In Silico Identification of Lead Compounds for Pseudomonas Aeruginosa PqsA Enzyme: Computational Study to Block Biofilm Formation

Machine learning and molecular simulation-based protocols to identify novel potential inhibitors for reverse transcriptase against HIV infections

Acquired immunodeficiency syndrome (AIDS) is a potentially fatal condition affecting the human immune system, which is attributed to the human immunodeficiency virus (HIV). The suppression of reverse transcriptase activity is a promising and feasible strategy for the therapeutic management of AIDS. In this study, we employed machine learning algorithms, such as support vector machines (SVM), k-nearest neighbor (k-NN), random forest (RF), and Gaussian naive base (GNB), which are fast and effective tools commonly used in drug design. For model training, we initially obtained a dataset of 5,159 compounds from BindingDB. The models were assessed using tenfold cross-validation to ensure their accuracy and reliability. Among these compounds, 1,645 compounds were labeled as active, having an IC50 below 0.49 µM, while 3,514 compounds were labeled "inactive against reverse transcriptase. Random forest achieved 86% accuracy on the train and test set among the different machine learning algorithms. Random forest model was then applied to an external ZINC dataset. Subsequently, only three hits-ZINC1359750464, ZINC1435357562, and ZINC1545719422-were selected based on the Lipinski Rule, docking score, and good interaction. The stability of these molecules was further evaluated by deploying molecular dynamics simulation and MM/GBSA, which were found to be -38.6013 ± 0.1103 kcal/mol for the Zidovudine/RT complex, -59.1761 ± 2.2926 kcal/mol for the ZINC1359750464/RT complex, -47.6292 ± 2.4206 kcal/mol for the ZINC1435357562/RT complex, and -50.7334 ± 2.5713 kcal/mol for the ZINC1545719422/RT complex.Communicated by Ramaswamy H. Sarma.

Consolidated knowledge-guided computational pipeline for therapeutic intervention against bacterial biofilms - a review

Biofilm-associated bacterial infections attributed to multifactorial antimicrobial resistance have caused worldwide challenges in formulating successful treatment strategies. In search of accelerated yet cost-effective therapeutics, several researchers have opted for bioinformatics-based protocols to systemize targeted therapies against biofilm-producing strains. The present review investigated the up-to-date computational databases and servers dedicated to anti-biofilm research to design/screen novel biofilm inhibitors (antimicrobial peptides/phytocompounds/synthetic compounds) and predict their biofilm-inhibition efficacy. Scrutinizing the contemporary in silico methods, a consolidated approach has been highlighted, referred to as a knowledge-guided computational pipeline for biofilm-targeted therapy. The proposed pipeline has amalgamated prominently employed methodologies in genomics, transcriptomics, interactomics and proteomics to identify potential target proteins and their complementary anti-biofilm compounds for effective functional inhibition of biofilm-linked pathways. This review can pave the way for new portals to formulate successful therapeutic interventions against biofilm-producing pathogens.

Sirtuin 1 inhibition: a promising avenue to suppress cancer progression through small inhibitors design

SIRT1 is a protein associated with vital cell functions such as gene regulation, metabolism, ageing, and cellular energy restoration. Its association with the tumor suppressor protein p53 is essential for controlling the growth of cells, apoptosis, and response to DNA damage. By raising p53 acetylation, encouraging apoptosis, and reducing cell proliferation, inhibiting SIRT1's catalytic domain, which interacts with p53, shows potential as a cancer treatment. The aim of the study is to find compounds that could inhibit SIRT1 and thus lower the proliferation of cancer cells. Employing molecular docking techniques, a virtual screening of ∼900 compounds (isolated from medicinal plants and derivatives) gave us 13 active compounds with good binding affinity. Additional evaluation of pharmacokinetic and pharmacodynamic properties led to the selection of eight compounds with desirable properties. Docking analysis confirmed stable interactions between the final eight compounds (C1-C8) and the SIRT1 catalytic domain. Molecular dynamics simulations show overall stability and moderate changes in protein structure upon compound binding. The compactness of the protein indicated the protein's tight packing upon the inhibitors binding. Binding free energy calculations revealed that compounds C2 (-49.96 ± 0.073 kcal/mol and C1 (-44.79 ± 0.077 kcal/mol) exhibited the highest energy, indicating strong binding affinity to the SIRT1 catalytic domain. These compounds, along with C8, C5, C6, C3, C4 and C7, showed promising potential as SIRT1 inhibitors. Based on their ability to reduce SIRT1 activity and increase apoptosis, the eight chemicals discovered in this work may be useful in treating cancer.Communicated by Ramaswamy H. Sarma.

Structure based virtual screening and molecular simulation study of FDA-approved drugs to inhibit human HDAC6 and VISTA as dual cancer immunotherapy

Cancer immunotherapy has significantly contributed to the treatment of various types of cancers mainly by targeting immune checkpoint inhibitors (ICI). Among them, V-domain immunoglobulin suppressor of T cell activation (VISTA) has been explored as a promising therapeutic target. Besides, histone deacetylase 6 (HDAC6) has been demonstrated to be efficacious target for several cancers. The current theoretical work was performed to explore the virtual repurposing of the FDA-approved drugs as inhibitors against these two (VISTA and HDAC6) cancers therapeutic targets. The crystal structure of the two proteins were downloaded from PDB and subjected to virtual screening by DrugRep webserver while using FDA-approved drugs library as ligands database. Our study revealed that Oxymorphone and Bexarotene are the top-ranked inhibitors of VISTA and HDAC6, respectively. The docking score of Bexarotene was predicted as - 10 kcal/mol while the docking score of Oxymorphone was predicted as - 6.2 kcal/mol. Furthermore, a total of 100 ns MD simulation revealed that the two drugs Oxymorphone and Bexarotene formed stable complexes with VISTA and HDAC6 drug targets. As compared to the standard drug the two drugs Oxymorphone and Bexarotene revealed great stability during the whole 100 ns MD simulation. The binding free energy calculation further supported the Root Mean Square Deviation (RMSD) result which stated that as compared to the ref/HDAC6 (- 18.0253 ± 2.6218) the binding free energy score of the Bexarotene/HDAC6 was good (- 51.9698 ± 3.1572 kcal/mol). The binding free energy score of Oxymorphone/VISTA and Ref/VISTA were calculated as - 36.8323 ± 3.4565, and - 21.5611 ± 4.8581 respectively. In conclusion, the two drugs deserve further consideration as cancer treatment option.

Machine Learning-Based Virtual Screening and Molecular Simulation Approaches Identified Novel Potential Inhibitors for Cancer Therapy

Cyclin-dependent kinase 2 (CDK2) is a promising target for cancer treatment, developing new effective CDK2 inhibitors is of great significance in anticancer therapy. The involvement of CDK2 in tumorigenesis has been debated, but recent evidence suggests that specifically inhibiting CDK2 could be beneficial in treating certain tumors. This approach remains attractive in the development of anticancer drugs. Several small-molecule inhibitors targeting CDK2 have reached clinical trials, but a selective inhibitor for CDK2 is yet to be discovered. In this study, we conducted machine learning-based drug designing to search for a drug candidate for CDK2. Machine learning models, including k-NN, SVM, RF, and GNB, were created to detect active and inactive inhibitors for a CDK2 drug target. The models were assessed using 10-fold cross-validation to ensure their accuracy and reliability. These methods are highly suitable for classifying compounds as either active or inactive through the virtual screening of extensive compound libraries. Subsequently, machine learning techniques were employed to analyze the test dataset obtained from the zinc database. A total of 25 compounds with 98% accuracy were predicted as active against CDK2. These compounds were docked into CDK2's active site. Finally, three compounds were selected based on good docking score, and, along with a reference compound, underwent MD simulation. The Gaussian naïve Bayes model yielded superior results compared to other models. The top three hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as CDK2 inhibitors.