Interaction Analysis by Fragment Molecular Orbital Method for Drug Discovery Research.

The use of computational methods in drug discovery research has increased substantially in recent years. Computational chemistry techniques, such as quantum chemical calculations and molecular dynamics simulations, continue to be widely used. In this review, we focused on drug discovery-related studies that employ fragment molecular orbital methods. Furthermore, we focused on inhibitor discovery, protein-protein interaction analysis, including antigen-antibody interaction analysis, and integration with molecular dynamics simulations.

How to Find a Fragment: Methods for Screening and Validation in Fragment-Based Drug Dscovery.

Fragment-based drug discovery (FBDD) is a crucial strategy for developing new drugs that have been applied to diverse targets, from neglected infectious diseases to cancer. With at least seven drugs already launched to the market, this approach has gained interest in both academics and industry in the last 20 years. FBDD relies on screening small libraries with about 1000-2000 compounds of low molecular weight (about 300 Da) using several biophysical methods. Because of the reduced size of the compounds, the chemical space and diversity can be better explored than large libraries used in high throughput screenings. This review summarises the most common biophysical techniques used in fragment screening and orthogonal validation. We also explore the advantages and drawbacks of the different biophysical techniques and examples of applications and strategies.

Binding adaptability of chemical ligands to polymorphic α-synuclein amyloid fibrils.

α-synuclein (α-syn) assembles into structurally distinct fibril polymorphs seen in different synucleinopathies, such as Parkinson's disease and multiple system atrophy. Targeting these unique fibril structures using chemical ligands holds diagnostic significance for different disease subtypes. However, the molecular mechanisms governing small molecules interacting with different fibril polymorphs remain unclear. Here, we investigated the interactions of small molecules belonging to four distinct scaffolds, with different α-syn fibril polymorphs. Using cryo-electron microscopy, we determined the structures of these molecules when bound to the fibrils formed by E46K mutant α-syn and compared them to those bound with wild-type α-syn fibrils. Notably, we observed that these ligands exhibit remarkable binding adaptability, as they engage distinct binding sites across different fibril polymorphs. While the molecular scaffold primarily steered the binding locations and geometries on specific sites, the conjugated functional groups further refined this adaptable binding by fine-tuning the geometries and binding sites. Overall, our finding elucidates the adaptability of small molecules binding to different fibril structures, which sheds light on the diagnostic tracer and drug developments tailored to specific pathological fibril polymorphs.

Comparative In Vitro and In Silico Analyses of Phytochemicals From Butea monosperma for Wound-Healing Potential in Human Cells.

Aim The objective of this study is to investigate the phytochemicals present in Butea monosperma and assess their potential for healing wounds using a computational comparative method. Materials and methods The phytochemical substances derived from B. monosperma were examined using a phytochemical test, Fourier-transform infrared (FTIR) spectroscopy, and gas chromatography-mass spectroscopy (GCMS). The chemical structures of these substances were investigated in silico using computational techniques to predict their wound-healing capacity. The molecular docking tests evaluate the binding strengths of the phytochemicals to specific proteins that play a major role in wound-healing mechanisms. The pharmacokinetic features of the substances were evaluated by analyzing their ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles. Results The computer analysis found several phytochemicals from B. monosperma that bind strongly to the proteins for wound healing: compounds such as hexanoic acid, 2,7-dimethyloct-7-en-5-yn-4-yl ester, 1,3,5-pentanetriol, 3-methyl-, and 2-butyne-1,4-diol. The ADMET analysis indicated favorable pharmacokinetic properties for the majority of the identified compounds, with low predicted toxicity. Conclusion Based on the in silico analysis, the phytochemicals in B. monosperma possess significant potential for use in wound-healing applications. These findings required additional in vitro and in vivo studies to confirm the effectiveness and safety of these drugs for improving wound healing. This study emphasizes the potential of B. monosperma as a source of innovative medicinal substances for wound care.

Accelerating Molecular Docking using Machine Learning Methods.

Virtual screening (VS) is one of the well-established approaches in drug discovery which speeds up the search for a bioactive molecule and, reduces costs and efforts associated with experiments. VS helps to narrow down the search space of chemical space and allows selecting fewer and more probable candidate compounds for experimental testing. Docking calculations are one of the commonly used and highly appreciated structure-based drug discovery methods. Databases for chemical structures of small molecules have been growing rapidly. However, at the moment virtual screening of large libraries via docking is not very common. In this work, we aim to accelerate docking studies by predicting docking scores without explicitly performing docking calculations. We experimented with an attention based long short-term memory (LSTM) neural network for an efficient prediction of docking scores as well as other machine learning models such as XGBoost. By using docking scores of a small number of ligands we trained our models and predicted docking scores of a few million molecules. Specifically, we tested our approaches on 11 datasets that were produced from in-house drug discovery studies. On average, by training models using only 7000 molecules we predicted docking scores of approximately 3.8 million molecules with R2 (coefficient of determination) of 0.77 and Spearman rank correlation coefficient of 0.85. We designed the system with ease of use in mind. All the user needs to provide is a csv file containing SMILES and their respective docking scores, the system then outputs a model that the user can use for the prediction of docking score for a new molecule.

Q-BioLiP: A Comprehensive Resource for Quaternary Structure-based Protein-ligand Interactions.

Since its establishment in 2013, BioLiP has become one of the widely used resources for protein-ligand interactions. Nevertheless, several known issues occurred with it over the past decade. For example, the protein-ligand interactions are represented in the form of single chain-based tertiary structures, which may be inappropriate as many interactions involve multiple protein chains (known as quaternary structures). We sought to address these issues, resulting in Q-BioLiP, a comprehensive resource for quaternary structure-based protein-ligand interactions. The major features of Q-BioLiP include: (1) representing protein structures in the form of quaternary structures rather than single chain-based tertiary structures; (2) pairing DNA/RNA chains properly rather than separation; (3) providing both experimental and predicted binding affinities; (4) retaining both biologically relevant and irrelevant interactions to alleviate the wrong justification of ligands' biological relevance; and (5) developing a new quaternary structure-based algorithm for the modelling of protein-ligand complex structure. With these new features, Q-BioLiP is expected to be a valuable resource for studying biomolecule interactions, including protein-small molecule interaction, protein-metal ion interaction, protein-peptide interaction, protein-protein interaction, protein-DNA/RNA interaction, and RNA-small molecule interaction. Q-BioLiP is freely available at https://yanglab.qd.sdu.edu.cn/Q-BioLiP/.

In Search for Low-Molecular-Weight Ligands of Human Serum Albumin That Affect Its Affinity for Monomeric Amyloid ß Peptide.

An imbalance between production and excretion of amyloid ß peptide (Aß) in the brain tissues of Alzheimer's disease (AD) patients leads to Aß accumulation and the formation of noxious Aß oligomers/plaques. A promising approach to AD prevention is the reduction of free Aß levels by directed enhancement of Aß binding to its natural depot, human serum albumin (HSA). We previously demonstrated the ability of specific low-molecular-weight ligands (LMWLs) in HSA to improve its affinity for Aß. Here we develop this approach through a bioinformatic search for the clinically approved AD-related LMWLs in HSA, followed by classification of the candidates according to the predicted location of their binding sites on the HSA surface, ranking of the candidates, and selective experimental validation of their impact on HSA affinity for Aß. The top 100 candidate LMWLs were classified into five clusters. The specific representatives of the different clusters exhibit dramatically different behavior, with 3- to 13-fold changes in equilibrium dissociation constants for the HSA-Aß40 interaction: prednisone favors HSA-Aß interaction, mefenamic acid shows the opposite effect, and levothyroxine exhibits bidirectional effects. Overall, the LMWLs in HSA chosen here provide a basis for drug repurposing for AD prevention, and for the search of medications promoting AD progression.

A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.

Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.

In vitro combination effects of plant-derived quercetin with synthetic bicalutamide on prostate cancer and normal cell lines: in silico comparison.

Prostate cancer is the second most frequent and the fifth greatest cause of death in men. Although diet has been connected to the prevalence of cancer in addition to other factors, the relation between cancer and prevention is weak. Treatment options are at risk due to cell resistance. To identify new combinations, we tried plant-derived quercetin with bicalutamide on cell lines. To determine the cytotoxicity and apoptotic potential of plant-derived quercetin and its combination, MTT [3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyl tetrazolium bromide] and dual stain assays were performed. In silico protein-ligand interaction was performed to support the in vitro findings. A thin layer, column, and high-performance chromatography were used to purify quercetin along with an authentic sample. In the cytotoxic study, quercetin was minimized by 80% similar to bicalutamide and a combination of quercetin and bicalutamide by 50% when compared to controls by 2%. Quercetin and bicalutamide showed a similar binding affinity for androgen receptors (9.7 and 9.8), hub genes (10.8 and 10.0), and a few other PCa-related genes (9.4 and 9.1). We propose to conclude that the combination of quercetin plus bicalutamide can be used for chemotherapy if additional in vivo studies are conducted. The intake of foods high in polyphenolic compounds can help to prevent prostate cancer. Examination of quercetin on several cell lines will provide a definite conclusion to combat cancers.

A new paradigm for applying deep learning to protein-ligand interaction prediction.

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

Databases of ligand-binding pockets and protein-ligand interactions.

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

MD investigation on the differences in the dynamic interactions between the specific ligand azamulin and two CYP3A isoforms, 3A4 and 3A5.

The unmarked potential drug molecule azamulin has been found to be a specific inhibitor of CYP3A4 and CYP3A5 in recent years, but this molecule also shows different binding ability and affinity to the two CYP3A isoforms. In order to explore the microscopic mechanism, conventional molecular dynamics (MD) simulation methods were performed to study the dynamic interactions between two isoforms and azamulin. The simulation results show that the binding of the ligand leads to different structural properties of two CYP3A proteins. First of all, compared with apo-CYP3A4, the binding of the ligand azamulin can lead to changes in the structural flexibility of CYP3A4, i.e., holo-CYP3A4 is more flexible than apo-CYP3A4. The structural changes of CYP3A5 are just the opposite. The ligand binding increases the rigidity of CYP3A5. Furthermore, the representative structures of the production phase in the MD simulation were in details analyzed to obtain the microscopic interactions between the ligand azamulin and two CYP3A isoforms at the atomic level. It is speculated that the difference of composition and interaction of the active sites is the fundamental cause of the change of structural properties of the two proteins.Communicated by Ramaswamy H. Sarma.

Biophysical insight into anti-amyloidogenic nature of novel ionic Co(II)(phen)(H2O)4]+[glycinate]- chemotherapeutic drug candidate against human lysozyme aggregation.

In the recent past, there has been an ever-increasing interest in the search for metal-based therapeutic drug candidates for protein misfolding disorders (PMDs) particularly neurodegenerative disorders such as Alzheimer's, Parkinson's, Prion's diseases, and amyotrophic lateral sclerosis. Also, different amyloidogenic variants of human lysozyme (HL) are involved in hereditary systemic amyloidosis. Metallo-therapeutic agents are extensively studied as antitumor agents, however, they are relatively unexplored for the treatment of non-neuropathic amyloidoses. In this work, inhibition potential of a novel ionic cobalt(II) therapeutic agent (CoTA) of the formulation [Co(phen)(H2O)4]+[glycinate]- is evaluated against HL fibrillation. Various biophysical techniques viz., dye-binding assays, dynamic light scattering (DLS), differential scanning calorimetry (DSC), electron microscopy, and molecular docking experiments validate the proposed mechanism of inhibition of HL fibrillation by CoTA. The experimental corroborative results of these studies reveal that CoTA can suppress and slow down HL fibrillation at physiological temperature and pH. DLS and 1-anilino-8-naphthalenesulfonate (ANS) assay show that reduced fibrillation in the presence of CoTA is marked by a significant decrease in the size and hydrophobicity of the aggregates. Fluorescence quenching and molecular docking results demonstrate that CoTA binds moderately to the aggregation-prone region of HL (Kb = 6.6 × 104 M-1), thereby, inhibiting HL fibrillation. In addition, far-UV CD and DSC show that binding of CoTA to HL does not cause any change in the stability of HL. More importantly, CoTA attenuates membrane damaging effects of HL aggregates against RBCs. This study identifies inorganic metal complexes as a therapeutic intervention for systemic amyloidosis.

Stabilization of the retromer complex: Analysis of novel binding sites of bis-1,3-phenyl guanylhydrazone 2a to the VPS29/VPS35 interface.

The stabilization of the retromer protein complex can be effective in the treatment of different neurological disorders. Following the identification of bis-1,3-phenyl guanylhydrazone 2a as an effective new compound for the treatment of amyotrophic lateral sclerosis, in this work we analyze the possible binding sites of this molecule to the VPS35/VPS29 dimer of the retromer complex. Our results show that the affinity for different sites of the protein assembly depends on compound charge and therefore slight changes in the cell microenvironment could promote different binding states. Finally, we describe a novel binding site located in a deep cleft between VPS29 and VPS35 that should be further explored to select novel molecular chaperones for the stabilization of the retromer complex.

KcsA-Kv1.x chimeras with complete ligand-binding sites provide improved predictivity for screening selective Kv1.x blockers.

Despite significant advances in the development of therapeutic interventions targeting autoimmune diseases and chronic inflammatory conditions, lack of effective treatment still poses a high unmet need. Modulating chronically activated T cells through the blockade of the Kv1.3 potassium channel is a promising therapeutic approach; however, developing selective Kv1.3 inhibitors is still an arduous task. Phage display-based high throughput peptide library screening is a rapid and robust approach to develop promising drug candidates; however, it requires solid-phase immobilization of target proteins with their binding site preserved. Historically, the KcsA bacterial channel chimera harboring only the turret region of the human Kv1.3 channel was used for screening campaigns. Nevertheless, literature data suggest that binding to this type of chimera does not correlate well with blocking potency on the native Kv1.3 channels. Therefore, we designed and successfully produced advanced KcsA-Kv1.3, KcsA-Kv1.1, and KcsA-Kv1.2 chimeric proteins in which both the turret and part of the filter regions of the human Kv1.x channels were transferred. These T+F (turret-filter) chimeras showed superior peptide ligand-binding predictivity compared to their T-only versions in novel phage ELISA assays. Phage ELISA binding and competition results supported with electrophysiological data confirmed that the filter region of KcsA-Kv1.x is essential for establishing adequate relative affinity order among selected peptide toxins (Vm24 toxin, Hongotoxin-1, Kaliotoxin-1, Maurotoxin, Stichodactyla toxin) and consequently obtaining more reliable selectivity data. These new findings provide a better screening tool for future drug development efforts and offer insight into the target-ligand interactions of these therapeutically relevant ion channels.

13 Cß-Valine and 13 Cγ-Leucine Methine Labeling To Probe Protein Ligand Interaction.

Precise information regarding the interaction between proteins and ligands at molecular resolution is crucial for effectively guiding the optimization process from initial hits to lead compounds in early stages of drug development. In this study, we introduce a novel aliphatic side chain isotope-labeling scheme to directly probe interactions between ligands and aliphatic sidechains using NMR techniques. To demonstrate the applicability of this method, we selected a set of Brd4-BD1 binders and analyzed 1 H chemical shift perturbation resulting from CH-π interaction of Hß -Val and Hγ -Leu as CH donors with corresponding ligand aromatic moieties as π acceptors.

Targeting the NTF2-like domain of G3BP1: Novel modulators of intracellular granule dynamics.

Stress granule (SG) is a temporary cellular structure that plays a crucial role in the regulation of mRNA and protein sequestration during various cellular stress conditions. SG enables cells to cope with stress more effectively, conserving vital energy and resources. Focusing on the NTF2-like domain of G3BP1, a key protein in SG dynamics, we explore to identify and characterize novel small molecules involved in SG modulation without external stressors. Through in silico molecular docking approach to simulate the interaction between various compounds and the NTF2-like domain of G3BP1, we identified three compounds as potential candidates that could bind to the NTF2-like domain of G3BP1. Subsequent immunofluorescence experiments demonstrated that these compounds induce the formation of SG-like, G3BP1-positive granules. Importantly, the granule formation by these compounds occurs independent from the phosphorylation of eIF2α, a common mechanism in SG formation, suggesting that it might offer a new strategy for influencing SG dynamics implicated in various diseases.

Structural and accessibility studies highlight the differential binding of clemizole to TRPC5 and TRPC6.

Transient Receptor Potential Canonical 5 (T RP C5) and T RP C6 channels play critical physiological roles in various cell types. Their involvement in numerous disease progression mechanisms has led to extensive searches for their inhibitors. Although several potent T RP C inhibitors have been developed and the structure of their binding sites were mapped using cryo electron microscopy, a comprehensive understanding of the molecular interactions within the inhibitor binding site of T RP Cs remains elusive. This study aimed to decipher the structural determinants and molecular mechanisms contributing to the differential binding of clemizole to T RP C5 and T RP C6, with a particular focus on the accessibility of binding site residues. This information can help better understand what molecular features allow for selective binding, which is a key characteristic of clinically effective pharmacological agents. Using computational methodologies, we conducted an in-depth molecular docking analysis of clemizole with T RP C5 and T RP C6 channels. The protein structures were retrieved from publicly accessible protein databases. Discovery Studio 2020 Client Visualizer and Chimera software facilitated our in-silico mutation experiments and enabled us to identify the critical structural elements influencing clemizole binding. Our study reveals key molecular determinants at the clemizole binding site, specifically outlining the role of residues' Accessible Surface Area (ASA) and Relative Accessible Surface Area (RASA) in differential binding. We found that lower accessibility of T RP C6 binding site residues, compared to those in T RP C5, could account for the lower affinity binding of clemizole to T RP C6. This work illuminates the pivotal role of binding site residue accessibility in determining the affinity of clemizole to T RP C5 and T RP C6. A nuanced understanding of the distinct binding properties between these homologous proteins may pave the way for the development of more selective inhibitors, promising improved therapeutic efficacy and fewer off-target effects. By demystifying the structural and molecular subtleties of T RP C inhibitors, this research could significantly accelerate the drug discovery process, offering hope to patients afflicted with T RP C-related diseases.

Novel N-(3-ethynyl Phenyl)-6,7-bis(2-methoxyethoxy)Quinazoline-4-amine Derivatives: Synthesis, Characterization, Anti-cancer Activity, In-silico and DFT Studies.

BACKGROUND: Cancer is one of the most common reasons for mortality in the world. A continuous effort to develop effective anti-cancer drugs with minimum side effects has become necessary. The use of small-molecule drugs has revolutionized cancer research by inhibiting cancer cell survival and proliferation. Quinazolines are a class of bioactive heterocyclic compounds with active pharmacophores in several anti-cancer drugs. Such small molecule inhibitors obstruct the significant signals responsible for cancer cell development, thus blocking these cell signals to prevent cancer development and spread. OBJECTIVE: In the current study, novel quinazoline derivatives structurally similar to erlotinib were synthesized and explored as novel anti-cancer agents. METHODS: All the synthesized molecules were confirmed by spectroscopic techniques like 1H NMR, 13C NMR, and ESI-MS. Various techniques were applied to study the protein-drug interaction, DFT analysis, Hirshfeld surface, and target prediction. The molecules were screened in vitro for their anti-cancer properties against 60 human tumor cell lines. The growth inhibitory properties of a few compounds were studied against the MCF7 breast cancer cell line. RESULTS: The activity of compounds 9f, 9o, and 9s were found to be active. However, compound 9f is more active when compared with other compounds. CONCLUSION: Some synthesized compounds were active against different cancer cell lines. The in-vitro study results were found to be in agreement with the predictions from in-silico data. The selected molecules were further subjected to get the possible mechanism of action against different cancer cells.

Induced Circular Dichroism Analysis of Thermally Induced Conformational Changes on Protein Binding Sites Under a Crowding Environment.

Protein-ligand interactions in crowded cellular environments play a crucial role in biological functions. The crowded environment can perturb the overall protein structure and local conformation, thereby influencing the binding pathway of protein-ligand reactions within the cellular milieu. Therefore, a detailed understanding of the local conformation is crucial for elucidating the intricacies of protein-ligand interactions in crowded cellular environments. In this study, we investigated the feasibility of induced circular dichroism (ICD) using 8-anilinonaphthalene-1-sulfonic acid (ANS) for local conformational analysis at the binding site in a crowding environment. Bovine serum albumin (BSA) concentration-dependent measurements were performed to assess the feasibility of ANS-ICD for analyzing protein interior binding sites. The results showed distinct changes in the ANS-ICD spectra of BSA solutions, indicating their potential for analyzing the internal conformation of proteins. Moreover, temperature-dependent measurements were performed in dilute and crowding environments, revealing distinct denaturation pathways of BSA binding sites. Principal component analysis of ANS-ICD spectral changes revealed lower temperature pre-denaturation in the crowded solution than that in the diluted solution, suggesting destabilization of binding sites owing to self-crowding repulsive interactions. The established ANS-ICD method can provide valuable conformational insights into protein-ligand interactions in crowded cellular environments.