Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking

A practical guide to large-scale docking

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

In silico prediction of nuclear receptor binding to polychlorinated dibenzofurans and its implication on endocrine disruption in humans and wildlife

Polychlorinated dibenzofurans (PCDFs) are known to cause endocrine disruption in humans and wildlife but the mechanisms underlying this disruption have not been adequately investigated. In this paper, the susceptibility of the endocrine system to disruption by PCDF congeners via nuclear receptor binding was studied using molecular docking simulation. Findings revealed that some PCDF congeners exhibit high probabilities of binding to androgen receptor in its agonistic and antagonistic conformations. In depth molecular docking analysis of the receptor-ligand complexes formed by PCDFs with androgen receptor in its agonistic and antagonistic conformations showed that, these complexes were stabilized by electrostatic, van der Waals, pi-effect and hydrophobic interactions. It was also observed that PCDF molecules mimic the modes of interaction observed in androgen-testosterone and androgen-bicalutamide complexes, utilizing between 65 and 83% of the amino acid residues used by the co-crystallized ligands for binding. This computational study suggests that some PCDF congeners may act as agonists and antagonists of androgen receptor in humans and wildlife via inapproprate binding to the receptor.

In silico methods and tools for drug discovery

In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.

Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface

The current pandemic outbreak clearly indicated the urgent need for tools allowing fast predictions of bioactivity of a large number of compounds, either available or at least synthesizable. In the computational chemistry toolbox, several such tools are available, with the main ones being docking and structure-activity relationship modeling either by classical linear QSAR or Machine Learning techniques. In this contribution, we focus on the comparison of the results obtained using different docking protocols on the example of the search for bioactivity of compounds containing N-N-C(S)-N scaffold at the S-protein of SARS-CoV-2 virus with ACE2 human receptor interface. Based on over 1800 structures in the training set we have predicted binding properties of the complete set of nearly 600000 structures from the same class using the Machine Learning Random Forest Regressor approach.

A modified binary particle swarm optimization with a machine learning algorithm and molecular docking for QSAR modelling of cholinesterase inhibitors

The acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitors play a key role in treating Alzheimer's disease. This study proposes an approach that integrates a modified binary particle swarm optimization (PSO) with a machine learning algorithm for building QSAR models to predict the activity of inhibitors for AChE and BuChE enzymes. More precisely, it uses a transfer function to convert the continuous search space of PSO to binary. Furthermore, it utilizes the concepts of catfish effect and chaotic map to improve exploration ability in searching for an optimum subset of descriptors for QSAR model constructions. Then, through a statistical method, it employs a machine learning algorithm to evaluate the fitness value of each candidate subset of features. Different combinations of four transfer functions with four machine learning algorithms, including K-nearest neighbour, multiple linear regression, support vector machine, and regression tree, were used to build several variants of the proposed algorithm. QSAR models constructed by each version were verified by internal and external validations. The best variants were selected based on a method called sum of ranking differences.

Pharmacophore Model for SARS-CoV-2 3CLpro Small-Molecule Inhibitors and in Vitro Experimental Validation of Computationally Screened Inhibitors

Among the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied intensively. One of the main drug target proteins proposed so far is the SARS-CoV-2 viral protease 3CLpro (also called Mpro), an essential component for viral replication. Ongoing ligand- and receptor-based computational screening efforts would be facilitated by an improved understanding of the electrostatic, hydrophobic, and steric features that characterize small-molecule inhibitors binding stably to 3CLpro and by an extended collection of known binders. Here, we present combined virtual screening, molecular dynamics (MD) simulation, machine learning, and in vitro experimental validation analyses, which have led to the identification of small-molecule inhibitors of 3CLpro with micromolar activity and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the 3CLpro binding pocket. Experimentally validated inhibitors using a ligand activity assay include natural compounds with the available prior knowledge on safety and bioavailability properties, such as the natural compound rottlerin (IC50 = 37 μM) and synthetic compounds previously not characterized (e.g., compound CID 46897844, IC50 = 31 μM). In combination with the developed pharmacophore model, these and other confirmed 3CLpro inhibitors may provide a basis for further similarity-based screening in independent compound databases and structural design optimization efforts to identify 3CLpro ligands with improved potency and selectivity. Overall, this study suggests that the integration of virtual screening, MD simulations, and machine learning can facilitate 3CLpro-targeted small-molecule screening investigations. Different receptor-, ligand-, and machine learning-based screening strategies provided complementary information, helping to increase the number and diversity of the identified active compounds. Finally, the resulting pharmacophore model and experimentally validated small-molecule inhibitors for 3CLpro provide resources to support follow-up computational screening efforts for this drug target.

Synthesis, antimicrobial activity, density functional modelling and molecular docking with COVID-19 main protease studies of benzoxazole derivative: 2-(p-chloro-benzyl)-5-[3-(4-ethly-1-piperazynl) propionamido]-benzoxazole

This study contains synthesis, antimicrobial activity, density functional modelling and molecular docking studies of benzoxazole derivative: 2-(p-chloro-benzyl)-5-[3-(4-ethly-1-piperazynl) propionamido]-benzoxazole. The synthetic procedure of investigated compound is given in detail. The newly synthesized benzoxazole compound and standard drugs were evaluated for their antimicrobial activity against some Gram-positive, Gram-negative bacteria and fungus C. albicans and their drug-resistant isolates. The benzoxazole compound has been characterized by using ¹H-NMR, IR and MASS spectrometry and elemental analysis techniques. The molecular structure of the compound in the ground state has been modelling using density functional theory (DFT) with B3LYP/6-311++g(d,p) level. The molecular docking of 2-(p-chloro-benzyl)-5-[3-(4-ethly-1-piperazynl) propionamido]-benzoxazole with COVID-19 main protease has been also performed by using optimized geometry and the experimentally determined dimensional structure of the main protease (M-pro) of COVID-19.

Induction of apoptosis, cytotoxicity and radiosensitization by novel 3,4-dihydroquinazolinone derivatives

Twenty new quinazolinone derivatives bearing a piperonyl moiety were designed and synthesized. The structures of the target compounds were in agreement with the microanalytical and spectral data. Compounds 4-10, 13, 14 and 17-27 were screened for their cytotoxic activity against HepG-2 and MCF-7 cancer cell lines. The target compounds showed IC₅₀ in the range of 2.46-36.85 µM and 3.87-88.93 µM for HepG-2 and MCF-7, respectively. The promising compounds 7, 19, 26 and 27 were selected to measure their EGFR inhibitory activity. The IC₅₀ values of the promising compounds were in the range of 146.9-1032.7 nM for EGFR in reference to Erlotinib (IC₅₀ = 96.6 nM). In further studies on compounds 7, 19, 26 and 27 using HepG-2 cell line, there was significant overexpression of p21 and downregulation of two members of IAPs protein family; Survivin and XIAP, relative to their controls. Annexin V-FITC and caspase-3 analyses have established a significant increase in early apoptosis. Moreover, the four selected compounds have impaired cell proliferation by cell cycle arrest at the G2/M phase compared to their respective control. Considering radiotherapy as the primary treatment for many types of solid tumors, the radiosensitizing abilities of compounds 7, 19, 26 and 27 were measured against HepG-2 and MCF-7 cell lines combined with a single dose of 8 Gy gamma radiation. Measurement of the IC₅₀ of the promising compounds after irradiation revealed their ability to sensitize the cells to the lethal effect of gamma irradiation (IC₅₀ = 1.56-4.32 µM and 3.06-5.93 µM for HepG-2 and MCF-7 cells, respectively). Molecular docking was performed to gain insights into the ligand-binding interactions of 7, 19, 26 and 27 inside the EGFR binding sites and revealed their essential interactions, explaining their good activity towards EGFR.

MCCS, a novel characterization method for protein-ligand complex

Delineating the fingerprint or feature vector of a receptor/protein will facilitate the structural and biological studies, as well as the rational design and development of drugs with high affinities and selectivity. However, protein is complicated by its different functional regions that can bind to some of its protein partner(s), substrate(s), orthosteric ligand(s) or allosteric modulator(s) where cogent methods like molecular fingerprints do not work well. We here elaborate a scoring-function-based computing protocol Molecular Complex Characterizing System to help characterize the binding feature of protein-ligand complexes. Based on the reported receptor-ligand interactions, we first quantitate the energy contribution of each individual residue which may be an alternative of MD-based energy decomposition. We then construct a vector for the energy contribution to represent the pattern of the ligand recognition at a receptor and qualitatively analyze the matching level with other receptors. Finally, the energy contribution vector is explored for extensive use in similarity and clustering. The present work provides a new approach to cluster proteins, a perspective counterpart for determining the protein characteristics in the binding, and an advanced screening technique where molecular docking is applicable.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19

The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.

4-Arylthiosemicarbazide derivatives as a new class of tyrosinase inhibitors and anti-Toxoplasma gondii agents

We report herein anti-proliferation effects of 4-arylthiosemicarbazides, with a cyclopentane substitution at N1 position, on highly virulent RH strain of Toxoplasma gondii. Among them, the highest in vitro anti-Toxoplasma activity was found with the meta-iodo derivative. Further experiments demonstrated inhibitory effects of thiosemicarbazides on tyrosinase (Tyr) activity, and good correlation was found between percentage of Tyr inhibition and IC_50Tg. To confirm the concept that thiosemicarbazides are able to disrupt tyrosine metabolism in Toxoplasma tachyzoites, the most potent Tyr inhibitors were tested for their efficacy of T. gondii growth inhibition. All of them significantly reduced the number of tachyzoites in the parasitophorous vacuoles (PVs) compared to untreated cells, as well as inhibited tachyzoites growth by impeding cell division. Collectively, these results indicate that compounds with the thiosemicarbazide scaffold are able to disrupt tyrosine metabolism in Toxoplasma tachyzoites by deregulation of their crucial enzyme tyrosine hydroxylase (TyrH).

Modeling the structural and reactivity properties of hydrazono methyl-4H-chromen-4-one derivatives-wavefunction-dependent properties, molecular docking, and dynamics simulation studies

This study explains the vibration and interaction of three pharmaceutically active hydrazine derivatives, (E)-3-((2-(2,5-difluorophenyl)hydrazono)methyl)-4H-chromen-4-one (DFH), (E)-3-((2-(4-(trifluoromethyl)phenyl)hydrazono)methyl)-4H-chromen-4-one (TMH), and (E)-3-((2-(3,5-bis(trifluoromethyl)phenyl)hydrazono)methyl)-4H-chromen-4-one (BPH) using theoretical approach. The trend in chemical reactivity and stability of the studied compounds was observed to show increasing stability and decreasing reactivity and this was obtained from orbital energies. The effect of bromine and chlorine atoms, instead of fluorine atoms, is also noted. Surface analysis on the covalent bond was attained by ELF and LOL analysis. Biological activities were predicted using molecular docking studies. Docking results were analyzed with standard drugs, 5-fluorouracil/piperine. Antitumor activity of hydrazine derivatives was found to be higher than reference ones. Molecular dynamics (MD) simulation was performed for 100 ns to validate the stability behavior of hydrazine derivatives with the dual specificity threonine tyrosine kinase (TTK) protein. RMSD, RMSF, Rg, SASA, and intermolecular analysis of DFH, TMH, and BPH with threonine tyrosine kinase forms stable ligand-protein interactions. The molecular and predictive biological properties of three pharmaceutically active hydrazine derivatives which can be helpful to researchers in future experimental validation through in vitro and in vivo studies.

Structure-Guided Computational Approaches to Unravel Druggable Proteomic Landscape of Mycobacterium leprae

Leprosy, caused by Mycobacterium leprae (M. leprae), is treated with a multidrug regimen comprising Dapsone, Rifampicin, and Clofazimine. These drugs exhibit bacteriostatic, bactericidal and anti-inflammatory properties, respectively, and control the dissemination of infection in the host. However, the current treatment is not cost-effective, does not favor patient compliance due to its long duration (12 months) and does not protect against the incumbent nerve damage, which is a severe leprosy complication. The chronic infectious peripheral neuropathy associated with the disease is primarily due to the bacterial components infiltrating the Schwann cells that protect neuronal axons, thereby inducing a demyelinating phenotype. There is a need to discover novel/repurposed drugs that can act as short duration and effective alternatives to the existing treatment regimens, preventing nerve damage and consequent disability associated with the disease. Mycobacterium leprae is an obligate pathogen resulting in experimental intractability to cultivate the bacillus in vitro and limiting drug discovery efforts to repositioning screens in mouse footpad models. The dearth of knowledge related to structural proteomics of M. leprae, coupled with emerging antimicrobial resistance to all the three drugs in the multidrug therapy, poses a need for concerted novel drug discovery efforts. A comprehensive understanding of the proteomic landscape of M. leprae is indispensable to unravel druggable targets that are essential for bacterial survival and predilection of human neuronal Schwann cells. Of the 1,614 protein-coding genes in the genome of M. leprae, only 17 protein structures are available in the Protein Data Bank. In this review, we discussed efforts made to model the proteome of M. leprae using a suite of software for protein modeling that has been developed in the Blundell laboratory. Precise template selection by employing sequence-structure homology recognition software, multi-template modeling of the monomeric models and accurate quality assessment are the hallmarks of the modeling process. Tools that map interfaces and enable building of homo-oligomers are discussed in the context of interface stability. Other software is described to determine the druggable proteome by using information related to the chokepoint analysis of the metabolic pathways, gene essentiality, homology to human proteins, functional sites, druggable pockets and fragment hotspot maps.

A computational insight into endocrine disruption by polychlorinated biphenyls via non-covalent interactions with human nuclear receptors

Production of polychlorinated biphenyls (PCBs) was banned a long time ago because of their harmful health effects but humans continue to be exposed to residual PCBs in the environment. In this study, the susceptibility of human nuclear receptors to binding by PCBs was investigated using molecular docking simulation. Findings revealed that PCBs belonging to ortho-substituted, mono-ortho-substituted and non-ortho-substituted congeners could bind to agonistic conformations of androgen (AR), estrogen (ER α and ER β), glucocorticoid (GR) and thyroid hormone (TR α and TR β) receptors as well as antagonistic conformation of androgen receptor (AR an) but only ortho-substituted and mono-ortho-substituted PCBs could bind to estrogen receptors in their antagonistic conformations (ER α an and ER β an). Further molecular docking analyses showed that PCBs mimic the modes of interaction observed for the co-crystallized ligands in the crystal structures of the affected receptors, utilizing 81%, 83%, 78%, 60%, 75%, 60%, 86%, 100% and 75% of the amino acid residues utilized by the co-crystallized ligands for binding in AR, AR an, ER α, ER α an, ER β, ER β an, GR, TR α and TR β respectively. This computational study suggests that PCBs may cause endocrine disruption via formation of non-covalent interactions with androgen, estrogen, glucocorticoid and thyroid hormone receptors.

Thiosemicarbazide Derivatives Decrease the ATPase Activity of Staphylococcus aureus Topoisomerase IV, Inhibit Mycobacterial Growth, and Affect Replication in Mycobacterium smegmatis

Compounds targeting bacterial topoisomerases are of interest for the development of antibacterial agents. Our previous studies culminated in the synthesis and characterization of small-molecular weight thiosemicarbazides as the initial prototypes of a novel class of gyrase and topoisomerase IV inhibitors. To expand these findings with further details on the mode of action of the most potent compounds, enzymatic studies combined with a molecular docking approach were carried out, the results of which are presented herein. The biochemical assay for 1-(indol-2-oyl)-4-(4-nitrophenyl) thiosemicarbazide (4) and 4-benzoyl-1-(indol-2-oyl) thiosemicarbazide (7), showing strong inhibitory activity against Staphylococcus aureus topoisomerase IV, confirmed that these compounds reduce the ability of the ParE subunit to hydrolyze ATP rather than act by stabilizing the cleavage complex. Compound 7 showed better antibacterial activity than compound 4 against clinical strains of S. aureus and representatives of the Mycobacterium genus. In vivo studies using time-lapse microfluidic microscopy, which allowed for the monitoring of fluorescently labelled replisomes, revealed that compound 7 caused an extension of the replication process duration in Mycobacterium smegmatis, as well as the growth arrest of bacterial cells. Despite some similarities to the mechanism of action of novobiocin, these compounds show additional, unique properties, and can thus be considered a novel group of inhibitors of the ATPase activity of bacterial type IIA topoisomerases.

Theoretical insights into molecular mechanism and energy criteria of PARP-2 enzyme inhibition by benzimidazole analogues

The emergence of poly (ADP-ribose) polymerase (PARP) inhibitors targeting a class of PARP enzymes has gained a great interest in cancer therapy. Majority of the PARP inhibitors are not isoform-selective which may cause unwanted off-target effects. In the present study, we explore the molecular mechanism and energy requirements for PARP-2 inhibition. This involves docking studies, frontier molecular orbital analysis, 500 ns molecular dynamics simulation (MD), binding free energy analysis and principal component analysis. The results clearly suggest the importance of hydrogen bonding (Gly429, Gln332, Ser470, Tyr455) and π-π stacking interactions (His428, Tyr455, Tyr462, Phe463, Tyr473) between residues and the inhibitor. Presence of lowest unoccupied molecular orbitals favors π-π stacking interactions and highest occupied molecular orbital orbital favors hydrogen-bonding interactions in the ligands. The stability of most active/PARP-2 complex is confirmed by hydrogen bonding and π-π stacking interaction parameters. Molecular-mechanics Poisson-Boltzmann surface area energy calculations showed that van der Waals and nonpolar solvation energy terms are crucial components for the stable binding of the ligands. Per residue analysis showed that tyrosine, histidine, and phenyl alanine residues are responsible for hydrophobic interactions with the ligands. Four new inhibitors are designed based on this study and the stability of PARP-2/inhibitor complex is validated by MD, density functional theory studies, and ADME/toxicity properties. Information from the present study can serve as a basis for designing new isoform-selective PARP-2 inhibitors.

FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening

To reduce time and cost, virtual ligand screening (VLS) often precedes experimental ligand screening in modern drug discovery. Traditionally, high-resolution structure-based docking approaches rely on experimental structures, while ligand-based approaches need known binders to the target protein and only explore their nearby chemical space. In contrast, our structure-based FINDSITE^comb2.0 approach takes advantage of predicted, low-resolution structures and information from ligands that bind distantly related proteins whose binding sites are similar to the target protein. Using a boosted tree regression machine learning framework, we significantly improved FINDSITE^comb2.0 by integrating ligand fragment scores as encoded by molecular fingerprints with the global ligand similarity scores of FINDSITE^comb2.0. The new approach, FRAGSITE, exploits our observation that ligand fragments, e.g., rings, tend to interact with stereochemically conserved protein subpockets that also occur in evolutionarily unrelated proteins. FRAGSITE was benchmarked on the 102 protein DUD-E set, where any template protein whose sequence identify >30% to the target was excluded. Within the top 100 ranked molecules, FRAGSITE improves VLS precision and recall by 14.3 and 18.5%, respectively, relative to FINDSITE^comb2.0. Moreover, the mean top 1% enrichment factor increases from 25.2 to 30.2. On average, both outperform state-of-the-art deep learning-based methods such as AtomNet. On the more challenging unbiased set LIT-PCBA, FRAGSITE also shows better performance than ligand similarity-based and docking approaches such as two-dimensional ECFP4 and Surflex-Dock v.3066. On a subset of 23 targets from DEKOIS 2.0, FRAGSITE shows much better performance than the boosted tree regression-based, vScreenML scoring function. Experimental testing of FRAGSITE's predictions shows that it has more hits and covers a more diverse region of chemical space than FINDSITE^comb2.0. For the two proteins that were experimentally tested, DHFR, a well-studied protein that catalyzes the conversion of dihydrofolate to tetrahydrofolate, and the kinase ACVR1, FRAGSITE identified new small-molecule nanomolar binders. Interestingly, one new binder of DHFR is a kinase inhibitor predicted to bind in a new subpocket. For ACVR1, FRAGSITE identified new molecules that have diverse scaffolds and estimated nanomolar to micromolar affinities. Thus, FRAGSITE shows significant improvement over prior state-of-the-art ligand virtual screening approaches. A web server is freely available for academic users at http:/sites.gatech.edu/cssb/FRAGSITE.

Concentration dependent SERS, DFT and molecular docking studies of a ureido derivative with antitubercular properties

Spectroscopic analysis, density functional theory (DFT) studies and surface enhanced Raman scattering (SERS) of antimycobactetial 4-[3-(4-acetylphenyl)ureido]-2-hydroxybenzoic acid (AUHB) have been studied on different silver sols. For Raman and SERS wavenumbers, very large changes are observed. Observed variations in the modes of ring may be due to surface π-electron interactions and presence of this indicated that poly substituted ring is more inclined than para substituted phenyl ring and assumes a inclined position for concentration 10^-3 M. Changes in orientation are seen in SERS spectra depending on concentration. In order to find electron-rich and poor sites of AUHB, molecular electrostatic potential was also constructed. The molecular docking results show that binding affinity and interactions with the receptor DprE1 may be supporting evidence for further studies in design further AUHB pharmaceutical applications. Based on antitubercular activity of 4-aminosalicylic acid (PAS) and urea derivatives we designed, synthesized and investigated mutual PAS-urea derivatives as potential antimycobacterial agents.

Spectroscopic investigations, concentration dependent SERS, and molecular docking studies of a benzoic acid derivative

Spectroscopic analysis, density functional theory (DFT) studies and surface enhanced Raman scattering of 4-((3-bromo-5-chloro-2-hydroxybenzylidene)amino)benzoic acid (BCHB) have been studied on different silver colloids concentrations in order to know the particular chemical species responsible for the spectra. For Raman and surface enhanced Raman scattering (SERS) wavenumbers, changes are observed. Observed variations in the modes of ring may be due to interaction of the π-electrons and presence of this indicated that RingII is more inclined than RingI and the BCHB assumes inclined orientation for concentration 10^-3 M. Changes in orientation are seen in SERS spectra depending on concentration. In order to determine the electron-rich and poor sites of BCHB, the molecular electrostatic potential was also constructed. The molecular docking studies show that the bindings and interactions with the receptors may be supporting evidence for further studies in design further BCHB pharmaceutical applications.

Spectroscopic investigations, concentration dependent SERS, and molecular docking studies of a hydroxybenzylidene derivative

Spectroscopic analysis, density functional theory (DFT) studies and surface enhanced Raman scattering (SERS) of (E)-N'-(5-chloro-2-hydroxybenzylidene)-4-trifluoromethyl) benzohydrazide (CHTB) have been studied on different silver colloids in order to know the particular chemical species responsible for the spectra. Very significant shifts are observed for Raman and SERS wavenumbers. Observed changes in the υ-ring modes may be due to surface interaction of the π-electrons and the presence of this suggested that RingII is more tilted in both cases than RingI and the molecule assumes a tilted orientation for the concentration 10^-3 M. Orientation changes are seen in concentration dependent SERS spectra. The molecular electrostatic potential has also been constructed to determine the electron rich and poor site of CHTB. The molecular docking studies indicate that the binding affinity and hydrogen bond interactions with the receptors may be supporting evidence for further studies in designing other pharmaceutical applications of CHTB.Communicated by Ramaswamy H. Sarma.

Identification of Novel Cyclin A2 Binding Site and Nanomolar Inhibitors of Cyclin A2-CDK2 Complex

BACKGROUND

Given the diverse roles of cyclin A2 both in cell cycle regulation and in DNA damage response, identifying small molecule regulators of cyclin A2 activity carries significant potential to regulate diverse cellular processes in both ageing/neurodegeneration and in cancer.

OBJECTIVE

Based on cyclin A2's recently discovered role in DNA repair, we hypothesized that small molecule inhibitors that were predicted to bind to both cyclin A2 and CDK2 will be useful as a radiosensitizer of cancer cells. In this study, we used structure-based drug discovery to find inhibitors that target both cyclin A2 and CDK2.

METHODS

Molecular dynamics simulations were used to generate diverse binding pocket conformations for application of the relaxed complex scheme. We then used structure-based virtual screening to find potential dual cyclin A2 and CDK2 inhibitors. Based on a consensus score of docked poses from Glide and AutoDock Vina, we identified about 40 promising hit compounds, where all PAINS scaffolds were removed from consideration. A biochemical luminescence assay of cyclin A2-CDK2 function was used for experimental verification.

RESULTS

Four lead inhibitors of cyclin A2-CDK2 complex have been identified using a relaxed complex scheme virtual screen have been verified in a biochemical luminescence assay of cyclin A2- CDK2 function. Two of the four lead inhibitors had inhibitory concentrations in the nanomolar range.

CONCLUSION

The four cyclin A2-CDK2 complex inhibitors are the first reported inhibitors that were specifically designed not to target the cyclin A2-CDK2 protein-protein interface. Overall, our results highlight the potential of combined advanced computational tools and biochemical verification to discover novel binding scaffolds.

Rational Design of PDZ Domain Inhibitors: Discovery of Small Organic Compounds Targeting PDZ Domains

PDZ domains, which belong to protein-protein interaction networks, are critical for regulating important biological processes such as scaffolding, trafficking, and signaling cascades. Interfering with PDZ-mediated interactions could affect these numerous biological processes. Thus, PDZ domains have emerged as promising targets to decipher biological phenomena and potentially treat cancer and neurological diseases. In this minireview, we focus on the discovery and design of small molecule inhibitors to modulate PDZ domains. These compounds interfere with endogenous protein partners from the PDZ domain by binding at the protein-protein interface. While peptides or peptidomimetic ligands were described to modulate PDZ domains, the focus of this review is on small organic compounds.

Synthesis of some quinazolinones inspired from the natural alkaloid L-norephedrine as EGFR inhibitors and radiosensitizers

A set of quinazolinones synthesized by the aid of L-norephedrine was assembled to generate novel analogues as potential anticancer and radiosensitizing agents. The new compounds were evaluated for their cytotoxic activity against MDA-MB-231, MCF-7, HepG-2, HCT-116 cancer cell lines and EGFR inhibitory activity. The most active compounds 5 and 6 were screened against MCF-10A normal cell line and displayed lower toxic effects. They proved their relative safety with high selectivity towards MDA-MB-231 breast cancer cell line. Measurement of the radiosensitizing activity for 5 and 6 revealed that they could sensitize the tumour cells after being exposed to a single dose of 8 Gy gamma radiation. Compound 5 was able to induce apoptosis and arrest the cell cycle at the G2-M phase. Molecular docking of 5 and 6 in the active site of EGFR was performed to gain insight into the binding interactions with the key amino acids.

Uncaria tomentosa (cat's claw): a promising herbal medicine against SARS-CoV-2/ACE-2 junction and SARS-CoV-2 spike protein based on molecular modeling

COVID-19 is a novel severe acute respiratory syndrome coronavirus. Currently, there is no effective treatment and vaccines seem to be the solution in the future. Virtual screening of potential drugs against the S protein of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) has provided small molecular compounds with a high binding affinity. Unfortunately, most of these drugs do not attach with the binding interface of the receptor-binding domain (RBD)–angiotensin-converting enzyme-2 (ACE-2) complex in host cells. Molecular modeling was carried out to evaluate the potential antiviral properties of the components of the medicinal herb Uncaria tomentosa (cat’s claw) focusing on the binding interface of the RBD–ACE-2 and the viral spike protein. The in silico approach starts with protein–ligand docking of 26 Cat’s claw key components followed by molecular dynamics simulations and re-docked calculations. Finally, we carried out drug-likeness calculations for the most qualified cat’s claw components. The structural bioinformatics approaches led to the identification of several bioactive compounds of U. tomentosa with potential therapeutic effect by dual strong interaction with interface of the RBD–ACE-2 and the ACE-2 binding site on SARS-CoV-2 RBD viral spike. In addition, in silico drug-likeness indices for these components were calculated and showed good predicted therapeutic profiles of these phytochemicals found in U. tomentosa (cat’s claw). Our findings suggest the potential effectiveness of cat’s claw as complementary and/or alternative medicine for COVID-19 treatment.

Communicated by Ramaswamy H. Sarma

Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks

The high-performance computational techniques have brought significant benefits for drug discovery efforts in recent decades. One of the most challenging problems in drug discovery is the protein-ligand binding pose prediction. To predict the most stable structure of the complex, the performance of conventional structure-based molecular docking methods heavily depends on the accuracy of scoring or energy functions (as an approximation of affinity) for each pose of the protein-ligand docking complex to effectively guide the search in an exponentially large solution space. However, due to the heterogeneity of molecular structures, the existing scoring calculation methods are either tailored to a particular data set or fail to exhibit high accuracy. In this paper, we propose a convolutional neural network (CNN)-based model that learns to predict the stability factor of the protein-ligand complex and exhibits the ability of CNNs to improve the existing docking software. Evaluated results on PDBbind data set indicate that our approach reduces the execution time of the traditional docking-based method while improving the accuracy. Our code, experiment scripts, and pretrained models are available at https://github.com/j9650/MedusaNet.

Carvacrol: An In Silico Approach of a Candidate Drug on HER2, PI3Kα, mTOR, hER-α, PR, and EGFR Receptors in the Breast Cancer

Carvacrol is a phenol monoterpene found in aromatic plants specially in Lamiaceae family, which has been evaluated in an experimental model of breast cancer. However, any proposed mechanism based on its antitumor effect has not been reported. In our previous study, carvacrol showed a protective effect on 7,12-dimethylbenz[α]anthracene- (DMBA-) induced breast cancer in female rats. The main objective in this research was to evaluate by using in silico study the carvacrol on HER2, PI3Kα, mTOR, hER-α, PR, and EGFR receptors involved in breast cancer progression by docking analysis, molecular dynamic, and drug-likeness evaluation. A multilevel computational study to evaluate the antitumor potential of carvacrol focusing on the main targets involved in the breast cancer was carried out. The in silico study starts with protein-ligand docking of carvacrol followed by ligand pathway calculations, molecular dynamic simulations, and molecular mechanics energies combined with the Poisson-Boltzmann (MM/PBSA) calculation of the free energy of binding for carvacrol. As result, the in silico study led to the identification of carvacrol with strong binding affinity on mTOR receptor. Additionally, in silico drug-likeness index for carvacrol showed a good predicted therapeutic profile of druggability. Our findings suggest that mTOR signaling pathway could be responsible for its preventive effect in the breast cancer.

Docking and QSAR of Aminothioureas at the SARS-CoV-2 S-Protein-Human ACE2 Receptor Interface

Docking of over 160 aminothiourea derivatives at the SARS-CoV-2 S-protein–human ACE2 receptor interface, whose structure became available recently, has been evaluated for its complex stabilizing potency and subsequently subjected to quantitative structure–activity relationship (QSAR) analysis. The structural variety of the studied compounds, that include 3 different forms of the N–N–C(S)–N skeleton and combinations of 13 different substituents alongside the extensive length of the interface, resulted in the failure of the QSAR analysis, since different molecules were binding to different parts of the interface. Subsequently, absorption, distribution, metabolism, and excretion (ADME) analysis on all studied compounds, followed by a toxicity analysis using statistical models for selected compounds, was carried out to evaluate their potential use as lead compounds for drug design. Combined, these studies highlighted two molecules among the studied compounds, i.e., 5-(pyrrol-2-yl)-2-(2-methoxyphenylamino)-1,3,4-thiadiazole and 1-(cyclopentanoyl)-4-(3-iodophenyl)-thiosemicarbazide, as the best candidates for the development of future drugs.

Combining fragment docking with graph theory to improve ligand docking for homology model structures

Computational protein–ligand docking is well-known to be prone to inaccuracies in input receptor structures, and it is challenging to obtain good docking results with computationally predicted receptor structures (e.g. through homology modeling). Here we introduce a fragment-based docking method and test if it reduces requirements on the accuracy of an input receptor structures relative to non-fragment docking approaches. In this method, small rigid fragments are docked first using AutoDock Vina to generate a large number of favorably docked poses spanning the receptor binding pocket. Then a graph theory maximum clique algorithm is applied to find combined sets of docked poses of different fragment types onto which the complete ligand can be properly aligned. On the basis of these alignments, possible binding poses of complete ligand are determined. This docking method is first tested for bound docking on a series of Cytochrome P450 (CYP450) enzyme–substrate complexes, in which experimentally determined receptor structures are used. For all complexes tested, ligand poses of less than 1 Å root mean square deviations (RMSD) from the actual binding positions can be recovered. Then the method is tested for unbound docking with modeled receptor structures for a number of protein–ligand complexes from different families including the very recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) protease. For all complexes, poses with RMSD less than 3 Å from actual binding positions can be recovered. Our results suggest that for docking with approximately modeled receptor structures, fragment-based methods can be more effective than common complete ligand docking approaches.

Chrysin Inhibits NF-κB-Dependent CCL5 Transcription by Targeting IκB Kinase in the Atopic Dermatitis-Like Inflammatory Microenvironment

Chrysin (5,7-dihydroxyflavone) is a natural polyphenolic compound that induces an anti-inflammatory response. In this study, we investigated the molecular mechanism underlying the chrysin-induced suppression of C-C motif chemokine ligand 5 (CCL5) gene expression in atopic dermatitis (AD)-like inflammatory microenvironment. We showed that chrysin inhibited CCL5 expression at the transcriptional level through the suppression of nuclear factor kappa B (NF-κB) in the inflammatory environment. Chrysin could bind to the ATP-binding pocket of the inhibitor of κB (IκB) kinase (IKK) and, subsequently, prevent IκB degradation and NF-κB activation. The clinical efficacy of chrysin in targeting IKK was evaluated in 2,4-dinitrochlorobenzene-induced skin lesions in BALB/c mice. Our results suggested that chrysin prevented CCL5 expression by targeting IKK to reduce the infiltration of mast cells to the inflammatory sites and at least partially attenuate the inflammatory responses. These findings suggested that chrysin might be useful as a platform for the design and synthesis of small-molecule IKK-targeting drugs for the treatment of chronic inflammatory diseases, such as AD.

ProteinsPlus: interactive analysis of protein-ligand binding interfaces

Due to the increasing amount of publicly available protein structures searching, enriching and investigating these data still poses a challenging task. The ProteinsPlus web service (https://proteins.plus) offers a broad range of tools addressing these challenges. The web interface to the tool collection focusing on protein–ligand interactions has been geared towards easy and intuitive access to a large variety of functionality for life scientists. Since our last publication, the ProteinsPlus web service has been extended by additional services as well as it has undergone substantial infrastructural improvements. A keyword search functionality was added on the start page of ProteinsPlus enabling users to work on structures without knowing their PDB code. The tool collection has been augmented by three tools: StructureProfiler validates ligands and active sites using selection criteria of well-established protein–ligand benchmark data sets, WarPP places water molecules in the ligand binding sites of a protein, and METALizer calculates, predicts and scores coordination geometries of metal ions based on surrounding complex atoms. Additionally, all tools provided by ProteinsPlus are available through a REST service enabling the automated integration in structure processing and modeling pipelines.

Molecular profiling of multidrug-resistant river water isolates: insights into resistance mechanism and potential inhibitors

Polluted waters are an important reservoir for antibiotic resistance genes and multidrug-resistant bacteria. This report describes the microbial community, antibiotic resistance genes, and the genetic profile of extended spectrum β-lactamase strains isolated from rivers at, Pune, India. ESBL-producing bacteria isolated from diverse river water catchments running through Pune City were characterized for their antibiotic resistance. The microbial community and types of genes which confer antibiotic resistance were identified followed by the isolation of antibiotic-resistant bacteria on selective media and their genome analysis. Four representative isolates were sequenced using next generation sequencing for genomic analysis. They were identified as Pseudomonas aeruginosa, Escherichia coli, and two isolates were Enterobacter cloacae. The genes associated with the multidrug efflux pumps, such as tolC, macA, macB, adeL, and rosB, were detected in the isolates. As MacAB-TolC is an ABC type efflux pump responsible for conferring resistance in bacteria to several antibiotics, potential efflux pump inhibitors were identified by molecular docking. The homology model of their MacB protein with that from Escherichia coli K12 demonstrated structural changes in different motifs of MacB. Molecular docking of reported efflux pump inhibitors revealed the highest binding affinity of compound MC207-110 against MacB. It also details the potential efflux pump inhibitors that can serve as possible drug targets in drug development and discovery.

Detailed quantum mechanical studies on bioactive benzodiazepine derivatives and their adsorption over graphene sheets

Estazolam (Z1) and related derivatives, adinazolam (Z2), alprazolam (Z3), 4-hydroxyalprazolam (Z4) and triazolam (Z5) have been studied by using various computational tools to analyze their geometry and spectral characteristics. The compounds were found to interact with graphene monolayer results shows that there is enhancement in various physico-chemical descriptors and surface enhanced Raman spectra (SERS). The various reactive descriptors obtained from the FMO analysis predict the reactive nature of the compound. The various lone pair/sigma to pi conjugation was analyzed using NBO formalism, which provides valuable information about intra molecular electron transfer which is vital in predicting the inherent stability of the molecule. Simulated electronic spectra using TD-DFT and CAM-B3LYP functional are discussed in detail with respect to electronic transitions and light harvesting efficiency. Suitability of candidates as a photo sensitizer in dye sensitized solar cells was studied and 4-Hydroxyalprazolam is identified as a suitable candidate. Nucleophilic and electrophilic regions of the molecules are identified using MESP, which adds to the reactivity information. It can be seen that the highest interaction energy has been obtained in the case of the Z5-graphene system, while the lowest interaction energy has been obtained in the case of the Z1-graphene system. Docking indicates that the ligands adsorbed over graphene also form stable complexes with the receptors as indicated by the high binding affinity energy values.