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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.

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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 Cys and allosteric Cys 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 Cys and Cys 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.

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 (M pro ) 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 M pro 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 M pro target.

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.

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.

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.