Molecular Mechanics Poisson-Boltzmann Surface Area Research Articles

Discovering broad-spectrum inhibitors for SARS-CoV-2 variants: a cheminformatics and biophysical approach targeting the main protease

The COVID-19 pandemic caused by SARS-CoV-2 still lacks effective antiviral drugs. Therefore, a thorough receptor-based virtual screening study was conducted to screen different natural and synthetic drug libraries, such as the Asinex Antiviral, Seaweed Metabolite Database, Medicinal Fungi Secondary Metabolite and Therapeutics Library, and Comprehensive Marine Natural Products Database comprising 6,827, 1,191, 1,830, and 45,000 compounds, respectively, against the main protease enzyme of SARS-CoV-2. Accordingly, three drug molecules (BBB-26580140, BDE-32007849, and LAS-51378804) are highlighted as the best binding molecules to the main protease S1 pocket. The docking binding energy scores of BBB-26580140, BDE-32007849, and LAS-51378804 were −13.02, −13.0, and −12.56 kcal/mol, respectively. Compared to the control Z1741970824 molecule with a binding energy score of −11.59 kcal/mol, the lead structures identified herein showed robust hydrophilic and van der Waals interactions with the enzyme active site residues, such as His41 and Cys145, and achieved highly stable binding modes. The simulations showed a stable structure of the main protease enzyme with the shortlisted leads in the pocket, and the network of binding interactions remained intact during the simulations. The overall molecular mechanics with generalized Born and surface area solvation binding energies of the BBB-26580140, BDE-32007849, LAS-51378804, and control molecules are −53.02, −56.85, −55.44, and −48.91 kcal/mol, respectively. Similarly, the net molecular mechanics Poisson–Boltzmann surface area binding energies of BBB-26580140, BDE-32007849, LAS-51378804, and control are −53.6, −57.61, −54.41, and −50.09 kcal/mol, respectively. The binding entropy energies of these systems showed lower free energies, indicating their stable nature. Furthermore, the binding energies were revalidated using the water swap method that considers the role of the water molecules in bridging the ligands to the enzyme active site residues. The compounds also revealed good ADMET properties and followed all major rules of drug-likeness. Thus, these compounds are predicted as promising leads and can be subjected to further experimental studies for evaluation of their biological activities.

Exploration of rutin derivatives as potential inhibitors of prostate cancer signaling pathways: A comprehensive in-silico study.

Prostate cancer is a widespread health issue that affects men worldwide. It is one of the most common forms of cancer, and its development is influenced by a combination of hereditary, epigenetic, environmental, age, and lifestyle factors. Given that it is the second most common cause of cancer-related deaths in men, it is crucial to comprehend its complex facets. Present research especially targets the 3-kinase/protein kinase B, Epidermal Growth Factor Receptor, and extracellular signal-related kinase pathways, which are known to be significantly involved in prostate cancer progression. Here, Rutin derivatives were screened against selected prostate cancer targets. Molecular docking was performed to identify favorable interactions and the most promising compound. Further, Density functional theory, pharmacokinetics, Molecular dynamics simulation, principal component analysis, free energy landscape analysis, and Molecular Mechanics Poisson-Boltzmann Surface Area provided additional insights into selecting the best drug candidate. Among all the selected rutin derivatives, RU4b1 has potent inhibitory action. We also performed predictive analysis to identify the distinct metabolic sites within the structure of RU4b1. RU4b1 also exhibits drug-like properties and potent antioxidant activity. The findings were also compared with standard drugs and reference molecules of the respective proteins, and it is noteworthy that RU4b1 exhibited superior action compared to the standard drugs and reference molecules. This study aims to contribute valuable insights into developing targeted therapies for prostate cancer, emphasizing the potential of rutin derivatives as effective anti-cancer agents.

Comprehensive Evaluation of End-Point Free Energy Methods in DNA-Ligand Interaction Predictions.

Deoxyribonucleic acid (DNA) serves as a repository of genetic information in cells and is a critical molecular target for various antibiotics and anticancer drugs. A profound understanding of small molecule interaction with DNA is crucial for the rational design of DNA-targeted therapies. While the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) approaches have been well established for predicting protein-ligand binding, their application to DNA-ligand interactions has been less explored. In this study, we systematically investigated the binding of 13 diverse small molecules to DNA, evaluating the accuracy of DNA-ligand interaction predictions across different solvation approaches, interior dielectric constants (εin), and molecular force fields. Our results demonstrate that MM/PBSA, using energy-minimized structures (the bsc1 force field and εin = 20), provides the best correlation (Rp = -0.742) with experimental binding affinities, surpassing the performance of rDock scoring functions (best Rp = -0.481). Notably, the interior dielectric constant was found to significantly impact DNA-ligand binding free energy predictions, especially for MM/PBSA. Moreover, both MM/PBSA and MM/GBSA predictions (εin = 16 or 20) exhibited superior performance in distinguishing native-like binding modes within the top-10 poses from decoys, compared to the molecular docking tools used in this study. However, the popular docking software PLANTS demonstrates notable efficacy in predicting the top-1 binding pose. Given the considerably higher computational cost of MM/PBSA, MM/GBSA rescoring with higher εin = 16 or 20 is more efficient for recognizing the native-like binding poses for DNA-ligand systems. This study presents the first detailed exploration of end-point free energy calculations in the context of DNA-ligand interactions and offers valuable insights for the application of the MM/PB(GB)SA methods in this domain.

Investigating the Inhibitory Effects of Paliperidone on RAGEs: Docking, DFT, MD Simulations, MMPBSA, MTT, Apoptosis, and Immunoblotting Studies

Chronic diseases such as diabetes and cancer are the leading causes of mortality worldwide. Receptors for Advanced Glycation End products (RAGEs) are ubiquitous factors that catalyse Advanced Glycation End products (AGEs), proteins, and lipids that become glycated from sugar ingestion. RAGEs are cell surface receptor proteins and play a broad role in mediating the effects of AGEs on cells, contributing to modifying biological macromolecules like proteins and lipids, which can cause Reactive Oxygen Species (ROS) generation, inflammation, and cancer. We targeted RAGE inhibition analysis and screening of United States Food and Drug Administration (FDA) libraries through molecular docking studies that identified the four most suitable FDA compounds: Zytiga, Paliperidone, Targretin, and Irinotecan. We compared them with the control substrate, Carboxymethyllysine, which showed good binding interaction through hydrogen bonding, hydrophobic interactions, and π-stacking at active site residues of the target protein. Following a 100 ns simulation run, the docked complex revealed that the Root Mean Square Deviation (RMSD) values of two drugs, Irinotecan (1.3 ± 0.2 nm) and Paliperidone (1.2 ± 0.3 nm), were relatively stable. Subsequently, the Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) determined that the Paliperidone molecule had a high negative energy of −13.49 kcal/mol, and the Absorption, Distribution, Metabolism, and Excretion (ADME) properties were in control for use in the mentioned cases. We extended this with many in vitro studies, including an immunoblotting assay, which revealed that RAGEs with High Mobility Group Box 1 (HMGB1) showed higher expression, while RAGEs with Paliperidone showed lower expressions. Furthermore, cell proliferation assay and Apoptosis assay (Annexin-V/PI staining) results revealed that Paliperidone was an effective anti-glycation and anti-apoptotic drug—however, more extensive in vivo studies are needed before its use.

Collagen Alpha 1(XI) Amino-Terminal Domain Modulates Type I Collagen Fibril Assembly.

The amino-terminal domain of collagen α1(XI) plays a key role in controlling fibrillogenesis. However, the specific mechanisms through which various isoforms of collagen α1(XI) regulate this process are not fully understood. We measured the kinetics of collagen type I self-assembly in the presence of specific collagen α1(XI) isoforms. Molecular dynamics simulations, protein-protein docking studies, and molecular mechanics Poisson-Boltzmann surface area were utilized to understand the molecular mechanisms. In vitro, in silico, and thermodynamic studies demonstrated an isoform-specific effect on self-assembly kinetics. Our results indicate isoform-specific differences in the rate constants, activation energy, and free energy of binding. These differences may result from isoform-specific interaction dynamics and modulation of steric hindrance due to the chemically distinct variable regions. We show that isoform A interacts with collagen type I due in part to the acidic variable region, increasing the activation energy of fibril growth while decreasing the rate constant during the growth phase. In contrast, the basic variable region of isoform B may result in less steric hindrance than isoform A. Isoform 0 demonstrated the highest activation energy and the lowest rate constant during the growth phase. Although the presence of isoforms reduced the rate constants for fibril growth, an increase in total turbidity during the plateau phase was observed compared to controls. Overall, these results are consistent with collagen α1(XI) NTD isoforms facilitating fibrillogenesis by increasing the final yield by reducing the rate of the lag and/or growth phases, while extending the duration of the growth phase.

In silico Analysis of Natural Iridoids as Primary Amoebic Meningoencephalitis Inhibitors: Molecular Docking, MD Simulation, MMPBSA, and DFT Analyses

Iridoids have demonstrated various activities, including anti-inflammatory, anticancer, cardioprotective, antiviral, hepatoprotective, antihyperglycemic, and antiparasitic effects. The brain-eating amoeba Naegleria fowleri is responsible for primary amoebic meningoencephalitis, a brain inflammation. In this study, 52 iridoids were selected through an extensive literature survey, and 22 of these iridoids passed the drug-likeness filter. The selected iridoids were molecularly docked against the N. fowleri CYP51 receptor, using voriconazole as a standard for comparison. The docking score for voriconazole was -7.6 kcal/mol, while the scores for 10-isovaleroyl-dihydropenstemide and Patrinalloside A were -8.9 and -8.6 kcal/mol, respectively. According to molecular dynamics (MD) simulation data, the interacting amino acid residues exhibited fluctuations within a specific range, with the Root Mean Square Deviation (RMSD) values stabilizing throughout the experiment. When interacting with the receptor linked to amoebic meningoencephalitis, 10-isovaleroyl-dihydropenstemide and Patrinalloside A showed free binding energies of -71.922 kJ/mol and -61.243 kJ/mol, respectively, based on Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) calculations. Furthermore, Fragment Molecular Orbital (FMO) and Molecular Electrostatic Potential (MEP) analyses of 10-isovaleroyl-dihydropenstemide and Patrinalloside A revealed potential nucleophilic-electrophilic attack zones, indicating they are chemically reactive. The analysis of both compounds' ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) indicated non-toxic behaviour. These findings suggest that natural iridoids have significant potential in combating primary amoebic meningoencephalitis.

Exploring DL-2-aminobutyric acid: DFT analysis and spectroscopic characterization

DL-2-aminobutyric acid (α-aminobutyric acid), an alpha amino acid, has been extensively investigated through experimental and theoretical methods. Theoretical analyses have been carried out using density functional theory. Spectroscopic techniques such as Fourier transform infrared and UV spectroscopy have been employed, with experimental spectra aligning closely with theoretical predictions. Studies of electronic transitions between unoccupied and occupied states have been performed in solvents like dimethyl sulfoxide (DMSO) and Methanol (MeOH). The Time Dependent Density Functional Theory (TD-DFT) method and Iterative Electrostatic Potential Continuum Model (IEPCM) model assessed charge transfer in these solvents. Vibrational analysis and other studies, including Frontier Molecular Orbital (FMO), natural bond analysis, and non-linear optical analysis, were based on structures optimized using the B3LYP method with the 6-311++G(d,p) basis set. Total potential energy distribution analysis has been done for the detailed analysis of internal coordinates to each vibrational mode. The atom in molecule theory was applied via electron localization functions to examine ellipticity, binding energy, and isosurface projections. Natural bond analysis was conducted to investigate interference energy, bond hybridization, and interactions between donor and acceptor sites. Reactive sites were identified through Fukui functions and molecular electrostatic potential analysis. Thermodynamic properties, including free energy, enthalpy, entropy, internal energy, and heat capacities at various temperatures, were computed using the Atomistica online calculator. The electrophilicity index, derived from FMO analysis, suggested the compound’s potential bioactivity. Molecular docking studies with various proteins revealed the lowest binding energy of −4.9 kcal/mol. α-Aminobutyric acid and its derivatives exhibit similar Absorption, distribution, metabolism, and excretion (ADME) properties, indicating their potential utility in drug development. Minimal conformational changes in the peptide backbone after equilibrium have been explained by using molecular dynamics simulations, and molecular mechanics Poisson–Boltzmann surface area calculation showed an average free binding energy of −23.48 ± 1.72 kcal/mol.

Advancing siRNA Therapeutics targeting MCT-4: A Multifaceted approach integrating Arithmetical Designing, Screening, and molecular dynamics validation.

Monocarboxylate transporter 4 (MCT-4) is involved in various metabolic processes which are crucial in maintaining cellular pH and energy metabolism, and thus influence the tumor microenvironment. The study is aimed to rationally design effective Small interfering RNA (siRNA) that can silence MCT-4. We utilized a comprehensive workflow integrating multiple tools such as siDirect version 2.0, Oligowalk and i-score designer, to evaluate sequence features and predict target site accessibility, Guanine-Cytosine (GC) content and thermodynamic stability. Five (M1, M2, M3, M4 and M5) siRNAs sequences were retrived and subjected to further scrutiny on the account of off-target elimation, sequence conservation, secondary structure formation, and thermodynamic properties. The M1 demonstrated off targets and the M2 sequence showed secondry conformation and therefore M3, M4 and M5 were considered for further evaluation. Additionally, molecular docking and simulations (50ns) were conducted with human Argonaute 2 protein (h-Arg-2). The post- molecular dynamics (MD) analysis revealed M4 (5'UUGAAGAAGACACUGACGG3') as a most appropriate siRNA candidate agsint MCT-4 on the basis of Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and H-Bond results. The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) analysis was also performed to further validate the selected siRNA candidates, which further affirmed M4 (5'UUGAAGAAGACACUGACGG3') as an potential candidate for future in-vitro and in-vivo evaluation.

Evaluation of Structure Prediction and Molecular Docking Tools for Therapeutic Peptides in Clinical Use and Trials Targeting Coronary Artery Disease.

This study evaluates the performance of various structure prediction tools and molecular docking platforms for therapeutic peptides targeting coronary artery disease (CAD). Structure prediction tools, including AlphaFold 3, I-TASSER 5.1, and PEP-FOLD 4, were employed to generate accurate peptide conformations. These methods, ranging from deep-learning-based (AlphaFold) to template-based (I-TASSER 5.1) and fragment-based (PEP-FOLD), were selected for their proven capabilities in predicting reliable structures. Molecular docking was conducted using four platforms (HADDOCK 2.4, HPEPDOCK 2.0, ClusPro 2.0, and HawDock 2.0) to assess binding affinities and interactions. A 100 ns molecular dynamics (MD) simulation was performed to evaluate the stability of the peptide-receptor complexes, along with Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculations to determine binding free energies. The results demonstrated that Apelin, a therapeutic peptide, exhibited superior binding affinities and stability across all platforms, making it a promising candidate for CAD therapy. Apelin's interactions with key receptors involved in cardiovascular health were notably stronger and more stable compared to the other peptides tested. These findings underscore the importance of integrating advanced computational tools for peptide design and evaluation, offering valuable insights for future therapeutic applications in CAD. Future work should focus on in vivo validation and combination therapies to fully explore the clinical potential of these therapeutic peptides.

Dual Targeting of MEK1 and Akt Kinase Identified SBL-027 as a Promising Lead Candidate to Control Cell Proliferations in Gastric Cancer.

Dual inhibition of Akt and MEK1 pathways offers a promising strategy to enhance treatment efficacy in gastric cancer. In this study, we employed computational approaches followed by in vitro validations. Our results demonstrate that SBL-027 exhibits robust and enduring interactions with Akt and MEK1 kinases, as evidenced by atomistic molecular dynamics simulations and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) based binding free energy estimates. The predicted Gibbs binding free energies indicate highly favorable interactions between SBL-027 and both Akt and MEK1 kinases. In vitro, SBL-027 displayed an IC50 value of 195.20nM against Akt and 239.10nM against MEK1 enzymes. The compound exhibited potent inhibition of cell proliferation in KATOIII and SNU-5 cells, with GI50 values of 490.70 and 615.14nM, respectively. Moreover, SBL-027 induced an increase in the sub G0/G1 population during the cell cycle of KATOIII and SNU-5 cells, while facilitating early and late-phase apoptosis in these cell lines. Notably, the compound significantly reduced the percentage of dual-positive cells expressing both MEK1 and Akt in gastric cancer cells. The strong binding affinity, stability, and favorable thermodynamics of SBL-027 along with the established in vitro efficacy highlight its potential as a lead compound for further preclinical and clinical development.

Influence of aqueous solutions of 2-(tetrafluoro(trifluoromethyl)-λ6-sulfanyl-ethan-1-ol (CF3SF4-ethanol) on the stabilization of the secondary structure of melittin: comparison with aqueous trifluoroethanol using molecular dynamics simulations and circular dichroism experiments.

The influence of aqueous solutions of 2-(tetrafluoro(trifluoromethyl)-λ6-sulfanyl-ethan-1-ol (CF3SF4-ethanol) and 2,2,2-trifluoroethanol (TFE) on the secondary structure of melittin was studied using circular dichroism (CD) and molecular dynamics (MD) simulations. In water, melittin transitions into a random coil. However, upon addition of even as little as 1% by volume of CF3SF4-ethanol, the secondary structure of melittin stabilizes as a helix. Contrarily, the addition of 40% by volume of TFE is required for the greatest helicity. Fluoroalcohols stabilize melittin's hydrophobic side chain residues, thereby enhancing the helical structure. Locally alcohol concentrations approach nearly 70-90% in the near vicinity of the hydrophobic side chains increasing hydrophobic interactions and reducing water-peptide hydrogen bonding. Using the molecular mechanics-Poisson Boltzmann surface area method (MMPBSA), the free energy of binding between the peptide and fluoroalcohols highlighted the role of nonpolar residues in stabilizing the secondary structure. Secondary structure content analysis (SESCA) validated the simulation results, confirming CF3SF4-ethanol as an effective, eco-friendly enhancer of helicity at low concentrations. The far UV circular dichroism (CD) spectrum of melittin in solutions containing TFE corroborates previous findings and likewise affirms that the addition of CF3SF4-ethanol to an aqueous solution can enhance helicity. The agreement between the experimental and calculated helicities highlights the potential of CF3SF4-ethanol. This study offers insights into peptide stabilization by fluoroalcohols, with implications for peptide-based therapeutic design.

A comprehensive high-throughput screening approach for discovering inhibitors targeting the menin-MLL1 interaction.

The prognosis for mixed-lineage leukemia (MLL), particularly in young children, remains a significant health concern due to the limited therapeutic options available. MLL refers to KMT2A chromosomal translocations that produce MLL fusion proteins. The protein menin, which is essential for the malignant potential of these MLL fusion proteins, offers novel targets for acute leukemia treatment. This study reports the identification of potential new inhibitors of MLL-mediated leukemia targeting menin through the screening of two distinct drug libraries and existing inhibitors. The 3D structure of the protein was retrieved from the Protein Data Bank (ID: 8IG0). The drug libraries, sourced from public repositories such as the 'Epigenetic Drug Library' and 'The FDA-anticancer Drug Library,' yielded top candidates like Tozaseritib and Panobinostat, which exhibited the highest binding energy scores in the Glide virtual screening module. Additionally, 31 known menin-MLL1 inhibitors were identified through PDB screening and subsequently docked with the menin protein. The top three inhibitors (M-525, M-808, and MI-89) were selected for further analysis. Five menin-ligand complexes were validated using molecular dynamics analysis and Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations to verify the stability and binding mechanisms.These findings provide insights into the molecular mechanisms of these drugs and lay the groundwork for future clinical development aimed at improving outcomes for acute myeloid leukemia (AML) patients.

Synthesis, Antileishmanial Activity, and in silico Study of 2-Hydroxy-3-(1,2,3-triazolyl)propyl Vanillin Derivatives

This study details the preparation, antileishmanial assay, and in silico analysis of twenty 2-hydroxy-3-(1,2,3-triazolyl)propyl vanillin derivatives. These compounds were synthesized in three steps and evaluated against Leishmania infantum, Leishmania amazonensis, and Leishmania braziliensis promastigotes. Compounds 3s and 3t were the most effective, showing good activity against all Leishmania species tested. Molecular docking indicated that all compounds bind favorably to the sterol 14α-demethylase (CYP51) enzyme from L. infantum. ADMET (absorption, distribution, metabolism, excretion and toxicity) analysis indicated good oral bioavailability, non-blood-brain barrier penetration, and high gastrointestinal absorption. Posaconazole and compounds 3e, 3s, and 3t remained stable in the CYP51 binding region during 100 ns molecular dynamics (MD) simulations. Root mean square deviation (RMSD) and root mean square fluctuations (RMSF) analyses from the MD trajectory revealed significant conformational fluctuations of the CYP51 N-terminal, suggesting occasional expulsion of 3e, potentially explaining its higher IC50 (half-maximal inhibitory concentration) values. Pairwise decomposition analyses from molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations highlighted the importance of hydrophobic residues in interacting with the synthesized derivatives.

A Chronobiological Approach: The Potential of Photoswitchable Drug Derivatives in the Treatment of Alzheimer’s Disease

Objective: Alzheimer’s disease (AD) is a progressive neurodegenerative condition significantly affecting cognitive functions, necessitating novel therapeutic approaches. Chronic progression of AD has an involvement of chronopathologic pattern. Traditional treatments primarily involve cholinesterase inhibitors like donepezil, rivastigmine, galantamine, and N-methyl-D-aspartate (NMDA) receptor antagonists such as memantine. These drugs face limitations due to their non-targeted systemic effects and side effects. Therefore, modifying these drugs with a chronobiological treatment approach could be of great importance in treating AD. Methods: In this study, the design of photoswitchable derivatives of these drugs was explored using azobenzene to control drug activity with light, potentially enhancing site-specific action and reducing side effects. The interactions of these derivatives were assessed with target binding sites through molecular docking, molecular dynamics simulations, and molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) calculations. Results: The photoswitchable derivatives demonstrated significantly enhanced binding affinities and stability compared to their traditional counterparts. For instance, photoswitchable derivatives of all tested drug molecules in their planar form showed increased binding energy against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and NMDA receptors. The MM/PBSA results supported these findings, indicating that the photoswitchable derivatives exhibit better stability and efficacy when interacting with their targets. Conclusion: The combination of photoswitchable features with traditional AD drugs could be a promising strategy. This hypothetical approach may not only improve the effectiveness of these drugs but also minimize the risk of negative side effects, opening up new possibilities for managing AD treatment by combining chronobiology and the effects of light.

Harnessing the Power of Machine Learning Guided Discovery of NLRP3 Inhibitors Towards the Effective Treatment of Rheumatoid Arthritis.

The NLRP3 inflammasome, plays a critical role in the pathogenesis of rheumatoid arthritis (RA) by activating inflammatory cytokines such as IL1β and IL18. Targeting NLRP3 has emerged as a promising therapeutic strategy for RA. In this study, a multidisciplinary approach combining machine learning, quantitative structure-activity relationship (QSAR) modeling, structure-activity landscape index (SALI), docking, molecular dynamics (MD), and molecular mechanics Poisson-Boltzmann surface area MM/PBSA assays was employed to identify novel NLRP3 inhibitors. The ChEMBL database was used to retrieve compounds with known IC50 values to train machine learning (ML) models using the Lazy Predict package. After data pre-processing, 401 non-redundant structures were selected for exploratory data analysis (EDA). PubChem and MACCS fingerprints were used to predict the inhibitory activities of the compounds. SALI was used to identify structurally similar compounds with significantly different biological activities. The compounds were docked using MOE to assess their binding affinities and interactions with key residues in NLRP3. The models were evaluated, and a comparative analysis revealed that the ensemble Random Forest (RF) model (PubChem fingerprints) with RMSE (0.731), R2 (0.622), and MAPE (8.988) and bootstrap aggregating model (MACCS fingerprints) with RMSE (0.687), R2 (0.666), and MAPE (9.216) on the testing set performed well, in accordance with the Organization for Economic Cooperation and Development (OECD) guidelines. Out of all docked compounds, the two most promising compounds (ChEMBL5289544 and ChEMBL5219789) with binding scores of -7.5 and -8.2 kcal/mol were further investigated by MD to evaluate their stability and dynamic behavior within the binding site. MD simulations (200 ns) revealed strong structural stability, flexibility, and interactions in the selected complexes. MM/PBSA binding free energy calculations revealed that van der Waals and electrostatic forces were the key drivers of the binding of the protein with ligands. The outcomes obtained can be used to design more potent and selective NLRP3 inhibitors as therapeutic agents for the treatment of inflammatory diseases such as RA. However, concerns related to the lack of large datasets, experimental validation, and high computational costs remain.

Natural Product Identification and Molecular Docking Studies of Leishmania Major Pteridine Reductase Inhibitors.

Background/Objectives: Pteridine reductase 1 (PTR1) has been one of the prime targets for discovering novel antileishmanial therapeutics in the fight against Leishmaniasis. This enzyme catalyzes the NADPH-dependent reduction of pterins to their tetrahydro forms. While chemotherapy remains the primary treatment, its effectiveness is constrained by drug resistance, unfavorable side effects, and substantial associated costs. Methods: This study addresses the urgent need for novel, cost-effective drugs by employing in silico techniques to identify potential lead compounds targeting the PTR1 enzyme. A library of 1463 natural compounds from AfroDb and NANPDB, prefiltered based on Lipinski's rules, was used to screen against the LmPTR1 target. The X-ray structure of LmPTR1 complexed with NADP and dihydrobiopterin (Protein Data Bank ID: 1E92) was identified to contain the critical residues Arg17, Leu18, Ser111, Phe113, Pro224, Gly225, Ser227, Leu229, and Val230 including the triad of residues Asp181-Tyr194-Lys198, which are critical for the catalytic process involving the reduction of dihydrofolate to tetrahydrofolate. Results: The docking yielded 155 compounds meeting the stringent criteria of -8.9 kcal/mol instead of the widely used -7.0 kcal/mol. These compounds demonstrated binding affinities comparable to the known inhibitors; methotrexate (-9.5 kcal/mol), jatrorrhizine (-9.0 kcal/mol), pyrimethamine (-7.3 kcal/mol), hardwickiic acid (-8.1 kcal/mol), and columbamine (-8.6 kcal/mol). Protein-ligand interactions and molecular dynamics (MD) simulation revealed favorable hydrophobic and hydrogen bonding with critical residues, such as Lys198, Arg17, Ser111, Tyr194, Asp181, and Gly225. Crucial to the drug development, the compounds were physiochemically and pharmacologically profiled, narrowing the selection to eight compounds, excluding those with potential toxicities. The five selected compounds ZINC000095486253, ZINC000095486221, ZINC000095486249, 8alpha-hydroxy-13-epi-pimar-16-en-6,18-olide, and pachycladin D were predicted to be antiprotozoal (Leishmania) with Pa values of 0.642, 0.297, 0.543, 0.431, and 0.350, respectively. Conclusions: This study identified five lead compounds that showed substantial binding affinity against LmPTR1 as well as critical residue interactions. A 100 ns MD combined with molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations confirmed the robust binding interactions and provided insights into the dynamics and stability of the protein-ligand complexes.

Novel Natural Candidates for Replacing Synthetic Additives in Nutraceutical and Pharmaceutical Areas: Two Senna Species (S. alata (L.) Roxb. and S. occidentalis (L.) Link).

Senna alata (L.) Roxb. and Senna occidentalis (L.) Link (family Fabaceae) are commonly used in different systems of traditional medicine to treat ailments. The present study was designed to determine the phytoconstituents, antioxidant, enzyme inhibition, and antimicrobial activities of the methanolic extract from the leaves of these two Senna species. A total of 75 phenolic compounds belonging to dihydroxybenzoic acids, dihydroxycinnamic acids, flavonoid C-glycosides, flavonoid O-glycosides, flavonoid aglycones, anthraquinone glycosides, and anthraquinone aglycones were identified. Flavonoid C-glycosides were only found in S. occidentalis while sennosides A, B, and C were only detected in S. alata. In line with its higher total phenolic and flavonoids contents, S. alata exerted significantly (p < 0.05) higher antiradical (2,2-diphenyl-1-picrylhydrazy (DPPH) = 58.36 mg trolox equivalent (TE)/g; 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) = 118.86 mg TE/g), ions reducing (cupric reducing antioxidant capacity (CUPRAC) = 93.85 mg TE/g; ferric reducing antioxidant power (FRAP) = 50.42 mg TE/g), and total antioxidant (1.39 mmol TE/g) activities than S. occidentalis. S. alata revealed significantly (p < 0.05) higher inhibitory effect against butyrylcholinesterase (1.67 mg galantamine equivalent (GALAE)/g), tyrosinase (45.07 mg KAE/g) 45.07 mg kojic acid equivalent (KAE)/g), α-glucosidase (0.73 mmol acarbose equivalent (ACAE)/g), and α-amylase (2.95 mmol ACAE/g) enzymes. Both species showed high antibacterial and antifungal activities with remarkable antifungal activity exerted by S. alata against Trichoderma viride (minimum inhibition concentration (MIC) 1 mg/mL), similar to that of Ketoconazole. The study utilized molecular docking, molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) free energy calculations, and molecular dynamics simulations to evaluate the binding interactions between anthraquinone glycosides and various bacterial enzymes, including targets from Escherichia coli and Staphylococcus aureus. The findings suggest that compounds like sennoside A, sennoside B, and chrysophanol exhibit strong binding affinities, stable interactions, and potential as antimicrobial inhibitors, especially against vital bacterial proteins such as MurE and 30S ribosome S3. In conclusion, our findings underscore the biopharmaceutical potential of these two Senna species, suggesting their significance as sources of bioactive agents for health-related applications.

Computational Exploration of Phenolic Compounds from Endophytic Fungi as α-Glucosidase Inhibitors for Diabetes Management.

Diabetes has become a global epidemic, affecting even the younger people on an alarming scale. Inhibiting intestinal α-glucosidase is one of the key approaches to managing type 2 diabetes (T2D). In the present study, phenolic compounds (PCs) produced by endophytic fungi as potential α-glucosidase inhibitors (AGIs) are explored through ADMET profiling, molecular docking, and molecular dynamics (MD) Simulations. After 150 PCs were screened for their drug-likeness and toxicity properties, 45 molecules were selected. These were subjected to molecular docking studies against human N-terminal maltase-glucoamylase (NtMGAM). Based on binding energy and IC50 values, the best five PCs from different chemical classes (depsidones, phenolic acids, butenolides, furanones, and polyketides) were studied for their binding dynamics with NtMGAM employing all-atom MD simulations. Among the five ligands analyzed, the methybutyrolactone III (BUT)-NtMGAM complex exhibited significantly higher active site flexibility, indicating a conformational change in response to ligand binding. BUT interacted specifically with both key residues, Asp443 and Phe575, critical for enzyme-inhibitor stability. These interactions, coupled with increased flexibility, suggest enhanced stabilization of BUT in the active site pocket. BUT also exhibited one of the most favorable toxicity profiles among molecules analyzed using ProTox 3.0. Molecular mechanics Poisson-Boltzmann surface area calculations confirmed that BUT had the highest binding energy (-35.01 kcal/mol) driven by substantial van der Waals and electrostatic interactions. Another butenolide derivative, aspernolide (ALD) ranked second in the binding energy score (-31.13 kcal/mol). These findings suggest that PCs possessing butenolide scaffolds, like BUT and ALD, hold great promise as potential AGIs for managing T2D. These findings, however, need to be further validated through in vivo experimentation.

Integrative computational pipeline for identifying binding‐enhancing mutations targeting the MBD2–p66α interaction: Implications for therapeutic applications

AbstractThis study presents a comprehensive computational pipeline to identify and evaluate potential stabilizing mutations for the coiled‐coil protein–protein interaction between methyl‐CpG‐binding domain protein 2 (MBD2) and transcriptional repressor p66‐alpha (p66α). The pipeline begins with the BeAtMuSiC program, which employs statistical potentials derived from known structures to predict candidate stabilizing mutations at the protein–protein interface. Out of 565 potential mutations, 10 single‐point mutations (K149I, K163I, A237F, K149L, K149M, K163L, R166M, R166W, K163F, and E155L) with the highest binding affinity were selected for further evaluation using rigorous alchemical free energy calculations. These alchemical simulations conducted using the double‐system/single‐box method, predicted changes in binding free energy (ΔΔG) upon mutation while maintaining charge neutrality. The Crooks–Gaussian intersection technique was employed to analyze the results, identifying K149I, K149L, and K163L as potentially enhancing binding affinity the most, while mutations like K163F, A237F, and E155L were predicted to destabilize the interaction significantly. Complementary conventional Molecular Dynamics Simulations provided further support for the alchemical predictions, revealing decreased flexibility, increased contacts, and more compact structures for the predicted stabilizing mutants compared with the wild‐type complex. Additionally, Molecular Mechanics Poisson–Boltzmann Surface Area (MM/PBSA) binding free energy calculations were performed, and their results were consistent with the direction of free energy change predicted by the alchemical approach. This multifaceted computational pipeline, combining predictive methods, alchemical simulations, and conventional analyses, offers valuable insights into modulating the binding affinity of the MBD2–p66α coiled‐coil interaction. The identified stabilizing mutations can create numerous opportunities across biotechnology, biomedical research, and synthetic biology.

Abstract A015: Design and deciphering of precision peptide inhibitors for cancer stemness using generative deep learning and molecular dynamics simulations

Abstract The use of artificial intelligence (AI) and machine learning (ML) for exploring vast amino acid sequence spaces has recently gained traction in the discovery of antibiotics and the design of biomaterials with a wide array of algorithms. However, despite some advancements, designing peptide inhibitors to specifically modulate protein-protein interactions remains a significant challenge. In this contribution, we explore the use of a Long Short-Term Memory (LSTM) network - a type of recurrent neural network - to model peptide sequences, given its ability to process sequential data and capture long-term dependencies, critical for peptide design. Our research focuses on developing precision peptides that target the interaction between the Notch intracellular domain (NICD) and CBF1/RBPJ transcription factors, key regulators of the Notch signaling pathway implicated in breast and pancreatic cancer stemness. We inferred hydrophobic, hydrophilic, van der Waals, and salt bridge interactions from experimentally determined three-dimensional protein complex structures, which were used for feature engineering via one-hot encoding. Peptides, each 20 amino acids in length, were generated using temperature scaling in the LSTM model. These peptides were then structurally optimized and subjected to molecular dynamics (MD) simulations, followed by molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) analysis to assess their interactions and binding affinities with the Notch receptor. The MD simulations provide valuable molecular-level insights into the peptide-Notch interactions, helping to evaluate their binding strength. Further biological testing is underway to validate the efficacy of these lead peptide inhibitors and elucidate their molecular mechanisms in targeting cancer stem cells associated with breast cancer. Citation Format: Gurudeeban Selvaraj, Satyavani Kaliamurthi, Gilles H. Peslherbe. Design and deciphering of precision peptide inhibitors for cancer stemness using generative deep learning and molecular dynamics simulations. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr A015