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The compilation of ligand and structure-based molecular modeling methods has become an important practice in virtual screening applied to drug discovery. This systematic review addresses and ranks various virtual screening strategies to drive the selection of the optimal method for studies that have as their starting point a multi-ligand investigation and investigation based on the protein structure of a therapeutic target. This study shows examples of applications and an evaluation based on the objective and problematic of a series of virtual screening studies present in the ScienceDirect® database. The results showed that the molecular docking technique is widely used in scientific production, indicating that approaches that use protein structure as a starting point are the most promising strategy for drug discovery that relies on virtual screening-based research.

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Vitiligo is a prevalent autoimmune disease affecting the quality of life and self-confidence. Total 25 phytochemicals from plants were screened by using four target proteins involved in the pathogenesis of vitiligo. The binding affinity of the ligands ranged between -10.3 and -4.5 kcal/mol. The top 10 phytochemicals i.e. rosmarinic acid, piperine, tamarixetin, desmethoxycurcumin, bisdemethoxycurcumin, isorhamnetin, quercetin, vicenin II, genkwanin, and aloe-emodin showed good inhibition with binding affinity ranged from -10.3 to 9.4 Kcal. The ADMET profiling revealed that these phytochemicals might be safe for the treatment of vitiligo. In MD simulation, rosmarinic acid, piperine, and tamarixetin with MAO-A formed stable complexes and the free binding energies of the complexes were -34.02 ± 6.94, -33.51 ± 2.65, and -27.17 ± 3.28. Furthermore, the ligands formed hydrogen bonds with targets, suggested that rosmarinic acid, piperine, and tamarixetin have potential to serve as lead compounds for developing novel therapeutics for vitiligo after in vitro and in vivo studies.

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Vacuolar-type H+-ATPases (V-ATPases) play a crucial role in the life cycle of agricultural pests and represent a promising target for the development of novel insecticides. In this study, S18, a derivative of vanillin acquired from Specs database using a structure-based virtual screening methodology, was first identified as a V-ATPase inhibitor. It binds to subunit A of the enzyme with a Kd of 1 nM and exhibits insecticidal activity against M. separata. Subsequently, using S18 as the lead compound, a new series of vanillin derivatives were rationally designed and efficiently synthesized. and their biological activities were assessed. Among them, compound 3b-03 showed the strongest insecticidal activity against M. separata by effectively targeting the V-ATPase subunit A with Kd of 0.803 µM. Isothermal titration calorimetric measurements and docking results provided insights into its interaction with subunit A of V-ATPase, which could facilitate future research aimed at the development of novel chemical insecticides.

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Coronavirus 2019 (COVID-19) is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) strain. Many anticancer compounds have been repurposed as effective anti-coronavirus agents and are currently in a clinical trial to be evaluated for treatment. Elacestrant is a novel selective estrogen receptor degrader (SERD). A fingerprint Tanimoto-based 2-dimensional similarity search was performed in the PubChem database using elacestrant as a prototype. The chemical compounds were downloaded, and virtual screening, molecular docking, and molecular dynamics were further used to identify the most active molecules in the binding pocket SARS-COV-2 main protease. Eight compounds with superior docking score, gscore, and glide binding energy were identified. Molecular dynamic simulations (MD) were performed at 100 ns to remove the false interactions between the receptor and the active ligands. The results showed that all the compounds displayed good stability. Further, the ADMET results showed that compounds CID58023104 was observed to be deemed a hit compound; hence, CID58023104 and could be optimize, derivatize, and explore for further development as an anti-coronavirus agent targeting SARS-COV-2 main protease.

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We here introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS). Ensemble VS is an established method for predicting protein/small-molecule (ligand) binding. Unlike traditional VS, which focuses on a single protein conformation, ensemble VS better accounts for protein flexibility by predicting binding to multiple protein conformations. Each compound is thus associated with a spectrum of scores (one score per protein conformation) rather than a single score. To effectively rank and prioritize the molecules for further evaluation (including experimental testing), researchers must select which protein conformations to consider and how best to map each compound's spectrum of scores to a single value, decisions that are system-specific. EnOpt uses machine learning to address these challenges. We perform benchmark VS to show that for many systems, EnOpt ranking distinguishes active compounds from inactive or decoy molecules more effectively than traditional ensemble VS methods. To encourage broad adoption, we release EnOpt free of charge under the terms of the MIT license.

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Leucine-rich repeat kinase 2 (LRRK2) has been reported to be associated with familial and idiopathic Parkinson's disease (PD) risk and is a promising target for drug discovery against PD. To identify novel and effective LRRK2 inhibitors, an ensemble virtual screening strategy by combining fingerprint similarity, complex-based pharmacophore and structure-based molecular docking was proposed and applied. Using this strategy, we finally selected 25 compounds from ∼1.7 million compounds for in vitro and in vivo tests. Firstly, the kinase inhibitory activity tests of compounds based on ADP-Glo assay identified three most potent compounds LY2023-19, LY2023-24 and LY2023-25 with IC50 of 556.4 nM, 218.1 nM and 22.4 nM for LRRK2 G2019S mutant, respectively. The further cellular experiments also indicated that three hit compounds significantly inhibited Ser935 phosphorylation of both wide-type and G2019S LRRK2 with IC50 ranging from 27 nM to 1674 nM in HEK293T cells. The MD simulations of three compounds and G2019S LRRK2 showed the hydrogen bond formed by Glu1948 and Ala1950 is crucial for the binding of LRRK2. Afterwards, 6-OHDA-induced PD zebrafish model was constructed to evaluate the neuroprotective effects of hit compounds. The locomotion of the 6-OHDA treated zebrafish larvae was improved after treatment with LY2023-24. The obtained results can provide valuable guidance for the development of PD drugs by targeting LRRK2.

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Pulmonary arterial hypertension (PAH) is a severe pulmonary vascular disorder marked by vascular remodeling, which is linked to the malignant phenotypes of pulmonary vascular cells. The prevailing therapeutic approaches for PAH tend to neglect the potential role of vascular remodeling, leading to the clinical prognosis remains poor. Previously, we first demonstrated that heat shock protein (Hsp110) was significantly activated to boost Hsp110-STAT3 interaction, which resulted in abnormal proliferation and migration of human pulmonary arterial endothelial cells (HPAECs) under hypoxia. In the present study, we initially postulated the allosteric site of Hsp110, performed a virtual screening and biological evaluation studies to discover novel Hsp110-STAT3 interaction inhibitors. Here, we identified compound 29 (AN-329/43448068) as the effective inhibitor of HPAECs proliferation and the Hsp110-STAT3 association with good druggability. In vitro, 29 significantly impeded the chaperone function of Hsp110 and the malignant phenotypes of HPAECs. In vivo, 29 remarkably attenuated pulmonary vascular remodeling and right ventricular hypertrophy in hypoxia-induced PAH rats (i.g). Altogether, our data support the conclusion that it not only provides a novel lead compound but also presents a promising approach for subsequent inhibitor development targeting Hsp110-STAT3 interaction.

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This study aimed to unravel the peptide profiles of six distinct yeast protein samples and identify novel umami peptides within them. Peptide characteristics analysis support the proposition that yeast protein peptide pools represent exceptional reservoirs of umami peptides. Nine potential umami peptides were screened using the iUmami_SCM, UMPred-FRL, Umami_YYDS, Umami-MRNN, Innovagen, Expasy-ProtParam, and ToxinPred tools. Peptides AGVEDVY, LFEQHPEYRK, AFDVQ, GPTVEEVD, NVVAGSDLR, ATNGSR, and VEVVALND (1 mg/mL) were confirmed to possess umami taste, and the first five peptides exhibited significant umami-enhancing effects on 0.35 % monosodium glutamate. Molecular docking indicated that peptide residues His, Arg, Tyr, Asp, Gln, Thr, Ser, and Glu primarily bound to His71, Ser107/109/148, Asp147/218, and Arg277 of T1R1 and Ser104/146, His145, Asp216, Tyr218, and Ala302 of T1R3 through hydrogen bonds. This study enriches the umami peptide repository for potential food additive use and establishes a theoretical foundation for exploring taste compounds in yeast proteins and their broader applications.

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Small molecule inhibitors targeting the bromodomain and extra-terminal domain (BET) family proteins have emerged as a promising class of anti-cancer drugs. Nevertheless, the clinical advancement of these agents has been significantly hampered by challenges related to their potency, oral bioavailability, or toxicity. In this study, virtual screening approaches were employed to discover novel inhibitors of the bromodomain-containing protein 4 (BRD4) by analyzing their comparable chemical structural features to established BRD4 inhibitors. Several of these compounds exhibited inhibitory effects on BRD4 activity ranging from 60 % to 70 % at 100 µM concentrations, while one compound also exhibited an 84 % inhibition of Sirtuin 2 (SIRT2) activity. Furthermore, a subset of structurally diverse compounds from the BRD4 inhibitors was selected to investigate their anti-cancer properties in both 2D and 3D cell cultures. These compounds exhibited varying effects on cell numbers depending on the specific cell line, and some of them induced cell cycle arrest in the G0/G1 phase in breast cancer (MDA-MB-231) cells. Moreover, all the compounds studied reduced the sizes of spheroids, and the most potent compound exhibited a 90 % decrease in growth at a concentration of 10 µM in T47D cells. These compounds hold potential as epigenetic regulators for future studies.

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Aim: To search for potential inhibitors to homoserine dehydrogenase (HSD) in Paracoccidioides brasiliensis the causative agent of paracoccidioidomycosis, an infection with a high mortality rate in Brazil.Materials & methods: The enzyme was modeled and used in the virtual screening of the compounds. The library was first screened by the Autodock, in which 66 molecules were better ranked than substrate, and then, also evaluated by the Molegro and Gold programs.Results: The HS23 and HS87 molecules were selected in common by the three programs, and ADME/Tox evaluation indicates they are not toxic. The molecular dynamics of PbHSD bonded to ligands showed stable complexes until 50 ns. To validate the results, compounds were purchased for assays of minimum inhibitory concentration (MIC), minimum fungicidal concentration (MFC), synergic profile with Amphotericin B (AmB) and cytotoxicity. The two molecules presented MIC of 32 µg/ml and MFC of 64 µg/ml against the P. brasiliensis (strain Pb18). They also showed synergistic activity with AmB and a lack of toxicity against Hela and Vero cell lines.Conclusion: These results suggest that the HS23 and HS87 are promising candidates as PbHSD inhibitors and may be used as hits for the development of new drugs against paracoccidioidomycosis.

[Box: see text].

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The identification of new compounds with potential activity against CXC chemokine receptor type 4 (CXCR4) has been broadly studied, implying several chemical families, particularly AMD3100 derivatives. Molecular modeling has played a pivotal role in the identification of new active compounds. But, has its golden age ended? A virtual library of 450,000 tetraamines of general structure 8 was constructed by using five spacers and 300 diamines, which were obtained from the corresponding commercially available cyclic amines. Diversity selection was performed to guide the virtual screening of the former database and to select the most representative set of compounds. Molecular docking on the CXCR4 crystal structure allowed us to rank the selection and identify those candidate molecules with potential antitumor activity against diffuse large B-cell lymphoma (DLBCL). Among them, compound A{17,18} stood out for being a non-symmetrical structure, synthetically feasible, and with promising activity against DLBCL in in vitro experiments. The focused study of symmetrical-related compounds allowed us to identify potential pre-hits (IC50~20 µM), evidencing that molecular design is still relevant in the development of new CXCR4 inhibitor candidates.

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Salty peptide as an important sodium substitute, which could reduce the risk of cardiovascular disease caused by excessive sodium intake. In this study, novel salty peptides were prepared and identified from enzymolysis extract of oysters by peptitomic identification, virtual screening and solid phase synthesis. Additionally, molecular simulation was used to study the taste mechanism of salty peptides. 316 peptides were identified in the enzymatic hydrolysates of oysters. 6 peptides, selected through virtual screening, were synthesized using solid-phase synthesis, and EK, LFE, LEY and DR were confirmed to possess a pleasing salty taste through electronic tongue evaluation. Molecular docking results indicated that these 4 peptides could enter the binding pocket within the transmembrane channel-like 4 (TMC4) cavity, wherein salt bridges, hydrogen bonds and attractive charges were the main binding forces. This study provides a rapid screening method for salty peptides in sea food products but possibly applied for other sources.

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Major depressive disorder (MDD) is a prevalent mental health condition characterized by persistent feelings of sadness and hopelessness, affecting millions globally. The precise molecular mechanisms underlying MDD remain elusive, necessitating comprehensive investigations. Our study integrates transcriptomic analysis, functional assays, and computational modeling to explore the molecular landscape of MDD, focusing on the DLPFC. We identify key genomic alterations and co-expression modules associated with MDD, highlighting potential therapeutic targets. Functional enrichment and protein-protein interaction analyses emphasize the role of astrocytes in MDD progression. Machine learning is employed to develop a predictive model for MDD risk assessment. Single-cell and spatial transcriptomic analyses provide insights into cell type-specific expression patterns, particularly regarding astrocytes. We have identified significant genomic alterations and co-expression modules associated with MDD in the DLPFC. Key genes involved in neuroactive ligand-receptor interaction pathways, notably in astrocytes, have been highlighted. Additionally, we developed a predictive model for MDD risk assessment based on selected key genes. Single-cell and spatial transcriptomic analyses underscored the role of astrocytes in MDD. Virtual screening of compounds targeting GPR37L1, KCNJ10, and PPP1R3C proteins has identified potential therapeutic candidates. In summary, our comprehensive approach enhances the understanding of MDD's molecular underpinnings and offers promising opportunities for advancing therapeutic interventions, ultimately aiming to alleviate the burden of this debilitating mental health condition. KEY MESSAGES: Our investigation furnishes insightful revelations concerning the dysregulation of astrocyte-associated processes in MDD. We have pinpointed specific genes, namely KCNJ10, PPP1R3C, and GPR37L1, as potential candidates warranting further exploration and therapeutic intervention. We incorporate a virtual screening of small molecule compounds targeting KCNJ10, PPP1R3C, and GPR37L1, presenting a promising trajectory for drug discovery in MDD.

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Antimicrobial resistance (AMR) poses a foremost threat to global health, necessitating innovative strategies for discovering antimicrobial agents. This review explores the role and recent advances of in-silico techniques in identifying novel antimicrobial agents and combating AMR giving few briefings of recent case studies of AMR. In-silico techniques, such as homology modeling, virtual screening, molecular docking, pharmacophore modeling, molecular dynamics simulation, density functional theory, integrated machine learning, and artificial intelligence, are systematically reviewed for their utility in discovering antimicrobial agents. These computational methods enable the rapid screening of large compound libraries, prediction of drug-target interactions, and optimization of drug candidates. The review discusses integrating in-silico approaches with traditional experimental methods and highlights their potential to accelerate the discovery of new antimicrobial agents. Furthermore, it emphasizes the significance of interdisciplinary collaboration and data-sharing initiatives in advancing antimicrobial research. Through a comprehensive discussion of the latest developments in in-silico techniques, this review provides valuable insights into the future of antimicrobial research and the fight against AMR. Supplementary Information: The online version contains supplementary material available at 10.1007/s12088-024-01355-x.

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Our research is dedicated to combating HIV by targeting its Matrix (MA) domain, which is crucial for viral assembly and replication. This strategy specifically aims to interrupt early-stage infection and deter drug resistance by focusing on this essential domain. Due to the MA domain's conservation across different HIV strains, our approach promises broad-spectrum efficacy, which is particularly crucial in regions marked by significant genetic diversity and resistance issues. In our study, we introduce CNP0269688, a natural product that exhibits high affinity for the HIV-1 Matrix. Through detailed molecular dynamics simulations, we have assessed the compound's structural stability and interaction dynamics, particularly its potential to hinder Protein-tRNA interactions. This analysis lays the groundwork for future experimental investigations. Our efforts are steps toward enhancing HIV treatment, reducing viral transmission, and curbing drug resistance, with the ultimate aim of controlling and eradicating the pandemic, thereby contributing significantly to public health and scientific advancement.

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Background: Mycobacterium tuberculosis (Mtb) carbonic anhydrases (CAs) are critical enzymes that regulate pH by converting CO2 to HCO3 -, essential for Mtb's survival in acidic environments. Inhibiting γ-CAs presents a potential target for novel antituberculosis drugs with unique mechanisms of action. Objective: This study aimed to explore the biological connections underlying Mtb pathogenesis and investigate the mechanistic actions of antituberculosis compounds targeting the Cas9 protein. Methods: We employed homology modeling and virtual screening to identify compounds with high binding affinities for Cas9 protein. This study used the homology modeling approach employing high-quality AlphaFold DB models for γ-CA. Furthermore, the systems biology approach was used for analyzing the integrated modelling of compounds, integrating data on genes, pathways, phenotypes, and molecular descriptors. Single-cell RNA sequencing was also conducted to profile gene expression. Results: Three compounds, F10921405, F08060425, and F14437079, potentially binding to Cas9 protein, have been identified. F10921405 and F08060425 showed significant overlap in their effects on pathways related to the immune response, while F14437079 displayed distinct mechanistic pathways. Expression profiling revealed high levels of genes such as PDE4D, ROCK2, ITK, MAPK10, and SYK in response to F1092-1405 and F0806-0425, and MMP2 and CALCRL in response to F1443-7079. These genes, which play a role in immune modulation and lung tissue integrity, are essential to fight against Mtb. Conclusion: The molecular relationship and pathways linked to the mentioned compounds give the study a holistic perspective of targeting Mtb, which is essential in designing specific therapeutic approaches. Subsequent research will involve experimental validation to demonstrate the efficacy of the promising candidates in Mtb infections.

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Mitragyna speciosa is famous for its addictive effect. On the other hand, this plant has good potential as an antioxidant agent, and so far, it was not explicitly explained what the most contributing compound in the leaves to that activity is. This study has been conducted using several computational methods to determine which compounds are the most active in interacting with cytochrome P450, myeloperoxidase, and NADPH oxidase proteins. First, virtual screening was carried out based on molecular docking, followed by profiling the properties of adsorption, distribution, metabolism, excretion, and toxicity (ADMET); the second one is the molecular dynamics (MD) simulations for 100 ns. The virtual screening results showed that three compounds acted as inhibitors for each protein: (-)-epicatechin, sitogluside, and corynoxeine. The ADMET profiles of the three compounds exhibit good drug ability and toxicity. The trajectories study from MD simulations predicts that the complexes of these three compounds with their respective target proteins are stable. Furthermore, these compounds identified in this computational study can be a potential guide for future experiments aimed at assessing the antioxidant properties through in vitro testing.

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Background: Mutations in the Spleen tyrosine kinase (Syk) protein have significant implications for its function and response to treatments. Understanding these mutations and identifying new inhibitors can lead to more effective therapies for conditions like autosomal dominant hyper-IgE syndrome (AD-HIES) and related immunological disorders. Objective: To investigate the impact of mutations in the Syk protein on its function and response to reference treatments, and to explore new inhibitors tailored to the mutational profile of Syk. Methods: We collected and analyzed mutations affecting the Syk protein to assess their functional impact. We screened 94 deleterious mutations in the kinase domain using molecular docking techniques. A library of 997 compounds with potential inhibitory activity against Syk was filtered based on Lipinski and Veber rules and toxicity assessments. We evaluated the binding affinity of reference inhibitors and 14 eligible compounds against wild-type and mutant Syk proteins. Molecular dynamics simulations were conducted to evaluate the interaction of Syk protein complexes with the reference inhibitor and potential candidate inhibitors. Results: Among the analyzed mutations, 60.5% were identified as deleterious, underscoring their potential impact on cellular processes. Virtual screening identified three potential inhibitors (IDs: 118558008, 118558000, and 118558092) with greater therapeutic potential than reference treatments, meeting all criteria and exhibiting lower IC50 values. Ligand 1 (ID: 118558000) demonstrated the most stable binding, favorable compactness, and extensive interaction with solvents. A 3D pharmacophore model was constructed, identifying structural features common to these inhibitors. Conclusion: This study found that 60.5% of reported mutations affecting the Syk protein are deleterious. Virtual screening revealed three top potential inhibitors, with ligand 1 (ID: 118558000) showing the most stable binding and favorable interactions. These inhibitors hold promise for more effective therapies targeting Syk-mediated signaling pathways. The pharmacophore model provides valuable insights for developing targeted therapies for AD-HIES and related disorders, offering hope for patients suffering from Hyper IgE syndrome with allergic symptoms.

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Molecular modeling and simulation serve an important role in exploring biological functions of proteins at the molecular level, which is complementary to experiments. CHARMM-GUI (https://www.charmm-gui.org) is a web-based graphical user interface that generates complex molecular simulation systems and input files, and we have been continuously developing and expanding its functionalities to facilitate various complex molecular modeling and make molecular dynamics simulations more accessible to the scientific community. Currently, covalent drug discovery emerges as a popular and important field. Covalent drug forms a chemical bond with specific residues on the target protein, and it has advantages in potency for its prolonged inhibition effects. Even though there are higher demands in modeling PDB protein structures with various covalent ligand types, proper modeling of covalent ligands remains challenging. This work presents a new functionality in CHARMM-GUI PDB Reader & Manipulator that can handle a diversity of ligand-amino acid linkage types, which is validated by a careful benchmark study using over 1,000 covalent ligand structures in RCSB PDB. We hope that this new functionality can boost the modeling and simulation study of covalent ligands.

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Protein kinase CK2 type α (CK2α) inhibitors are expected to be a new anticancer drug and a treatment for nephritis. Virtual screening for CK2α inhibitors has been conducted and active compounds with various scaffolds have been obtained. Research on compound optimization is currently in progress for some of them with the aim of improving their activity. This process involves the combination of various computational chemistry methods and crystal analyses. In this review, case studies of structure-based compound designs that have efficiently improved the activity of screening hit compounds, including compounds with a thiadiazole ring and a purine scaffold, are introduced.

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