective inhibitors against it. Recently, research has focused on covalent inhibitors based on their characteristic, highly-selective warheads and ability to prevent drug resistance. This prompted us to screen for new covalent inhibitors of Nedd4-1 using a combination of computational approaches. However, this task proved challenging due to the limited number of electrophilic moieties available in virtual libraries. Therefore, we opted to divide an existing covalent Nedd4-1 inhibitor into two parts: a non-covalent binding group and a pre-selected α, ß-unsaturated ester that forms the covalent linkage with the protein. A non-covalent pharmacophore model was built based on molecular interactions at the binding site. The pharmacophore was then subjected to virtual screening to identify structurally similar hit compounds. Multiple filtrations were implemented prior to selecting four hits, which were validated with a covalent conjugation and later assessed by molecular dynamic simulations. The results showed that, of the four hit molecules, Zinc00937975 exhibited advantageous molecular groups, allowing for favourable interactions with one of the characteristic cysteine residues. Predictive pharmacokinetic analysis further justified the compound as a potential lead molecule, prompting its recommendation for confirmatory biological evaluation. Our inhouse, refined, pharmacophore model approach serves as a robust method that will encourage screening for novel covalent inhibitors in drug discovery.

In silico design and analysis of NS4B inhibitors against hepatitis C virus.

The hepatitis C virus is a communicable disease that gradually harms the liver leading to cirrhosis and hepatocellular carcinoma. Important therapeutic interventions have been reached since the discovery of the disease. However, its resurgence urges the need for new approaches against this malady. The NS4B receptor is one of the important proteins for Hepatitis C Virus RNA replication that acts by mediating different viral properties. In this work, we opt to explore the relationships between the molecular structures of biologically tested NS4B inhibitors and their corresponding inhibitory activities to assist the design of novel and potent NS4B inhibitors. For that, a set of 115 indol-2-ylpyridine-3-sulfonamides (IPSA) compounds with inhibitory activity against NS4B is used. A hybrid genetic algorithm combined with multiple linear regressions (GA-MLR) was implemented to construct a predictive model. This model was further used and applied to a set of compounds that were generated based on a pharmacophore modeling study combined with virtual screening to identify structurally similar lead compounds. Multiple filtrations were implemented for selecting potent hits. The selected hits exhibited advantageous molecular features, allowing for favorable inhibitory activity against HCV. The results showed that 7 out of 1285 screened compounds, were selected as potent candidate hits where Zinc14822482 exhibits the best predicted potency and pharmacophore features. The predictive pharmacokinetic analysis further justified the compounds as potential hit molecules, prompting their recommendation for a confirmatory biological evaluation. We believe that our strategy could help in the design and screening of potential inhibitors in drug discovery.Communicated by Ramaswamy H. Sarma.

Host Cell Proteases Mediating SARS-CoV-2 Entry: An Overview.

The outbreak of the SARS-CoV-2 virus in late 2019 and the spread of the COVID-19 pandemic have caused severe health and socioeconomic damage worldwide. Despite the significant research effort to develop vaccines, antiviral treatments, and repurposed therapeutics to effectively contain the catastrophe, there are no available effective vaccines or antiviral drugs that can limit the threat of the disease, so the infections continue to expand. To date, the search for effective treatment remains a global challenge. Therefore, it is imperative to develop therapeutic strategies to contain the spread of SARS-CoV-2. Like other coronaviruses, SARS-CoV-2 invades and infects human host cells via the attachment of its spike envelope glycoprotein to the human host cell receptor hACE2. Subsequently, several host cell proteases facilitate viral entry via proteolytic cleavage and activation of the S protein. These host cell proteases include type II transmembrane serine proteases (TTSPs), cysteine cathepsins B and L, furin, trypsin, and Factor Xa, among others. Given the critical role of the host cell proteases in coronavirus pathogenesis, their inhibition by small molecules has successfully targeted SARS-CoV-2 in vitro, suggesting that host cell proteases are attractive therapeutic targets for SARS-CoV-2 infection. In this review, we focus on the biochemical properties of host cell proteases that facilitate the entry of SARS-CoV-2, and we highlight therapeutic small molecule candidates that have been proposed through in silico research.

Covalent Versus Non-covalent Enzyme Inhibition: Which Route Should We Take? A Justification of the Good and Bad from Molecular Modelling Perspective.

The pace and efficiency of drug target strategies have been emanating debates among researchers in the field of drug development. Covalent inhibitors possess significant advantages over non-covalent inhibitors, such that covalent warheads can target rare residues of a particular target protein, thus leading to the development of highly selective inhibitors. However, toxicity can be a real challenge related to this class of therapeutics. From the challenges of irreversible drug toxicity to the declining reactivity of reversible drugs, herein we provide justifications from the computational point of view. It was evident that both classes had its merits; however, with the increase in drug resistance, covalent inhibition seemed more suitable. There also seems to be enhanced selectivity of the covalent systems, proving its use as a therapeutic regimen worldwide. We believe that this study will assist researchers in making informed decisions on which drug class to choose as lead compounds in the drug discovery pipeline.

Selective Covalent Inhibition of "Allosteric Cys121" Distort the Binding of PTP1B Enzyme: A Novel Therapeutic Approach for Cancer Treatment.

Covalent inhibition targeting noncatalytic residues is rapidly gaining attention in drug discovery. Protein tyrosine phosphatases 1B (PTP1B) is an attractive target for therapeutic interventions in cancer and other diseases. Two binding sites of PTP1B enzyme were identified, catalytic and allosteric. The catalytic site is deep and narrow which protects the active site amino acid residue Cys215 from covalent inhibition, whereas the allosteric site is more hydrophobic and less conserved with Cys121 residue, to which covalent inhibitors can bind. A recent experimental report highlighted that a highly selective inhibitor, 73U, was found to bind covalently in the allosteric region of PTP1B enzyme. Using a robust covalent simulations protocol which was developed in-house, we explore the origin and impact of covalent inhibition upon inhibitor binding to allosteric site. For this, covalently bound and apo enzymes were investigated. Results revealed that allosteric covalent inhibition has ensued in a significant disturbance in the overall network of interaction between Cys121 and other nearby residues, more specifically Tyr124 and His214. The covalent inhibition also exhibited better protein stability as evident from positive correlation between residues in the allosteric site and multiple van der Waal, hydrogen bond and ionic interactions. Surface analysis revealed an increase in the accessible surface area in order to facilitate for the covalent inhibitor to sink in. These findings indicate that exploring allosteric covalent mechanism of PTP1B enzyme offers an opportunity to develop novel PTP1B covalent inhibitors with high potency and selectivity for cancer and other diseases.

Exploring the Structural Mechanism of Covalently Bound E3 Ubiquitin Ligase: Catalytic or Allosteric Inhibition?

Covalent inhibition has recently gained a resurgence of interest in several drug discovery areas. The expansion of this approach is based on evidence elucidating the selectivity and potency of covalent inhibitors when bound to particular amino acids of a biological target. The Nedd4-1, an E3 ubiquitin ligase, is characterized by two covalent binding sites, of which catalytic Cyscat and allosteric Cysallo are enclosed. This enzyme has demonstrated inhibition at both the above-mentioned binding sites; however, a detailed molecular understanding of the structural mechanism of inhibition upon Cyscat and Cysallo binding remains vague. This prompted us to provide the first account of investigating the preferential covalent binding mode and the underlying structural and molecular dynamic implications. Based on the molecular dynamic analyses, it was evident that although both catalytic and allosteric covalent binding led to greater stability of the enzyme, a preferential covalent mechanism of inhibition was seen in the allosteric-targeted system. This was supported by a more favorable binding energy in the allosteric site compared to the catalytic site, in addition to the larger number of residue interactions and stabilizing hydrogen bonds occurring in the allosteric covalent bound complex. The fundamental dynamic analysis presented in this report compliments, as well as adds to previous experimental findings, thus leading to a crucial understanding of the structural mechanism by which Nedd4-1 is inhibited. The findings from this study may assist in the design of more target-specific Nedd4-1 covalent inhibitors exploring the surface-exposed cysteine residues.

Reversible versus irreversible inhibition modes of ERK2: a comparative analysis for ERK2 protein kinase in cancer therapy.

AIM: Irreversible covalent drug inhibition is an emerging paradigm; however, critical gaps in unraveling the efficacy of molecular determinants still persist. METHODOLOGY: We compare two ERK2 inhibitors with different binding modes. A 5-7-Oxozeaenol is selective inhibitor which irreversibly binds ERK2 by the formation of covalent bond with Cys166 while 5-iodotubercidin binds noncovalently. Result & discussion: Covalent inhibition showed greater protein stability, favorable binding energetics (irreversible inhibition binding free energy [ΔGbind] = -40.4354 kcal/mol and reversible inhibition ΔGbind = -26.2515 kcal/mol); higher correlation in residual movement and multiple van der Waals interactions as evident from residue interaction analysis. CONCLUSION: This investigation of the different inhibition modes of ERK2 would assist toward the design of more potent and highly site-specific covalent inhibitors in cancer therapy.

In Silico SAR Studies of HIV-1 Inhibitors.

Quantitative Structure Activity Relationships (QSAR or SAR) have helped scientists to establish mathematical relationships between molecular structures and their biological activities. In the present article, SAR studies have been carried out on 89 tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepine (TIBO) derivatives using different classifiers, such as support vector machines, artificial neural networks, random forests, and decision trees. The goal is to propose classification models that will be able to classify TIBO compounds into two groups: high and low inhibitors of HIV-1 reverse transcriptase. Each molecular structure was encoded by 10 descriptors. To check the validity of the established models, all of them were subjected to various validation tests: internal validation, Y-randomization, and external validation. The established classification models have been successful. The correct classification rates reached 100% and 90% in the learning and test sets, respectively. Finally, molecular docking analysis was carried out to understand the interactions between reverse transcriptase enzyme and the TIBO compounds studied. Hydrophobic and hydrogen bond interactions led to the identification of active binding sites. The established models could help scientists to predict the inhibition activity of untested compounds or of novel molecules prior to their synthesis. Therefore, they could reduce the trial and error process in the design of human immunodeficiency virus (HIV) inhibitors.

Covalent simulations of covalent/irreversible enzyme inhibition in drug discovery: a reliable technical protocol.

AIM: Irreversible covalent inhibition of biological targets in disease pathogenesis is an emerging field in drug design. Computational techniques have assumed a critical role in understanding covalent enzyme inhibition. However, a gap currently exists with regards to the reliability and reproducibility of currently available protocols available in literature and open scientific forums. METHODOLOGY/RESULTS: Appropriate ligand and protein target are selected, docked covalently or noncovalently using respective docking tools. Both components are subjected to premolecular dynamic preparations. This was followed by parameterization of the ligand, protein and covalent complex, respectively. The production runs were initiated and the resulting trajectories are saved and analyzed. CONCLUSION: This protocol is reliable and reproducible, hence would advance the development of irreversible covalent inhibitors toward disease treatment.

An update on the discovery and development of selective heat shock protein inhibitors as anti-cancer therapy.

INTRODUCTION: Over the years, not a single HSP inhibitor has progressed into the post-market phase of drug development despite the success recorded in various pre-clinical and clinical studies. The inability of existing drugs to specifically target oncogenic HSPs has majorly accounted for these setbacks. Recent combinatorial strategies that incorporated computer-aided drug design (CADD) techniques are geared towards the development of highly specific HSP inhibitors with increased activities and minimal toxicities. Areas covered: In this review, strategic therapeutic approaches that have recently aided the development of selective HSP inhibitors were highlighted. Also, the significant contributions of CADD techniques over the years were discussed in detail. This article further describes promising computational paradigms and their applications towards the discovery of highly specific inhibitors of oncogenic HSPs. Expert opinion: The recent shift towards highly selective and specific HSP inhibition has shown great promise as evidenced by the development of paralog/isoform-selective HSP drugs. It could be further augmented with computer-aided drug design strategies, which incorporate reliable methods that would greatly enhance the design and optimization of novel inhibitors with improved activities and minimal toxicities.

Covalent Inhibition in Drug Discovery: Filling the Void in Literature.

The serendipitous discovery of covalent inhibitors and their characteristic potency of inducing irreversible and complete inhibition in therapeutic targets have caused a paradigm shift from the use of non-covalent drugs in disease treatment. This has caused a significant evolution in the field of covalent targeting to understand their inhibitory mechanisms and facilitate the systemic design of novel covalent modifiers for 'undruggable' targets. Computational techniques have evolved over the years and have significantly contributed to the process of drug discovery by mirroring the pattern of biological occurrences thereby providing insights into the dynamics and conformational transitions associated with biomolecular interactions. Moreover, our previous contributions towards the systematic design of selective covalent modifiers have revealed the various setbacks associated with the use of these conventional techniques in the study of covalent systems, hence there is a need for distinct approaches. In this review, we highlight the modifications and development of computational techniques suitable for covalent systems, their lapses, shortcomings and recent advancements.