Calculated Binding Energies Research Articles

Integrative Machine Learning, Virtual Screening, and Molecular Modeling for BacA-Targeted Anti-Biofilm Drug Discovery Against Staphylococcal Infections

The rise in antibiotic-resistant Staphylococcal infections necessitates innovative approaches to identify new therapeutic agents. This study investigates the application of machine learning models to identify potential phytochemical inhibitors against BacA, a target related to Staphylococcal infections. Active compounds were retrieved from BindingDB while the decoy was generated from DUDE. The RDKit was utilized for feature engineering. Machine learning models such as k-nearest neighbors (KNN), the support vector machine (SVM), random forest (RF), and naive Bayes (NB) were trained on an initial dataset consisting of 226 active chemicals and 2550 inert compounds. Accompanied by an MCC of 0.93 and an accuracy of 96%, the RF performed better. Utilizing the RF model, a library of 9000 phytochemicals was screened, identifying 300 potentially active compounds, of which 192 exhibited drug-like properties and were further analyzed through molecular docking studies. Molecular docking results identified Ergotamine, Withanolide E, and DOPPA as top inhibitors of the BacA protein, accompanied by interaction affinities of −8.8, −8.1, and −7.9 kcal/mol, respectively. Molecular dynamics (MD) was applied for 100 ns to these top hits to evaluate their stability and dynamic behavior. RMSD, RMSF, SASA, and Rg analyses showed that all complexes remained stable throughout the simulation period. Binding energy calculations using MMGBSA analysis revealed that the BacA_Withanolide E complex exhibited the most favorable binding energy profile with significant van der Waals interactions and a substantial reduction in gas-phase energy. It also revealed that van der Waals interactions contributed significantly to the binding stability of Withanolide E, while electrostatic interactions played a secondary role. The integration of machine learning models with molecular docking and MD simulations proved effective in identifying promising phytochemical inhibitors, with Withanolide E emerging as a potent candidate. These findings provide a pathway for developing new antibacterial agents against Staphylococcal infections, pending further experimental validation and optimization.

Computational Screening of HPLC-Identified Phytochemicals from Corn Silk (Stigma Maydis) as Protein Tyrosine phosphatase 1B (PTP1B) Inhibitors for the Management of Type 2 Diabetes

Protein tyrosine phosphatase 1B (PTP1B) has emerged as a promising therapeutic target for the treatment of the escalating global burden of disease, diabetes, due to its critical role in insulin signaling regulation. Natural products, like those derived from a traditional plant like Corn silk, offer a valuable source of innovative antidiabetic compounds, but their bioactive constituents and molecular mechanisms remain poorly understood. This study aimed at identifying the standard constituents of Corn silk using a High-performance liquid chromatography (HPLC) approach and investigates the binding energy of potential ligands against protein tyrosine phosphatase 1B (PTP1B) target protein using a computational approach in analogy to the standard drug of diabetes. Based on molecular docking, induced-fit docking, calculation of free binding energy, and ADMET screening. 15 compounds were identified, with which most of the ligands displayed significant inhibitory activity with docking scores, ranging from -4.037 to -8.497 kcal/mol when compared with the control drug, metformin, and glyburide, with -4.624 and -0.976 kcal/mol respectively. The five top-scoring compounds showed good hydrophobic communications and hydrogen bonding associations to hinder PTP1B. The ADMET profiles of most of the compounds fell within desirable ranges, and subsequent MM-GBSA post-docking analysis validated the binding stability of selected compounds to the PTP1B protein structure. This study showed that compounds from Corn silk like Maysin, Orientin, Quercetin, Caffeic acid, and Ferulic acid are worth further investigation as promising PTP1B inhibitors as an antidiabetic agent.

An in-silico study reveals that a C-terminal fragment of the adhesion protein Fibulin7 (Fbln7-C) regulates the activation of integrin α5β1 through dynamics of VWA and the hybrid domain in the β1 subunit

A C-terminal fragment of the adhesion protein Fibulin7 (Fbln7-C) binds to monocytes and neutrophils via integrin α5β1, regulating their adhesion and immunological functions through Erk and STAT signaling pathways. It also inhibits cell binding, spreading, and migration on fibronectin. However, the precise structural components of Fbln7-C that interact with various domains of integrin α5β1 and contribute to its regulatory effects are not entirely understood. This study investigated the structural dynamics of Fibulin7 fragments and the mechanisms by which Fbln7-C regulates α5β1 integrin activation using protein modeling, protein-protein docking, molecular dynamics simulation (MDS), and binding free energy calculations. An energy-minimized model of α5β1 integrin, Fibulin7 full length (Fbln7-FL), and Fbln7-C was developed and validated using 100 ns MDS. Additionally, protein-protein docking was used to confirm Fbln7-C’s better integrin binding ability over Fbln7-FL. A 500 ns MDS on the docked Fbln7-C integrin complex revealed the regulatory effects of Fbln7-C on arginine-glycine-aspartic acid (RGD) bound integrin α5β1. The simulation studies showed that Fbln7-C’s attachment to activated α5β1 integrin increased the distance between the RGD and its interacting residues on both integrin subunits, shifting the RGD ligand from its original binding position and inactivating the integrin. Further analysis using free energy landscape (FEL), principal component analysis (PCA), and binding energy calculation validated the alteration in α5β1 integrin’s structural dynamics following Fbln7-C binding. This could relate to obstruction in the outward swing of the integrin’s hybrid domain and result in the low-affinity, inactive headpiece conformation of the α5β1 integrin.

Computational approach towards repurposing of FDA approved drug molecules: strategy to combat antibiotic resistance conferred by Pseudomonas aeruginosa

Antimicrobial resistance is recognized as a major worldwide public health dilemma in the current century. Pseudomonas aeruginosa, a Gram-negative opportunistic pathogen, causes nosocomial infections like respiratory tract infections, urinary tract infections, dermatitis, and cystic fibrosis. It manifests antibiotic resistance via intrinsic, acquired, and adaptive pathways, where efflux pumps function in the extrusion of antibiotics from the cell. MexB protein, part of the tripartite efflux pumps MexAB-OprM present in P.aeruginosa, expels the penems and β-lactam antibiotics, thereby enhancing Pseudomonas resistance. The current study was intended to screen around 1602 clinically approved drugs to understand their ability to inhibit the MexB protein. Amongst them, the top 5 drug molecules were selected based on the binding energies for analyzing their physio-chemical and toxicity properties. Lomitapide was found to have the maximum negative binding energy followed by Nilotinib, whereas Nilotinib’s number of hydrogen bonds was higher than that of Lomitapide. ADMET study revealed that all 5 drug molecules had limited solubility. Also, Lomitapide and Venetoclax showed low bioavailability scores, while Nilotinib, Eltrombopag, and Conivaptan demonstrated higher potential for therapeutic levels. A molecular dynamic simulation study of the 5 drugs against MexB was carried out for 200 nanoseconds. The RMSD, RMSF, Hydrogen bond formation, Radius of gyration, SASA, PCA, DCCM, DSSP and MM-PBSA binding energy calculation along with demonstrated high stability of the MexB-Nilotinib complex with lesser distortions. Our study concludes, that Nilotinib is a potential inhibitor and can be developed as a therapeutic agent against MexB protein for controlling P. aeruginosa infections.

Molecular Docking of Endolysins for Studying Peptidoglycan Binding Mechanism.

Endolysins of bacteriophages, which degrade the bacterial cell wall peptidoglycan, are applicable in many industries to deal with biofilms and bacterial infections. While multi-domain endolysins have both enzymatically active and cell wall-binding domains, single-domain endolysins consist only of an enzymatically active domain, and their mechanism of peptidoglycan binding remains unexplored, for this is a challenging task experimentally. This research aimed to explore the binding mechanism of endolysins using computational approaches, namely molecular docking and bioinformatical tools, and analyze the performance of these approaches. The docking engine Autodock Vina 1.1.2 and the 3D-RISM module of AmberTools 24 were studied in the current work and used for receptor-ligand affinity and binding energy calculations, respectively. Two possible mechanisms of single-domain endolysin-ligand binding were predicted by Autodock Vina and verified by the 3D-RISM. As a result, the previously obtained experimental results on peptidoglycan binding of the isolated gamma phage endolysin PlyG enzymatically active domain were supported by molecular docking. Both methods predicted that single-domain endolysins are able to bind peptidoglycan, with Autodock Vina being able to give accurate numerical estimates of protein-ligand affinities and 3D-RISM providing comparative values.

Evaluating the insecticidal potential of alkaloids for the management of Thrips palmi: in vivo and in silico perspectives

Insecticidal potential of seven commonly available alkaloids against melon thrips (Thrips palmi Karny) was investigated through in vivo experiments and the bioactivity was explained via in silico approaches. In vivo screening showed highest mortality of T. plami larvae for reserpine (43%), closely followed by tropinone (41%) after 24 h of incubation. After 48 h, tropinone surpassed reserpine with 83% mortality, indicating its prolonged insecticidal activity. A detailed bioassay of tropinone revealed its LC50 values as 1187.9 and 686.9 µg mL−1 after 24 and 48 h, respectively. While studying the molecular interactions between the alkaloids and four physiologically important target proteins of T. palmi, tropinone demonstrated the highest ligand efficiency and lowest predicted inhibitory constant, particularly when forming complexes with CathB protein. However, binding energy calculations of the docked complexes showed most favorable binding of reserpine with CathB. To clear the ambiguity, considering both binding energy and ligand efficiency as the evaluation parameters, a molecular dynamics study was carried out, which predicted higher stability of CathB-tropinone complex than CathB-reserpine complex in terms of the total energy of the system. These in silico findings aligned well with the in vivo results, confirming tropinone as a promising candidate for effective thrips management programs.

Substrate Specificity of ABCB Transporters Predicted by Docking Simulations Can Be Confirmed by Experimental Tests.

ATP-binding cassette (ABC) transporters, particularly those of subfamily B, are involved in cell detoxification, multidrug resistance, drug treatment pharmacodynamics, and also ecological adaptation. In this regard, ABCB transporters may play a decisive role in the co-evolution between plants and herbivores. Cardenolides, toxic steroid glycosides, are secondary plant metabolites that defend plants against herbivores by targeting their sodium-potassium ATPase. Despite their toxicity, several herbivorous insects such as the large milkweed bug (Oncopeltus fasciatus) have evolved adaptations to tolerate cardenolides and sequester them for their own defense. We investigate the role of two ABCB transporters of O. fasciatus for the paracellular transport of cardenolides by docking simulations and ATPase assays. Cardenolide binding of OfABCB1 and OfABCB2 is predicted by docking simulations and calculated binding energies are compared with substrate specificities determined in ATPase assays. Both tested ABCB transporters showed activity upon exposure to cardenolides and Km values that agreed well with the predictions of our docking simulations. We conclude that docking simulations can help identify transporter binding regions and predict substrate specificity, as well as provide deeper insights into the structural basis of ABC transporter function.

IDENTIFYING POTENTIAL hENR INHIBITORS AGAINST PROSTATE CANCER EMPLOYING IN SILICO DRUG REPURPOSING APPROACH

Objective: This study employed an in silico drug repurposing strategy to identify potential human enoyl acyl carrier protein reductase (hENR) inhibitors. Methods: The co-crystallized ligand triclosan was used as a reference standard. Initially, FDA-approved drugs from the Drug Bank database were docked against the hENR and compounds with appreciable binding affinities with the protein were shortlisted. The binding energy calculations, ADME analysis, and induced-fit docking results of shortlisted compounds led to the identification of two top hits, DB07676 and DB11399, which were further subjected to molecular dynamics simulation. Results: Of 2,509 ligands docked via High Throughput Virtual Screening (HTVS), the top 250 were assessed with Standard Precision (SP) and the top 25 with Extra Precision (XP) mode. Thirteen compounds were selected based on interactions and XP scores, ranging from-15.245 to-10.031. Relative binding free energies of ligands DB07676 and DB11399 were-54.18 and-61.38 kcalmol-1, respectively. ADME analysis confirmed that both ligands followed Lipinski's Rule, though DB11399 had a high log P, which could be addressed by adding polar groups. Induced Fit scores for DB07676 and DB11399 were-10.592 and-11.220, respectively. Molecular Dynamics simulations confirmed superior stability of these complexes with RMSD ranging from 1.2 to 3.5 Å for the protein and 1.7 to 5.2 Å for the ligand with DB07676-protein complex and 1.4 to 3.0 Å for the protein and 1.1 to 5.8 Å for the ligand with DB11399-protein complex. Conclusion: Our final findings suggested that DB07676 and DB11399 could be potential lead compounds as hENR inhibitors.

Molecular insights from structural dynamics of HER2-inhibitor complexes pave the way for new breast cancer drugs

The human epidermal growth factor receptor 2 (HER2) is closely associated with the development and progression of breast cancer, making it a critical target for therapeutic interventions. In this study, we employed a comprehensive computational drug discovery strategy to identify potential inhibitors of HER2. Our approach combined virtual screening, re-docking procedures, molecular dynamics (MD) simulations, and free energy landscape analysis using principal component analysis (PCA). From the extensive PubChem library, we initially screened 733 compounds for their binding potential to HER2, using docking scores as a primary filter. These scores ranged notably from −11.172 to −7.028 kcal/mol, indicating substantial binding capacities. Following this screening, we selected four promising compounds (PubChem CID 166029206, 166544027, 21031510, and 11712721) along with a control compound (70I) for in-depth analysis. Utilizing the Amber software suite for MD simulations, we conducted 200-nanosecond simulations to assess the interactions and binding efficiencies of these selected compounds with HER2. We analysed the molecular interactions through various parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), and hydrogen bond formation patterns, free binding energy calculations. The PCA-based free energy landscape analysis revealed that these compounds consistently occupied a distinct low-energy basin, indicating their high stability and strong binding affinity for the HER2. This detailed analysis provided insights into the stability and conformational dynamics of these potential inhibitors when bound to the HER2. Our findings pave the way for further experimental validation and development of these compounds as therapeutic agents in breast cancer treatment.

An in silico approach uncovering the competency of oncolytic human adenovirus 52 for targeted breast cancer virotherapy

Breast cancer remains a major health threat throughout the world specifically in women above 30 years of age however, it is rarely known to affect men as well. It is characterized by the abnormal division of cells in the breast tissue resulting in the development of breast malignancies. Various risk factors contributing to breast cancer include age, family history, genetic mutations (chiefly in BRCA1 and BRCA2 genes) along with hormonal imbalances (oestrogen, progesterone, HER2). Early detection which can be obtained through frequent rounds of self-examination, mammographic scanning, and clinical assessment plays a crucial role in the prevention of the disease. In addition, appropriate diagnosis assists in better therapeutic responses. This study highlights the considerable health risks associated with the conventional treatment procedures which arise and increased demand of advanced, secure, and risk-free treatment alternatives. Oncolytic viruses are potentially apparent for the aim of improving cancer therapeutics with reduced side effects. These viruses act as the fundamental therapeutic agent themselves that selectively target and kill malignant cells without harm to healthy tissues. The key objective of the research is to provide evidence that Human Adenovirus 52 is a potent oncolytic virus and to highlight its capacity to target and eliminate cancer cells with precision while causing the least amount of harm to healthy tissues. Validating the in-silico method entails evaluating the precision and dependability of the computational modelling by contrasting the in-silico predictions with the findings from the experiments rank as the secondary objective. The workflow of this research utilized in-silico computational drug designing approaches including retrieval of tertiary structures of both the target Breast Cancer Type 1 Susceptibility Protein (BRCA1) and the viral Human Adenovirus 52 protein, their validation generating Ramachandran Plots determining favoured amino acid residue angles and prediction of their active residues. Furthermore, the study focused on the molecular dynamics docking of proteins, interpretation of molecular interactions between the docked complex, as well as the assessment of the molecular dynamic simulations (MD) in addition to their MMGBSA binding energy calculations. A successful docking between BRCA1 and Adenovirus protein provided a significant score of 329.2 +/- 24.3, furthermore, MD simulations showed a high RMSD peak at 2.8 Å, RMSF were maximum at 3.5 Å with highest protein–protein interaction, the radius of gyration was stable throughout the simulation representing elastic stability along with a high energy interaction value of - 7882 kCal/mol. Moreover, the MMGBSA calculation results showed a notable release of binding free energy of - 68.96 kCal/mol demonstrating effective bond formation between the docked complex. These findings propose the effectiveness of Human Adenovirus 52 to treat cancer. The selected oncolytic Human Adenovirus 52 is a potential candidate for the target specific treatment of breast cancer through virotherapy. This computer-aided drug discovery presents significant potential in targeting cancer cells and would assist in the development of potent drug reagents for the cancer therapy.

Probing marine macroalgal phlorotannins as an antibacterial candidate against Salmonella typhi: Molecular docking and dynamics simulation approach

The increasing prevalence of drug-resistant bacterial strains, including multidrug-resistant Salmonella typhi, has raised significant concerns about the effectiveness of traditional antimicrobial treatments. To address this issue, the study employed computational techniques to evaluate the potential of natural phlorotannins derived from marine algae as alternative antibacterial agents against S. typhi. A total of 104 phlorotannins were retrieved from PubChem and subjected to molecular docking with three specific target proteins of S. typhi. Among the compounds tested, Compound-10 a derivative of dieckol exhibited the highest affinity for the Omptin family outer membrane protease [pgtE] protein and for the cell invasion protein sipB protein. The docking results revealed strong interactions between the phlorotannins and the target proteins, indicating their potential as antimicrobial agents. Additionally, pharmacokinetic studies using the QikProp module demonstrated that the top-ranked compounds showed favourable drug-like properties with good druggable efficiency, moderate gut-blood barrier transport, and high human oral absorption. Most compounds passed the rule of three and five drug-likeness criteria, indicating their potential as drug candidates. Furthermore, MM-GBSA analysis provided relative binding affinity estimations, ranking the compounds based on their calculated binding energies. These results align reasonably well with experimental binding affinities, reinforcing the potential of the identified compounds as potent binders to their target proteins. This study highlights the promising antibacterial potential of marine phlorotannins against S. typhi. Further in vitro studies are warranted to validate these compounds' efficacy and anti-microbial activity, ultimately paving the way for developing new therapeutic strategies to combat drug-resistant Salmonella infections.

Exploring the Impact of Protein Chain Selection in Binding Energy Calculations with DFT.

Calculation of binding free energies between a protein and a ligand are highly desired for computer-aided drug design. Here we approximate the binding energies of ABL1, an enzyme which is the target for drugs used in the treatment of chronic myeloid leukaemia, with minimal models and density functional theory (DFT). Starting from the crystal structures of protein-drug complexes, we estimated the binding free energies having used all available individual molecules (protein chains) within each structure, not only a single one as commonly used, in order to see if the choice of the protein chain is important in such calculations. Differences were observed between chains in the same file. Energy decomposition analysis (EDA) revealed that the most important factors for binding were exchange, repulsion and electrostatics. The desolvation term varied dramatically between the inhibitors (between 4.2 and 92.3 kcal/mol). All functionals showed similar patterns in the EDA and in discriminating between the ligands. Non-covalent interactions (NCI) analysis was used to further explain the differences between protein chains and functionals. Overall, it is shown that small minimal models of a drug binding site can be useful to infer on the suitability of an initial crystal structure for further analysis such as EDA.

Cu Based Dilute Alloys for Tuning the C2+ Selectivity of Electrochemical CO2 Reduction.

Electrochemical CO2 reduction is a promising technology for replacing fossil fuel feedstocks in the chemical industry but further improvements in catalyst selectivity need to be made. So far, only copper-based catalysts have shown efficient conversion of CO2 into the desired multi-carbon (C2+) products. This work explores Cu-based dilute alloys to systematically tune the energy landscape of CO2 electrolysis toward C2+ products. Selection of the dilute alloy components is guided by grand canonical density functional theory simulations using the calculated binding energies of the reaction intermediates CO*, CHO*, and OCCO* dimer as descriptors for the selectivity toward C2+ products. A physical vapor deposition catalyst testing platform is employed to isolate the effect of alloy composition on the C2+/C1 product branching ratio without interference from catalyst morphology or catalyst integration. Six dilute alloy catalysts are prepared and tested with respect to their C2+/C1 product ratio using different electrolyzer environments including selected tests in a 100-cm2 electrolyzer. Consistent with theory, CuAl, CuB, CuGa and especially CuSc show increased selectivity toward C2+ products by making CO dimerization energetically more favorable on the dominant Cu facets, demonstrating the power of using the dilute alloy approach to tune the selectivity of CO2 electrolysis.

Evaluating the estrogen degradation potential of laccase and peroxidase from Bacillus ligniniphilus L1 through integrated computational and experimental approaches

This study investigated the degradation potential of laccase and catalase-peroxidase from the extremophilic marine bacterium Bacillus ligniniphilus L1 against endogenous and synthetic estrogen compounds using an integrated computational and experimental approach. Molecular docking identified five estrogen compounds exhibiting reliable bindings with enzymes, which were then subjected to enzyme activity assays. The degradation potential of the two enzymes against five selected estrogen compounds were investigated and compared with their commercial counterparts. Laccase from L1 showed higher degradation potential against estrone (47.02 % without and 62.21 % with ABTS) compared to commercial laccase (39 % without and 54.20 % with ABTS). For estradiol valerate, commercial laccase showed a slightly higher degradation (52.47 %) than L1 laccase (49.94 %), but with ABTS, L1 laccase performed better (74.15 % vs. 68.03 %). Notably, L1 catalase-peroxidase demonstrated significantly higher degradation for all tested compounds compared to its commercial counterpart with efficiencies of 96.16 %, 89.09 %, 74.94 %, 64.91 %, and 62.80 % against estropipate, quinestrol, estradiol valerate, estriol and estrone, respectively, revealing its potential for commercial applications. Molecular dynamics simulations revealed the interaction and stability of enzyme-estrogen complexes, with MMGBSA binding energy calculations supporting experimental results. These findings highlight the usefulness of the computational approach in elucidating the molecular mechanisms underlying enzyme-mediated bioremediation of environmental contaminants.

Targeting SARS-CoV-2 Proteins: In Silico Investigation with Polypyridyl-Based Zn(II)-Curcumin Complexes.

Herein, we have selected eight Zn(II)-based complexes viz., [Zn(bpy)(acac)Cl] (1), [Zn(phen)(acac)Cl] (2), [Zn(dppz)(acac)Cl] (3), [Zn(dppn)(acac)Cl] (4), [Zn(bpy)(cur)Cl] (5), [Zn(phen)(cur)Cl] (6), [Zn(dppz)(cur)Cl] (7), [Zn(dppn)(cur)Cl] (8), where bpy=2,2'-bipyridine, phen=1,10-phenanthroline, dppz=benzo[i]dipyrido[3,2-a:2',3'-c]phenazine, dppn=naphtho[2,3-i]dipyrido[3,2-a:2',3'-c]phenazine, acac=acetylacetonate, cur=curcumin and performed in silico molecular docking studies with the viral proteins, i. e., spike protein (S), Angiotensin-converting enzyme II Receptor protein (ACE2), nucleocapsid protein (N), main protease protein (Mpro), and RNA-dependent RNA polymerase protein (RdRp) of SARS-CoV-2. The binding energy calculations, visualization of the docking orientation, and analysis of the interactions revealed that these complexes could be potential inhibitors of the viral proteins. Among complexes 1-8, complex 6 showed the strongest binding affinity with S and ACE2 proteins. 4 exerted better binding affinity in the case of the N protein, whereas 8 presented the highest binding affinities with Mpro and RdRp among all the complexes. Overall, the study indicated that Zn(II) complexes have the potential as alternative and viable therapeutic solutions for COVID-19.

An Extensive Computational Investigation of Mycobacterium tuberculosis Pantothenate Synthetase Inhibitors from Diverse‐Lib Compounds Library

AbstractAntibiotics have played a crucial role in significantly reducing the incidence of tuberculosis (TB) infection worldwide. Even before the mid‐20th century, the mortality rate of TB onset within five years was around 50 %. So, the introduction of antibiotics has changed the scenario of TB from a serious threat to a manageable one. However, the emergence of resistance to anti‐TB drugs poses a significant challenge. So, to overcome this situation the therapeutic approaches and drug targets need to be reformed. This study focused on finding potential inhibitors by targeting Pantothenate Synthetase, a crucial enzyme for Mycobacterium tuberculosis (Mtb) survival, through computational drug discovery methods. Molecular docking and virtual screening were employed to identify potential inhibitors from Diverse‐lib. Four compounds, namely CID2813602, 24357538, CID753354, and CID4798023, exhibited strong binding energies and stable interaction with the target protein. Further assessment of these compounds through MD simulation and Post MD simulation showed significant dynamic stability. The minimum energy transition calculated using the free energy landscape analysis of these compounds when docked with Pantothenate Synthetase confirmed the stability of each complex due to its minimum energy production. The free binding energy calculation of each complex also showed the intramolecular interaction contributes to the strong binding affinity of the compounds within the enzyme's active site clarifying their mechanisms of action. This research showcases the effectiveness of computational methods in promptly identifying potential anti‐TB drugs, paving the way for future experimental validation and optimization. It holds promise for the development of new treatments targeting drug‐resistant TB strains.

Chemical space-informed machine learning models for rapid predictions of x-ray photoelectron spectra of organic molecules

Abstract We present machine learning models based on kernel-ridge regression for predicting X-ray photoelectron spec- tra of organic molecules originating from the ionization energies of 1s electrons in carbon (C), nitrogen (N), oxygen (O), and fluorine (F) atoms. We constructed training dataset through high-throughput calculations of core-electron binding energies (CEBEs) for 12,880 small organic molecules in the bigQM7ω dataset, employing the ∆-SCF formalism coupled with meta-GGA-DFT and a variationally converged basis set. The models are cost-effective, as they require the atomic coordinates of a molecule generated using universal force fields while estimating the target-level CEBEs corresponding to DFT-level equilibrium geometry. We explore transfer learning by utilizing the atomic environment feature vectors learned using a graph neural network framework in kernel-ridge regression. Additionally, we enhance accuracy using the ∆-machine learning framework by leveraging inexpensive baseline spectra derived from Kohn–Sham eigenvalues. Upon application to 208 com- binatorially substituted uracil molecules, larger than those in the training set, our analyses reveal that while the models may not yield quantitatively accurate predictions of CEBEs on a molecule-by-molecule basis, they do exhibit a strong linear correlation, which proves valuable for virtual high-throughput screening purposes. We present the dataset and models as the Python module, cebeconf, to facilitate further explorations.

Structural insights into the toxicity of type II ribosome inactivating proteins (RIPs): a molecular dynamics study

Ribosome Inactivating Proteins (RIPs) act by irreversibly depurinating the 28S rRNA ricin-sarcin loop (SRL) of the eukaryotic ribosome resulting in protein synthesis inhibition. In general, they consist of two variants: Type I which is single chained (∼30 kDa), and Type II, a more toxic variant which is a Type I N-glycosidase chain covalently linked to a lectin chain. These proteins are believed to play a pivotal role in defence mechanisms. Intriguingly, non-toxic variants of such toxic proteins do exist in nature. To explore their mode of action, in the present study we have selected three toxic (Ricin, Ebulin and HmRIP) as well as two non-toxic (BGSL and SGSL) RIPs and performed molecular docking and molecular dynamic simulations with the SRL loop. This study throws light on the structural stability and plasticity of the toxic and non-toxic RIP complexes. Furthermore, analysis of the active site cavity volume and binding free energy calculations reveal that the SRL, particularly the specific adenine (A4605), is relatively unstable in the case of non-toxic RIPs which is also supported by the free binding energy calculations, and the pocket size analysis indicates the abnormal increase in active site cavity volume of non-toxic RIPs with time. This first-of-its-kind comprehensive study of toxic and non-toxic RIPs gives insights about the mode of action and the dynamic nature of their interaction with the SRL loop. These observations will be helpful in the development of toxoids against RIPs and also in designing novel therapeutic approaches against human diseases.

Phytobioinformatics screening of ayurvedic plants for potential α-glucosidase inhibitors in diabetes management

The enzyme α-glucosidase in the small intestine regulates blood glucose levels and stimulates the hydrolysis of oligosaccharides and polysaccharides, increasing glucose levels in the body. Inhibiting this enzyme slows glucose digestion and absorption and as a result post-prandial blood glucose levels remain low, causing decreased insulin demand. Here, we investigated the ayurvedic antidiabetic plants and virtually screened an in-house library of 478 phytochemicals of these plants against the human α-glucosidase. We identified 11 secondary metabolites, including palmitic acid α-monoglyceride, (+)-(2 R)-6-propionyloxyethyl-4′,5,7-trihydroxyisoflavanone, Abruquinone E, and Aurantiamide Acetate, among others, showed stronger interactions with the receptor than the native ligand N-acetyl cysteine. Surprisingly, except one, all of these metabolites were from Abrus precatorius L. [Fabaceae] affirming its ethnopharmacological use against diabetes. The stability of the interactions between the ligands and receptor protein was evaluated through Molecular Dynamic (MD) simulation trajectories including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), H bonds, β-factor analysis, and binding energy calculation through MM/GBSA method. The efficacy of top metabolites in inhibiting α-glucosidase is depicted in pharmacophore analysis. A comprehensive pharmacokinetics analysis confirmed the druggability, safety, and efficiency of top drug candidates. Additionally, we predicted the interactions of these top metabolites within the biological system. The medicinal properties described in this study will help develop active drug candidates for therapeutic purposes. Further experiments are recommended to prove the effectiveness of these metabolites in inhibiting the α-glucosidase enzyme for exploring their potential in the treatment of diabetes.

Synthesis of Novel Indolyl Aryl Sulfone-clubbed Hydrazone Derivatives as Potential HIV-1 Non-Nucleoside Reverse Transcriptase Inhibitors: Molecular Modeling and QSAR Studies.

Non-Nucleoside Reverse Transcriptases Inhibitors (NNRTIs) are among the most extensively studied enzymes for understanding the biology of Human Immunodeficiency Viruses (HIV) and designing inhibitors for managing HIV infections. Indolyl aryl sulfones (IASs), an underexplored class of potent NNRTIs, require further exploration for the development of newer drugs for HIV. In this context, we synthesized a series of novels by Indolyl Aryl Sulfones with a hydrazone moiety at the carboxylate site of the indole nucleus. A 2D-QSAR model was developed to predict Reverse Transcriptase inhibitory activity against wild-type RT (WT-RT) enzyme. The model was successfully applied to predict the HIV-1 inhibitory activity of known Indolyl Aryl Sulfones. Considering the reliability, robustness, and reproducibility of the 2D-QSAR model, we made an in-silico prediction of the RT inhibition for our synthesized compounds (1-14). Molecular docking and dynamics simulations established our synthesized Indolyl Aryl Sulfones, particularly compounds 23, 24, and 28, as effective NNRTIs by stabilizing HIV reverse transcriptase's structure. Binding energy calculations revealed compound 28 as the strongest inhibitor (-43.21 ± 0.09 kcal/mol), followed by 23 (-40.94 ± 0.10 kcal/mol) and 24 (-39.18±0.08 kcal/mol), emphasizing their binding affinity towards HIV reverse transcriptase. In summary, the synthesized Indolyl Aryl Sulfones, particularly compounds 23, 24, and 28, demonstrate significant potential as Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) against HIV. These results highlight the promising role of these compounds in developing novel NNRTIs for managing HIV infections.