Ligand-based virtual screening and inductive learning for identification of SIRT1 inhibitors in natural products

Identification of phytoestrogens as sirtuin inhibitor against breast cancer: Multitargeted approach

Despite progress in diagnosis and treatment strategies, breast cancer remains a primary risk to female health as indicated by second most cancer-deaths globally caused by this cancer. High risk mutation is linked to prognosis of breast cancer. Due to high resistance of breast cancer against current therapies, there is necessity of novel treatment strategies. Sirtuins are signaling proteins belonging to histone deacetylase class III family, known to control several cellular processes. Therefore, targeting sirtuins could be one of the approaches to treat breast cancer. Several plants synthesize phytoestrogens which exhibit structural and physiological similarities to estrogens and have been recognized to possess anticancer activity. In our study, we investigated several phytoestrogens for sirtuin inhibition by conducting molecular docking studies, and in-vitro studies against breast cancer cell lines. In molecular docking studies, we identified coumestrol possessing high binding energy with sirtuin proteins 1-3 as compared to other phytoestrogens. The molecular dynamic studies showed stable interaction of ligand and protein with higher affinity at sirtuin proteins 1-3 binding sites. In cell proliferation assay and colony formation assay using breast cancer cell lines (MCF-7 and MDAMB-231) coumestrol caused significant reduction in cell proliferation and number of colonies formed. Further, the flow cytometric analysis showed that coumestrol induces intracellular reactive oxygen species and the western blot analysis revealed reduction in the level of SIRT-1 expression in breast cancer cell lines. In conclusion, in-silico data and in-vitro studies suggest that the phytoestrogen coumestrol has sirtuin inhibitory activity against breast cancer.

Targeting Sirtuin 1 for therapeutic potential: Drug repurposing approach integrating docking and molecular dynamics simulations

Identifying novel therapeutic agents is a fundamental challenge in contemporary drug development, especially in the context of complex diseases like cancer, neurodegenerative disorders, and metabolic syndromes. Here, we present a comprehensive computational study to identify potential inhibitors of SIRT1 (Sirtuin 1), a critical protein involved in various cellular processes and disease pathways. Leveraging the concept of drug repurposing, we employed a multifaceted approach that integrates molecular docking and molecular dynamics (MD) simulations to predict the binding affinities and dynamic behavior of a diverse set of FDA-approved drugs from DrugBank against the SIRT1. Initially, compounds were shortlisted based on their binding affinities and interaction analyses to identify safe and promising binding partners for SIRT1. Among these candidates, Doxercalciferol and Timiperone emerged as potential candidates, displaying notable affinity, efficiency, and specificity towards the binding pocket of SIRT1. Extensive evaluation revealed that these identified compounds boast a range of favorable biological properties and prefer binding to the active site of SIRT1. To delve deeper into the interactions, all-atom MD simulations were conducted for 500 nanoseconds (ns). These simulations assessed the conformational dynamics, stability, and interaction mechanism of the SIRT1-Doxercalciferol and SIRT1-Timiperone complexes. The MD simulations illustrated that the SIRT1-Doxercalciferol and SIRT1-Timiperone complexes maintain stability over a 500 ns trajectory. These insightful outcomes propose that Doxercalciferol and Timiperone hold promise as viable scaffolds for developing potential SIRT1 inhibitors, with implications for tackling complex diseases such as cancer, neurodegenerative disorders, and metabolic syndromes.

Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products

Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.

Virtual Screening in the Identification of Sirtuins’ Activity Modulators

Sirtuins are NAD⁺-dependent deac(et)ylases with different subcellular localization. The sirtuins’ family is composed of seven members, named SIRT-1 to SIRT-7. Their substrates include histones and also an increasing number of different proteins. Sirtuins regulate a wide range of different processes, ranging from transcription to metabolism to genome stability. Thus, their dysregulation has been related to the pathogenesis of different diseases. In this review, we discussed the pharmacological approaches based on sirtuins’ modulators (both inhibitors and activators) that have been attempted in in vitro and/or in in vivo experimental settings, to highlight the therapeutic potential of targeting one/more specific sirtuin isoform(s) in cancer, neurodegenerative disorders and type 2 diabetes. Extensive research has already been performed to identify SIRT-1 and -2 modulators, while compounds targeting the other sirtuins have been less studied so far. Beside sections dedicated to each sirtuin, in the present review we also included sections dedicated to pan-sirtuins’ and to parasitic sirtuins’ modulators. A special focus is dedicated to the sirtuins’ modulators identified by the use of virtual screening.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Targeting Epigenetic Regulators Using Machine Learning: Potential Sirtuin 2 Inhibitors

Sirtuin 2 (SIRT2) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase that has been identified as a target for many diseases, including Parkinson’s disease (PD) and leukemia. Using 234 SIRT2 inhibitors from the ZINC15 database, we generated molecular descriptors with PaDEL and constructed a machine-learning (ML) model for the binary classification of SIRT2 inhibitors. To predict compounds with novel inhibitory mechanisms, we then applied the model on the ZINC15/FDA subset, yielding 107 potential SIRT2 inhibitors. For validation of these substances, we employed the binding analysis software AutoDock Vina to perform virtual screening, with which 43 compounds were considered best inhibitors at the [Formula: see text][Formula: see text]kcal/mol binding affinity threshold. Our results demonstrate the potential of ligand-based (LB) ML techniques in conjunction with receptor-based virtual screening (RBVS) to facilitate the drug discovery or repurposing.

Navigating into the Chemical Space of Monoamine Oxidase Inhibitors by Artificial Intelligence and Cheminformatics Approach

The monoamine oxidase (MAO) enzyme class is a prevalent target for many neurodegenerative and depressive disorders. Even though scrutinization of many promising drugs for the treatment of MAO inhibition has been carried out in recent times, a conclusive structural requirement for potent activity needs to be developed. Numerous approaches have been examined for the identification of structural features for potent MAO inhibitors (MAOIs) that mainly involve an array of computational studies, synthetic approaches, and biological evaluation. In this paper, we have analyzed ∼2200 well-known MAOIs to expand perceptions in the chemical space of MAOIs. The physicochemical properties of the MAOIs disclosed a discernible hydrophobic feature making a bunch discrete from the central nervous system (CNS) acting drugs, as exposed using the principal component analysis (PCA). The Murcko scaffold structure study revealed unfavorable and favorable scaffold structures, in both data sets, with the highest biological activity shown by the 3-phenyl-2H-chromen-2-one scaffold. This scaffold showed a polypharmacological effect. R-group disintegration and automatic structure-activity relationship (SAR) study resulted in identification of substructures responsible for the inhibitory bioactivity of the MAO-A and MAO-B enzymes. Moreover, with activity cliff analysis, significant biological activity was detected by simple molecular conversion in the chemical compound structure. In addition, we used the machine learning tool to generate a hypothesis wherein pyrazole, benzene ring, and amide containing structural functionalities can exhibit potential biological activities. This hypothesis revealed that CNS target drugs, C4155, C13390, C21265, C43862, C31524, C24810, C37100, C42075, and C43644, could be repurposed as valuable candidates for the MAO-B enzyme. For researchers, this study will bring new perceptions in the discovery and development of MAOIs and direct lead and hit optimization for the progress of small molecules beneficial for MAO-targeting associated diseases.

Machine learning approaches for elucidating the biological effects of natural products

Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.

A prospective compound screening contest identified broader inhibitors for Sirtuin 1

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

Mimicking Strategy for Protein-Protein Interaction Inhibitor Discovery by Virtual Screening

As protein–protein interactions (PPIs) are highly involved in most cellular processes, the discovery of PPI inhibitors that mimic the structure of the natural protein partners is a promising strategy toward the discovery of PPI inhibitors. In this review, we discuss recent advances in the application of virtual screening for identifying mimics of protein partners. The classification and function of the mimicking protein partner inhibitor discovery by virtual screening are described. We anticipate that this review would be of interest to medicinal chemists and chemical biologists working in the field of protein–protein interaction inhibitors or probes.

Molecular docking analysis of imine stilbene analogs and evaluation of their anti-aging activity using yeast and mammalian cell models

The NAD⁺-dependent histone deacetylase SIRT1 was shown to be associated with aging and longevity. A stilbene, resveratrol (RV) was shown to exert anti-aging activity by stimulating the SIRT1 activity. However, the utility of RV is limited by its low bioavailability and structural instability. It is thus envisaged to test imine stilbene (IMS) analogs of RV for their potential anti-aging activity. In the present study, molecular docking analysis of five IMS analogs (3a, 3b, 3c, 3d and 3e) against the SIRT1 protein has been carried out. All the five IMS analogs displayed enhanced binding affinity towards SIRT1; three out of five IMS analogs (3a, 3 b, 3e) showed significantly higher affinity with lower binding energies (-9.58, -9.54, and -9.82 kcal mol^-1) than RV (-8.11 kcal mol^-1). Further, experimental validation of anti-aging activity was performed by measuring the chronological life span in vitro using yeast and cellular replicative senescence (CRS) in mammalian cell line models. All IMS analogs extended the chronological life span in yeast as compared to untreated cells as well as RV treated cells. Enhanced anti-aging activity was also observed in an analogous mammalian cell line model upon treatment with either RV or IMS analogs. The results thus suggest that most of the IMS analogs tested may serve as potent drug lead molecules with anti-aging activity.

Pharmacokinetic profiling of anticancer phytocompounds using computational approach

INTRODUCTION

Natural products exhibit diverse scaffolds and are considered as suitable candidates for development of leads. However, poor pharmacokinetics often acts as a hindrance during the drug discovery process.

OBJECTIVE

With a view of exploring the absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of plant-based anticancer compounds, open-access databases (NPACT, CancerHSP and TaxKB) were analysed to identify molecules having properties favourable for drug ability.

METHODOLOGY

Our workflow involved identification of molecules capable of passing each of the ADMET barriers based on physicochemical properties of molecules, and physiological barriers and factors.

RESULTS

The results revealed that out of 5086 phytomolecules, 63% were orally absorbable and 52% distributable. Also, an appreciable proportion of these compounds (45%) could be metabolised and excreted. Furthermore, 28% were found to be non-toxic for cardio toxicity and central nervous system (CNS) activity. Additionally, comparison against known anticancer drugs (reference dataset) revealed that the three libraries exhibit similar trends, thus providing additional confidence to the predictions. Overall, 28% of the molecular dataset was found to have suitable pharmacokinetic properties. We have also discussed a few natural products which exhibit favourable ADMET as well as low nano-micromolar in vitro anticancer activity.

CONCLUSION

We have created an interactive database (ADMETCan), which provides access to predicted ADMET of these anticancer phytomolecules. The ease of availability of this dataset is expected to minimise failure rate of these compounds and thus is expected to be beneficial to the scientific community involved in anticancer identification and development.

TNF Receptor Type II as an Emerging Drug Target for the Treatment of Cancer, Autoimmune Diseases, and Graft-Versus-Host Disease: Current Perspectives and In Silico Search for Small Molecule Binders

There is now compelling evidence that TNF receptor type II (TNFR2) is predominantly expressed on CD4⁺Foxp3⁺ regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs), and plays a major role in the expansion and function of Tregs and MDSCs. Consequently, targeting of TNFR2 by either antagonists or agonists may represent a novel strategy in the treatment of cancer and autoimmune diseases, by downregulating or upregulating suppressor cell activity. The advance in the understanding of complex structure of TNFR2 and its binding with TNF at molecular levels offers opportunity for structure-guided drug discovery. This article reviews the current evidences regarding the decisive role of TNFR2 in immunosuppressive function of Tregs and MDSCs, and the current effort to develop novel TNFR2-targeting therapeutic agents in the treatment of cancer, autoimmune diseases, and graft-versus-host disease. To shed light on the potential TNFR2-targeting small molecules, we for the first time performed virtual screening of 400,000 natural compounds against the two TNF-binding sites, regions 3 and 4, of TNFR2. Our result showed that the top hits at region 4 had slightly higher docking energies than those at region 3. Nevertheless, free energy calculation from the TNF–TNFR2 molecular dynamics simulation revealed that the binding strength of TNF in region 3 is only one-tenth of that in region 4. This suggests that region 3 is a potentially more viable binding site to be targeted by small molecules than region 4. Therefore, the effectiveness in targeting region 3 of TNFR2 deserves further investigation.

Identification of Bichalcones as Sirtuin Inhibitors by Virtual Screening and In Vitro Testing

Sirtuins are nicotinamide adenine dinucleotide (NAD⁺)-dependent class III histone deacetylases, which have been linked to the pathogenesis of numerous diseases, including HIV, metabolic disorders, neurodegeneration and cancer. Docking of the virtual pan-African natural products library (p-ANAPL), followed by in vitro testing, resulted in the identification of two inhibitors of sirtuin 1, 2 and 3 (sirt1–3). Two bichalcones, known as rhuschalcone IV (8) and an analogue of rhuschalcone I (9), previously isolated from the medicinal plant Rhus pyroides, were shown to be active in the in vitro assay. The rhuschalcone I analogue (9) showed the best activity against sirt1, with an IC₅₀ value of 40.8 µM. Based on the docking experiments, suggestions for improving the biological activities of the newly identified hit compounds have been provided.

How much successful are the medicinal chemists in modulation of SIRT1: A critical review

Silent information regulator two homologue one (SIRT1) is the most widely studied member of the sirtuin family related to histone deacetylases class III super-family using nicotinamide adenine dinucleotide (NAD(+)) as its cofactor. It is located in the nucleus but also modulates the targets in cytoplasm and mainly acts as transacetylase rather than deacetylase. SIRT1 specifically cleaves the nicotinamide ribosyl bond of NAD(+) and transfers the acetyl group from proteins to their co-substrate through an ADP- ribose-peptidyl imidate intermediate. It has been indicated that SIRT1 and its histone as well as non histone targets are involved in a wide range of biological courses including metabolic diseases, age related diseases, viral infection, inflammation, tumor-cell growth and metastasis. Modulation of SIRT1 expression may present a new insight in the discovery of a number of therapeutics. This review summarizes studies about SIRT1 and mainly focuses on the various modulators of SIRT1 evolved by natural as well as synthetic means.