Identification of potential histone deacetylase1 (HDAC1) inhibitors using multistep virtual screening approach including SVM model, pharmacophore modeling, molecular docking and biological evaluation

The application of WaterMap-guided structure-based virtual screening in novel drug discovery

INTRODUCTION

Nowadays, it is widely accepted that water molecules play a key role in binding a ligand to a molecular target. Neglecting water molecules in the process of molecular recognition was the result of several failures of the structure-based drug discovery campaigns. The application of WaterMap, in particular WaterMap-guided molecular docking, enables the reasonably accurate and quick description of the location and energetics of water molecules at the ligand-protein interface.

AREAS COVERED

In this review, the authors shortly discuss the importance of water in drug design and discovery and provide a brief overview of the computational approaches used to predict the solvent-related effects for the purposes of presenting WaterMap in the context of other available techniques and tools. A concise description of WaterMap concept is followed by the presentation of WaterMap-assisted virtual screening literature published between 2013 and 2023.

EXPERT OPINION

In recent years, WaterMap software has been extensively used to support structure-based drug design, in particular structure-based virtual screening. Indeed, it is a useful tool to rescore docking results considering water molecules in the binding pocket. Although WaterMap allows for the consideration of the dynamic behavior of water molecules in the binding site, for best accuracy, its application in conjunction with other techniques such as molecular mechanics-generalized Born surface area of FEP (Free Energy Perturbation) is recommended.

Machine learning-based predictive modeling, virtual screening and biological evaluation studies for identification of potential inhibitors of MMP-13

Matrix Metalloproteinase-13 (MMP-13) is a collagenase that regulates the homeostasis of the extracellular matrix (ECM) and basement membrane, as well as the breakdown of type II collagen. Recent research studies on the molecular and cellular mechanisms of cartilage degradation suggest that MMP-13 overexpression triggers osteoarthritis and is considered a promising target for osteoarthritis treatment. The present work employs machine learning-based virtual screening and structure-based rational drug design approaches to identify potential inhibitors of MMP-13 with diverse chemical scaffolds. The twelve top-scoring screened compounds were subjected to biological evaluation to validate the robustness and predictive modeling of ML-based Virtual Screening. It was observed that eight compounds exhibited approximately 44%-60% inhibition at 0.1 µM concentration, and the IC50 lies in the range of 1.9-2.3 µM against MMP-13. Interestingly, two of the compounds, DP01473 and RH01617, showed potent dose-dependent inhibitory activity. Compound DP01473 inhibited MMP-13 by 44%, 50%, and 70%, while compound RH01617 inhibited MMP-13 by 54%, 55%, and 57% at 0.1 μM, 1 μM, and 10 μM concentrations, respectively, and can be further optimized for the design and development of more potent MMP-13 inhibitors.Communicated by Ramaswamy H. Sarma.

Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

A quantitative structural analysis of AR-42 derivatives as HDAC1 inhibitors for the identification of promising structural contributors

Alteration and abnormal epigenetic mechanisms can lead to the aberration of normal biological functions and the occurrence of several diseases. The histone deacetylase (HDAC) family of enzymes is one of the prime regulators of epigenetic functions modifying the histone proteins, and thus, regulating epigenetics directly. HDAC1 is one of those HDACs which have important contributions to cellular epigenetics. The abnormality of HDAC is correlated to the occurrence, progression, and poor prognosis in several disease conditions namely neurodegenerative disorders, cancer cell proliferation, metastasis, chemotherapy resistance, and survival in various cancers. Therefore, the progress of potent and effective HDAC1 inhibitors is one of the prime approaches to combat such diseases. In this study, both regression and classification-based molecular modelling studies were conducted on some AR-42 derivatives as HDAC1 inhibitors to elucidate the crucial structural aspects that are responsible for regulating their biological responses. This study revealed that the molecular polarizability, van der Waals volume, the presence of aromatic rings as well as the higher number of hydrogen bond acceptors might affect prominently their inhibitory activity and might be responsible for proper fitting and interactions at the HDAC1 active site to pertain effective inhibition.

Combining structure-based pharmacophore modeling and machine learning for the identification of novel BTK inhibitors

Bruton's tyrosine kinase (BTK) is a critical enzyme which is involved in multiple signaling pathways that regulate cellular survival, activation, and proliferation, making it a major cancer therapeutic target. We applied the novel integrated structure-based pharmacophore modeling, machine learning, and other in silico studies to screen the Korean chemical database (KCB) to identify the potential BTK inhibitors (BTKi). Further evaluation of these inhibitors on three different human cancer cell lines showed significant cell growth inhibitory activity. Among the 13 compounds shortlisted, four demonstrated consistent cell inhibition activity among breast, gastric, and lung cancer cells (IC50 below 3 μM). The selected compounds also showed significant kinase inhibition activity (IC50 below 5 μM). The current study suggests the potential of these inhibitors for targeting BTK malignant tumors.

Anti-Colon Cancer Activity of Novel Peptides Isolated from In Vitro Digestion of Quinoa Protein in Caco-2 Cells

Quinoa peptides are the bioactive components obtained from quinoa protein digestion, which have been proved to possess various biological activities. However, there are few studies on the anticancer activity of quinoa peptides, and the mechanism has not been clarified. In this study, the novel quinoa peptides were obtained from quinoa protein hydrolysate and identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The anticancer activity of these peptides was predicted by PeptideRanker and evaluated using an antiproliferative assay in colon cancer Caco-2 cells. Combined with the result of histone deacetylase 1 (HDAC1) inhibitory activity assay, the highly anticancer activity peptides FHPFPR, NWFPLPR, and HYNPYFPG were screened and further investigated. Molecular docking was used to analyze the binding site between peptides and HDAC1, and results showed that three peptides were bound in the active pocket of HDAC1. Moreover, real-time quantitative polymerase chain reaction (RT-qPCR), and Western blot showed that the expression of HDAC1, NFκB, IL-6, IL-8, Bcl-2 was significantly decreased, whereas caspase3 expression showed a remarkable evaluation. In conclusion, quinoa peptides may have the potential to protect against cancer development by inhibiting HDAC1 activity and regulating the expression of the cancer-related genes, which indicates that these peptides could be explored as functional foods to alleviate colon cancer.

Discovery of novel hit compounds as potential HDAC1 inhibitors: The case of ligand- and structure-based virtual screening

Histone deacetylases (HDACs) as an important family of epigenetic regulatory enzymes are implicated in the onset and progression of carcinomas. As a result, HDAC inhibition has been proven as a compelling strategy for reversing the aberrant epigenetic changes associated with cancer. However, non-selective profile of most developed HDAC inhibitors (HDACIs) leads to the occurrence of various side effects, limiting their clinical utility. This evidence provides a solid ground for ongoing research aimed at identifying isoform-selective inhibitors. Among the isoforms, HDAC1 have particularly gained increased attention as a preferred target for the design of selective HDACIs. Accordingly, in this paper, we have developed a reliable virtual screening process, combining different ligand- and structure-based methods, to identify novel benzamide-based analogs with potential HDAC1 inhibitory activity. For this purpose, a focused library of 736,160 compounds from PubChem database was first compiled based on 80% structural similarity with four known benzamide-based HDAC1 inhibitors, Mocetinostat, Entinostat, Tacedinaline, and Chidamide. Our inclusive in-house 3D-QSAR model, derived from pharmacophore-based alignment, was then employed as a 3D-query to discriminate hits with the highest predicted HDAC1 inhibitory activity. The selected hits were subjected to subsequent structure-based approaches (induced-fit docking (IFD), MM-GBSA calculations and molecular dynamics (MD) simulation) to retrieve potential compounds with the highest binding affinity for HDAC1 active site. Additionally, in silico ADMET properties and PAINS filtration were also considered for selecting an enriched set of the best drug-like molecules. Finally, six top-ranked hit molecules, CID_38265326, CID_56064109, CID_8136932, CID_55802151, CID_133901641 and CID_18150975 were identified to expose the best stability profiles and binding mode in the HDAC1 active site. The IFD and MD results cooperatively confirmed the interactions of the promising selected hits with critical residues within HDAC1 active site. In summary, the presented computational approach can provide a set of guidelines for the further development of improved benzamide-based derivatives targeting HDAC1 isoform.

Non-Hydroxamate Zinc-Binding Groups as Warheads for Histone Deacetylases

Histone deacetylases (HDACs) remove acetyl groups from acetylated lysine residues and have a large variety of substrates and interaction partners. Therefore, it is not surprising that HDACs are involved in many diseases. Most inhibitors of zinc-dependent HDACs (HDACis) including approved drugs contain a hydroxamate as a zinc-binding group (ZBG), which is by far the biggest contributor to affinity, while chemical variation of the residual molecule is exploited to create more or less selectivity against HDAC isozymes or other metalloproteins. Hydroxamates have a propensity for nonspecificity and have recently come under considerable suspicion because of potential mutagenicity. Therefore, there are significant concerns when applying hydroxamate-containing compounds as therapeutics in chronic diseases beyond oncology due to unwanted toxic side effects. In the last years, several alternative ZBGs have been developed, which can replace the critical hydroxamate group in HDACis, while preserving high potency. Moreover, these compounds can be developed into highly selective inhibitors. This review aims at providing an overview of the progress in the field of non-hydroxamic HDACis in the time period from 2015 to present. Formally, ZBGs are clustered according to their binding mode and structural similarity to provide qualitative assessments and predictions based on available structural information.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

A combined model based on feature selection and support vector machine for PM2.5 prediction

With the increasing attention to the environment and air quality, PM2.5 has been paid more and more attention. It is expected to excavate useful information in meteorological data to predict air pollution, however, the air quality is greatly affected by meteorological factors, and how to establish an effective air quality prediction model has always been a problem that people urgently need to solve. This paper proposed a combined model based on feature selection and Support Vector Machine (SVM) for PM2.5 prediction. Firstly, aiming at the influence of meteorological factors on PM2.5, a feature selection method based on linear causality is proposed to find out the causality between features and select the features with strong causality, so as to remove the redundant features in air pollution data and reduce the workload of data analysis. Then, a method based on SVM is proposed to analyze and solve the nonlinear problems in the data, for reducing the prediction error, a method of particle swarm optimization is also used to optimize SVM parameters. Finally, the above methods are combined into a prediction model, which is suitable for the current air pollution control. 12 representative data sets on the UCI (University of California, Irvine) website are used to verify the combined model, and the experimental results show that the model is feasible and effective.

Targeting Mycobacterium Tuberculosis Enoyl-Acyl Carrier Protein Reductase Using Computational Tools for Identification of Potential Inhibitor and their Biological Activity

Enoyl-acyl carrier protein reductase (InhA) of type II fatty acid synthase system is involved in the synthesis of mycolic acids which is a major component of the bacterial cell wall. Since they are the key enzymes playing a very significant role in the FASII pathway of the bacterium. In this study, we have developed a workflow for identification of InhA inhibitors by utilizing in silico virtual screening approaches based on various machine learning algorithms followed by pharmacophore based virtual screening. The hits screened from the models were further subjected to molecular docking. Further, based on the XP docking score best twenty compounds were subjected to molecular dynamics study. Finally, nine compounds were shortlisted on the basis of best stable ligand RMSD, c-alpha RMSD, and RMSF plot for biological evaluation studies. Experimental validation of the shortlisted compounds identified one compound JFD01724 having potent inhibitory activity and was able to inhibit the growth of mycobacterium tuberculosis. Further medicinal chemistry efforts may help to improve the inhibitory potency of the identified compound.

Identification of HDAC6 selective inhibitors: pharmacophore based virtual screening, molecular docking and molecular dynamics simulation

HDAC6 regulates the expression and activity of various tumor-related proteins, but currently there is no selective inhibitor targeting HDAC6 for clinical application. In order to discover novel HDAC6 inhibitors, virtual screening methods comprised of pharmacophore based virtual screening, molecular docking and molecular dynamics (MD) simulations were employed. 15 molecules were obtained after virtual screening. After in vitro bioassays, two of the hits showed inhibition activity against HDAC6, among which the inhibition activity of G1 to HDAC6 reached 81% at concentration of 20 μM. In addition, the inhibitory activity against HDAC1 and HDAC10 demonstrated that G1 and G10 were highly selective to HDAC6. The analysis of the binding modes of G1 and G10 provides a reference for further development of highly active HDAC6 inhibitors. Communicated by Ramaswamy H. Sarma.