Comparative evaluation of eight docking tools for docking and virtual screening accuracy

Design and Optimization of Novel Pyrimidine-Morpholine Hybrids Through Computational Approaches for SRC Kinase Inhibitory Activity

Src (non receptor tyrosine kinase) plays a role in multiple pathways leading to tumor survival, proliferation and metastasis. Inhibiting Src kinase would be a therapeutic benefit in Src dependent cancers. Most of the nitrogen containing heterocyclic moieties found to possess variety of biological activities. Combination of heterocyclic nucleus to active hybrids has proven to be a successful method of approach to augment biological activities. Hence a series of pyrimidine-morpholine hybrids were designed and its shape similarity studies calculated with the standard Dasatinib using Tanimoto coefficient. Designed molecules were docked with human tyrosine kinase (PDB ID: 2SRC) using AutoDock vina. Docked poses were ranked based on their binding affinities which are then compared with a reference. The studies revealed that docking of hybrid molecules with 2SRC showed promising interactions with affordable ADMET properties. The stability of highly docked complex was analyzed by molecular simulation studies and the results confirmed the docking outcomes thereby making it as a potential SRC kinase inhibitor. Hence these novel pyrimidine hybrids can be considered as lead molecules for developing novel druggable moieties for breast cancer research.

Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

Novel applications of metformin in the treatment of septic myocardial injury based on metabolomics and network pharmacology

BACKGROUND

While metformin has shown promise in treating septic myocardial injury (SMI), its underlying mechanisms and impact on metabolic disturbances remain poorly understood.

METHODS

This study employed an integrated approach of metabolomics and network pharmacology to identify key targets and pathways through which metformin may act against SMI. Findings were validated using a lipopolysaccharide (LPS)-induced mouse model.

RESULTS

Metformin was found to counter myocardial metabolic disruptions, indicated by the reversal of 49 metabolites primarily involved in purine metabolism, pantothenate and CoA biosynthesis, and histidine metabolism. In vivo, metformin significantly improved survival rates and cardiac function, reduced cardiomyocyte apoptosis, and inhibited inflammation and oxidative stress in LPS-induced mice. Integrated analyses identified 27 potential targets for metformin in SMI treatment. KEGG pathway analysis revealed significant enrichment in TNF, HIF-1, IL-17, and PI3K/AKT signaling pathways, while protein-protein interaction analysis pinpointed ten core targets, including IL6, IL1B, CCL2, CASP3, MMP9, HIF1A, IGF1, NOS3, MMP2, and LEP. Molecular docking and dynamics simulations demonstrated metformin's high affinity for these core targets. Further, RT-qPCR and Western blot analyses confirmed that metformin modulates core target expression to mitigate SMI. Notably, our data underscore the importance of PI3K/AKT and MMP2/MMP9 signaling pathways in SMI therapy.

CONCLUSION

This study elucidates the metabolic and molecular mechanisms of metformin in SMI treatment, supporting its potential repurposing for SMI.

Identification of phytomolecules as isoform and mutation specific PI3K-α inhibitor for protection against breast cancer using e-pharmacophore modeling and molecular dynamics simulations

PI3K-α mutation plays a critical role in cancer development, notably in breast cancer, particularly within HR + /HER2- subtypes. These mutations drive tumor growth and survival by activating the PI3K/AKT/mTOR pathway, which is essential for cell proliferation and survival. Our research aimed to identify natural compounds that can inhibit mutant and specific isoforms of PI3K-α to prevent tumor progression. e-Pharmacophore model was generated using Receptor-Ligand complex using the Inavolisib drug (PDB:8EXV) and phase screening was performed using the Molport database of natural compounds. Through molecular docking studies we identified seven promising compounds for further molecular dynamics simulations. Among these, three compounds-STOCK1N-85097, STOCK1N-85998, and STOCK1N-86060-showed significant stability and interaction with PI3K-α. These compounds demonstrated favorable results in several parameters, including RMSD, RMSF, Rg, SASA, PCA, FEL, and total energy evaluations. Therefore, these compounds are projected to function as PI3K-α inhibitors and because of its natural origin it can possess fewer side effects than the conventional medicine, which should be validated by proper in vivo and in vitro models.

Pharmacophore mapping, 3D QSAR, molecular docking, and ADME prediction studies of novel Benzothiazinone derivatives

In the quest to combat tuberculosis, DprE1, a challenging target for novel anti-tubercular agents due to its small size and membrane location, has been a focus of research. DprE1 catalyzes the transformation of DPR into Ketoribose DPX, with Benzothiazinone emerging as a potent pharmacophore for inhibiting DprE1. Clinical trial drugs such as BTZ043, BTZ038, PBTZ169, and TMC-207 have shown promising results as DprE1 inhibitors. This study employed pharmacophore mapping of Pyrazolopyridine, Dinitrobenzamide, and Benzothiazinone derivatives to identify crucial features for eliciting a biological response. Benzothiazinone (Ligand code: 73) emerged as a reference ligand with a fitness score of 3.000. ROC analysis validated the pharmacophore with an excellent score of 0.71. To build a 3D QSAR model, a series of Benzothiazinone congeneric derivatives were explored. The model exhibited strong performance, with a standard deviation of 0.1531, a correlation coefficient for the training set (R2) value of 0.9754, and a correlation coefficient for test set Q2 value of 0.7632, indicating robust predictive capabilities. Contour maps guided the design of novel benzothiazinone derivatives, emphasizing steric, electrostatic, hydrophobic, H-bond acceptor, and H-bond donor groups for structure-activity relationships. Docking studies against PDB ID: 4NCR demonstrated favorable scores, with interactions aligning well with the in-built ligand 26 J. Docking validation via RMSD values supported the reliability of the docking results. This comprehensive approach aids in the design of novel benzothiazinone derivatives with potential anti-tubercular properties, contributing to the development of novel anti-tubercular agents which can be pivotal in the eradication of tuberculosis.

Unlocking the Potential of Clustering and Classification Approaches: Navigating Supervised and Unsupervised Chemical Similarity

BACKGROUND

The field of toxicology has witnessed substantial advancements in recent years, particularly with the adoption of new approach methodologies (NAMs) to understand and predict chemical toxicity. Class-based methods such as clustering and classification are key to NAMs development and application, aiding the understanding of hazard and risk concerns associated with groups of chemicals without additional laboratory work. Advances in computational chemistry, data generation and availability, and machine learning algorithms represent important opportunities for continued improvement of these techniques to optimize their utility for specific regulatory and research purposes. However, due to their intricacy, deep understanding and careful selection are imperative to align the adequate methods with their intended applications.

OBJECTIVES

This commentary aims to deepen the understanding of class-based approaches by elucidating the pivotal role of chemical similarity (structural and biological) in clustering and classification approaches (CCAs). It addresses the dichotomy between general end point-agnostic similarity, often entailing unsupervised analysis, and end point-specific similarity necessitating supervised learning. The goal is to highlight the nuances of these approaches, their applications, and common misuses.

DISCUSSION

Understanding similarity is pivotal in toxicological research involving CCAs. The effectiveness of these approaches depends on the right definition and measure of similarity, which varies based on context and objectives of the study. This choice is influenced by how chemical structures are represented and the respective labels indicating biological activity, if applicable. The distinction between unsupervised clustering and supervised classification methods is vital, requiring the use of end point-agnostic vs. end point-specific similarity definition. Separate use or combination of these methods requires careful consideration to prevent bias and ensure relevance for the goal of the study. Unsupervised methods use end point-agnostic similarity measures to uncover general structural patterns and relationships, aiding hypothesis generation and facilitating exploration of datasets without the need for predefined labels or explicit guidance. Conversely, supervised techniques demand end point-specific similarity to group chemicals into predefined classes or to train classification models, allowing accurate predictions for new chemicals. Misuse can arise when unsupervised methods are applied to end point-specific contexts, like analog selection in read-across, leading to erroneous conclusions. This commentary provides insights into the significance of similarity and its role in supervised classification and unsupervised clustering approaches. https://doi.org/10.1289/EHP14001.

A computational study to identify Sesamol derivatives as NRF2 activator for protection against drug-induced liver injury (DILI)

Drug-induced liver injury can be caused by any drugs, their metabolites, or natural products due to the inefficient functioning of drug-metabolizing enzymes, resulting in reactive oxygen species generation and leading to oxidative stress-induced cell death. For protection against oxidative stress, our cell has various defense mechanisms. One of the mechanisms is NRF2 pathway, when activated, protects the cell against oxidative stress. Natural antioxidants such as Sesamol have reported pharmacological activity (hepatoprotective & cardioprotective) and signaling pathways (NRF2 & CREM) altering potential. A Computational analysis was done using molecular docking, IFD, ADMET, MM-GBSA, and Molecular dynamic simulation of the Schrödinger suite. A total of 63,345 Sesamol derivatives were downloaded for the PubChem database. The protein structure of KEAP1-NRF2 (PDB: 4L7D) was downloaded from the RCSB protein database. The molecular docking technique was used to screen compounds that can form an interaction similar to the co-crystalized ligand (1VX). Based on MM-GBSA, docking score, and interactions, ten compounds were selected for ADMET profiling and IFD. After IFD, five compounds (66867225, 46148111, 12444939, 123892179, & 94817569) were selected for molecular dynamics simulation (MDS). Protein-ligand complex stability was assessed during MDS. The selected compounds (66867225, 46148111, 12444939, 123892179, & 94817569) complex with KEAP1 protein shows good stability and bond retentions. In our study, we observed that the selected compounds show good interaction, PCA, Rg, binding free energy, and ADMET profile. We can conclude that the selected compounds can act as NRF2 activators, which should be validated using proper in-vivo/in-vitro models.

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review

BACKGROUND

In the search for better anticancer drugs, computer-aided drug design (CADD) techniques play an indispensable role in facilitating the lengthy and costly drug discovery process especially when natural products are involved. Anthraquinone is one of the most widely-recognized natural products with anticancer properties. This review aimed to systematically assess and synthesize evidence on the utilization of CADD techniques centered on the anthraquinone scaffold for cancer treatment.

METHODS

The conduct and reporting of this review were done in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guideline. The protocol was registered in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904) and also published recently. The search strategy was designed based on the combination of concept 1 "CADD or virtual screening", concept 2 "anthraquinone" and concept 3 "cancer". The search was executed in PubMed, Scopus, Web of Science and MedRxiv on 30 June 2023.

RESULTS

Databases searching retrieved a total of 317 records. After deduplication and applying the eligibility criteria, the final review ended up with 32 articles in which 3 articles were found by citation searching. The CADD methods used in the studies were either structure-based alone (69%) or combined with ligand-based methods via parallel (9%) or sequential (22%) approaches. Molecular docking was performed in all studies, with Glide and AutoDock being the most popular commercial and public software used respectively. Protein data bank was used in most studies to retrieve the crystal structure of the targets of interest while the main ligand databases were PubChem and Zinc. The utilization of in-silico techniques has enabled a deeper dive into the structural, biological and pharmacological properties of anthraquinone derivatives, revealing their remarkable anticancer properties in an all-rounded fashion.

CONCLUSION

By harnessing the power of computational tools and leveraging the natural diversity of anthraquinone compounds, researchers can expedite the development of better drugs to address the unmet medical needs in cancer treatment by improving the treatment outcome for cancer patients.

Astaxanthin targets IL-6 and alleviates the LPS-induced adverse inflammatory response of macrophages

Numerous natural compounds are recognized for their anti-inflammatory properties attributed to antioxidant effects and the modulation of key inflammatory factors. Among them, astaxanthin (AST), a potent carotenoid antioxidant, remains relatively underexplored regarding its anti-inflammatory mechanisms and specific molecular targets. In this study, human monocytic leukemia cell-derived macrophages (THP-1) were selected as experimental cells, and lipopolysaccharides (LPS) served as inflammatory stimuli. Upon LPS treatment, the oxidative stress was significantly increased, accompanied by remarkable cellular damage. Moreover, LPSs escalated the expression of inflammation-related molecules. Our results demonstrate that AST intervention could effectively alleviate LPS-induced oxidative stress, facilitate cellular repair, and significantly attenuate inflammation. Further exploration of the anti-inflammatory mechanism revealed AST could substantially inhibit NF-κB translocation and activation, and mitigate inflammatory factor production by hindering NF-κB through the antioxidant mechanism. We further confirmed that AST exhibited protective effects against cell damage and reduced the injury from inflammatory cytokines by activating p53 and inhibiting STAT3. In addition, utilizing network pharmacology and in silico calculations based on molecular docking, molecular dynamics simulation, we identified interleukin-6 (IL-6) as a prominent core target of AST anti-inflammation, which was further validated by the RNA interference experiment. This IL-6 binding capacity actually enabled AST to curb the positive feedback loop of inflammatory factors, averting the onset of possible inflammatory storms. Therefore, this study offers a new possibility for the application and development of astaxanthin as a popular dietary supplement of anti-inflammatory or immunomodulatory function.

Computational investigation of turmeric phytochemicals targeting PTR1 enzyme of Leishmania species

In this study, we used in silico techniques to identify available parasite treatments, representing a promising therapeutic avenue. Building upon our computational initiatives aimed at discovering natural inhibitors for various target enzymes from parasites causing neglected tropical diseases (NTDs), we present novel findings on three turmeric-derived phytochemicals as inhibitors of Leishmania pteridine reductase I (PTR1) through in silico methodologies. PTR1, a crucial enzyme in the unique folate metabolism of trypanosomatid parasites, holds established therapeutic significance. Employing MOE software, a molecular docking analysis assesses the efficacy of turmeric phytochemicals against Leishmania PTR1. Validation of the docking protocol is confirmed with an RMSD value of 2. Post-docking, compounds displaying notable interactions with critical residues and binding affinities ranging between -6 and -8 kcal/mol are selected for interaction pattern exploration. Testing twelve turmeric phytochemicals, including curcumin, zingiberene, curcumol, curcumenol, eugenol, bisdemethoxycurcumin, tetrahydrocurcumin, tryethylcurcumin, turmerones, turmerin, demethoxycurcumin, and turmeronols, revealed binding affinities ranging from -5.5 to -8 kcal/mol. Notably, curcumin, demethoxycurcumin, and bisdemethoxycurcumin exhibit binding affinities within -6.5 to -8 kcal/mol and establish substantial interactions with catalytic residues. These phytochemicals hold promise as lead structures for rational drug design targeting Leishmania spp. PTR in future applications. This work underscores the potential of these identified phytochemicals in the development of more effective inhibitors, demonstrating their relevance in addressing neglected tropical diseases caused by parasites.

EIF4A3 targeted therapeutic intervention in glioblastoma multiforme using phytochemicals from Indian medicinal plants - an integration of phytotherapy into precision onco-medicine

Glioblastoma Multiforme (GBM), an aggressive brain tumor (grade-IV astrocytoma), poses treatment challenges. Poor prognosis results from the rapid growth, highlighting the role of EIF4A3 in regulating non-coding RNAs. EIF4A3 promotes the expression of several non-coding RNAs, viz, Circ matrix metallopeptidase 9 (MMP9), a prominent oncogene, by interacting with the upstream region of the circMMP9 mRNA transcript and acts on cell proliferation, migration, and invasion of GBM. However, research shows that EIF4A3 knockdown inhibits glioblastoma progression and increases apoptosis. In this study, we explored the efficiency of the phytochemicals from plants like Withania somnifera and Castanea sativa with potential anti-glioblastoma effects as obtained from the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database. Consequently, we have performed a virtual screening of the compounds against the protein EIF4A3. We further investigated the efficiency of the shortlisted compounds based on docking scores evaluated using GOLD, AutoDock4.2, LeDock, and binding free energy analyses using Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA). Among the phytochemicals studied so far, several Withania-specific compounds from Withania somnifera and a single dietary compound, viz., Thiamine from Castanea sativa, have exhibited comparatively good blood-brain barrier permeability, significant binding affinity towards the EIF4A3, and good ADMET properties. Furthermore, we have verified the interaction stability of the lead molecules with EIF4A3 using MD simulations. Thus, the present study offers an opportunity to develop drug candidates targeting glioblastoma caused by EIF4A3 over-expression, integrating phytotherapy into precision oncology to create tailored and precise natural treatment strategies for cancer.

Exploring the mechanism of metformin action in Alzheimer's disease and type 2 diabetes based on network pharmacology, molecular docking, and molecular dynamic simulation

Background

Metformin, which has been shown to be highly effective in treating type 2 diabetes (T2D), is also believed to be valuable for Alzheimer's disease (AD). Computer simulation techniques have emerged as an innovative approach to explore mechanisms.

Objective

To study the potential mechanism of metformin action in AD and T2D.

Methods

The chemical structure of metformin was obtained from PubChem. The targets of metformin were obtained from PubChem, Pharm Mapper, Batman, SwissTargetPrediction, DrugBank, and PubMed. The pathogenic genes of AD and T2D were retrieved from the GeneCards, OMIM, TTD, Drugbank, PharmGKB, and DisGeNET. The intersection of metformin with the targets of AD and T2D is represented by a Venn diagram. The protein-protein interaction (PPI) and core targets networks of intersected targets were constructed by Cytoscape 3.7.1. The enrichment information of GO and Kyoto Encyclopedia of Gene and Genomics (KEGG) pathways obtained by the Metascape was made into a bar chart and a bubble diagram. AutoDockTools, Pymol, and Chem3D were used for the molecular docking. Gromacs software was used to perform molecular dynamics (MD) simulation of the best binding target protein.

Results

A total of 115 key targets of metformin for AD and T2D were obtained. GO analysis showed that biological process mainly involved response to hormones and the regulation of ion transport. Cellular component was enriched in the cell body and axon. Molecular function mainly involved kinase binding and signal receptor regulator activity. The KEGG pathway was mainly enriched in pathways of cancer, neurodegeneration, and endocrine resistance. Core targets mainly included TP53, TNF, VEGFA, HIF1A, IL1B, IGF1, ESR1, SIRT1, CAT, and CXCL8. The molecular docking results showed best binding of metformin to CAT. MD simulation further indicated that the CAT-metformin complex could bind well and converge relatively stable at 30 ns.

Conclusion

Metformin exerts its effects on regulating oxidative stress, gluconeogenesis and inflammation, which may be the mechanism of action of metformin to improve the common pathological features of T2D and AD.

In Silico Exploration of Microtubule Agent Griseofulvin and Its Derivatives Interactions with Different Human β-Tubulin Isotypes

Tubulin isotypes are known to regulate microtubule stability and dynamics, as well as to play a role in the development of resistance to microtubule-targeted cancer drugs. Griseofulvin is known to disrupt cell microtubule dynamics and cause cell death in cancer cells through binding to tubulin protein at the taxol site. However, the detailed binding mode involved molecular interactions, and binding affinities with different human β-tubulin isotypes are not well understood. Here, the binding affinities of human β-tubulin isotypes with griseofulvin and its derivatives were investigated using molecular docking, molecular dynamics simulation, and binding energy calculations. Multiple sequence analysis shows that the amino acid sequences are different in the griseofulvin binding pocket of βI isotypes. However, no differences were observed at the griseofulvin binding pocket of other β-tubulin isotypes. Our molecular docking results show the favorable interaction and significant affinity of griseofulvin and its derivatives toward human β-tubulin isotypes. Further, molecular dynamics simulation results show the structural stability of most β-tubulin isotypes upon binding to the G1 derivative. Taxol is an effective drug in breast cancer, but resistance to it is known. Modern anticancer treatments use a combination of multiple drugs to alleviate the problem of cancer cells resistance to chemotherapy. Our study provides a significant understanding of the involved molecular interactions of griseofulvin and its derivatives with β-tubulin isotypes, which may help to design potent griseofulvin analogues for specific tubulin isotypes in multidrug-resistance cancer cells in future.

Identification of inhibitors as drug candidates against Chagas disease

Chagas disease, after more than a century after its discovery, is still a major public health problem. It is estimated that approximately 10 million people worldwide are infected with T. cruzi. However, the situation is more critical in Latin America and other regions where the disease is endemic. The largest number of cases occurs in Brazil, Argentina, and Mexico as more than 100 million people in these regions are located in areas with a high risk of contamination by the vector. The need for new therapeutic alternatives is urgent, as the available drugs have severe limitations such as low efficacy and high toxicity. From this scenario, in this work, we employed the virtual screening technique using cruzain and BDF2 as key biological targets for the survival of the parasite. Our objective was to identify potential inhibitors of T. cruzi trypomastigotes, which could be considered drug candidates against Chagas disease. For this, we employed different in silico methodologies and the obtained results were corroborated using in vitro biological assays. For the VS studies, a database containing synthetic compounds was simulated at the binding site of cruzain and BDF2. In addition, pharmacophoric models were constructed in the initial phases of VS, as well as other advanced analyses (molecular dynamics simulations, calculations of binding free energy, and ADME prediction) were carried out and the results allowed the selection of potential inhibitors of T. cruzi. Based on the obtained data, 32 different compounds commercially available were subjected to biological tests against the trypomastigote form of T. cruzi. As result, 11 of those compounds displayed significant activity against T. cruzi and can be considered potential candidates for the treatment of Chagas disease.

Kaempferol has potential anti-coronavirus disease 2019 (COVID-19) targets based on bioinformatics analyses and pharmacological effects on endotoxin-induced cytokine storm

COVID-19 has infected 272 million patients and caused 5.33 million deaths around the world, and it remains the main global threat. Previous studies revealed that Chinese traditional medicine is an effective treatment for COVID-19 infection. This study aims to reveal the pharmacological effects of kaempferol, which is the active component of Radix Bupleuri and Tripterygii Radix, and potential mechanisms for the treatment of COVID-19. Here, we employed the bioinformatics methods to filter the anti-COVID-19 candidate genes of kaempferol, which mainly enriched in inflammation (TNF, JUN, etc.) and virus infection (AKT1, JNK, etc.). The Transcription levels of AKT1, JNK and JUN were significantly reduced by kaempferol treatment in the LPS-activated macrophages. In addition, kaempferol reduced the secretion of inflammatory factors by LPS-stimulated macrophages, inhibited MAPK/NF-κB signaling and regulated macrophage polarization to M2 type in vitro, and suppressed endotoxin-induced cytokine storm and improved survival in mice. Molecular docking analysis demonstrated that kaempferol was probable to bind the COVID-19 protein 5R84 and formatted hydrogen bond with the residues, the free binding energy of which was lower than the original ligand. In summary, our current work indicates that kaempferol has anti-COVID-19 potential through the reduction of COVID-19-induced body dysfunction and molecule-protein interaction, and bioinformatics results clarify that some of these key target genes might serve as potential molecular markers for detecting COVID-19.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Docking-Based Prediction of Peptide Binding to MHC Proteins

Major histocompatibility complex (MHC) proteins are the most polymorphic and polygenic proteins in humans. They bind peptides, derived from cleavage of different pathogenic antigens, and are responsible for presenting them to T cells. The peptides recognized by the T cell receptors are denoted as epitopes and they trigger an immune response.In this chapter, we describe a docking protocol for predicting the peptide binding to a given MHC protein using the software tool GOLD. The protocol starts with the construction of a combinatorial peptide library used in the docking and ends with the derivation of a quantitative matrix (QM) accounting for the contribution of each amino acid at each peptide position.

Discovery of Mcl-1 inhibitors through virtual screening, molecular dynamics simulations and in vitro experiments

As a member of the B-cell lymphoma 2 (Bcl-2) protein family, the myeloid leukemia cell differentiation protein (Mcl-1) can inhibit apoptosis and plays an active role in the process of tumor escape from apoptosis. Therefore, inhibition of Mcl-1 protein can effectively promote the apoptosis of tumor cells and may also reduce tumor cell resistance to drugs targeting other anti-apoptotic proteins. This research is dedicated to the development of Mcl-1 inhibitors, aiming to provide more references for lead compounds with different scaffolds for the development of targeted anticancer drugs. We obtained a series of small molecules with a common core skeleton through molecular docking from Specs database and searched the core structure in ZINC database for more similar small molecules. Collecting these small molecules for preliminary experimental screening, we found a batch of active compounds, and selected two small molecules with the strongest inhibitory activity on B16F10 cells: compound 7 and compound 1. Their IC₅₀s are 7.86 ± 1.25 and 24.72 ± 1.94 μM, respectively. These two compounds were also put into cell scratch test for B16F10 cells and cell viability assay of other cell lines. Furthermore, through molecular dynamics (MD) simulation analysis, we found that compound 7 formed strong binding with the key P2, P3 pocket and ARG 263 of Mcl-1. Finally, ADME results showed that compound 7 performs well in terms of drug similarity. In conclusion, this study provides hits with co-scaffolds that may aid in the design of effective clinical drugs targeting Mcl-1 and the future drug development.

In Silico Structure-Based Vaccine Design

Structure-based vaccine design (SBVD) is an important technique in computational vaccine design that uses structural information on a targeted protein to design novel vaccine candidates. This increasing ability to rapidly model structural information on proteins and antibodies has provided the scientific community with many new vaccine targets and novel opportunities for future vaccine discovery. This chapter provides a comprehensive overview of the status of in silico SBVD and discusses the current challenges and limitations. Key strategies in the field of SBVD are exemplified by a case study on design of COVID-19 vaccines targeting SARS-CoV-2 spike protein.

Preparing a database of corrected protein structures important in cell signaling pathways

Background and purpose

Precise structures of macromolecules are important for structure-based drug design. Due to the limited resolution of some structures obtained from X-ray diffraction crystallography, differentiation between the NH and O atoms can be difficult. Sometimes a number of amino acids are missing from the protein structure. In this research, we intend to introduce a small database that we have prepared for providing the corrected 3D structure files of proteins frequently used in structure-based drug design protocols.

Experimental approach

3454 soluble proteins belonging to the cancer signaling pathways were collected from the PDB database from which a dataset of 1001 was obtained. All were subjected to corrections in the protein preparation step. 896 protein structures out of 1001 were corrected successfully and the decision on the remained 105 proposed twelve for homology modeling to correct the missing residues. Three of them were subjected to molecular dynamics simulation for 30 ns.

Findings / Results

896 corrected proteins were perfect and homology modeling on 12 proteins with missing residues in the backbone resulted in acceptable models according to Ramachandran, z-score, and DOPE energy plots. RMSD, RMSF, and Rg values verified the stability of the models after 30 ns molecular dynamics simulation.

Conclusion and implication

A collection of 1001 proteins were modified for some defects such as adjustment of the bond orders and formal charges, and addition of missing side chains of residues. Homology modeling corrected the amino missing backbone residues. This database will be completed for quite a lot of water-soluble proteins to be uploaded to the internet.

A novel glycosylated indolocarbazole derivative LCS1269 effectively inhibits growth of human cancer cells in vitro and in vivo through driving of both apoptosis and senescence by inducing of DNA damage and modulating of AKT/mTOR/S6K and ERK pathways

In recent decades, indolocarbazole glycosides containing sugar moieties have attracted attention due to their diverse anti-tumor activities. In the present study, a series of new indolo [2,3-a]pyrrolo [3,4-c]carbazole derivatives were synthesized for the first time. First of all, we have shown that compound 6e (LCS1269) had the most pronounced effect on inhibiting tumor growth in the transferable solid and non-solid murine tumors as compared with other synthesized indolocarbazole derivatives. The results of the in vivo nude mice xenoraft study also confirmed that LCS1269 treatment strongly suppressed the growth of human colon cancer SW620 xenografts. It is important to note that the antiproliferative activity of LCS1269 against three human cancer cell lines (MCF-7, HCT-116 and A549) was considerably higher than that against the non-tumor cell lines (immortalized breast cells and normal embryonic fibroblasts). Furthermore, the treatment of MCF-7, HCT-116 and A549 cells with LCS1269 caused the statistically significant inhibition of anchorage-dependent and anchorage-independent colony formation. We further revealed that LCS1269 treatment of investigated human cancer cells resulted in the DNA damage and G2/M cell cycle arrest followed by the decrease of mitochondrial membrane potential with subsequent initiation of intrinsic apoptosis and the triggering of senescence via p53-dependent mechanisms. In addition, our western blotting findings and molecular docking data suppose that LCS1269 could at least partially attenuate cancer cells growth by modulation of AKT/mTOR/S6K and ERK signaling pathways. Therefore, we concluded that LCS1269 might be the promising compound for implementation and probable use in the clinical practice.

Disease-Ligand Identification Based on Flexible Neural Tree

In order to screen the disease-related compounds of a traditional Chinese medicine prescription in network pharmacology research accurately, a new virtual screening method based on flexible neural tree (FNT) model, hybrid evolutionary method and negative sample selection algorithm is proposed. A novel hybrid evolutionary algorithm based on the Grammar-guided genetic programming and salp swarm algorithm is proposed to infer the optimal FNT. According to hypertension, diabetes, and Corona Virus Disease 2019, disease-related compounds are collected from the up-to-date literatures. The unrelated compounds are chosen by negative sample selection algorithm. ECFP6, MACCS, Macrocycle, and RDKit are utilized to numerically characterize the chemical structure of each compound collected, respectively. The experiment results show that our proposed method performs better than classical classifiers [Support Vector Machine (SVM), random forest (RF), AdaBoost, decision tree (DT), Gradient Boosting Decision Tree (GBDT), KNN, logic regression (LR), and Naive Bayes (NB)], up-to-date classifier (gcForest), and deep learning method (forgeNet) in terms of AUC, ROC, TPR, FPR, Precision, Specificity, and F1. MACCS method is suitable for the maximum number of classifiers. All methods perform poorly with ECFP6 molecular descriptor.

Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling

Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.

Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA

The recent availability of large numbers of GPCR crystal structures has provided an unprecedented opportunity to evaluate their performance in virtual screening protocols using established benchmarking datasets. In this study, we evaluated the ability of MM/GBSA in consensus scoring-based virtual screening enrichment together with nine classical scoring functions, using the GPCR-Bench dataset consisting of 24 GPCR crystal structures and 254,646 actives and decoys. While the performance of consensus scoring was modest overall, combinations which included MM/GBSA performed relatively well compared to combinations of classical scoring functions. Combinations of MM/GBSA and good-performing scoring functions provided the highest proportion of improvements, with improvements observed in 32% and 19% of all combinations across all targets at the EF_1% and EF_5% levels respectively. Combinations of MM/GBSA and poor-performing scoring functions still outperformed classical scoring functions, with improvements observed in 26% and 17% of all combinations at the EF_1% and EF_5% levels. In comparison, only 14-22% and 6-11% of combinations of classical scoring functions produced improvements at EF_1% and EF_5% respectively. Efforts to improve performance by increasing the number of scoring functions in consensus scoring to three were mostly ineffective. We also observed that consensus scoring performed better for individual scoring functions possessing initially low enrichment factors, potentially implying their benefits are more relevant in such scenarios. Overall, this study demonstrated the first implementation of MM/GBSA in consensus scoring using the GPCR-Bench dataset and could provide a valuable benchmark of the performance of MM/GBSA in comparison to classical scoring functions in consensus scoring for GPCRs.

One-Pot Synthesis and in Silico Molecular Docking Studies of Arylselanyl Hydrazides as Potential Antituberculosis Agents

The present study reports a simple two-step method for the synthesis of arylselanyl hydrazide derivatives using hypophosphorous acid and polyethylene glycol (H₃ PO₂ /PEG-400) as an alternative reducing system and hydrazine hydrate (NH₂ NH₂ ⋅xH₂ O/50-60 %). This single-vessel procedure was employed with methyl acrylate 2a and methyl bromoacetate 2b using diaryl diselenides to generate the nucleophile species to produce, respectively, 3-(arylselanyl)propane-hydrazides 4a-e and 2-(arylselanyl)acetohydrazides 5a-e with good yields by accelerating the reduction of -Se-Se- bond, when compared to available methods. The synthesized molecules are structurally similar to the isoniazid (INH). Therefore, we perform in silico molecular docking studies, using the lactoperoxidase enzyme, in order to verify whether the INH Se derivatives could interact in a similar way to INH at the active site of the mammalian enzyme. The in silico results indicated a similar type of interaction of the arylselanyl hydrazide derivatives with that of INH. In view of the similar in silico interaction of the selenium derivatives of INH, the arylselanyl hydrazide derivatives reported here should be tested against Mycobacterium tuberculosis in vitro.

Structure Prediction and Potential Inhibitors Docking of Enterovirus 2C Proteins

Human enterovirus infections are mostly asymptomatic and occasionally could be severe and life-threatening. The conserved non-structural 2C from enteroviruses protein is a promising target in antiviral therapies against human enteroviruses. Understanding of 2C-drug interactions is crucial for developing the potential antiviral agents. While functions of enterovirus 2C proteins have been widely studied, three-dimensional structure information of 2C is limited. In this study, the structures of 2C proteins from 20 enteroviruses were simulated and reconstructed using I-TASSER programs. Subsequent docking studies of the known 22 antiviral inhibitors for 2C proteins were performed to uncover the inhibitor-binding characteristics of 2C. Among the potential inhibitors, the compound hydantoin exhibited the highest broad-spectrum antiviral activities with binding to 2C protein. The anti-enteroviral activity of GuaHCL, compound 19b, R523062, compound 12a, compound 12b, quinoline analogs 12a, compound 19d, N6-benzyladenosine, dibucaine derivatives 6i, TBZE-029, fluoxetine analogs 2b, dibucaine, 2-(α-hydroxybenzyl)-benzimidazole (HBB), metrifudil, pirlindole, MRL-1237, quinoline analogs 10a, zuclopenthixol, fluoxetine, fluoxetine HCl, and quinoline analogs 12c showed a trend of gradual decrease. In addition, the free energy with 22 compounds binding to EV 2C ranged from -0.35 to -88.18 kcal/mol. Our in silico studies will provide important information for the development of pan-enterovirus antiviral agents based on 2C.

A Set of Experimentally Validated Decoys for the Human CC Chemokine Receptor 7 (CCR7) Obtained by Virtual Screening

We present a state-of-the-art virtual screening workflow aiming at the identification of novel CC chemokine receptor 7 (CCR7) antagonists. Although CCR7 is associated with a variety of human diseases, such as immunological disorders, inflammatory diseases, and cancer, this target is underexplored in drug discovery and there are no potent and selective CCR7 small molecule antagonists available today. Therefore, computer-aided ligand-based, structure-based, and joint virtual screening campaigns were performed. Hits from these virtual screenings were tested in a CCL19-induced calcium signaling assay. After careful evaluation, none of the in silico hits were confirmed to have an antagonistic effect on CCR7. Hence, we report here a valuable set of 287 inactive compounds that can be used as experimentally validated decoys.

In Silico Pesticide Discovery for New Anti-Tobacco Mosaic Virus Agents: Reactivity, Molecular Docking, and Molecular Dynamics Simulations

Considerable data are available regarding the molecular genetics of the tobacco mosaic virus. The disease caused by the tobacco mosaic virus is still out of control due to the lack of an efficient functional antagonist chemical molecule. Extensive research was carried out to try to find effective new anti-tobacco mosaic virus agents, however no study could find an effective agent which could completely inhibit the disease caused by the virus. In recent years, molecular docking, combined with molecular dynamics, which is considered to be one of the most important methods of drug discovery and design, were used to evaluate the type of binding between the ligand and its protein enzyme. The aim of the current work was to assess the in silico anti-tobacco mosaic virus activity for a selection of 41 new and 2 reference standard compounds. These compounds were chosen to examine their reactivity and binding efficiency with the tobacco mosaic virus coat protein (PDB ID: 2OM3). A comparison was made between the activity of the selected compounds and that for ningnanmycin and ribavirin, which are common inhibitors of plant viruses. The simulation results obtained from the molecular docking and molecular dynamics showed that two compounds of the antofine analogues could bind with the tobacco mosaic virus coat protein receptor better than ningnanmycin and ribavirin.

Application of molecular docking in elaborating molecular mechanisms and interactions of supramolecular cyclodextrin

The cyclodextrin (CD)-based supramolecular nanomedicines have attracted growing interest because of their superior characteristics, including desirable biocompatibility, low toxicity, unique molecular structure and easy functionalization. The smart structures of CD impart host-guest interaction for meeting the multifunctional needs of disease therapy. However, it faces challenges in formulation design and inclusion mechanism clarification of the functional supramolecular assemblies owing to the complicated structures and mechanisms. Fortunately, molecular docking helps the researchers to comprehend the interaction between the drug and the target molecule for achieving high-through screening from the database. In this review, we summarized the category and characteristics of molecular docking along with the properties and applications of CD. Significantly, we highlighted the application of molecular docking in elaborating molecular mechanisms and simulating complex structures at molecular levels. The issues and development of CD and molecular docking were also presented to provide beneficial reference and new insights for supramolecular nano-systems.

Computational Approaches for Supporting Combination Therapy in the Post-Aducanumab Era in Alzheimer’s Disease

With the approval of aducanumab on the “Accelerated Approval Pathway” and the recognition of amyloid load as a surrogate marker, new successful therapeutic approaches will be driven by combination therapy as was the case in oncology after the launch of immune checkpoint inhibitors. However, the sheer number of therapeutic combinations substantially complicates the search for optimal combinations. Data-driven approaches based on large databases or electronic health records can identify optimal combinations and often using artificial intelligence or machine learning to crunch through many possible combinations but are limited to the pharmacology of existing marketed drugs and are highly dependent upon the quality of the training sets. Knowledge-driven in silico modeling approaches use multi-scale biophysically realistic models of neuroanatomy, physiology, and pathology and can be personalized with individual patient comedications, disease state, and genotypes to create ‘virtual twin patients’. Such models simulate effects on action potential dynamics of anatomically informed neuronal circuits driving functional clinical readouts. Informed by data-driven approaches this knowledge-driven modeling could systematically and quantitatively simulate all possible target combinations for a maximal synergistic effect on a clinically relevant functional outcome. This approach seamlessly integrates pharmacokinetic modeling of different therapeutic modalities. A crucial requirement to constrain the parameters is the access to preferably anonymized individual patient data from completed clinical trials with various selective compounds. We believe that the combination of data- and knowledge driven modeling could be a game changer to find a cure for this devastating disease that affects the most complex organ of the universe.

Molecular docking, molecular dynamics simulations and reactivity, studies on approved drugs library targeting ACE2 and SARS-CoV-2 binding with ACE2

The recent new contagion coronavirus 2019 (COVID-19) disease is a new generation of severe acute respiratory syndrome coronavirus-2 SARS-CoV-2 which infected millions confirmed cases and hundreds of thousands death cases around the world so far. Molecular docking combined with molecular dynamics is one of the most important tools of drug discovery and drug design, which it used to examine the type of binding between the ligand and its protein enzyme. Global reactivity has important properties, which enable chemists to understand the chemical reactivity and kinetic stability of compounds. In this study, molecular docking and reactivity were applied for eighteen drugs, which are similar in structure to chloroquine and hydroxychloroquine, the potential inhibitors to angiotensin-converting enzyme (ACE2). Those drugs were selected from DrugBank. The reactivity, molecular docking and molecular dynamics were performed for two receptors ACE2 and [SARS-CoV-2/ACE2] complex receptor in two active sites to find a ligand, which may inhibit COVID-19. The results obtained from this study showed that Ramipril, Delapril and Lisinopril could bind with ACE2 receptor and [SARS-CoV-2/ACE2] complex better than chloroquine and hydroxychloroquine. This new understanding should help to improve predictions of the impact of such alternatives on COVID-19.Communicated by Ramaswamy H. Sarma.

Identification of novel topoisomerase II alpha inhibitors by virtual screening, molecular docking, and bioassay

Breast cancer is one of the most common tumors, and its treatment still leaves room for improvement. Topoisomerase II alpha is a potential target for the treatment of human diseases such as breast cancer. In this article, we attempted to discover a novel anticancer drug. We have used the topoisomerase II alpha protein-Homo sapiens (Human) to hierarchically screen the Maybridge database. Based on their docking score, the top hit compounds have been assayed for inhibition in a topoisomerase II pBR322 DNA relaxation assay in vitro. Candidate compound 6 (CP6) was found to have the best inhibitory effect for topoisomerase II among the 20 tested compounds. In addition, CP6 had potent cytotoxicity against eight tested tumor cell lines. At the same time, CP6 was shown to have potential anti-multidrug resistance capabilities. This study identifies CP6, which can contribute to the development of new topoisomerase II inhibitors as anticancer agents.

Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis

Mycobacterium tuberculosis is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are urgently required to combat this growing health emergency. Alongside this, increased knowledge of gene essentiality in the pathogenic organism and larger compound databases can aid in the discovery of new drug compounds. The number of protein structures, X-ray based and modelled, is increasing and now accounts for greater than > 80% of all predicted M. tuberculosis proteins; allowing novel targets to be investigated. This review will focus on structure-based in silico approaches for drug discovery, covering a range of complexities and computational demands, with associated antimycobacterial examples. This includes molecular docking, molecular dynamic simulations, ensemble docking and free energy calculations. Applications of machine learning onto each of these approaches will be discussed. The need for experimental validation of computational hits is an essential component, which is unfortunately missing from many current studies. The future outlooks of these approaches will also be discussed.

Overview of Viral Pneumonia Associated With Influenza Virus, Respiratory Syncytial Virus, and Coronavirus, and Therapeutics Based on Natural Products of Medicinal Plants

Viral pneumonia has been a serious threat to global health, especially now we have dramatic challenges such as the COVID-19 pandemic. Approximately six million cases of community-acquired pneumonia occur every year, and over 20% of which need hospital admission. Influenza virus, respiratory virus, and coronavirus are the noteworthy causative agents to be investigated based on recent clinical research. Currently, anaphylactic reaction and inflammation induced by antiviral immunity can be incriminated as causative factors for clinicopathological symptoms of viral pneumonia. In this article, we illustrate the structure and related infection mechanisms of these viruses and the current status of antiviral therapies. Owing to a set of antiviral regiments with unsatisfactory clinical effects resulting from side effects, genetic mutation, and growing incidence of resistance, much attention has been paid on medicinal plants as a natural source of antiviral agents. Previous research mainly referred to herbal medicines and plant extracts with curative effects on viral infection models of influenza virus, respiratory virus, and coronavirus. This review summarizes the results of antiviral activities of various medicinal plants and their isolated substances, exclusively focusing on natural products for the treatment of the three types of pathogens that elicit pneumonia. Furthermore, we have introduced several useful screening tools to develop antiviral lead compounds.

Characterization and structure-activity relationship of novel umami peptides isolated from Thai fish sauce

Fish sauce has a prominent umami flavor. In this study, umami peptides were isolated, purified and identified from Thai fish sauce, and their structure-activity relationships were analyzed. Six novel umami peptides were characterized and verified by using sensory evaluation and a electronic tongue. Molecular docking with T1R1/T1R3 receptors showed that the interaction forces were mainly hydrogen bonds, electrostatic interaction and van der Waals force. In the constructed three dimensional quantitative structure-activity relationship model (3D-QSAR) model, the regression coefficient (R2) for the degree of dispersion between the predicted molecular and the experimental values of the six peptides was 0.976. The association between the structure and activity of umami peptides was revealed through 3D-QSAR. Results showed that the spatial effect was significant for long chain peptides.

Computational Drug Repurposing Resources and Approaches for Discovering Novel Antifungal Drugs against Candida albicans N-Myristoyl Transferase

Candida albicans is a yeast that is an opportunistic fungal pathogen and also identified as ubiquitous polymorphic species that is mainly linked with major fungal infections in humans, particularly in the immunocompromised patients including transplant recipients, chemotherapy patients, HIV-infected patients as well as in low-birth-weight infants. Systemic Candida infections have a high mortality rate of around 29 to 76%. For reducing its infection, limited drugs are existing such as caspofungin, fluconazole, terbinafine, and amphotericin B, etc. which contain unlikable side effects and also toxic. This review intends to utilize advanced bioinformatics technologies such as Molecular docking, Scaffold hopping, Virtual screening, Pharmacophore modeling, Molecular dynamics (MD) simulation for the development of potentially new drug candidates with a drug-repurpose approach against Candida albicans within a limited time frame and also cost reductive.

Identification, taste characterization and molecular docking study of novel umami peptides from the Chinese anchovy sauce

BACKGROUND

Fish sauce has a subtle flavor with prominent umami and salty taste, and is accompanied by a certain sweetness and bitterness. In order to identify a wider range of umami peptides, Chinese southern and northern anchovy sauce were selected for the study.

RESULTS

Seventeen peptides were obtained by separation and purification, and their taste activity was predicted. Through the taste characterization and descriptive analysis, it was found that the synthesized peptides were umami and umami-enhancing peptides. Seventeen umami peptides were simulated and embedded into the umami receptor T1R1/T1R3 by inserting into the Venus flytrap domain (VFTD) of the T1R3 subunit; the interaction forces were mainly hydrogen bonding, electrostatic interaction, van der Waals force and hydrophobic interaction. According to the docking interaction energies, long-chain peptides may be easier to bind to the receptor than short-chain peptides. Asp196, Glu128 and Glu197 were the main binding sites for docking, and could affect umami synergism.

CONCLUSION

For the first time, novel umami peptides in Chinese anchovy sauce have been reported. This study is helpful for discovering umami marine resource peptides, and can provide a basis for further understanding the flavor system of anchovy sauce. © 2020 Society of Chemical Industry.

Investigation of New Inhibitors of UDP-N-Acetylglucosamine Enolpyruvyl Transferase (MurA) by Virtual Screening with Antibacterial Assessment

BACKGROUND

Considering the interesting role in the peptidoglycan biosynthesis pathway, the enzyme UDP-N-acetylglucosamine enolpyruvyl transferase is an attractive target to develop new antibacterial agents. It catalyzes the first key step of this pathway and its inhibition leads to bacterial cell death. Fosfomycin is known as the natural inhibitor of MurA.

OBJECTIVE

The study aimed to introduce new inhibitors of MurA by virtual screening of different chemical compounds libraries, and test the best scored "virtual hits" against three pathogenic bacteria: Escherichia coli, Bacillus subtilis and Staphylococcus aureus.

METHODS

A virtual screening of the structural analogues of fosfomycin downloaded from the Pub- Chem database was performed. Moreover, French National Chemical Library and ZINC database were also utilized to identify new structures different from fosfomycin. FlexX was the software used for this study. The antibacterial testing was divided into two methods: disk diffusion and broth dilution.

RESULTS

A set of virtual hits was found to have better energy score than that of fosfomycin, seven of them were tested in vitro. In addition, the disk diffusion method explored four compounds that exhibited antibacterial activity: CID-21680357 (fosfomycin analogue), AB-00005001, ZINC04658565, and ZINC901335. The testing was continued by broth dilution method for both compounds CID-21680357 and ZINC901335 to determine their minimum inhibitory concentrations, and ZINC901335 had the best value with 457μg/ml against Staphylococcus aureus.

CONCLUSION

Four compounds were found and proven in silico and in vitro to have antibacterial activity, namely CID-21680357, AB-00005001, ZINC04658565, and ZINC901335.

Synthesis, In vitro, and Docking Analysis of C-3 Substituted Coumarin Analogues as Anticancer Agents

BACKGROUND

Breast cancer (BC) is a leading cause of cancer-related deaths in women next to skin cancer. Estrogen receptors (ERs) play an important role in the progression of BC. Current anticancer agents have several drawbacks such as serious side effects and the emergence of resistance to chemotherapeutic drugs. As coumarins possess minimum side effects along with multidrug reversal activity, it has a tremendous ability to regulate a diverse range of cellular pathways that can be explored for selective anticancer activity.

OBJECTIVES

Synthesis and evaluation of new coumarin analogues for anti-proliferative activity on human breast cancer cell line MCF-7 along with exploration of binding interaction of the compounds for ER-α target protein by molecular docking.

METHODS

In this study, the anti-proliferative activity of C-3 substituted coumarins analogues (1-17) has been evaluated against estrogen receptor-positive MCF-7 breast cancer cell lines. Molecular interactions and ADME study of the compounds were analyzed by using Schrodinger software.

RESULTS

Among the synthesized analogues, 12 and 13 show good antiproliferative activity with IC50 values 1 and 1.3 μM, respectively. Molecular docking suggests a remarkable binding pose of all the seventeen compounds. Compounds 12 and 13 were found to exhibit a docking score of -4.10 kcal/mol and -4.38 kcal/mol, respectively.

CONCLUSION

Compounds 12 and 13 showed the highest activity followed by 1 and 5. ADME properties of all compounds were in the acceptable range. The active compounds can be taken for lead optimization and mechanistic interventions for their in vivo study in the future.

Hybrid In Silico and TR-FRET-Guided Discovery of Novel BCL-2 Inhibitors

B-Cell lymphoma 2 (BCL-2) regulates cell death in humans. In this study, combined multiscale in silico approaches and in vitro studies were employed. A small-molecule library that includes more than 210 000 compounds was used. The predicted therapeutic activity value (TAV) of the compounds in this library was computed with the binary cancer quantitative structure-activity relationships (QSAR) model. The molecules with a high calculated TAV were used in 26 individual toxicity QSAR models. As a result of this screening protocol, 288 nontoxic molecules with high predicted TAV were identified. These selected hits were then screened against the BCL-2 target protein using hybrid docking and molecular dynamics (MD) simulations. The interaction energies of identified compounds were compared with two known BCL-2 inhibitors. Then, the short MD simulations were carried out by initiating the best docking poses of 288 molecules. Average MM/GBSA energies were computed, and long MD simulations were employed to selected hits. The same calculations were also applied for two known BCL-2 inhibitors. Moreover, a five-site (AHRRR) structure-based pharmacophore model was constructed, and this model was used in the screening of the same database. On the basis of hybrid data-driven ligand identification study, final hits were selected and used in in vitro studies. Based on results of the time-resolved fluorescence resonance energy transfer (TR-FRET) analysis, further filtration was carried out for the U87-MG cell line tests. MTT cell proliferation assay analysis results showed that selected three potent compounds were significantly effective on glioma cells.

Synthesis, in vitro and computational studies of novel glycosyl-1, 2, 3-1H-triazolyl methyl benzamide derivatives as potential α-glucosidase inhibitory activity

A series of novel glycosyl-1,2,3-1H-triazolyl methyl benzamide analogues were synthesized by the unambiguous strategy and evaluated for α-glucosidase inhibitory activity. Glycosyl benzamide exhibited a dose-dependent inhibition of α-glucosidase activity. The In-vitro α-glucosidase inhibition activity results indicated that all the synthesized triazolyl methyl benzamide compounds (IC₅₀ values ranging from 25.3 ± 0.8 to 118.5 ± 5.3 μM) exhibited more inhibitory activity in comparison with the standard drug acarbose (IC₅₀ = 750.0 ± 12.5 μM). Among all, the 3 deacetylated glycosyl methyl benzamide derivatives (4c, 4d and 4f) showed promising α-glucosidase enzyme inhibitory activities with IC₅₀ value 25.3 ± 0.8, 26.1 ± 1.5 and 30.6 ± 2.1 respectively. Furthermore, these compounds were subjected to molecular docking and molecular dynamics simulation studies. The molecular docking studies were performed between (PDB ID: 3A4A) target protein and these synthesized molecules. The compounds displayed good docking energies in the range of -7.5 to -7.8 Kcal/mol. This work could be used as an initial approach in identifying potential novel molecules with the promising activity of type-2 diabetes mellitus.

Property-Unmatched Decoys in Docking Benchmarks

Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.

Signature-based approaches for informed drug repurposing: targeting CNS disorders

CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.

Screening coronavirus and human proteins for sialic acid binding sites using a docking approach

<abstract> <p>The initial step of interaction of some pathogens with the host is driven by the interaction of glycoproteins of either side <italic>via</italic> endcaps of their glycans. These end caps consist of sialic acids or sugar molecules. Coronaviruses (CoVs), including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are found to use this route of interaction. The strength and spatial interactions on the single molecule level of sialic acids with either the spike (S) protein of SARS coronaviruses, or human angiotensin-converting enzyme 2 (ACE2) and furin are probed and compared to the binding modes of those sugar molecules which are present in glycans of glycoproteins. The protocol of using single molecules is seen as a simplified but effective mimic of the complex mode of interaction of the glycans. Averaged estimated binding energies from a docking approach result in preferential binding of the sialic acids to a specific binding site of the S protein of human coronavirus OC43 (HCoV-OC43). Furin is proposed to provide better binding sites for sialic acids than ACE2, albeit outweighed by sites for other sugar molecules. Absolute minimal estimated binding energies indicate weak binding affinities and are indifferent to the type of sugar molecules and the proteins. Neither the proposed best binding sites of the sialic acids nor those of the sugar molecules overlap with any of the cleavage sites at the S protein and the active sites of the human proteins.</p> </abstract>

In vitro anti-leishmanial activity of Prunus armeniaca fractions on Leishmania tropica and molecular docking studies

Prunus armeniaca (L.) is a member of the Rosaceae, subfamily Prunoideae, shows anticancer, antitubercular, antimutagenic, antimicrobial, antioxidant, and cardioprotective activities. Here we fractionated the leaves extract of this highly medicinally important plant for antileishmanial activity. In the current study, the leaves extract was fractionated and characterized using column and thin layer chromatography by n-hexane, ethyl acetate, and methanol solvents. Twelve fractions were isolated and subjected for evaluation of their cytotoxicity and in vitro antileishmanial activity against promastigotes and amastigotes of Leishmania tropica. Among all fractions used, the fraction (F7) exhibited the strongest antileishmanial activity. The bioactive fraction was further characterized by spectroscopy (FTIR, UV-Vis), and GC-MS analysis. The in silico docking was carried out to find the active site of PTR1. All derived fractions exhibited toxicity in the safety range IC₅₀ > 100 μg/ml. The fraction (F7) showed significantly the highest antipromastigotes activity with IC₅₀11.48 ± 0.82 μg/ml and antiamastigotes activity with IC₅₀ 21.03 ± 0.98 μg/ml compared with control i.e. 11.60 ± 0.70 and 22.03 ± 1.02 μg/ml respectively. The UV-Vis spectroscopic analysis revealed the presence of six absorption peaks and the FTIR spectrum revealed the presence of alkane, aldehyde, carboxylic acid, thiols, alkynes, and carbonyls compounds The GC-MS chromatogram exhibited the presence of nine compounds: (a) benzeneethanol, alpha, beta dimethyl, (b)carbazic acid, 3-(1 propylbutylidene)-, ethyl ester, (c)1, 2-benzenedicarboxylic acid, diisooctyl ester, (d)benzeneethanamine a-methyl, (e)2aminononadecane, (f)2-heptanamine-5-methyl, (g)cyclobutanol, (h)cyclopropyl carbine, and (i)nitric acid, nonyl ester. Among all compounds, the 1, 2-benzenedicarboxylic acid, diisooctyl ester bound well to the PTR1 receptor. Fraction (F7) showed acceptable results with no cytotoxicity. However, in vivo studies are required in the future.