Utility of homology models in the drug discovery process

Structure-based drug design for TSPO: Challenges and opportunities

The translocator protein 18 kDa (TSPO) is an evolutionarily conserved mitochondrial transmembrane protein implicated in various neuropathologies and inflammatory conditions, making it a longstanding diagnostic and therapeutic target of interest. Despite the development of various classes of TSPO ligand chemotypes, and the elucidation of bacterial and non-human mammalian experimental structures, many unknowns exist surrounding its differential structural and functional features in health and disease. There are several limitations associated with currently used computational methodologies for modelling the native structure and ligand-binding behaviour of this enigmatic protein. In this perspective, we provide a critical analysis of the developments in the uses of these methods, outlining their uses, inherent limitations, and continuing challenges. We offer suggestions of unexplored opportunities that exist in the use of computational methodologies which offer promise for enhancing our understanding of the TSPO.

Exploring SDHI fungicide resistance in Botrytis cinerea through genetic transformation system and AlphaFold model-based molecular docking

BACKGROUND

Gray mold caused by Botrytis cinerea is one of the most serious diseases affecting strawberry. Succinate dehydrogenase inhibitor (SDHI) fungicides have been used for more than a decade to control the disease. Monitoring resistance and improving in-depth understanding of resistance mechanisms are essential for the control of B. cinerea.

RESULTS

In this study, resistance monitoring of a SDHI fungicide boscalid was conducted on B. cinerea isolated from strawberries in Korea during 2020 and 2021, with resistance rates of 76.92% and 72.25%, respectively. In resistant strains, mutations P225F/H and H272R were found in SdhB, with P225F representing the dominant mutation type. Simultaneous mutations G85A, I93V, M158V, and V168I in SdhC were detected in 54.84% of sensitive strains. Sensitivity profiles of different Sdh genotypes of B. cinerea strains to six SDHIs were determined in vitro and in vivo. In addition, the mutation(s) were genetically validated through in situ SdhB (SdhC) expression. Docking assays between SDHIs and AlphaFold model-based Sdh complexes revealed generally consistent patterns with their in vitro phenotypes.

CONCLUSION

Resistance of B. cinerea to SDHI fungicide on strawberry was systematically investigated in this study. Deciphering of SDHI resistance through the genetic transformation system and AlphaFold model-based molecular docking will provide valuable insights into other target site-based fungicide resistance in fungal pathogens. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

AlphaFold2 structures guide prospective ligand discovery

AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ2 and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules and comparing results with those obtained from docking against the experimental structures. Hit rates were high and similar for the experimental and AF2 structures, as were affinities. Success in docking against the AF2 models was achieved despite differences between orthosteric residue conformations in the AF2 models and the experimental structures. Determination of the cryo-electron microscopy structure for one of the more potent 5-HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based drug design.

AlphaFold2 structures template ligand discovery

AlphaFold2 (AF2) and RosettaFold have greatly expanded the number of structures available for structure-based ligand discovery, even though retrospective studies have cast doubt on their direct usefulness for that goal. Here, we tested unrefined AF2 models prospectively, comparing experimental hit-rates and affinities from large library docking against AF2 models vs the same screens targeting experimental structures of the same receptors. In retrospective docking screens against the σ2 and the 5-HT2A receptors, the AF2 structures struggled to recapitulate ligands that we had previously found docking against the receptors' experimental structures, consistent with published results. Prospective large library docking against the AF2 models, however, yielded similar hit rates for both receptors versus docking against experimentally-derived structures; hundreds of molecules were prioritized and tested against each model and each structure of each receptor. The success of the AF2 models was achieved despite differences in orthosteric pocket residue conformations for both targets versus the experimental structures. Intriguingly, against the 5-HT2A receptor the most potent, subtype-selective agonists were discovered via docking against the AF2 model, not the experimental structure. To understand this from a molecular perspective, a cryoEM structure was determined for one of the more potent and selective ligands to emerge from docking against the AF2 model of the 5-HT2A receptor. Our findings suggest that AF2 models may sample conformations that are relevant for ligand discovery, much extending the domain of applicability of structure-based ligand discovery.

Comprehensive computational investigation for ligand recognition and binding dynamics of SdiA: a degenerate LuxR -type receptor in Klebsiella pneumoniae

SdiA is a LuxR-type receptor that controls the virulence of Klebsiella pneumoniae, a Gram-negative bacterium that causes various infections in humans. SdiA senses exogenous acyl-homoserine lactones (AHLs) and autoinducer-2 (AI-2), two types of quorum sensing signals produced by other bacterial species. However, the molecular details of how SdiA recognizes and binds to different ligands and how this affects its function and regulation in K. pneumoniae still need to be better understood. This study uses computational methods to explore the protein-ligand binding dynamics of SdiA with 11 AHLs and 2 AI-2 ligands. The 3D structure of SdiA was predicted through homology modeling, followed by molecular docking with AHLs and AI-2 ligands. Binding affinities were quantified using MM-GBSA, and complex stability was assessed via Molecular Dynamics (MD) simulations. Results demonstrated that SdiA in Klebsiella pneumoniae exhibits a degenerate binding nature, capable of interacting with multiple AHLs and AI-2. Specific ligands, namely C10-HSL, C8-HSL, 3-oxo-C8-HSL, and 3-oxo-C10-HSL, were found to have high binding affinities and formed critical hydrogen bonds with key amino acid residues of SdiA. This finding aligns with the observed preference of SdiA for AHLs having 8 to 10 carbon-length acyl chains and lacking hydroxyl groups. In contrast, THMF and HMF demonstrated poor binding properties. Furthermore, AI-2 exhibited a low affinity, corroborating the inference that SdiA is not the primary receptor for AI-2 in K. pneumoniae. These findings provide insights into the protein-ligand binding dynamics of SdiA and its role in quorum sensing and virulence of K. pneumoniae.

Inhibition of Uridine 5'-diphospho-glucuronosyltransferases A10 and B7 by vitamins: insights from in silico and in vitro studies

Uridine 5'-diphospho-glucuronosyltransferases (UGTs) have been considered as a family of enzymes responsible for the glucuronidation process, a crucial phase II detoxification reaction. Among the various UGT isoforms, UGTs A10 and B7 have garnered significant attention due to their broad substrate specificity and involvement in the metabolism of numerous compounds. Recent studies have suggested that certain vitamins may exert inhibitory effects on UGT activity, thereby influencing the metabolism of drugs, environmental toxins, and endogenous substances, ultimately impacting their biological activities. In the present study, the inhibition potential of vitamins (A, B1, B2, B3, B5, B6, B7, B9, D3, E, and C) on UGT1A10 and UGT2B7 was determined using in silico and in vitro approaches. A 3-dimensional model of UGT1A10 and UGT2B7 enzymes was built using Swiss Model, ITASSER, and ROSETTA and verified using Ramachandran plot and SAVES tools. Molecular docking studies revealed that vitamins interact with UGT1A10 and UGT2B7 enzymes by binding within the active site pocket and interacting with residues. Among all vitamins, the highest binding affinity predicted by molecular docking was - 8.61 kcal/mol with vitamin B1. The in vitro studies results demonstrated the inhibition of the glucuronidation activity of UGTs by vitamins A, B1, B2, B6, B9, C, D, and E, with IC50 values of 3.28 ± 1.07 µg/mL, 24.21 ± 1.11 µg/mL, 3.69 ± 1.02 µg/mL, 23.60 ± 1.08 µg/mL, 6.77 ± 1.08 µg/mL, 83.95 ± 1.09 µg/ml, 3.27 ± 1.13 µg/mL and 3.89 ± 1.12 µg/mL, respectively. These studies provided the valuable insights into the mechanisms underlying drug-vitamins interactions and have the potential to guide personalized medicine approaches, optimizing therapeutic outcomes, and ensuring patient safety. Indeed, further research in the area of UGT (UDP-glucuronosyltransferase) inhibition by vitamins is essential to fully understand the clinical relevance and implications of these interactions. UGTs play a crucial role in the metabolism and elimination of various drugs, toxins, and endogenous compounds in the body. Therefore, any factors that can modulate UGT activity, including vitamins, can have implications for drug metabolism, drug-drug interactions, and overall health.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00182-0.

In silico designing of multiepitope-based-peptide (MBP) vaccine against MAPK protein express for Alzheimer's disease in Zebrafish

Understanding the role of the mitogen-activated protein kinases (MAPKs) signalling pathway is essential in advancing treatments for neurodegenerative disorders like Alzheimer's. In this study, we investigate in-silico techniques involving computer-based methods to extract the MAPK1 sequence. Our applied methods enable us to analyze the protein's structure, evaluate its properties, establish its evolutionary relationships, and assess its prevalence in populations. We also predict epitopes, assess their ability to trigger immune responses, and check for allergenicity using advanced computational tools to understand their immunological properties comprehensively. We apply virtual screening, docking, and structure modelling to identify promising drug candidates, analyze their interactions, and enhance drug design processes. We identified a total of 30 cell-targeting molecules against the MAPK1 protein, where we selected top 10 CTL epitopes (PAGGGPNPG, GGGPNPGSG, SAPAGGGPN, AVSAPAGGG, AGGGPNPGS, ATAAVSAPA, TAAVSAPAG, ENIIGINDI, INDIIRTPT, and NDIIRTPTI) for further evaluation to determine their potential efficacy, safety, and suitability for vaccine design based on strong binding potential. The potential to cover a large portion of the world's population with these vaccines is substantial-88.5 % for one type and 99.99 % for another. In exploring the molecular docking analyses, we examined a library of compounds from the ZINC database. Among them, we identified twelve compounds with the lowest binding energy. Critical residues in the MAPK1 protein, such as VAL48, LYS63, CYS175, ASP176, LYS160, ALA61, LEU165, TYR45, SER162, ARG33, PRO365, PHE363, ILE40, ASN163, and GLU42, are pivotal for interactions with these compounds. Our result suggests that these compounds could influence the protein's behaviour. Moreover, our docking analyses revealed that the predicted peptides have a strong affinity for the MAPK1 protein. These peptides form stable complexes, indicating their potential as potent inhibitors. This study contributes to the identification of new drug compounds and the screening of their desired properties. These compounds could potentially help reduce the excessive activity of MAPK1, which is linked to Alzheimer's disease.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

eEF2K Inhibitor Design: The Progression of Exemplary Structure-Based Drug Design

The α-kinase, eEF2K, phosphorylates the threonine 56 residue of eEF2 to inhibit global peptide elongation (protein translation). As a master regulator of protein synthesis, in combination with its unique atypical kinase active site, investigations into the targeting of eEF2K represents a case of intense structure-based drug design that includes the use of modern computational techniques. The role of eEF2K is incredibly diverse and has been scrutinized in several different diseases including cancer and neurological disorders-with numerous studies inhibiting eEF2K as a potential treatment option, as described in this paper. Using available crystal structures of related α-kinases, particularly MHCKA, we report how homology modeling has been used to improve inhibitor design and efficacy. This review presents an overview of eEF2K related drug discovery efforts predating from the 1990's, to more recent in vivo studies in rat models. We also provide the reader with a basic introduction to several approaches and software programs used to undertake such drug discovery campaigns. With the recent exciting publication of an eEF2K crystal structure, we present our view regarding the future of eEF2K drug discovery.

Machine Learning and Computational Chemistry for the Endocannabinoid System

Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure-activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.

In silico activity and ADMET profiling of phytochemicals from Ethiopian indigenous aloes using pharmacophore models

In silico profiling is used in identification of active compounds and guide rational use of traditional medicines. Previous studies on Ethiopian indigenous aloes focused on documentation of phytochemical compositions and traditional uses. In this study, ADMET and drug-likeness properties of phytochemicals from Ethiopian indigenous aloes were evaluated, and pharmacophore-based profiling was done using Discovery Studio to predict therapeutic targets. The targets were examined using KEGG pathway, gene ontology and network analysis. Using random-walk with restart algorithm, network propagation was performed in CODA network to find diseases associated with the targets. As a result, 82 human targets were predicted and found to be involved in several molecular functions and biological processes. The targets also were linked to various cancers and diseases of immune system, metabolism, neurological system, musculoskeletal system, digestive system, hematologic, infectious, mouth and dental, and congenital disorder of metabolism. 207 KEGG pathways were enriched with the targets, and the main pathways were metabolism of steroid hormone biosynthesis, lipid and atherosclerosis, chemical carcinogenesis, and pathways in cancer. In conclusion, in silico target fishing and network analysis revealed therapeutic activities of the phytochemicals, demonstrating that Ethiopian indigenous aloes exhibit polypharmacology effects on numerous genes and signaling pathways linked to many diseases.

Efficacy Evaluation of SDF-1α-Based Polypeptides in an Acute Myocardial Infarction Model Using Structure-Based Drug Design

Stromal cell-derived factor-1 alpha (SDF-1α, CXCL12) mediates the migration of circulating cells to desired sites for tissue development, homeostasis, and regeneration and can be used to promote cardiac regeneration by recruiting stem cells. However, the use of SDF-1α in the injured heart necessitates not only higher binding affinity to its receptor, CXCR4+, but also better robustness against enzymatic degradation than other SDF-1 isoforms. Here, we conduct a screening of SDF-1α analog peptides that were designed by structure-based drug design (SBDD), a type of computer-aided drug design (CADD). We have developed in vitro and in vivo methods that enable us to estimate the effect of peptides on the migration of human mesenchymal stem cells (hMSCs) and cardiac regeneration in acute myocardial infarction (AMI)-induced animals, respectively. We demonstrate that one type of SDF-1α analog peptide, SDP-4, among the four analog peptides preselected by SBDD, is more potent than native SDF-1α for cardiac regeneration in myocardial infarction. It is interesting to note that the migratory effects of SDP-4 determined by a wound healing assay, a Transwell assay, and a 2D migration assay are comparable to those of SDF-1α. These results suggest that in vivo, as well as in vitro, screening of peptides developed by SBDD is a quintessential process to the development of a novel therapeutic compound for cardiac regeneration. Our finding also has an implication that the SDP-4 peptide is an excellent candidate for use in the regeneration of an AMI heart.

Conserved coronavirus proteins as targets of broad-spectrum antivirals

Coronaviruses are a class of single-stranded, positive-sense RNA viruses that have caused three major outbreaks over the past two decades: Middle East respiratory syndrome-related coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). All outbreaks have been associated with significant morbidity and mortality. In this study, we have identified and explored conserved binding sites in the key coronavirus proteins for the development of broad-spectrum direct acting anti-coronaviral compounds and validated the significance of this conservation for drug discovery with existing experimental data. We have identified four coronaviral proteins with highly conserved binding site sequence and 3D structure similarity: PLpro, Mpro, nsp10-nsp16 complex(methyltransferase), and nsp15 endoribonuclease. We have compiled all available experimental data for known antiviral medications inhibiting these targets and identified compounds active against multiple coronaviruses. The identified compounds representing potential broad-spectrum antivirals include: GC376, which is active against six viral Mpro (out of six tested, as described in research literature); mycophenolic acid, which is active against four viral PLpro (out of four); and emetine, which is active against four viral RdRp (out of four). The approach described in this study for coronaviruses, which combines the assessment of sequence and structure conservation across a viral family with the analysis of accessible chemical structure - antiviral activity data, can be explored for the development of broad-spectrum drugs for multiple viral families.

Structural Elucidation of Rift Valley Fever Virus L Protein towards the Discovery of Its Potential Inhibitors

Rift valley fever virus (RVFV) is the causative agent of a viral zoonosis that causes a significant clinical burden in domestic and wild ruminants. Major outbreaks of the virus occur in livestock, and contaminated animal products or arthropod vectors can transmit the virus to humans. The viral RNA-dependent RNA polymerase (RdRp; L protein) of the RVFV is responsible for viral replication and is thus an appealing drug target because no effective and specific vaccine against this virus is available. The current study reported the structural elucidation of the RVFV-L protein by in-depth homology modeling since no crystal structure is available yet. The inhibitory binding modes of known potent L protein inhibitors were analyzed. Based on the results, further molecular docking-based virtual screening of Selleckchem Nucleoside Analogue Library (156 compounds) was performed to find potential new inhibitors against the RVFV L protein. ADME (Absorption, Distribution, Metabolism, and Excretion) and toxicity analysis of these compounds was also performed. Besides, the binding mechanism and stability of identified compounds were confirmed by a 50 ns molecular dynamic (MD) simulation followed by MM/PBSA binding free energy calculations. Homology modeling determined a stable multi-domain structure of L protein. An analysis of known L protein inhibitors, including Monensin, Mycophenolic acid, and Ribavirin, provide insights into the binding mechanism and reveals key residues of the L protein binding pocket. The screening results revealed that the top three compounds, A-317491, Khasianine, and VER155008, exhibited a high affinity at the L protein binding pocket. ADME analysis revealed good pharmacodynamics and pharmacokinetic profiles of these compounds. Furthermore, MD simulation and binding free energy analysis endorsed the binding stability of potential compounds with L protein. In a nutshell, the present study determined potential compounds that may aid in the rational design of novel inhibitors of the RVFV L protein as anti-RVFV drugs.

A Benchmark Dataset for Evaluating Practical Performance of Model Quality Assessment of Homology Models

Protein structure prediction is an important issue in structural bioinformatics. In this process, model quality assessment (MQA), which estimates the accuracy of the predicted structure, is also practically important. Currently, the most commonly used dataset to evaluate the performance of MQA is the critical assessment of the protein structure prediction (CASP) dataset. However, the CASP dataset does not contain enough targets with high-quality models, and thus cannot sufficiently evaluate the MQA performance in practical use. Additionally, most application studies employ homology modeling because of its reliability. However, the CASP dataset includes models generated by de novo methods, which may lead to the mis-estimation of MQA performance. In this study, we created new benchmark datasets, named a homology models dataset for model quality assessment (HMDM), that contain targets with high-quality models derived using homology modeling. We then benchmarked the performance of the MQA methods using the new datasets and compared their performance to that of the classical selection based on the sequence identity of the template proteins. The results showed that model selection by the latest MQA methods using deep learning is better than selection by template sequence identity and classical statistical potentials. Using HMDM, it is possible to verify the MQA performance for high-accuracy homology models.

Enhancing preclinical drug discovery with artificial intelligence

Artificial intelligence (AI) is becoming an integral part of drug discovery. It has the potential to deliver across the drug discovery and development value chain, starting from target identification and reaching through clinical development. In this review, we provide an overview of current AI technologies and a glimpse of how AI is reimagining preclinical drug discovery by highlighting examples where AI has made a real impact. Considering the excitement and hyperbole surrounding AI in drug discovery, we aim to present a realistic view by discussing both opportunities and challenges in adopting AI in drug discovery.

Assessing the binding properties of CASP14 targets and models

An important question is how well the models submitted to CASP retain the properties of target structures. We investigate several properties related to binding. First we explore the binding of small molecules as probes, and count the number of interactions between each residue and such probes, resulting in a binding fingerprint. The similarity between two fingerprints, one for the X-ray structure and the other for a model, is determined by calculating their correlation coefficient. The fingerprint similarity weakly correlates with global measures of accuracy, and GDT_TS higher than 80 is a necessary but not sufficient condition for the conservation of surface binding properties. The advantage of this approach is that it can be carried out without information on potential ligands and their binding sites. The latter information was available for a few targets, and we explored whether the CASP14 models can be used to predict binding sites and to dock small ligands. Finally, we tested the ability of models to reproduce protein-protein interactions by docking both the X-ray structures and the models to their interaction partners in complexes. The analysis showed that in CASP14 the quality of individual domain models is approaching that offered by X-ray crystallography, and hence such models can be successfully used for the identification of binding and regulatory sites, as well as for assembling obligatory protein-protein complexes. Success of ligand docking, however, often depends on fine details of the binding interface, and thus may require accounting for conformational changes by simulation methods.

Dasatinib-SIK2 Binding Elucidated by Homology Modeling, Molecular Docking, and Dynamics Simulations

Salt-inducible kinases (SIKs) are calcium/calmodulin-dependent protein kinase (CAMK)-like (CAMKL) family members implicated in insulin signal transduction, metabolic regulation, inflammatory response, and other processes. Here, we focused on SIK2, which is a target of the Food and Drug Administration (FDA)-approved pan inhibitor N-(2-chloro-6-methylphenyl)-2-(6-(4-(2-hydroxyethyl)piperazin-1-yl)-2-methylpyrimidin-4-ylamino)thiazole-5-carboxamide (dasatinib), and constructed four representative SIK2 structures by homology modeling. We investigated the interactions between dasatinib and SIK2 via molecular docking, molecular dynamics simulation, and binding free energy calculation and found that dasatinib showed strong binding affinity for SIK2. Binding free energy calculations suggested that the modification of various dasatinib regions may provide useful information for drug design and to guide the discovery of novel dasatinib-based SIK2 inhibitors.

Behavior of Chemokine Receptor 6 (CXCR6) in Complex with CXCL16 Soluble form Chemokine by Molecular Dynamic Simulations: General Protein‒Ligand Interaction Model and 3D-QSAR Studies of Synthetic Antagonists

The CXCR6‒CXCL16 axis is involved in several pathological processes, and its overexpression has been detected in different types of cancer, such as prostate, breast, ovary, and lung cancer, along with schwannomas, in which it promotes invasion and metastasis. Moreover, this axis is involved in atherosclerosis, type 1 diabetes, primary immune thrombocytopenia, vitiligo, and other autoimmune diseases, in which it is responsible for the infiltration of different immune system cells. The 3D structure of CXCR6 and CXCL16 has not been experimentally resolved; therefore, homology modeling and molecular dynamics simulations could be useful for the study of this signaling axis. In this work, a homology model of CXCR6 and a soluble form of CXCL16 (CXCR6‒CXCL16s) are reported to study the interactions between CXCR6 and CXCL16s through coarse-grained molecular dynamics (CG-MD) simulations. CG-MD simulations showed the two activation steps of CXCR6 through a decrease in the distance between the chemokine and the transmembrane region (TM) of CXCR6 and transmembrane rotational changes and polar interactions between transmembrane segments. The polar interactions between TM3, TM5, and TM6 are fundamental to functional conformation and the meta-active state of CXCR6. The interactions between D77-R280 and T243-TM7 could be related to the functional conformation of CXCR6; alternatively, the interaction between Q195-Q244 and N248 could be related to an inactive state due to the loss of this interaction, and an arginine cage broken in the presence of CXCL16s allows the meta-active state of CXCR6. A general protein‒ligand interaction supports the relevance of TM3‒TM5‒TM6 interactions, presenting three relevant pharmacophoric features: HAc (H-bond acceptor), HDn (H-bond donator), and Hph (hydrophobic), distributed around the space between extracellular loops (ECLs) and TMs. The HDn feature is close to TM3 and TM6; likewise, the HAc and Hph features are close to ECL1 and ECL2 and could block the rotation and interactions between TM3‒TM6 and the interactions of CXCL16s with the ECLs. Tridimensional quantitative structure-activity relationships (3D-QSAR) models show that the positive steric (VdW) and electrostatic fields coincide with the steric and positive electrostatic region of the exo-azabicyclo[3.3.1]nonane scaffold in the best pIC50 ligands. This substructure is close to the E274 residue and therefore relevant to the activity of CXCR6. These data could help with the design of new molecules that inhibit chemokine binding or antagonize the receptor based on the activation mechanism of CXCR6 and provoke a decrease in chemotaxis caused by the CXCR6‒CXCL16 axis.

Cardiovascular effects of farnesol and its β-cyclodextrin complex in normotensive and hypertensive rats

Farnesol (FAR) is a sesquiterpene alcohol with a range of reported biological effects including cardioprotective, antioxidant and antiarrhythmic properties. However, due to its volatility, the use of drug incorporation systems, such as cyclodextrins, have been proposed to improve its pharmacological properties. Thus, the aim of this study was to evaluate and characterize the cardiovascular effects of FAR alone, and to investigate the antihypertensive effects of FAR complexed with β-cyclodextrin (βCD) in rats. Mean arterial pressure (MAP) and heart rate (HR) were measured before and after intravenous administration of FAR (0,5; 2,5; 5 and 7,5 mg/kg) in normotensive rats, and after oral acute administration (200 mg/kg) of FAR and FAR/βCD complex in NG-nitro-L-arginine-methyl-ester (L-NAME) hypertensive rats. In normotensive animals, FAR induced dose-dependent hypotension associated with bradycardia. These effects were not affected by pre-treatment with L-NAME or indomethacin (INDO), but were partially attenuated by atropine. Pre-treatment with hexamethonium (HEXA) only affected hypotension. In the hypertensive rats, FAR/βCD potentialized the antihypertensive effect when compared to FAR alone. Molecular docking experiments demonstrated for the first time that FAR has affinity to bind to the M₃ and M₂ muscarinic, and nicotinic receptors through hydrogen bonds in the same residues as known ligands. In conclusion, our results demonstrated that FAR induced hypotension associated with bradycardia, possibly through the muscarinic and nicotinic receptors. The inclusion complex with βCD improved the antihypertensive effects of FAR, which can be relevant to improve current cardiovascular therapy using volatile natural components.

Protein-ligand structure determination with the NMR molecular replacement tool, NMR2

We recently reported on a new method called NMR Molecular Replacement that efficiently derives the structure of a protein-ligand complex at the interaction site. The method was successfully applied to high and low affinity complexes covering ligands from peptides to small molecules. The algorithm used in the NMR Molecular Replacement program has until now not been described in detail. Here, we present a complete description of the NMR Molecular Replacement implementation as well as several new features that further reduce the time required for structure elucidation.

Computational Study of C-X-C Chemokine Receptor (CXCR)3 Binding with Its Natural Agonists Chemokine (C-X-C Motif) Ligand (CXCL)9, 10 and 11 and with Synthetic Antagonists: Insights of Receptor Activation towards Drug Design for Vitiligo

Vitiligo is a hypopigmentary skin pathology resulting from the death of melanocytes due to the activity of CD8+ cytotoxic lymphocytes and overexpression of chemokines. These include CXCL9, CXCL10, and CXCL11 and its receptor CXCR3, both in peripheral cells of the immune system and in the skin of patients diagnosed with vitiligo. The three-dimensional structure of CXCR3 and CXCL9 has not been reported experimentally; thus, homology modeling and molecular dynamics could be useful for the study of this chemotaxis-promoter axis. In this work, a homology model of CXCR3 and CXCL9 and the structure of the CXCR3/Gαi/0βγ complex with post-translational modifications of CXCR3 are reported for the study of the interaction of chemokines with CXCR3 through all-atom (AA-MD) and coarse-grained molecular dynamics (CG-MD) simulations. AA-MD and CG-MD simulations showed the first activation step of the CXCR3 receptor with all chemokines and the second activation step in the CXCR3-CXCL10 complex through a decrease in the distance between the chemokine and the transmembrane region of CXCR3 and the separation of the βγ complex from the α subunit in the G-protein. Additionally, a general protein-ligand interaction model was calculated, based on known antagonists binding to CXCR3. These results contribute to understanding the activation mechanism of CXCR3 and the design of new molecules that inhibit chemokine binding or antagonize the receptor, provoking a decrease of chemotaxis caused by the CXCR3/chemokines axis.

Opportunities and Challenges for Biosensors and Nanoscale Analytical Tools for Pandemics: COVID-19

Biosensors and nanoscale analytical tools have shown huge growth in literature in the past 20 years, with a large number of reports on the topic of 'ultrasensitive', 'cost-effective', and 'early detection' tools with a potential of 'mass-production' cited on the web of science. Yet none of these tools are commercially available in the market or practically viable for mass production and use in pandemic diseases such as coronavirus disease 2019 (COVID-19). In this context, we review the technological challenges and opportunities of current bio/chemical sensors and analytical tools by critically analyzing the bottlenecks which have hindered the implementation of advanced sensing technologies in pandemic diseases. We also describe in brief COVID-19 by comparing it with other pandemic strains such as that of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) for the identification of features that enable biosensing. Moreover, we discuss visualization and characterization tools that can potentially be used not only for sensing applications but also to assist in speeding up the drug discovery and vaccine development process. Furthermore, we discuss the emerging monitoring mechanism, namely wastewater-based epidemiology, for early warning of the outbreak, focusing on sensors for rapid and on-site analysis of SARS-CoV2 in sewage. To conclude, we provide holistic insights into challenges associated with the quick translation of sensing technologies, policies, ethical issues, technology adoption, and an overall outlook of the role of the sensing technologies in pandemics.

How 'Protein-Docking' Translates into the New Emerging Field of Docking Small Molecules to Nucleic Acids?

In this review, we retraced the '40-year evolution' of molecular docking algorithms. Over the course of the years, their development allowed to progress from the so-called 'rigid-docking' searching methods to the more sophisticated 'semi-flexible' and 'flexible docking' algorithms. Together with the advancement of computing architecture and power, molecular docking's applications also exponentially increased, from a single-ligand binding calculation to large screening and polypharmacology profiles. Recently targeting nucleic acids with small molecules has emerged as a valuable therapeutic strategy especially for cancer treatment, along with bacterial and viral infections. For example, therapeutic intervention at the mRNA level allows to overcome the problematic of undruggable proteins without modifying the genome. Despite the promising therapeutic potential of nucleic acids, molecular docking programs have been optimized mostly for proteins. Here, we have analyzed literature data on nucleic acid to benchmark some of the widely used docking programs. Finally, the comparison between proteins and nucleic acid targets docking highlighted similarity and differences, which are intrinsically related to their chemical and structural nature.

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Molecular Architect: A User-Friendly Workflow for Virtual Screening

Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.

Presence of carbohydrate binding modules in extracellular region of class C G-protein coupled receptors (C GPCR): An in silico investigation on sweet taste receptor

Sweet taste receptor (STR) is a C GPCR family member and a suggested drug target for metabolic disorders such as diabetes. Detailed characteristics of the molecule as well as its ligand interactions mode are yet considerably unclear due to experimental study limitations of transmembrane proteins. An in silico study was designed to find the putative carbohydrate binding sites on STR. To this end, α-D-glucose and its α-1,4-oligomers (degree of polymerization up to 14) were chosen as probes and docked into an ensemble of different conformations of the extracellular region of STR monomers (T1R2 and T1R3), using AutoDock Vina. Ensembles had been sampled from an MD simulation experiment. Best poses were further energy-minimized in the presence of water molecules with Amber14 forcefield. For each monomer, four distinct binding regions consisting of one or two binding pockets could be distinguished. These regions were further investigated with regard to hydrophobicity and hydrophilicity of the residues, as well as residue compositions and non-covalent interactions with ligands. Popular binding regions showed similar characteristics to carbohydrate binding modules (CBM). Observation of several conserved or semi-conserved residues in these binding regions suggests a possibility to extrapolate the results to other C GPCR family members. In conclusion, presence of CBM in STR and, by extrapolation, in other C GPCR family members is suggested, similar to previously proposed sites in gut fungal C GPCRs, through transcriptome analyses. STR modes of interaction with carbohydrates are also discussed and characteristics of non-covalent interactions in C GPCR family are highlighted.

Perspectives towards antiviral drug discovery against Ebola virus

Ebola virus disease (EVD), caused by Ebola viruses, resulted in more than 11 500 deaths according to a recent 2018 WHO report. With mortality rates up to 90%, it is nowadays one of the most deadly infectious diseases. However, no Food and Drug Administration‐approved Ebola drugs or vaccines are available yet with the mainstay of therapy being supportive care. The high fatality rate and absence of effective treatment or vaccination make Ebola virus a category‐A biothreat pathogen. Fortunately, a series of investigational countermeasures have been developed to control and prevent this global threat. This review summarizes the recent therapeutic advances and ongoing research progress from research and development to clinical trials in the development of small‐molecule antiviral drugs, small‐interference RNA molecules, phosphorodiamidate morpholino oligomers, full‐length monoclonal antibodies, and vaccines. Moreover, difficulties are highlighted in the search for effective countermeasures against EVD with additional focus on the interplay between available in silico prediction methods and their evidenced potential in antiviral drug discovery.

Introducing a New Model of Sweet Taste Receptor, a Class C G-protein Coupled Receptor (C GPCR)

The structure of sweet taste receptor (STR), a heterodimer of class C G-protein coupled receptors comprising T1R2 and T1R3 molecules, is still undetermined. In this study, a new enhanced model of the receptor is introduced based on the most recent templates. The improvement, stability, and reliability of the model are discussed in details. Each domain of the protein, i.e., VFTM, CR, and TMD, were separately constructed by hybrid-model construction methods and then assembled to build whole monomers. Overall, 680 ns molecular dynamics simulation was performed for the individual domains, the whole monomers and the heterodimer form of the VFTM orthosteric binding site. The latter's structure obtained from 200 ns simulation was docked with aspartame; among various binding sites suggested by FTMAP server, the experimentally suggested binding domain in T1R2 was retrieved. Local three-dimensional structures and helices spans were evaluated and showed acceptable accordance with the template structures and secondary structure predictions. Individual domains and whole monomer structures were found stable and reliable to be used. In conclusion, several validations have shown reliability of the new and enhanced models for further molecular modeling studies on structure and function of STR and C GPCRs.

In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors

The Crimean-Congo Hemorrhagic Fever virus (CCHFV) is a segmented negative single-stranded RNA virus (-ssRNA) which causes severe hemorrhagic fever in humans with a mortality rate of ~50%. To date, no vaccine has been approved. Treatment is limited to supportive care with few investigational drugs in practice. Previous studies have identified viral RNA dependent RNA Polymerase (RdRp) as a potential drug target due to its significant role in viral replication and transcription. Since no crystal structure is available yet, we report the structural elucidation of CCHFV-RdRp by in-depth homology modeling. Even with low sequence identity, the generated model suggests a similar overall structure as previously reported RdRps. More specifically, the model suggests the presence of structural/functional conserved RdRp motifs for polymerase function, the configuration of uniform spatial arrangement of core RdRp sub-domains, and predicted positively charged entry/exit tunnels, as seen in sNSV polymerases. Extensive pharmacophore modeling based on per-residue energy contribution with investigational drugs allowed the concise mapping of pharmacophoric features and identified potential hits. The combination of pharmacophoric features with interaction energy analysis revealed functionally important residues in the conserved motifs together with in silico predicted common inhibitory binding modes with highly potent reference compounds.

Anti-Dengue, Cytotoxicity, Antifungal, and In Silico Study of the Newly Synthesized 3-O-Phospo-α-D-Glucopyranuronic Acid Compound

The aim of the current study was to synthesize new bioactive compounds and evaluate their therapeutic relevance. The chemical structure of compound 7 (methyl 3-O-phospo-α-D-glucopyranuronic acid was elucidated by physical and advance spectral technique. Also, this compound was assessed for various in vitro biological screening. The results showed that compound 7 has promising antifungal activity against selected fungal strains. Computational study was also carried out to find antimalarial efficacy of the synthesized compounds. Compounds (2-7) were tested for cytotoxicity by MTT assay, and no considerable cytotoxicity was observed. Molecular docking study was performed to predict the binding modes of new compound (7). The docking results revealed that the compound has strong attraction towards the target protein, as characterized by good bonding networks. On the basis of the acquired results, it can be predicted that compound (7) might show good inhibitory activity against dengue envelope protein.

Molecular modelling and competitive inhibition of a Mycobacterium tuberculosis multidrug-resistance efflux pump

Tuberculosis is a major cause of mortality and morbidity in developing countries, and the emergency of multidrug and extensive drug resistance cases is an utmost issue on the control of the disease. Despite the efforts on the development of new antibiotics, eventually there will be strains resistant to them as well. Efflux plays an important role in the evolution of resistance in Mycobacterium tuberculosis. Tap is an important efflux pump associated with tuberculosis resistant to isoniazid, rifampicine and ofloxacin and with multidrug resistance. The development of efflux inhibitors for Tap could raise the effectiveness of second line drugs and reduce the duration of the current treatment. Therefore the objective of this study is to build a reliable molecular model of Tap efflux pump and test the possible competitive inhibition between efflux inhibitors and antibiotics in the optimized structure. We built twenty five Tap models with molecular modelling to elect the best according to the results of the validation analysis. The elected model went through to a 100 ns molecular dynamics simulation in a lipid bilayer, and the resulting optimized structure was used in docking studies to test if the used efflux inhibitors may act via competitive inhibition on antibiotics. The validation results pointed the model built by Phyre2 as the closest to a possible native Tap structure, and therefore it was the elected model. RSMD analysis revealed the model is stable, where the predicted binding site stabilized between 15 and 20 ns, maintaining the RMSD at around 0.35 Å throughout the molecular dynamics simulation in a lipid bilayer. Therefore this model is reliable and can also be used for further studies. The docking studies showed a possibility of competitive inhibition by NUNL02 on ofloxacin and bedaquiline, and by verapamil on ofloxacin and rifampicin. This presents the possibility that NUNL02 and verapamil are possible inhibitors of Tap efflux and highlights the importance of including efflux inhibitors as adjuvants to the tuberculosis therapy, as it indicates a possible extrusion of ofloxacin, rifampicin and bedaquilin by Tap.

Critical assessment of approaches for molecular docking to elucidate associations of HLA alleles with adverse drug reactions

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All software assessed could dock Abacavir back into the risk allele structure but not always predict the exact binding mode.

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Most docking software assessed can distinguish between risk and control alleles.

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Docking performance can be degraded by using a homology model.

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Receptor flexibility can negatively affect the docking performance for complex HLA examples.

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Using AutoDockFR cannot compensate for the added difficulty of docking to the unbound target.

Adverse drug reactions have been linked with genetic polymorphisms in HLA genes in numerous different studies. HLA proteins have an essential role in the presentation of self and non-self peptides, as part of the adaptive immune response. Amongst the associated drugs-allele combinations, anti-HIV drug Abacavir has been shown to be associated with the HLA-B*57:01 allele, and anti-epilepsy drug Carbamazepine with B*15:02, in both cases likely following the altered peptide repertoire model of interaction. Under this model, the drug binds directly to the antigen presentation region, causing different self peptides to be presented, which trigger an unwanted immune response. There is growing interest in searching for evidence supporting this model for other ADRs using bioinformatics techniques. In this study, in silico docking was used to assess the utility and reliability of well-known docking programs when addressing these challenging HLA-drug situations. The overall aim was to address the uncertainty of docking programs giving different results by completing a detailed comparative study of docking software, grounded in the MHC-ligand experimental structural data – for Abacavir and to a lesser extent Carbamazepine - in order to assess their performance. Four docking programs: SwissDock, ROSIE, AutoDock Vina and AutoDockFR, were used to investigate if each software could accurately dock the Abacavir back into the crystal structure for the protein arising from the known risk allele, and if they were able to distinguish between the HLA-associated and non-HLA-associated (control) alleles. The impact of using homology models on the docking performance and how using different parameters, such as including receptor flexibility, affected the docking performance were also investigated to simulate the approach where a crystal structure for a given HLA allele may be unavailable. The programs that were best able to predict the binding position of Abacavir were then used to recreate the docking seen for Carbamazepine with B*15:02 and controls alleles.

It was found that the programs investigated were sometimes able to correctly predict the binding mode of Abacavir with B*57:01 but not always. Each of the software packages that were assessed could predict the binding of Abacavir and Carbamazepine within the correct sub-pocket and, with the exception of ROSIE, was able to correctly distinguish between risk and control alleles. We found that docking to homology models could produce poorer quality predictions, especially when sequence differences impact the architecture of predicted binding pockets. Caution must therefore be used as inaccurate structures may lead to erroneous docking predictions. Incorporating receptor flexibility was found to negatively affect the docking performance for the examples investigated. Taken together, our findings help characterise the potential but also the limitations of computational prediction of drug-HLA interactions. These docking techniques should therefore always be used with care and alongside other methods of investigation, in order to be able to draw strong conclusions from the given results.

Computer-Aided Drug Design in Epigenetics

Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation, and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field.

Biological and functional relevance of CASP predictions

Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo-sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo-sites), and Ten sites containing important motifs, loops, or key residues with important disease-associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best-ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand-binding sites, most prediction methods have higher performance on apo-sites than holo-sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein-protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein-protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template.

The NMR2 Method to Determine Rapidly the Structure of the Binding Pocket of a Protein–Ligand Complex with High Accuracy

Structural characterization of complexes is crucial for a better understanding of biological processes and structure-based drug design. However, many protein–ligand structures are not solvable by X-ray crystallography, for example those with low affinity binders or dynamic binding sites. Such complexes are usually targeted by solution-state NMR spectroscopy. Unfortunately, structure calculation by NMR is very time consuming since all atoms in the complex need to be assigned to their respective chemical shifts. To circumvent this problem, we recently developed the Nuclear Magnetic Resonance Molecular Replacement (NMR2) method. NMR2 very quickly provides the complex structure of a binding pocket as measured by solution-state NMR. NMR2 circumvents the assignment of the protein by using previously determined structures and therefore speeds up the whole process from a couple of months to a couple of days. Here, we recall the main aspects of the method, show how to apply it, discuss its advantages over other methods and outline its limitations and future directions.

Strategies of Virtual Screening in Medicinal Chemistry

Virtual screening represents an effective computational strategy to rise-up the chances of finding new bioactive compounds by accelerating the time needed to move from an initial intuition to market. Classically, the most pursued approaches rely on ligand- and structure-based studies, the former employed when structural data information about the target is missing while the latter employed when X-ray/NMR solved or homology models are instead available for the target. The authors will focus on the most advanced techniques applied in this area. In particular, they will survey the key concepts of virtual screening by discussing how to properly select chemical libraries, how to make database curation, how to applying and- and structure-based techniques, how to wisely use post-processing methods. Emphasis will be also given to the most meaningful databases used in VS protocols. For the ease of discussion several examples will be presented.

Fragment-Based Ligand Designing

Fragment-based drug design strategies have been used in drug discovery since it was first demonstrated using experimental structural biology techniques such as nuclear magnetic resonance (NMR) and X-ray crystallography. The underlying idea is that existing or new chemical entities with known desirable properties may serve both as tool compounds and as starting points for hit-to-lead expansion. Despite the recent advancements, there remain challenges to overcome, such as assembly of the synthetically feasible structures, development of scoring functions to correlate structure and their activities, and fine tuning of the promising molecules. This chapter first covers the theoretical background needed to understand the concepts and the challenges related to the field of study, followed by the description of important protocols and related software. Case studies are presented to demonstrate practical applications.

The Bioactive Protein-Ligand Conformation of GluN2C-Selective Positive Allosteric Modulators Bound to the NMDA Receptor

N-methyl-d-aspartate (NMDA) receptors are ligand-gated, cation-selective channels that mediate a slow component of excitatory synaptic transmission. Subunit-selective positive allosteric modulators of NMDA receptor function have therapeutically relevant effects on multiple processes in the brain. A series of pyrrolidinones, such as PYD-106, that selectively potentiate NMDA receptors that contain the GluN2C subunit have structural determinants of activity that reside between the GluN2C amino terminal domain and the GluN2C agonist binding domain, suggesting a unique site of action. Here we use molecular biology and homology modeling to identify residues that line a candidate binding pocket for GluN2C-selective pyrrolidinones. We also show that occupancy of only one site in diheteromeric receptors is required for potentiation. Both GluN2A and GluN2B can dominate the sensitivity of triheteromeric receptors to eliminate the actions of pyrrolidinones, thus rendering this series uniquely sensitive to subunit stoichiometry. We experimentally identified NMR-derived conformers in solution, which combined with molecular modeling allows the prediction of the bioactive binding pose for this series of GluN2C-selective positive allosteric modulators of NMDA receptors. These data advance our understanding of the site and nature of the ligand-protein interaction for GluN2C-selective positive allosteric modulators for NMDA receptors.

Dataset generated for Dissection of mechanisms of Trypanothione Reductase and Tryparedoxin Peroxidase through dynamic network analysis and simulations in leishmaniasis

Leishmaniasis is the second largest parasitic killer disease caused by the protozoan parasite Leishmania, transmitted by the bite of sand flies. It's endemic in the eastern India with 165.4 million populations at risk with the current drug regimen. Three forms of leishmaniasis exist in which cutaneous is the most common form caused by Leishmania major. Trypanothione Reductase (TryR), a flavoprotein oxidoreductase, unique to thiol redox system, is considered as a potential target for chemotherapy for trypanosomatids infection. It is involved in the NADPH dependent reduction of Trypanothione disulphide to Trypanothione. Similarly, is Tryparedoxin Peroxidase (Txnpx), for detoxification of peroxides, an event pivotal for survival of Leishmania in two disparate biological environment. Fe-S plays a major role in regulating redox balance. To check for the closeness between human homologs of these proteins, we have carried the molecular clock analysis followed by molecular modeling of 3D structure of this protein, enabling us to design and test the novel drug like molecules. Molecular clock analysis suggests that human homologs of TryR i.e. Glutathione Reductase and Txnpx respectively are highly diverged in phylogenetic tree, thus, they serve as good candidates for chemotherapy of leishmaniasis. Furthermore, we have done the homology modeling of TryR using template of same protein from Leishmania infantum (PDB ID: 2JK6). This was done using Modeller 9.18 and the resultant models were validated. To inhibit this target, molecular docking was done with various screened inhibitors in which we found Taxifolin acts as common inhibitors for both TryR and Txnpx. We constructed the protein-protein interaction network for the proteins that are involved in the redox metabolism from various Interaction databases and the network was statistically analysed.