End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design

Disruption of the eEF1A1/ARID3A/PKC-δ Complex by Neferine Inhibits Macrophage Glycolytic Reprogramming in Atherosclerosis

Glycolytic reprogramming of macrophages is a decisive factor in atherosclerosis (AS) plaque formation. Eukaryotic elongation factor 1A1 (eEF1A1) plays an important role in protein synthesis, ubiquitination degradation, and nuclear translocation. However, the potential function of eEF1A1 in AS has not yet been fully understood. Here, the natural small molecule neferine (Nef), which targets eEF1A1 to suppress macrophage glycolytic reprogramming is discovered. In this work, chemical genetics and non-modified target confirmation assays are used to confirm that eEF1A1 is a direct target of Nef. Mechanically, Nef disrupted the formation of the eEF1A1/ARID3A/PKC-δ complex, inhibits phosphorylation of ARID3A at Thr491, and consequently prevents its nuclear translocation. Meanwhile, it is verified that ARID3A is a transcriptional regulator of enolase 2 (ENO2), an important enzyme in the glycolytic process. Nef suppresses ENO2 transcription activation by affecting ARID3A binding to the promoter region of ENO2, which results in macrophage glycolytic reprogramming inhibition and transformation of macrophages from M1 to M2. Collectively, these findings provide an attractive future direction for AS therapy by inhibiting ARID3A/ENO2-mediated macrophage glycolytic reprogramming by targeting eEF1A1.

A Comparative Molecular Dynamics Study of Food-Derived Compounds as PD-L1 Inhibitors: Insights Across Six Flavonoid Subgroups

In this study, we investigated the inhibitory potential of 60 flavonoids from six distinct subgroups on the programmed cell death ligand 1 (PD-L1) dimer through molecular docking and dynamics simulations. Using AutoDock Vina for docking, the binding poses and affinities were evaluated, revealing an average binding affinity of -8.5 kcal/mol for the flavonoids. Among them, ginkgetin exhibited the highest binding free energy of -46.73 kcal/mol, indicating a strong interaction with PD-L1, while diosmin followed closely, with -44.96 kcal/mol. Molecular dynamics simulations were used to further elucidate the dynamic interactions and stability of the flavonoid-PD-L1 complexes, with the analyses showing minimal root mean square deviation (RMSD) and favorable root mean square fluctuation (RMSF) profiles for several compounds, particularly formononetin, idaein, and neohesperidin. Additionally, contact number and hydrogen bond analyses were performed, which highlighted ginkgetin and diosmin as key flavonoids with significant binding interactions, evidenced by their stable conformations and robust molecular interactions throughout the simulations. Ultimately, a cell-based assay confirmed their ability to inhibit the proliferation of cancer cells. These results, validated through cell-based assays, indicate that the strategy of identifying natural compounds with anticancer activity using computational modeling is highly effective.

Density functional theory and molecular dynamics simulation-based bioprospection of Agathosma betulina essential oil metabolites against protein tyrosine phosphatase 1B for interventive antidiabetic therapy

Type II diabetes mellitus (T2DM) is characterized by elevated blood glucose due to impaired insulin secretion/sensitivity. While conventional antihyperglycemic medications like biguanides, sulfonylureas, and other agents are commonly used, their long-term use can have side effects, prompting research into natural alternatives. This study bioprospects the antidiabetic potential of metabolites in Agathosma betulina (Buchu) essential oil through computational analysis of their ability to inhibit protein tyrosine phosphatase 1B (PTP1B), a therapeutic diabetes target. Molecular dynamic simulation, supported by DFT analysis, revealed that compounds linalylanthranilate (-20.18 kcal/mol) and γ-diosphenol (-16.49 kcal/mol) found in the oil exhibited stronger PTP1B inhibition than ursolic acid (-15.98 kcal/mol). The compounds showed favorable drug-like properties complying with Lipinski's rules. This study provides the first evidence that these Buchu oil compounds could potentially serve as PTP1B inhibitors to enhance insulin receptor sensitivity, showing promise for T2DM treatment. Further validation through safety and clinical studies is recommended.

Identification of rheochrysin as a potential anti-cancer inhibitor of NAD(P) H quinone oxidoreductase 1 through ensemble virtual screening, molecular dynamics, MM-GBSA and DFT

NAD(P)H quinone oxidoreductase 1 (NQO1) plays a multifaceted role in humans and its overexpression is associated with several cancers thereby making it a promising target for anticancer therapy. Moreover, the homodimeric NQO1 protein consists of two active sites formed because of the spatial orientation of the individual monomers. Therefore, ensemble virtual screening (eVS) was performed against both the active sites and multiple crystal structures of NQO1 to account for protein flexibility and predict the binding affinities of potential inhibitors. Since natural products show high structural diversity, ADMET adherent molecules from the ZINC natural product (NP) catalogue were selected for eVS. As a result, two NPs (rheochrysin and pulmatin) were identified as the top strong binders across the virtual screening results. These molecules were then subjected to 250 ns molecular dynamics simulations, MM-GBSA and per residue decomposition analysis which indicated that they exhibit stable protein-ligand interactions in the active sites of NQO1 protein. Furthermore, DFT calculations showed that these molecules exhibited higher energy gap indicating that they are stable with low reactivity. The results indicate that the naturally occurring compound rheochrysin (ZINC04098695) found in the traditional medicinal plant Rheum palmatum exhibits lower binding free energy suggesting its inhibitory potential against NQO1.

An Implicit Solvation Model for Binding Free Energy Estimation in Nonaqueous Solution

Implicit solvation models permit the approximate description of solute-solvent interactions, where water is the most often considered solvent due to its relevance in biological systems. The use of other solvents is less common but is relevant for applications such as in nuclear magnetic resonance (NMR) or chromatography. As an example, chloroform is commonly used in anisotropic NMR to measure residual dipolar couplings (RDCs) of chiral analytes weakly aligned by an alignment medium. They can be calculated from molecular dynamics (MD) simulations with explicit solvent, but it is computationally expensive, because tens of microseconds-long MD trajectories should be collected. Here, we develop a computational protocol and numerical implementation for binding free energies of rigid organic molecules to poly-γ-benzyl-l-glutamate using an implicit solvation model of chloroform. The model parameters are fit to alchemical binding free energies obtained from MD simulations in explicit chloroform and compared to the MD results and another implicit solvation model. Possible applications of the method are docking or Monte Carlo simulations based on a physically meaningful scoring function for the fast prediction of interaction poses of ligands for selective binding or the alignment of analytes.

Biophysics-guided uncertainty-aware deep learning uncovers high-affinity plastic-binding peptides

Plastic pollution, particularly microplastics (MPs), poses a significant global threat to ecosystems and human health, necessitating innovative remediation strategies. Biocompatible and biodegradable plastic-binding peptides (PBPs) offer a potential solution through targeted adsorption and subsequent MP detection or removal from the environment. A challenge in discovering plastic-binding peptides is the vast combinatorial space of possible peptides (i.e., over 1015 for 12-mer peptides), which far exceeds the sample sizes typically reachable by experiments or biophysics-based computational methods. One step towards addressing this issue is to train deep learning models on experimental or biophysical datasets, permitting faster and cheaper evaluations of peptides. However, deep learning predictions are not always accurate, which could waste time and money due to synthesizing and evaluating false positives. Here, we resolve this issue by combining biophysical modeling data from Peptide Binder Design (PepBD) algorithm, the predictive power and uncertainty quantification of evidential deep learning, and metaheuristic search methods to identify high-affinity PBPs for several common plastics. Molecular dynamics simulations show that the discovered PBPs have greater median adsorption free energies for polyethylene (5%), polypropylene (18%), and polystyrene (34%) relative to PBPs previously designed by PepBD. The impact of including uncertainty quantification in peptide design is demonstrated by the increasing improvement in the median adsorption free energy with decreasing uncertainty. This robust framework accelerates peptide discovery, paving the way for effective, bio-inspired solutions to MP remediation.

Mechanistic insights into the phosphorylation-regulated a disordered protein interaction module

The TFIIS N-terminal domain (TND) is a crucial protein scaffold that selectively recognizes disordered ligands, known as TND-interacting motifs (TIMs). Understanding the specific mechanisms of TND-TIM interactions is essential for deciphering the transcription machinery. Here, we investigated the conformational ensembles of the TND-TIM interaction module using molecular dynamics simulations. The study revealed that the experimental structures of TND-TIM complexes, including P75-PogZ and P75-IWS1, maintained stable conformations during microsecond-long simulations, even when the linked proteins between TND and TIM were removed or when TIM was phosphorylated. Conversely, both P75-ASK and HRP2-IWS1, prepared based on the structure of P75-IWS1, are unstable in simulations; for example, the helix-1 of TIMs shifts from their initial binding site on TND. However, phosphorylation enhances TND-TIM interactions and rapidly stabilizes the complex structure. A general rule for phosphorylation regulation of TND-TIM interactions is identified: the phosphoryl group of TIM forms hydrogen bonds with the positively charged side chains of TND residues, promoting dynamic correlation between TND and the Ser-containing acidic linker of TIM, and enhancing residue-residue interactions among helix-1 and FXGF motif of TIM with TND. These phosphorylation-induced changes resulted in a higher affinity between TND and TIM. Our study provides insights into the phosphorylation-regulated TND-TIM interaction module at an atomic level, facilitating a deeper understanding of the molecular mechanisms of protein interactome assembly in transcription machinery.

Discovery of potential VEGFR-2 inhibitors from natural products by virtual screening and molecular dynamics simulation

Hepatocellular carcinoma (HCC) is the most common cancer worldwide and vascular endothelial growth factor receptor-2 (VEGFR-2) is an important target in the development of inhibitors for the treatment of liver cancer. So far, however, there are no effective drugs targeting VEGFR-2 to achieve complete treatment of liver cancer. In this study, we employed molecular docking, molecular dynamics simulations, molecular mechanics generalized Born surface area (MM-GBSA) method, quantum mechanics/molecular mechanics (QM/MM) calculations and steered molecular dynamics simulations to discover the potential inhibitors from COCONUT database targeting VEGFR-2. The molecular docking analyses of 13 743 natural compounds targeting VEGFR-2 identified 96 molecules as promising candidates. Our molecular dynamics simulations revealed that only 5 candidate-docking systems remained stable over 100 ns of production run. Then, steered molecular dynamics simulations showed that CNP0076764, CNP0028810, CNP0177683 and CNP0107283 had higher mean force values than that of sorafenib, reflecting the high potential of candidate molecules. A detailed analysis of the binding modes revealed that Leu840, Val848, Lys868, Glu885, Leu889, Val899, Val916, Leu1035, Cys1045, Asp1046 and Phe1047 play key roles in binding the inhibitors. Overall, this study shows evidence that the four natural products obtained from the COCONUT database could be further used as anti-cancer inhibitors, which provides theoretical guidance for designing new drugs.

Mechanistic Insights into CYP199A4-Catalyzed α-Hydroxyketone Formation and Hydrogen Bond-Assisted C-C Bond Cleavage Catalyzed by the CYP199A4 F182L Mutant

CYP199A4 is a cytochrome P450 and can catalyze the hydroxylation of 4-propionylbenzoic acid (4-pIBA) to generate α-hydroxyketone with high stereoselectivity. The F182L mutant of CYP199A4 (F182L-CYP199A4) has been shown to support the cleavage of the C-C bond between the carbonyl and hydroxyl groups of α-hydroxyketone, whereas wild-type CYP199A4 cannot. To uncover how the Phe182 regulates substrate reactivity, we conducted classical molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) MD simulations on these systems. The results predicted that the formation of α-hydroxyketone preferentially led to the (S)-enantiomer. Moreover, the findings revealed that the F182L-CYP199A4 facilitated the formation of a hydrogen bond between the α-hydroxyketone and the reactive peroxoanion (POA) species. This interaction stabilized the α-hydroxyketone near POA and promoted the subsequent C-C bond cleavage. The mechanism of α-hydroxyketone formation and the subsequent C-C bond cleavage were elucidated by employing the hybrid density functional theory (DFT). The α-hydroxyketone formation mechanism involved C-H hydroxylation of 4-pIBA with a rate-limiting energy barrier of 17.1 kcal/mol. The C-C bond cleavage of α-hydroxyketone catalyzed by F182L-CYP199A4 occurred via a radical attack mechanism.

Computational Modeling of Pharmaceuticals with an Emphasis on Crossing the Blood-Brain Barrier

The discovery and development of new pharmaceutical drugs is a costly, time-consuming, and highly manual process, with significant challenges in ensuring drug bioavailability at target sites. Computational techniques are highly employed in drug design, particularly to predict the pharmacokinetic properties of molecules. One major kinetic challenge in central nervous system drug development is the permeation through the blood-brain barrier (BBB). Several different computational techniques are used to evaluate both BBB permeability and target delivery. Methods such as quantitative structure-activity relationships, machine learning models, molecular dynamics simulations, end-point free energy calculations, or transporter models have pros and cons for drug development, all contributing to a better understanding of a specific characteristic. Additionally, the design (assisted or not by computers) of prodrug and nanoparticle-based drug delivery systems can enhance BBB permeability by leveraging enzymatic activation and transporter-mediated uptake. Neuroactive peptide computational development is also a relevant field in drug design, since biopharmaceuticals are on the edge of drug discovery. By integrating these computational and formulation-based strategies, researchers can enhance the rational design of BBB-permeable drugs while minimizing off-target effects. This review is valuable for understanding BBB selectivity principles and the latest in silico and nanotechnological approaches for improving CNS drug delivery.

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.

Computational Elucidation of a Monobody Targeting the Phosphatase Domain of SHP2

Src homology 2 (SH2) domain-containing phosphatase 2 (SHP2) is a key regulator in cellular signaling pathways because its dysregulation has been implicated in various pathological conditions, including cancers and developmental disorders. Despite its importance, the molecular basis of SHP2's regulatory mechanism remains poorly understood, hindering the development of effective targeted therapies. In this study, we utilized the high-specificity monobody Mb11 to investigate its interaction with the SHP2 phosphatase domain (PTP) using multiple replica molecular dynamics simulations. Our analyses elucidate the precise mechanisms through which Mb11 achieves selective recognition and stabilization of the SHP2-PTP domain, identifying key residues and interaction networks essential for its high binding specificity and regulatory dynamics. Furthermore, the study highlights the pivotal role of residue C459 in preserving the structural integrity and functional coherence of the complex, acting as a central node within the interaction network and underpinning its stability and efficiency. These findings have significantly advanced the understanding of the mechanisms underlying SHP2's involvement in disease-related signaling and pathology while simultaneously paving the way for the rational design of targeted inhibitors, offering significant implications for therapeutic strategies in SHP2-associated diseases and contributing to the broader scope of precision medicine.

QSAR-driven screening uncovers and designs novel pyrimidine-4,6-diamine derivatives as potent JAK3 inhibitors

This study presents a robust and integrated methodology that harnesses a range of computational techniques to facilitate the design and prediction of new inhibitors targeting the JAK3/STAT pathway. This methodology encompasses several strategies, including QSAR analysis, pharmacophore modeling, ADMET prediction, covalent docking, molecular dynamics (MD) simulations, and the calculation of binding free energies (MM/GBSA). An efficacious QSAR model was meticulously crafted through the employment of multiple linear regression (MLR). The initial MLR model underwent further refinement employing an artificial neural network (ANN) methodology aimed at minimizing predictive errors. Notably, both MLR and ANN exhibited commendable performance, showcasing R2 values of 0.89 and 0.95, respectively. The model's precision was assessed via leave-one-out cross-validation (CV) yielding a Q2 value of 0.65, supplemented by rigorous Y-randomization. , The pharmacophore model effectively differentiated between active and inactive drugs, identifying potential JAK3 inhibitors, and demonstrated validity with an ROC value of 0.86. The newly discovered and designed inhibitors exhibited high inhibitory potency, ranging from 6 to 8, as accurately predicted by the QSAR models. Comparative analysis with FDA-approved Tofacitinib revealed that the new compounds exhibited promising ADMET properties and strong covalent docking (CovDock) interactions. The stability of the new discovered and designed inhibitors within the JAK3 binding site was confirmed through 500 ns MD simulations, while MM/GBSA calculations supported their binding affinity. Additionally, a retrosynthetic study was conducted to facilitate the synthesis of these potential JAK3/STAT inhibitors. The overall integrated approach demonstrates the feasibility of designing novel JAK3/STAT inhibitors with robust efficacy and excellent ADMET characteristics that surpass Tofacitinib by a significant margin.Communicated by Ramaswamy H. Sarma.

Rational design of thiazolidine-4-one-gallic acid hybrid derivatives as selective partial PPARγ modulators: an in-silico approach for type 2 diabetes treatment

Type 2 diabetes mellitus is a bipolar metabolic disorder characterized by abnormalities in insulin production from β-cells and insulin resistance. Thiazolidinediones are potent anti-diabetic agents that act through the modulation of the peroxisome proliferator-activated receptor γ (PPARγ), a nuclear receptor. However, their full agonistic activity leads to severe side effects by stabilizing Helix12 through strong hydrogen bonding with the TYR473 residue. Partial and selective PPARγ modulators (GW0072, GQ16, VSP-51, MRL-20, MBX-213, INT131) have demonstrated superior results compared to full agonists without causing adverse effects, as reported in existing data. To address this uncertainty and advance therapeutic options, we identified and designed a novel class of compounds (A1-A23) based on a hybrid structure combining phenolic and Thiazolidine-4-one's moieties. Our rational drug design strategy incorporated structural-activity relationship principle, and validated the docking studies through calculated the root mean square deviation. Additionally, we conducted molecular docking, binding energy, molecular dynamics simulations, and post-molecular dynamics calculations to evaluate the dynamics behavior between the ligands and protein. The selected ligands demonstrated highly favorable docking scores and binding energies, comparable to the co-crystal (rosiglitazone) such as A12 (-13.9 kcal/mol and -86.2 kcal/mol), A1 (-11.1 kcal/mol and -79.5 kcal/mol), A13 (-11.3 kcal/mol and -91.4 kcal/mol), and the co-crystal itself (-9.8 kcal/mol and -76 kcal/mol), respectively. Finally, the MD revealed that, the selected ligands were equally contributed for stabilization of Helix12 and β-sheets. It was concluded, the designed ligands (A12, A1, and A13) exhibited weaker hydrogen-bond interactions with specific residue TYR473 which partially modulated the PPARγ protein.Communicated by Ramaswamy H. Sarma.

Identification of promising inhibitors against breast cancer disease by targeting NUDIX hydrolase 5 (NUDT5) biomolecule

It is well documented that NUDT5 enzyme inhibition in breast cancer cell lines arrest cancer cells growth, invasiveness and migration. The NUDT5 enzyme enhances breast cancer aggressiveness and act as key regulator of oncogenic pathways. Similarly, the NUDT5 enzyme plays a primer role in ATP-dependent cellular processes and proliferation in breast cancer. Thus, the NUDT5 enzyme plays a profound contribution in promoting breast cancers carcinogenesis and could be an ideal target for anti-cancer drug discovery. In this work, LAS_51382001, LAS_51177972 and LAS_51380924 with binding energy of -12.64 kcal/mol, -11.59 kcal/mol and -10.01 kcal/mol, respectively were filtered as lead molecules. The control molecule binding energy was -10.87 kcal/mol. The system dynamics were found uniform in molecular dynamics simulation studies and observed with no major structural changes. Among the leads, the LAS_51177972 showed the most stable binding energy values. The MM-GBSA binding energy of the compound was -37.07 kcal/mol and MM-PBSA binding energy of -43.56 kcal/mol. Similarly, the compound revealed very stable WaterSwap absolute binding energy values; Bennett's, TI and FEP energy of -36.2 kcal/mol, -36.13 kcal/mol and -36.58 kcal/mol, respectively. Similarly, the leads reported very favorable physicochemical properties, water solubility, pharmacokinetics, druglikeness and medicinal chemistry properties. In a nutshell, the compounds are potent in term of the current computational study however, need to be subjected to experimental studies.Communicated by Ramaswamy H. Sarma.

Allosteric modulation of NF1 GAP: Differential distributions of catalytically competent populations in loss-of-function and gain-of-function mutants

Neurofibromin (NF1), a Ras GTPase-activating protein (GAP), catalyzes Ras-mediated GTP hydrolysis and thereby negatively regulates the Ras/MAPK pathway. NF1 mutations can cause neurofibromatosis type 1 manifesting tumors, and neurodevelopmental disorders. Exactly how the missense mutations in the GAP-related domain of NF1 (NF1GRD) allosterically impact NF1 GAP to promote these distinct pathologies is unclear. Especially tantalizing is the question of how same-domain, same-residue NF1GRD variants exhibit distinct clinical phenotypes. Guided by clinical data, we take up this dilemma. We sampled the conformational ensembles of NF1GRD in complex with GTP-bound K-Ras4B by performing molecular dynamics simulations. Our results show that mutations in NF1GRD retain the active conformation of K-Ras4B but with biased propensities of the catalytically competent populations of K-Ras4B-NF1GRD complex. In agreement with clinical depiction and experimental tagging, compared to the wild type, NF1GRD E1356A and E1356V mutants effectively act through loss-of-function and gain-of-function mechanisms, leading to neurofibromatosis and developmental disorders, respectively. Allosteric modulation of NF1GRD GAP activity through biasing the conformational ensembles in the different states is further demonstrated by the diminished GAP activity by NF1GRD isoform 2, further manifesting propensities of conformational ensembles as powerful predictors of protein function. Taken together, our work identifies a NF1GRD hotspot that could allosterically tune GAP function, suggests targeting Ras oncogenic mutations by restoring NF1 catalytic activity, and offers a molecular mechanism for NF1 phenotypes determined by their distinct conformational propensities.

Identification of potential drug molecules against fibroblast growth factor receptor 3 (FGFR3) by multi-stage computational-biophysics correlate

The fibroblast growth factor receptor 3 (FGFR3) is warranted as a promising therapeutic target in bladder cancer as it is described in 75% of papillary bladder tumors. Considering this, the present study was conducted to use different approaches of computer-aided drug discovery (CADD) to identify the best binding compounds against the active pocket of FGFR3. Compared to control pyrimidine derivative, the study identified three promising lead structures; BDC_24037121, BDC_21200852, and BDC_21206757 with binding energy value of -14.80 kcal/mol, -12.22 kcal/mol, and -11.67 kcal/mol, respectively. The control molecule binding energy score was -9.85 kcal/mol. The compounds achieved deep pocket binding and produced balanced interactions of hydrogen bonds and van der Waals. The FGFR3 enzyme residues such as Leu478, Lys508, Glu556, Asn562, Asn622, and Asp635. The molecular dynamic (MD) simulation studies additionally validated the docked conformation stability with respect to FGFR3 with a mean root mean square deviation (RMSD) value of < 3 Å. The root mean square fluctuation (RMSF) complements the complexes structural stability and the residues showed less fluctuation in the presence of compounds. The Poisson-Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) methods revalidated compounds better binding and highlighted van der Waals energy to dominate the overall net energy. The docked stability was additionally confirmed by WaterSwap and AMBER normal mode entropy energy analyses. In a nutshell, the compounds shortlisted in this study are promising in term of theoretical binding affinity for FGFR3 but experimental validation is needed.Communicated by Ramaswamy H. Sarma.

Identification of synthetically tractable MERS-CoV main protease inhibitors using structure-based virtual screening and molecular dynamics potential of mean force (PMF) calculations

The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is a potentially lethal infection that presents a substantial threat to health, especially in Middle East nations. Given that no FDA-approved specific therapy for MERS infection exists, designing and discovering a potent antiviral therapy for MERS-CoV is crucial. One pivotal strategy for inhibiting MERS replication is to focus on the viral main protease (Mpro). In this study, we identify potential novel Mpro inhibitors employing structure-based virtual screening of our recently reported Ugi reaction-derived library (URDL) consisting of cherry-picked molecules from the literature. The key features of the URDL library include synthetic tractability (1-2 pot synthesis) of the molecules scaffold and unexplored chemical space. The hits were ranked based on the docking score, MM-GBSA free energy of binding, and the interaction pattern with the active site residues. A molecular dynamics (MD) simulation study was performed for the first two top-ranked compounds to analyze the stability and free binding energy based on the molecular mechanics Poisson-Boltzmann surface area. The potential mean force calculated from the steered molecular dynamics (SMD) simulations of the hits indicates improved H-bond potential, enhanced conformational stability, and binding affinity toward the target, compared to the cocrystallized ligand. The discovered hits represent novel synthetically tractable scaffolds as potential MERS-CoV Mpro inhibitors.Communicated by Ramaswamy H. Sarma.

Exploring the constitutive activation mechanism of the class A orphan GPR20

GPR20, an orphan G protein-coupled receptor (GPCR), shows significant expression in intestinal tissue and represents a potential therapeutic target to treat gastrointestinal stromal tumors. GPR20 performs high constitutive activity when coupling with Gi. Despite the pharmacological importance of GPCR constitutive activation, determining the mechanism has long remained unclear. In this study, we explored the constitutive activation mechanism of GPR20 through large-scale unbiased molecular dynamics simulations. Our results unveil the allosteric nature of constitutively activated GPCR signal transduction involving extracellular and intracellular domains. Moreover, the constitutively active state of the GPR20 requires both the N-terminal cap and Gi protein. The N-terminal cap of GPR20 functions like an agonist and mediates long-range activated conformational shift. Together with the previous study, this study enhances our knowledge of the self-activation mechanism of the orphan receptor, facilitates the drug discovery efforts that target GPR20.

Investigation of ritonavir analogs antiretroviral natural compounds against SARS-CoV-2 envelope protein

Since the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported from Wuhan, China, there has been a surge in scientific research to find a permanent cure for the disease. The main challenge in effective drug discovery is the continuously mutating nature of the SARS-CoV-2 virus. Thus, we have used the I-TASSER modeling to predict the structure of the SARS-CoV-2 viral envelope protein followed by combinatorial computational assessment to predict its putative potential small molecule inhibitors. As early treatment with ritonavir in combination was associated with faster time to clinical improvement and/or virological clearance, we aimed to retrieve analogs of ritonavir to find ideal inhibitors for SARS-CoV-2 viral envelope protein. The collected ligands were screened against the predicted binding pocket of viral envelope protein using extra precision (XP) docking protocol and the first four best-docked compounds were studied for complex stability using 300 ns all-atom molecular dynamics simulations embedding within the cellular membrane. Among the selected compounds, ZINC64859171 and ZINC1221429 showed considerable stability and interactions by comparison to the reference compound, i.e., Ritonavir (ZINC3944422). Moreover, the post-simulation analysis suggested the considerable binding affinity and induced conformation changes in the respective docked complexes against Ritonavir. Altogether, the obtained results demonstrated the putative potential of screened ritonavir analogs, i.e., ZINC64859171, against the envelope protein of SARS-CoV-2 and can be considered for further drug development in the treatment of the COVID-19 pandemic.Communicated by Ramaswamy H. Sarma.

Rationalizing protein-ligand interactions via the effective fragment potential method and structural data from classical molecular dynamics

The Effective Fragment Potential (EFP) method, a polarizable quantum mechanics-based force field for describing non-covalent interactions, is utilized to calculate protein-ligand interactions in seven inactive cyclin-dependent kinase 2-ligand complexes, employing structural data from molecular dynamics simulations to assess dynamic and solvent effects. Our results reveal high correlations between experimental binding affinities and EFP interaction energies across all the structural data considered. Using representative structures found by clustering analysis and excluding water molecules yields the highest correlation (R2 of 0.95). In addition, the EFP pairwise interaction energy decomposition analysis identifies critical interactions between the ligands and protein residues and provides insight into their nature. Overall, this study indicates the potential applications of the EFP method in structure-based drug design.

Uncovering the Binding Mechanism of Mutated Omicron Variants via Computational Strategies

The COVID-19 pandemic, triggered by the SARS-CoV-2 virus, has resulted in nearly 630 million cases and 6.60 million fatalities globally, as of November 2022. SARS-CoV-2, a species of the Coronaviridae family, has a single-stranded positive-sense RNA genome as well as four main structural proteins (S, E, M, and N) required for viral entrance into target cells. The spike protein (S) influences this entry through interactions with human angiotensin-converting enzyme 2 (hACE2) receptor. The World Health Organization (WHO) recognized numerous variants of concern (VOCs) that involve Alpha, Beta, Gamma, Delta, and Omicron, having multiple mutations within the spike protein, altering infection rates and immunity evasion. The Omicron variant, featuring 50 mutations, mainly within the spike protein's receptor-binding domain (RBD), has a higher transmission rate as compared to other variants. This study focused on two recent Omicron subvariants, XBB.1.5 and CH.1.1, which are known for their high affinity for the human ACE2 receptor. Utilizing an in silico strategy, a total of 1.65 μs molecular dynamics (MD) simulations were performed to assess the stability as well as binding details of these subvariants along with the wild-type Omicron variants. The comprehensive structural stability of the spike protein-hACE2 complexes was evaluated by using numerous parameters including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (R g), and principal component analysis (PCA). Moreover, the binding free energies have been determined using the MM-GBSA approach to provide insights into the binding affinities of these variants. Evaluation revealed that the unbound mutant frameworks (SM and TM) displayed higher degrees of instability in comparison to the wild-type (WT) Omicron variant. In contrast, the WT-hACE2 of the Omicron variant complex was less stable than the subvariants, SM-hACE2 and TM-hACE2 complexes. Binding free energy calculations employing MM-PBSA disclosed higher binding energy values for the SM-hACE2 and TM-hACE2 complexes, suggesting a more stable and ordered binding interaction. The observed increase in transmissibility of the new XBB.1.5 and CH.1.1 subvariants, in comparison to the wild-type Omicron, appears to be due to this greater stability and ordered binding.

End-Point Affinity Estimation of Galectin Ligands by Classical and Semiempirical Quantum Mechanical Potentials

The use of quantum mechanical potentials in protein-ligand affinity prediction is becoming increasingly feasible with growing computational power. To move forward, validation of such potentials on real-world challenges is necessary. To this end, we have collated an extensive set of over a thousand galectin inhibitors with known affinities and docked them into galectin-3. The docked poses were then used to systematically evaluate several modern force fields and semiempirical quantum mechanical (SQM) methods up to the tight-binding level under consistent computational workflow. Implicit solvation models available with the tested methods were used to simulate solvation effects. Overall, the best methods in this study achieved a Pearson correlation of 0.7-0.8 between the computed and experimental affinities. There were differences between the tested methods in their ability to rank ligands across the entire ligand set as well as within subsets of structurally similar ligands. A major discrepancy was observed for a subset of ligands that bind to the protein via a halogen bond, which was clearly challenging for all the tested methods. The inclusion of an entropic term calculated by the rigid-rotor-harmonic-oscillator approximation at SQM level slightly worsened correlation with experiment but brought the calculated affinities closer to experimental values. We also found that the success of the prediction strongly depended on the solvation model. Furthermore, we provide an in-depth analysis of the individual energy terms and their effect on the overall prediction accuracy.

Acetohydroxyacid Synthase (AHAS) Inhibitors as Antitubercular Agents: Insights From Molecular Docking and Dynamics Simulations

Acetohydroxyacid synthase (AHAS) is a vital enzyme in Mycobacterium tuberculosis, the pathogen causing tuberculosis (TB), involved in branched-chain amino acid synthesis. Targeting AHAS for drug design against TB offers a promising strategy due to its essentiality in bacterial growth. In current investigation, we have designed 160 novel compounds by leveraging key scaffolds identified through structure-based drug design (SBDD) methodologies. Subsequently, these compounds underwent molecular docking analysis to elucidate their potential interactions with the AHAS protein. The Top 4 compounds (with docking score above -8.2 kcal/mol) resulting from the docking studies were subjected to rigorous molecular dynamics simulations, spanning a runtime of 100 ns, to assess their stability across various parameters including root mean square deviation (RMSD), root mean square fluctuation (RMSF), secondary structure elements (SSEs), radius of gyration (rGyr), solvent accessible surface area (SASA), and MM-GBSA free energy values. Remarkably, compounds KG 98 and KG 131 exhibited superior stability profiles across all analyzed parameters. From the detailed interactions analysis, it was found that the nitrogen containing heterocyclic rings (1,3,5-triazine/imidazole) are essential to have the potential binding interactions with the AHAS enzyme. Some of the interactions were persisted for more than 75% of the simulated time, which shows the strength of the interactions. The findings suggest these lead molecules as promising candidates for AHAS inhibition, a potential avenue for TB treatment and management.

Targeting cyclin-dependent kinase 11: a computational approach for natural anti-cancer compound discovery

Cancer, a leading global cause of death, presents considerable treatment challenges due to resistance to conventional therapies like chemotherapy and radiotherapy. Cyclin-dependent kinase 11 (CDK11), which plays a pivotal role in cell cycle regulation and transcription, is overexpressed in various cancers and is linked to poor prognosis. This study focused on identifying potential inhibitors of CDK11 using computational drug discovery methods. Techniques such as pharmacophore modeling, virtual screening, molecular docking, ADMET predictions, molecular dynamics simulations, and binding free energy analysis were applied to screen a large natural product database. Three pharmacophore models were validated, leading to the identification of several promising compounds with stronger binding affinities than the reference inhibitor. ADMET profiling indicated favorable drug-like properties, while molecular dynamics simulations confirmed the stability and favorable interactions of top candidates with CDK11. Binding free energy calculations further revealed that UNPD29888 exhibited the strongest binding affinity. In conclusion, the identified compound shows potential as a CDK11 inhibitor based on computational predictions, suggesting their future application in cancer treatment by targeting CDK11. These computational findings encourage further experimental validation as anti-cancer agents.

Evaluating cutinase from Fusarium oxysporum as a biocatalyst for the degradation of nine synthetic polymer

Plastic poses a significant environmental impact due to its chemical resilience, leading to prolonged and degradation times and resulting in widespread adverse effects on global flora and fauna. Cutinases are essential enzymes in the biodegradation process of synthetic polymers like polyethylene terephthalate (PET), which recognized organisms can break down. Here, we used molecular dynamics and binding free energy calculations to explore the interaction of nine synthetic polymers, including PET, with Cutinase from Fusarium oxysporum (FoCut). According to our findings, the polymers poly(ethylene terephthalate) (PET), poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBH), poly(butylene succinate) (PBS), poly(butylene adipate-co-terephthalate) (PBAT) and poly(ε-caprolactone) (PCL) can bind to the Cutinase enzyme from F. oxysporum, indicating potential biodegradation activity for these polymers. PET exhibited the highest binding affinity (- 34.26 kcal/mol). Besides PET, the polymers PHBH, PBS, PBAT, and PCL also demonstrated significant affinities for the FoCut enzyme, with binding values of - 18.44, - 29.71, - 22.78, and - 22.26 kcal/mol, respectively. Additionally, analysis of the phylogenetic tree of cutinases produced by different organisms demonstrated that even though the organisms belong to different kingdoms, the cutinase from F. oxysporum (FoCut) showed biological similarity in its activity in degrading polymers with the cutinase enzyme from the bacterium Kineococcus radiotolerans and the fungus Moniliophthora roreri. Furthermore, the phylogenetic analysis demonstrated that the PETase enzyme has a very high similarity with the bacterial cutinase enzyme than with the fungal cutinase, therefore demonstrating that the PETase enzyme from Ideonella sakaiensis can easily be a modified bacterial cutinase enzyme that created a unique feature in biodegrading only the pet polymer through an evolutionary process due to its environment and its biochemical need for carbon. Our data demonstrate that bacterial cutinase enzymes have the same common ancestor as the PETase enzyme. Therefore, cutinases and PETase are interconnected through their biological similarity in biodegrading polymers. We demonstrated that important conserved regions, such as the Ser-Asp-His catalytic triad, exist in the enzyme's catalytic site and that all Cut enzymes from different organisms have the same region to couple with the polymer structures.

Identifying Natural Products as Feline Coronavirus Mpro Inhibitors by Structural-Based Virtual Screening and Enzyme-Based Assays

The main protease (Mpro) is a pivotal target in the life cycle of feline coronavirus (FCoV), which causes a high mortality feline disease, feline infectious peritonitis (FIP). Virtual screening was performed against the feline coronavirus Mpro to find active compounds with low toxicity from a library of natural products. Eighty-six compounds were selected by using the rank of docking score and binding pose analysis. In the enzyme-based assay, 12 compounds showed a more than 40% inhibitory effect on Mpro at a concentration of 200 μmol/L. The IC50 values of theaflavin 3,3'-digallate (25.0 μmol/L), sennoside C (25.2 μmol/L), pinocembrin-galloyl-HHDP-G (33.3 μmol/L), and thonningianin A (50.6 μmol/L) were determined. In addition, curcuminoids (51.7-64.3% under 200 μmol/L) and flavonoids (41.3-60.3% under 200 μmol/L) also exhibited certain inhibitory effects on Mpro. Molecular dynamics simulations and binding free energy calculations were employed to reveal the atomic details of the binding of these compounds with Mpro. The results showed that most of the compounds formed significant interactions with key residues on the catalytic site, such as His-41, Cys-144, and Glu-165. These compounds could serve as a starting point to develop FCoV Mpro inhibitors with high potency.

Adenocarpine, Marmesin, and Lycocernuine from Ficus benjamina as Promising Inhibitors of Aldose Reductase in Diabetes: A Bioinformatics-Guided Approach

Diabetes affects approximately 422 million people worldwide, leading to 1.5 million deaths annually and causing severe complications such as kidney failure, neuropathy, and cardiovascular disease. Aldose reductase (AR), a key enzyme in the polyol pathway, is an important therapeutic target for managing these complications. The high cost, severe side effects, and rising drug resistance in traditional diabetes treatments underscore the urgent need for novel AR-targeting antidiabetic agents. Ficus benjamina used in traditional medicine demonstrates promising potential for diabetes management. This study investigated the antidiabetic potential of F. benjamina phytocompounds targeting AR receptor employing a structure-based drug design approach to identify potential antidiabetic drug agents. Using molecular docking, ADMET analysis, molecular dynamics (MD) simulation, MM/GBSA, MM/PBSA, and DFT calculations, we identified three promising lead compounds: adenocarpine (- 9.2 kcal/mol), marmesin (- 8.8 kcal/mol), and lycocernuine (- 8.4 kcal/mol). These compounds presented favorable pharmacokinetic, pharmacodynamic, and toxicity profiles, with a 500-ns MD simulation confirming their stability, supported by PCA and Gibbs FEL analysis. MM/GBSA study identified adenocarpine (- 72.53 kcal/mol) as the best compound, outperforming marmesin (- 70 kcal/mol) and lycocernuine (- 61.95 kcal/mol). DFT analysis revealed that adenocarpine exhibited the highest molecular reactivity (3.914 eV), while lycocernuine demonstrated the greatest kinetic stability (6.377 eV). Marmesin and lycocernuine showed increased reactivity upon transitioning from the free states (4.441 eV and 6.377 eV, respectively) to the bound states (4.359 eV and 6.231 eV, respectively). These results could lead to the development of adenocarpine, marmesin, and lycocernuine as novel drug candidates for diabetes, warranting further in vitro and in vivo validation.

Flexible framework of computing binding free energy using the energy representation theory of solution

Host-guest binding plays a crucial role in the functionality of various systems, and its efficiency is often quantified using the binding free energy, which represents the free-energy difference between the bound and dissociated states. Here, we propose a methodology to compute the binding free energy based on the energy representation (ER) theory of solution, which enables us to evaluate the free-energy difference between the systems of interest with the molecular dynamics (MD) simulations. Unlike the other free-energy methods, such as the Bennett acceptance ratio (BAR), the ER theory does not require the MD simulations for hypothetical intermediate states connecting the systems of interest, leading to reduced computational costs. By constructing the thermodynamic cycle of the binding process that is suitable for the ER theory, a robust calculation of the binding free energy is realized. We apply the present method to the self-association of N-methylacetamide in different solvents and the binding of aspirin to β-cyclodextrin (CD) in water. In the former case, the present method estimates that the binding free energy decreases as the solvent polarity decreases. This trend is consistent with the experimental finding. For the latter system, the binding free energies for the two representative CD-aspirin bound complexes, primary (P) and secondary (S) complexes, are estimated to be -5.2 ± 0.1 and -5.03 ± 0.09 kcal mol-1, respectively. These values are satisfactorily close to those from the BAR method [-4.2 ± 0.2 and -4.1 ± 0.2 kcal mol-1 for P and S, respectively]. Furthermore, the interaction-energy component analysis reveals that the van der Waals interaction between aspirin and CD dominantly contributes to the stabilization of the bound complexes, which is in harmony with the well-known binding mechanism in the CD systems.

Unraveling the Molecular Architecture of Mosquito D1-Like Dopamine Receptors: Insights Into Ligand Binding and Structural Dynamics for Insecticide Development

Vector-borne diseases pose a severe threat to human life, contributing significantly to global mortality. Understanding the structure-function relationship of the vector proteins is pivotal for effective insecticide development due to their involvement in drug resistance and disease transmission. This study reports the structural and dynamic features of D1-like dopamine receptors (DARs) in disease-causing mosquito species, such as Aedes aegypti, Culex quinquefasciatus, Anopheles gambiae, and Anopheles stephensi. Through molecular modeling and simulations, we describe the common structural fold of mosquito DARs within the G-protein-coupled receptor family, highlighting the importance of an orthosteric and enlarged binding pocket. The orthosteric binding pocket, resembling a cage-like structure, is situated ~15 Å deep within the protein, with two serine residues forming the roof and an aspartate residue, along with two conserved water molecules (W1 and W2), forming the floor. The side walls are composed of two phenylalanine residues on one side and a valine residue on the other. The antagonist binding site, an enlarged binding pocket (EBP) near the entrance cavity, can accommodate ligands of varying sizes. The binding energy of dopamine is observed to be ~2-3 kcal/mol higher than that of the antagonist molecules amitriptyline, asenapine, and flupenthixol in mosquito DARs. These antagonist molecules bind to EBP, which obstructs dopamine movement toward the active site, thereby inhibiting signal transduction. Our findings elucidate the molecular architecture of the binding pockets and the versatility of DARs in accommodating diverse ligands, providing a foundational framework for future drug and insecticide development.

Development of a recombinant Ang1 variant with enhanced Tie2 binding and its application to attenuate sepsis in mice

The angiopoietin (Ang)-Tie axis, critical for endothelial cell function and vascular development, is a promising therapeutic target for treating vascular disorders and inflammatory conditions like sepsis. This study aimed to enhance the binding affinity of recombinant Ang1 variants to the Tie2 and explore their therapeutic potential. Structural insights from the Ang1-Tie2 complex enabled the identification of key residues within the Ang1 receptor binding domain (RBD) critical for Tie2 interaction. Molecular dynamics simulations revealed that Met436Arg (M436R) and Ala451Asp (A451D) could improve Ang1's Tie2 binding affinity. One variant, Ang1-RBDA451D, demonstrated a 100-fold increase compared to the wild type. Cellular assays revealed that Ang1A451D enhanced Tie2 phosphorylation, promoting endothelial cell migration and tube formation. In vivo, this variant effectively reduced inflammatory cytokines and attenuated organ damage in septic mice. These findings highlight Ang1A451D as a promising therapeutic candidate for vascular diseases, offering notable clinical potential for mitigating sepsis-related vascular dysfunction.

Sucrose and Gibberellic Acid Binding Stabilize the Inward-Open Conformation of AtSWEET13: A Molecular Dynamics Study

In plants, sugar will eventually be exported transporters (SWEETs) facilitate the translocation of mono- and disaccharides across membranes and play a critical role in modulating responses to gibberellin (GA3), a key growth hormone. However, the dynamic mechanisms underlying sucrose and GA3 binding and transport remain elusive. Here, we employed microsecond-scale molecular dynamics (MD) simulations to investigate the influence of sucrose and GA3 binding on SWEET13 transporter motions. While sucrose exhibits high flexibility within the binding pocket, GA3 remains firmly anchored in the central cavity. Binding of both ligands increases the average channel radius along the transporter's principal axis. In contrast to the apo form, which retains its initial conformation throughout the simulation, ligand-bound complexes undergo a significant conformational transition characterized by further opening of the intracellular gate relative to the inward-open crystal structure (5XPD). This opening is driven by ligand-induced bending of helix V, stabilizing the inward-open state. Sucrose binding notably enhances the flexibility of the intracellular gate and amplifies anticorrelated motions between the N- and C-terminal domains at the intracellular side, suggesting an opening-closing motion of these domains. Principal component analysis revealed that this gating motion is most pronounced in the sucrose complex and minimal in the apo form, highlighting sucrose's ability to induce high-amplitude gating. Our binding free energy calculations indicate that SWEET13 has lower binding affinity for sucrose compared to GA3, consistent with its role in sugar transport. These results provide insight into key residues involved in sucrose and GA3 binding and transport, advancing our understanding of SWEET13 dynamics.

Insight into the enhancement and mechanism of saltiness perception by salty peptides from bovine bone

Food-derived salty peptides have been considered promising substitutes for sodium salt. In this work, three novel salty dipeptides Asp-Pro (DP), Asp-Arg (DR), and Arg-Glu (RE) were identified from bovine bone hydrolysates. The salt reduction rates were 76.85 %, 77.28 %, and 73.72 % by the three peptides (2 mg/mL) in a NaCl concentration of 0.203 g/100 mL, respectively. According to Stevens' law, a non-linear relationship between saltiness intensity and concentration was quantified, showing a slower increase in the sensory intensity perception compared with the changes in physical concentration (β < 1). In molecular detail, electrostatic energy and van der Waals energy were the main energetic contributions to forming stable complexes. The binding of salty peptides to TMC4 was driven by hydrogen bonding and salt bridge, and the main binding sites were Glu319, Ala579, and Thr581. These results could provide new insight into the salt-enhancing property and interaction mechanism of salty peptides as novel sodium substitutes.

From Antipsychotic to Neuroprotective: Computational Repurposing of Fluspirilene as a Potential PDE5 Inhibitor for Alzheimer's Disease

Phosphodiesterase 5 (PDE5) inhibitors have shown great potential in treating Alzheimer's disease by improving memory and cognitive function. In this study, we evaluated fluspirilene, a drug commonly used to treat schizophrenia, as a potential PDE5 inhibitor using computational methods. Molecular docking revealed that fluspirilene binds strongly to PDE5, supported by hydrophobic and aromatic interactions. Molecular dynamics simulations confirmed that the fluspirilene-PDE5 complex is stable and maintains its structural integrity over time. Binding energy calculations further highlighted favorable interactions, indicating that the drug forms a strong and stable bond with PDE5. Additional analyses, including studies of protein dynamics and energy landscape mapping, revealed how the drug interacts dynamically with PDE5, adapting to different conformations and maintaining stability. These findings suggest that fluspirilene may modulate PDE5 activity, potentially offering therapeutic benefits for Alzheimer's disease. This study provides strong evidence for repurposing fluspirilene as a treatment for Alzheimer's and lays the foundation for further experimental and clinical investigations.

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.

Multiple Topology Replica Exchange of Expanded Ensembles for Multidimensional Alchemical Calculations

Relative free energy (RFE) calculations are now widely used in academia and the industry, but their accuracy is often limited by poor sampling of the complexes' conformational ensemble. To help address conformational sampling problems when simulating many relative binding free energies, we developed a novel method termed multiple topology replica exchange of expanded ensembles (MT-REXEE). This method enables parallel expanded ensemble calculations, facilitating iterative RFE computations while allowing conformational exchange between parallel transformations. These iterative transformations can be adaptable to any set of systems with a common backbone or central substructure. We demonstrate that the MT-REXEE method maintains thermodynamic cycle closure to the same extent as standard expanded ensemble calculations for both solvation free energy and relative binding free energy calculations. The transformations tested involve systems that incorporate diverse heavy atoms and multisite perturbations of a small molecule core resembling multisite λ dynamics, without necessitating modifications to the MD code. Our initial implementation is in GROMACS. We outline a systematic approach for the topology setup and provide instructions on how to perform inter-replica coordinate modifications. This work shows that MT-REXEE can be used to perform accurate and reproducible free energy estimates and prompts expansion to more complex test systems and other molecular dynamics simulation infrastructures.

Advanced Mass-Spectra-Based Machine Learning for Predicting the Toxicity of Traditional Chinese Medicines

Traditional Chinese medicine (TCM) has been a cornerstone of health care for centuries, valued for its preventive and therapeutic properties. However, recent decades have revealed significant toxicological concerns associated with TCMs due to their complex chemical compositions. Traditional QSAR (quantitative structure-activity relationships) models, which predict toxicity based on chemical structures, face challenges with the intricate nature of TCM compounds. In this study, we effectively resolved this issue by correlating the toxicity of TCMs with advanced analytical descriptors from electron ionization mass spectra (EI-MS) data. The optimal classification model achieved a balanced accuracy of over 0.74. Through interpretable machine learning models, we identified specific toxic components, such as 13-hexyloxacyclotridec-10-en-2-one and loliolide. We applied molecular dynamics (MD) simulations to explore the interactions of identified toxic components with crucial protein targets, using hepatic cytochrome P450 3A4 as an example. This novel approach not only enhances our understanding of the toxicological profiles of TCMs but also maximizes their therapeutic benefits while minimizing adverse effects. More importantly, our findings support the application of analytical descriptor-based machine learning in predicting the toxicity of unknown mixtures in the real environment.

Essential Considerations for Free Energy Calculations of RNA-Small Molecule Complexes: Lessons from the Theophylline-Binding RNA Aptamer

Alchemical free energy calculations are widely used to predict the binding affinity of small molecule ligands to protein targets; however, the application of these methods to RNA targets has not been deeply explored. We systematically investigated how modeling decisions affect the performance of absolute binding free energy calculations for a relatively simple RNA model system: theophylline-binding RNA aptamer with theophylline and five analogs. The goal of this investigation was 2-fold: (1) understanding the performance levels we can expect from absolute free energy calculations for a simple RNA complex and (2) learning about practical modeling considerations that impact the success of RNA-binding predictions, which may be different from the best practices established for protein targets. We learned that magnesium ion (Mg2+) placement is a critical decision that impacts affinity predictions. When information regarding Mg2+ positions is lacking, implementing RNA backbone restraints is an alternative way of stabilizing the RNA structure that recapitulates prediction accuracy. Since mistakes in Mg2+ placement can be detrimental, omitting magnesium ions entirely and using RNA backbone restraints are attractive as a risk-mitigating approach. We found that predictions are sensitive to modeling experimental buffer conditions correctly, including salt type and ionic strength. We explored the effects of sampling in the alchemical protocol, choice of the ligand force field (GAFF2/OpenFF Sage), and water model (TIP3P/OPC) on predictions, which allowed us to give practical advice for the application of free energy methods to RNA targets. By capturing experimental buffer conditions and implementing RNA backbone restraints, we were able to compute binding affinities accurately (mean absolute error (MAE) = 2.2 kcal/mol, Pearson's correlation coefficient = 0.9, Kendall's τ = 0.7). We believe there is much to learn about how to apply free energy calculations for RNA targets and how to enhance their performance in prospective predictions. This study is an important first step for learning best practices and special considerations for RNA-ligand free energy calculations. Future studies will consider increasingly complicated ligands and diverse RNA systems and help the development of general protocols for therapeutically relevant RNA targets.

Computational screening and molecular dynamics of natural compounds targeting the SH2 domain of STAT3: a multitarget approach using network pharmacology

SH2 (Src Homology 2) domains play a crucial role in phosphotyrosine-mediated signaling and have emerged as promising drug targets, particularly in cancer therapy. STAT3 (Signal Transducer and Activator of Transcription 3), which contains an SH2 domain, plays a pivotal role in cancer progression and immune evasion because it facilitates the dimerization of STAT3, which is essential for their activation and subsequent nuclear translocation. SH2 domain-mediated STAT3 inhibition disrupts this binding, reduces phosphorylation of STAT3, and impairs dimerization. This study employed an in silico approach to screen potential natural compounds that could target the SH2 domain of STAT3 and inhibit its function. The phytomolecules (182455) were retrieved from the ZINC 15 database and were docked using various modes like HTVS, SP, and XP. The phytomolecules exhibiting higher binding affinity were selected. MM-GBSA was performed to determine binding free energy, and the QikProp tool was utilized to assess the pharmacokinetic properties of potential hit compounds, narrowing down the list of candidates. Molecular dynamics simulations, thermal MM-GBSA, and WaterMap analysis were performed on compounds that exhibited favorable binding affinities and pharmacokinetic characteristics. Based on docking scores and binding interactions, ZINC255200449, ZINC299817570, ZINC31167114, and ZINC67910988 were identified as potential STAT3 inhibitors. ZINC67910988 demonstrated superior stability in molecular dynamics simulation and WaterMap analysis. Furthermore, DFT was performed to determine energetic and electronic properties, and HOMO and LUMO sites were predicted for electronic structure calculation. Additionally, network pharmacology was performed to map the compounds' interactions within biological networks, highlighting their multitarget potential. Compound-target networks elucidate the relationships between compounds and multiple targets, along with their associated pathways and help to minimize off-target effects. The identified lead compound showed strong potential as a STAT3 inhibitor, warranting further validation through in vitro and in vivo studies.

Design, Synthesis, and Antitumor Activity Evaluation of 2-Phenylthiazole-5-Carboxylic Acid Derivatives Targeting Transactivation Response RNA-Binding Protein 2

Transactivation response (TAR) RNA-binding protein 2 (TRBP) plays a critical role in microRNA (miRNA) biosynthesis, with aberrant expression linked to various cancers. Previously, we identified CIB-3b, a phenyloxazole derivative that disrupts the TRBP-Dicer interaction in hepatocellular carcinoma (HCC). In this study, we optimized this scaffold and substituent, leading to the discovery of CIB-L43, a 2-phenylthiazole-5-carboxylic acid derivative with nanomolar inhibitory activity (EC50 = 0.66 nM). CIB-L43 demonstrated superior TRBP binding affinity (KD = 4.78 nM) and enhanced disruption of TRBP-Dicer interactions (IC50 = 2.34 μM). Mechanistically, CIB-L43 suppressed oncogenic miR-21 biosynthesis, increasing PTEN and Smad7 expression and inhibiting AKT and TGF-β signaling, thereby reducing HCC cell proliferation and migration. In vivo, CIB-L43 exhibited favorable pharmacokinetics, including 53.9% oral bioavailability, and comparable antitumor efficacy to first-line anticancer drug, sorafenib, with lower toxicity. CIB-L43 emerges as a promising HCC treatment candidate with potent TRBP inhibition and favorable drug-like properties.

Plasticity of BioPhi-driven humanness optimization in ScFv-CD99 binding affinity validated through AlphaFold, HADDOCK, and MD simulations

BioPhi-guided humanization was utilized to enhance the humanness of a humanized single-chain variable fragment targeting CD99, leading to the development of two variants: HuScFvMT99/3BP and HuScFvMT99/3HY. The HuScFvMT99/3BP variant incorporated framework region modifications, leading to modest improvements in humanness, particularly in the VH domain, although the VL domain remained suboptimal. To address this limitation, HuScFvMT99/3HY was designed by combining the VL domain of wild-type with the VH domain of HuScFvMT99/3BP. Molecular dynamics simulations employing AlphaFold2, AlphaFold3, and HADDOCK were performed to evaluate the HuScFv-CD99 peptide complexes. AF2-based simulations demonstrated enhanced binding free energy (ΔGbinding) for both variants compared to HuScFvMT99/3WT. However, ΔGbinding values obtained from AF3 and HD simulations were inconsistent, with HuScFvMT99/3BP exhibiting the weakest binding affinity. While ΔGbinding patterns derived from AlphaFold3 and HADDOCK simulations aligned, amino acid decomposition analysis revealed variations in the interaction coordinates of the predicted complexes. Root-mean-square deviation analysis indicated improved structural stability for HuScFvMT99/3BP (0.975 Å) and HuScFvMT99/3HY (1.075 Å) relative to HuScFvMT99/3WT (1.225 Å). Biolayer interferometry further confirmed that HuScFvMT99/3WT exhibited the highest binding affinity (KD = 1.35 × 10⁻⁷ M) compared to HuScFvMT99/3BP (KD = 2.64 × 10⁻⁷ M) and HuScFvMT99/3HY (KD = 3.95 × 10⁻⁷ M). Supporting evidence was provided by ELISA and flow cytometry experiments. PITHA analysis revealed a high immunogenicity risk for all variants, despite HuScFvMT99/3HY displaying improved humanness, a larger complementarity-determining region (CDR) cavity, and a more hydrophobic CDR-H3 loop. These findings highlight the delicate balance between enhancing humanness and preserving the structural and functional integrity critical for therapeutic antibody development.

Discovery of novel fused-heterocycle-bearing diarypyrimidine derivatives as HIV-1 potent NNRTIs targeting tolerant region I for enhanced antiviral activity and resistance profile

As an important part of anti-AIDS therapy, HIV-1 non-nucleoside reverse transcriptase inhibitors are plagued by resistance and toxicity issues. Taking our reported XJ-18b1 as lead compound, we designed a series of novel diarypyrimidine derivatives by employing a scaffold hopping strategy to discover potent NNRTIs with improved anti-resistance properties and drug-like profiles. The most active compound 3k exhibited prominent inhibitory activity against wild-type HIV-1 (EC50 = 0.0019 μM) and common mutant strains including K103 N (EC50 = 0.0019 μM), L100I (EC50 = 0.0087 μM), E138K (EC50 = 0.011 μM), along with low cytotoxicity and high selectivity index (CC50 = 21.95 μM, SI = 11478). Additionally, compound 3k demonstrated antiviral activity against HIV-2 with EC50 value of 6.14 μM. The enzyme-linked immunosorbent assay validated that 3k could significantly inhibit the activity of HIV-1 reverse transcriptase (IC50 = 0.025 μM). Furthermore, molecular dynamics simulation studies were performed to illustrate the potential binding mode and binding free energy of the RT-3k complex, and in silico prediction revealed that 3k possessed favorable drug-like profiles. Collectively, 3k proved to be a promising lead compound for further optimization to obtain anti-HIV drug candidates.

Exploring pyrazolines as potential inhibitors of NSP3-macrodomain of SARS-CoV-2: synthesis and in silico analysis

COVID-19 has proved to be a global health crisis during the pandemic, and the emerging JN.1 variant is a potential threat. Therefore, finding alternative antivirals is of utmost priority. In the current report, we present the synthesis of new and potential anti-viral pyrazoline compounds. Here we report a chemical scheme where β-aryl β-anilino ketones react with phenyl hydrazine in potassium hydroxide to give the corresponding 3,5-diarylpyrazoline. The protocol is applicable to a variety of β-amino ketones and tolerates several functional groups. This method is efficient and proceeds regioselectivity since the β-Anilino group acts as a protecting group for alkenes of chalcones. We identified the NSP3-microdomain (Mac-1) of SARS-CoV-2 as a putative target for newly synthesized triaryl-2-pyrazoline compounds. The molecular dynamics simulation-based free energy estimation suggests compounds 7a, 7d, 7 g, 7i, 7k, and 7 L as promising Mac-1 inhibitors. The detailed structural inspection of MD simulation trajectories sheds light on the structural and functional dynamics involved in the SARS-CoV-2 Mac-1. The data presented here is expected to guide the design and development of better anti-SARS-CoV-2 therapies.

Molecular Photoswitching Unlocks Glucose Oxidase for Synergistically Reinforcing Fenton Reactions for Antitumor Chemodynamic Therapy

We have developed a new type of nanoparticles with potent antitumor activity photoactivatable via the combination of molecular photoswitching of spiropyran (SP) and enzymatic reaction of glucose oxidase (GOx). As two key processes involved therein, Fe(III)-to-Fe(II) photoreduction in Fe(III) metal-organic frameworks (MOFs) brings about the release of free Fe2+/Fe3+ while the photoswitching of SP to merocyanine (MC) unlocks the enzymatic activity of GOx that was pre-passivated by SP. The release of free Fe3+ boosts its hydrolysis and therefore enables the acidification of microenvironment, which is further reinforced by one of the products of the GOx-mediated glucose oxidation reaction, gluconic acid (GlcA). Based on the generation of Fe2+ and acidic milieu together with another product of the oxidation reaction, hydrogen peroxide (H2O2), these two processes jointly present triple enabling factors for generating lethal hydroxyl radicals (⋅OH) species via Fenton reactions and therefore oxidative stress capable of inhibiting tumor. The antitumor potency of such nanoparticle is verified in tumor-bearing model mice in vivo, proclaiming its potential as a potent and safe agent based on the unique mechanism of optically manipulating enzyme activity for synergistic antitumor therapeutics with high spatial precision, enhanced efficacy and minimized side effects.

Deciphering the conformational stability of MazE7 antitoxin in Mycobacterium tuberculosis from molecular dynamics simulation study

MazEF Toxin-antitoxin (TA) systems are associated with the persistent phenotype of the pathogen, Mycobacterium tuberculosis (Mtb), aiding their survival. Though extensively studied, the mode of action between the antitoxin-toxin and DNA of this family remains largely unclear. Here, the important interactions between MazF7 toxin and MazE7 antitoxin, and how MazE7 binds its promoter/operator region have been studied. To elucidate this, molecular dynamics (MD) simulation has been performed on MazE7, MazF7, MazEF7, MazEF7-DNA, and MazE7-DNA complexes to investigate how MazF7 and DNA affect the conformational change and dynamics of MazE7 antitoxin. This study demonstrated that the MazE7 dimer is disordered and one monomer (Chain C) attains stability after binding to the MazF7 toxin. Both the monomers (Chain C and Chain D) however are stabilized when MazE7 binds to DNA. MazE7 is also observed to sterically inhibit tRNA from binding to MazF7, thus suppressing its toxic activity. Comparative structural analysis performed on all the available antitoxins/antitoxin-toxin-DNA structures revealed MazEF7-DNA mechanism was similar to another TA system, AtaRT_E.coli. Simulation performed on the crystal structures of AtaR, AtaT, AtaRT, AtaRT-DNA, and AtaR-DNA showed that the disordered AtaR antitoxin attains stability by AtaT and DNA binding similar to MazE7. Based on these analyses it can thus be hypothesized that the disordered antitoxins enable tighter toxin and DNA binding thus preventing accidental toxin activation. Overall, this study provides crucial structural and dynamic insights into the MazEF7 toxin-antitoxin system and should provide a basis for targeting this TA system in combating Mycobacterium tuberculosis.Communicated by Ramaswamy H. Sarma.

Molecular basis of the CYFIP2 and NCKAP1 autism-linked variants in the WAVE regulatory complex

The WAVE regulatory pentameric complex regulates actin remodeling. Two components of it (CYFIP2 and NCKAP1) are encoded by genes whose genetic mutations increase the risk for autism spectrum disorder (ASD) and related neurodevelopmental disorders. Here, we use a newly developed computational protocol and hotspot analysis to uncover the functional impact of these mutations at the interface of the correct isoforms of the two proteins into the complex. The mutations turn out to be located on the surfaces involving the largest number of hotspots of the complex. Most of them decrease the affinity of the proteins for the rest of the complex, but some have the opposite effect. The results are fully consistent with the available experimental data. The observed changes in the WAVE regulatory complex stability might impact on complex activation and ultimately play a role in the aberrant pathway of the complex, leading to the cell derangement associated with the disease.

Insights into in vitro and in silico studies of α-glucosidase inhibitors isolated from the leaves of Grewia optiva (Malvaceae)

α-Glucosidase plays a critical role in glucose metabolism by breaking down complex carbohydrates into simpler sugars for intestinal absorption. Due to the side effects of current α-glucosidase inhibitors, there is increasing interest in exploring alternative therapeutic options. Inspired by the traditional uses of Grewia optiva J.R.Drumm. ex Burret (Malvaceae family) as an anti-diabetic herb, we isolated gnaphaffine A (1), a cyclic glycosylated homolignan, together with kaempferol derivatives (trans-tiliroside 2, cis-tiliroside 3, and astragalin 4) from the ethyl acetate fraction. In vitro antioxidant assays revealed that 1 exhibited anti-DPPH• and anti-ABTS+• activity (IC50 of 39.42 and 52.84 μg/mL, respectively), comparable to ascorbic acid (IC50 of 43.34 and 47.56 μg/mL, respectively). Moreover, 1 demonstrated a seven-fold stronger inhibition of α-glucosidase activity than acarbose (IC50 of 8.2 and 57.8 μg/mL, respectively). Importantly, 1 was non-toxic to AC16 normal cardiomyocyte cell lines. Computational analyses identified two key factors contributing to the α-glucosidase inhibitory activity of 1: (a) hydrogen bonding interactions with catalytic residues (E277 and D352) and (b) a calculated ∆Gbind of -51.20 kcal/mol. Furthermore, 3 showed the most favorable in silico binding profile, with the highest ∆Gbind (-55.89 kcal/mol) and higher hydrogen bond occupancy compared to 1 and 2. These findings suggest that 1 and 3 may serve as promising lead compounds for the development of new α-glucosidase drugs.

Molecular dynamics of transferrin receptor binder peptides: unlocking blood-brain barrier for enhanced CNS drug delivery

A cystine-dense peptide (CDP) named TfRB1 was identified for its ability to bind to the transferrin receptor (TfR). CDPs are stabilized by their disulfide bonds, and variants of TfRB1 - specifically TfRB1G1, TfRB1G2, and TfRB1G3 - are explored for their potential to transport molecules across the blood-brain barrier (BBB) into the central nervous system (CNS). This study employed molecular modeling and dynamics simulations to characterize the interactions between these TfRB1 variants and TfR. Binding free energy calculations showed a strong correlation with experimental binding affinities of -10.99 kcal/mol for TfRB1G2 and -13.18 kcal/mol for TfRB1G3, with a relative error of 1.98%. The key forces driving these interactions include electrostatic and van der Waals forces, with mutations in TfRB1G3 (T9M and A13D) enhancing its binding affinity through improved interactions with residues such as Arg633. The free energy landscape analysis revealed that TfRB1G3 maintains the N-terminal residues of TfR in an α-helical conformation, unlike TfRB1G2. Per-residue free energy decomposition identified key residues - Leu619, Arg629, Tyr643, and Phe650 - as crucial for TfR binding, underscoring their competitive nature with transferrin. Additionally, Glu612, which is favorable for binding in TfRB1G2, becomes unfavorable in TfRB1G3. Conversely, Arg633 shifts from unfavorable in TfRB1G2 to favorable in TfRB1G3, compensating for the loss of favorable interaction with Glu612. These findings provide valuable molecular insights into the TfRB1 peptides' potential as drug carriers, highlighting their capability to deliver molecules to the CNS and compete with transferrin for BBB transport.

Probing the interaction mechanisms between three β-lactam antibiotics and penicillin-binding proteins of Escherichia coli by molecular dynamics simulations

The presence of antibiotic residues in the aquatic environments poses great potential risks to the aquatic organisms, and even human health. Elucidating the interaction mechanisms between antibiotics and biomacromolecules is crucial for accurately assessing and preventing their potential risks. Therefore, the toxicity of three beta-lactam antibiotics on Escherichia coli (E. coli) was investigated by using the time-dependent toxicity microplate analysis method in this study. Then, molecular docking and molecular dynamics simulation technologies were used to elucidate the potential molecular interactions between β-lactam antibiotics and penicillin-binding proteins of E. coli, and their correlation with the physical and chemical behaviors observed in the physiological and biochemical experiments. The results show that three antibiotics exert inhibitory effects on E. coli cells by modifying their membrane permeability, and even more severe cell damage including rupture, wrinkling, adhesion, indentation, elongation and size alterations. But, toxic effect of the three antibiotics on E. coli varies, and toxicity order is followed by meropenem > cefoperazone > amoxicillin. Van der Waals forces play a vital role in the molecular interactions between the three antibiotics penicillin binding protein of E. coli and the sequence of binding free energy is consistent with the observed toxicity order. Shape compensation is the principal determinant for the binding of antibiotics to penicillin binding proteins, which pertains to the drug-induced alteration in the three-dimensional conformation of penicillin binding proteins.

Mechanisms for translating chiral enantiomers separation research into macroscopic visualization

Chirality is a common phenomenon in nature, including the dominance preference of small biomolecules, the special spatial conformation of biomolecules, and the biological and physiological processes triggered by chirality. The selective chiral recognition of molecules in nature from up-bottom or bottom-up is of great significance for living organisms. Such as the transcription of DNA, the recognition of membrane proteins, and the catalysis of enzymes all involve chiral recognition processes. The selective recognition between these macromolecules is mainly achieved through non covalent interactions such as hydrophobic interactions, ammonia bonding, electrostatic interactions, metal coordination, van der Waals forces, and π-π stacking. Researchers have been committed to studying how to convert this weak non covalent interaction into macroscopic visualization, which has further understood of the interactions between chiral molecules and is of great significance for simulating the interactions between molecules in living organisms. This article reviews several models of chiral recognition mechanisms, the interaction forces involved in the chiral recognition process, and the research progress of chiral recognition mechanisms. The outlook in this review points out that studying chiral recognition interactions provides an important bridge between chiral materials and the life sciences, providing an ideal platform for studying chiral phenomena in biological systems.