Applications and Success Stories in Virtual Screening

Computational Assessment of the Phytochemicals of Panax ginseng C.A. Meyer Against Dopamine Receptor D1 for Early Huntington's Disease Prophylactics

A herb, Panax ginseng C.A. Meyer has been used traditionally for the treatment of various diseases. In this work, its chemical components have been explored by computational methods for the possibility of therapeutic potential against early Huntington's disease. The molecular docking calculations against dopamine receptor D1 (PDB ID: 7X2F) involved in pathogenesis of early Huntington's disease gave the binding affinities (kcal/mol) of schizandrin (-10.530), ergosterol (-10.124), protopanaxadiol (-9.650), panaxydol (-9.399), diphenhydramine (-9.358), and panasenoside (-9.358). The values for native ligand (-7.748) and some selected drugs, Nefazodone (-9.880), Risperidone (-9.752), and Haloperidol (-9.712) were higher revealing weaker interactions. The stability assessment of top protein-ligand adducts in terms of various geometrical and thermodynamical parameters extracted from 200 ns molecular dynamics simulations pointed to schizandrin, protopanaxadiol, and panasenoside as hit molecules. The minimal translational and rotational motion of the docked ligands at orthosteric pocket of the receptor at near physiological conditions hinted at the probability of it restricting or inhibiting over-activation of DRD1. The sustained thermodynamic spontaneity of complex formation reaction augmented the inferences derived from spatial results. The phytochemicals from Panax ginseng could be used in the prophylactics of early Huntington's disease and recommendation is made for further evaluation by experimental work.

Ligand-Based Virtual Screening as a Path to New Chemotypes for Candidate PET Radioligands for Imaging Tauopathies

Ligand-based virtual screening (LBVS) has rarely been tested as a method for discovering new structural scaffolds for PET radioligand development. This study used LBVS to discover potential chemotype leads for developing radioligands for PET imaging of tauopathies. ZINC12, a free database of over 12 million commercially available compounds, was searched to discover novel scaffolds based on similarities to four query compounds. Thirteen high-ranking hits were purchased and assayed for their ability to compete against three tritiated radioligands at their distinct binding sites in Alzheimer's disease brain tissue. Three hits were 2-substituted 6-methoxy naphthalenes. Synthetic elaboration of this new chemotype yielded three new ligands (25, 26, and 28) with high affinity for the [3H]6 (flortaucipur) neurofibrillary tangle binding site. Compound 28 showed remarkably high affinity (Ki, 7 nM) and other desirable properties for a candidate PET radioligand, including low topological polar surface area, moderate computed log D, and amenability for labeling with carbon-11. LBVS appears to be uniquely valuable for discovering new chemotypes for candidate PET radioligands.

Natural Products for the Prevention and Control of the COVID-19 Pandemic: Sustainable Bioresources

Background: The world has been unprecedentedly hit by a global pandemic which broke the record of deadly pandemics that faced humanity ever since its existence. Even kids are well-versed in the terminologies and basics of the SARS-CoV-2 virus and COVID-19 now. The vaccination program has been successfully launched in various countries, given that the huge global population of concern is still far behind to be vaccinated. Furthermore, the scarcity of any potential drug against the COVID-19-causing virus forces scientists and clinicians to search for alternative and complementary medicines on a war-footing basis. Aims and Objectives: The present review aims to cover and analyze the etiology and epidemiology of COVID-19, the role of intestinal microbiota and pro-inflammatory markers, and most importantly, the natural products to combat this deadly SARS-CoV-2 virus. Methods: A primary literature search was conducted through PubMed and Google Scholar using relevant keywords. Natural products were searched from January 2020 to November 2020. No timeline limit has been imposed on the search for the biological sources of those phytochemicals. Interactive mapping has been done to analyze the multi-modal and multi-target sources. Results and Discussion: The intestinal microbiota and the pro-inflammatory markers that can serve the prognosis, diagnosis, and treatment of COVID-19 were discussed. The literature search resulted in yielding 70 phytochemicals and ten polyherbal formulations which were scientifically analyzed against the SARS-CoV-2 virus and its targets and found significant. Retrospective analyses led to provide information about 165 biological sources that can also be screened if not done earlier. Conclusion: The interactive analysis mapping of biological sources with phytochemicals and targets as well as that of phytochemical class with phytochemicals and COVID-19 targets yielded insights into the multitarget and multimodal evidence-based complementary medicines.

Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein-Inhibitor Complexes in Relevance to Developing Parkinson's Therapeutics

Monoamine oxidase B (MAOB) is expressed in the mitochondrial membrane and has a key role in degrading various neurologically active amines such as benzylamine, phenethylamine and dopamine with the help of Flavin adenine dinucleotide (FAD) cofactor. The Parkinson’s disease associated symptoms can be treated using inhibitors of MAO-B as the dopamine degradation can be reduced. Currently, many inhibitors are available having micromolar to nanomolar binding affinities. However, still there is demand for compounds with superior binding affinity and binding specificity with favorable pharmacokinetic properties for treating Parkinson’s disease and computational screening methods can be majorly recruited for this. However, the accuracy of currently available force-field methods for ranking the inhibitors or lead drug-like compounds should be improved and novel methods for screening compounds need to be developed. We studied the performance of various force-field-based methods and data driven approaches in ranking about 3753 compounds having activity against the MAO-B target. The binding affinities computed using autodock and autodock-vina are shown to be non-reliable. The force-field-based MM-GBSA also under-performs. However, certain machine learning approaches, in particular KNN, are found to be superior, and we propose KNN as the most reliable approach for ranking the complexes to reasonable accuracy. Furthermore, all the employed machine learning approaches are also computationally less demanding.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

Investigation of the binding mode of a novel cruzain inhibitor by docking, molecular dynamics, ab initio and MM/PBSA calculations

Chagas disease remains a major health problem in South America, and throughout the world. The two drugs clinically available for its treatment have limited efficacy and cause serious adverse effects. Cruzain is an established therapeutic target of Trypanosoma cruzi, the protozoan that causes Chagas disease. Our group recently identified a competitive cruzain inhibitor (compound 1) with an IC₅₀ = 15 µM that is also more synthetically accessible than the previously reported lead, compound 2. Prior studies, however, did not propose a binding mode for compound 1, hindering understanding of the structure-activity relationship and optimization. Here, the cruzain binding mode of compound 1 was investigated using docking, molecular dynamics (MD) simulations with ab initio derived parameters, ab initio calculations, and MM/PBSA. Two ligand protonation states and four binding poses were evaluated. A careful ligand parameterization method was employed to derive more physically meaningful parameters than those obtained by automated tools. The poses of unprotonated 1 were unstable in MD, showing large conformational changes and diffusing away from the binding site, whereas the protonated form showed higher stability and interaction with negatively charged residues Asp161 and Cys25. MM/PBSA also suggested that these two residues contribute favorably to binding of compound 1. By combining results from MD, ab initio calculations, and MM/PBSA, a binding mode of 1 is proposed. The results also provide insights for further optimization of 1, an interesting lead compound for the development of new cruzain inhibitors.

Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening

High-throughput screening (HTS) campaigns are routinely performed in pharmaceutical companies to explore activity profiles of chemical libraries for the identification of promising candidates for further investigation. With the aim of improving hit rates in these campaigns, data-driven approaches have been used to design relevant compound screening collections, enable effective hit triage and perform activity modeling for compound prioritization. Remarkable progress has been made in the activity modeling area since the recent introduction of large-scale bioactivity-based compound similarity metrics. This is evidenced by increased hit rates in iterative screening strategies and novel insights into compound mode of action obtained through activity modeling. Here, we provide an overview of the developments in data-driven approaches, elaborate on novel activity modeling techniques and screening paradigms explored and outline their significance in HTS.

Identification of estrogen receptor α ligands with virtual screening techniques

Utilization of computer-aided molecular discovery methods in virtual screening (VS) is a cost-effective approach to identify novel bioactive small molecules. Unfortunately, no universal VS strategy can guarantee high hit rates for all biological targets, but each target requires distinct, fine-tuned solutions. Here, we have studied in retrospective manner the effectiveness and usefulness of common pharmacophore hypothesis, molecular docking and negative image-based screening as potential VS tools for a widely applied drug discovery target, estrogen receptor α (ERα). The comparison of the methods helps to demonstrate the differences in their ability to identify active molecules. For example, structure-based methods identified an already known active ligand from the widely-used bechmarking decoy molecule set. Although prospective VS against one commercially available database with around 100,000 drug-like molecules did not retrieve many testworthy hits, one novel hit molecule with pIC50 value of 6.6, was identified. Furthermore, our small in-house compound collection of easy-to-synthesize molecules was virtually screened against ERα, yielding to five hit candidates, which were found to be active in vitro having pIC50 values from 5.5 to 6.5.

Evaluation of potential flavonoid inhibitors of glyoxalase-I based on virtual screening and in vitro studies

Glyoxalase-I (GLO-I) is a component of the ubiquitous detoxification system involved in the conversion of methylglyoxal (MG) to d-lactate in the glycolytic pathway. MG toxicity arises from its ability to form advanced glycation end products. GLO-I has been reported to be frequently overexpressed in various types of cancer cells. In this study, we performed structure-based virtual screening of focused flavonoids commercial library to identify potential and specific inhibitors of GLO-I. The compounds were ranked based on Glide extra precision docking score and five hits (curcumin, quercetin, morin, naringin and silibinin) were selected on the basis of their interaction with active site amino acid residues of GLO-I. Mixed mode QM/MM calculation was performed on the top-scoring hit to ascertain the role of zinc ion in ligand binding. In addition, the identified hits were subjected to MM/GBSA binding energy prediction, ADME prediction and similarity studies. The hits were tested in vitro for cell viability, and GLO-I inhibition. Naringin (ST072162) was found to be most potent inhibitor of GLO-I among the identified hits with highest glide XP dock score of -14.906. These findings suggest that naringin could be a new scaffold for designing inhibitors against GLO-I with potential application as anticancer agents.

Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5-HT(3)A, Histamine H(1), and Histamine H(4) Receptors

In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.

Binding modes of peptidomimetics designed to inhibit STAT3

STAT3 is a transcription factor that has been found to be constitutively activated in a number of human cancers. Dimerization of STAT3 via its SH2 domain and the subsequent translocation of the dimer to the nucleus leads to transcription of anti-apoptotic genes. Prevention of the dimerization is thus an attractive strategy for inhibiting the activity of STAT3. Phosphotyrosine-based peptidomimetic inhibitors, which mimic pTyr-Xaa-Yaa-Gln motif and have strong to weak binding affinities, have been previously investigated. It is well-known that structures of protein-inhibitor complexes are important for understanding the binding interactions and designing stronger inhibitors. Experimental structures of inhibitors bound to the SH2 domain of STAT3 are, however, unavailable. In this paper we describe a computational study that combined molecular docking and molecular dynamics to model structures of 12 peptidomimetic inhibitors bound to the SH2 domain of STAT3. A detailed analysis of the modeled structures was performed to evaluate the characteristics of the binding interactions. We also estimated the binding affinities of the inhibitors by combining MMPB/GBSA-based energies and entropic cost of binding. The estimated affinities correlate strongly with the experimentally obtained affinities. Modeling results show binding modes that are consistent with limited previous modeling studies on binding interactions involving the SH2 domain and phosphotyrosine(pTyr)-based inhibitors. We also discovered a stable novel binding mode that involves deformation of two loops of the SH2 domain that subsequently bury the C-terminal end of one of the stronger inhibitors. The novel binding mode could prove useful for developing more potent inhibitors aimed at preventing dimerization of cancer target protein STAT3.