VAD-MM/GBSA: A Variable Atomic Dielectric MM/GBSA Model for Improved Accuracy in Protein-Ligand Binding Free Energy Calculations

Searching for novel MDM2/MDMX dual inhibitors through a drug repurposing approach

Disruption of p53-MDM2/MDMX interaction by smaller inhibitors is a promising therapeutic intervention gaining tremendous interest. However, no MDM2/MDMX inhibitors have been marketed so far. Drug repurposing is a validated, practical approach to drug discovery. In this regard, we employed structure-based virtual screening in a reservoir of marketed drugs and identified nintedanib as a new MDM2/MDMX dual inhibitor. The computational structure analysis and biochemical experiments uncover that nintedanib binds MDM2/MDMX similarly to RO2443, a dual MDM2/MDMX inhibitor. Furthermore, the mechanistic study reveals that nintedanib disrupts the physical interaction of p53-MDM2/MDMX, enabling the transcriptional activation of p53 and the subsequent cell cycle arrest and growth inhibition in p53+/+ cancer cells. Lastly, structural minimisation of nintedanib yields H3 with the equivalent potency. In summary, this work provides a solid foundation for reshaping nintedanib as a valuable lead compound for the further design of MDM2/MDMX dual inhibitors.

Toxicological study on ibuprofen and selenium in freshwater mussel Lamellidens marginalis and exploring the microbial cytochrome through modelling and quantum mechanics approaches for its toxicity degradation in contaminated environment

Toxicological stress in aquatic organisms is caused by the discharge of hundreds of toxic pollutants and contaminants among which the current study concentrates on the toxic effect of non-steroidal anti-inflammatory drug ibuprofen (IBF) and the trace element selenium (Se). In this study, IBF and Se toxicity on freshwater mussel Lamellidens marginalis was studied for 14 days, and in silico predictions for their degradation were made using Molecular modelling and Quantum Mechanical approaches. The degrading propensity of cytochrome c oxidase proteins from Trametes verticillatus and Thauera selenatis (Turkey tail fungi and Gram-negative bacteria) is examined into atom level. The results of molecular modelling study indicate that ionic interactions occur in the T. selenatis-HEME bound complex by Se interacting directly with HEME, and in the T. versicolor-HEME bound complex by IBF bound to a nearby region of HEME. Experimental and theoretical findings suggest that, the toxicological effects of Se and IBF pollution can be reduced by bioremediation with special emphasis on T. versicolor, and T. selenatis, which can effectively interact with Se and IBF present in the environment and degrade them. Besides, this is the first time in freshwater mussel L. marginalis that ibuprofen and selenium toxicity have been studied utilizing both experimental and computational methodologies for their bioremediation study.

SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening

The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.

Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach

BACKGROUND

Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth.

OBJECTIVES/AIMS

This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model.

METHODS

This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP.

RESULTS

The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis.

CONCLUSION

Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.

Advanced molecular mechanisms of modified DRV compounds in targeting HIV-1 protease mutations and interrupting monomer dimerization

HIV-1 protease (PR) plays a crucial role in the treatment of HIV as a key target. The global issue of emerging drug resistance is escalating, and PR mutations pose a substantial challenge to the effectiveness of inhibitors. HIV-1 PR is an ideal model for studying drug resistance to inhibitors. The inhibitor, darunavir (DRV), exhibits a high genetic barrier to viral resistance, but with mutations of residues in the PR, there is also some resistance to DRV. Inhibitors can impede PR in two ways: one involves binding to the active site of the dimerization protease, and the other involves binding to the PR monomer, thereby preventing dimerization. In this study, we aimed to investigate the inhibitory effect of DRV with a modified inhibitor on PR, comparing the differences between wild-type and mutated PR, using molecular dynamics simulations. The inhibitory effect of the inhibitors on PR monomers was subsequently investigated. And molecular mechanics Poisson-Boltzmann surface area evaluated the binding free energy. The energy contribution of individual residues in the complex was accurately calculated by the alanine scanning binding interaction entropy method. The results showed that these inhibitors had strong inhibitory effects against PR mutations, with GRL-142 exhibiting potent inhibition of both the PR monomer and dimer. Improved inhibitors could strengthen hydrogen bonds and interactions with PR, thereby boosting inhibition efficacy. The binding of the inhibitor and mutation of the PR affected the distance between D25 and I50, preventing their dimerization and the development of drug resistance. This study could accelerate research targeting HIV-1 PR inhibitors and help to further facilitate drug design targeting both mechanisms.

In silico study of inhibition activity of boceprevir drug against 2019-nCoV main protease

Boceprevir drug is a ketoamide serine protease inhibitor with a linear peptidomimetic structure that exhibits inhibition activity against 2019-nCoV main protease. This paper reports electronic properties of boceprevir and its molecular docking as well as molecular dynamics simulation analysis with protein receptor. For this, the equilibrium structure of boceprevir has been obtained by DFT at B3LYP and ωB97XD levels with 6-311+G(d,p) basis set in gas and water mediums. HOMO-LUMO and absorption spectrum analysis have been used to evaluate the boceprevir's toxicity and photosensitivity, respectively. Molecular docking simulation has been performed to test the binding affinity of boceprevir with 2019-nCoV MPRO; which rendered a variety of desirable binding locations between the ligand and target protein's residue positions. The optimum binding location has been considered for molecular dynamics simulation. The findings have been addressed to clarify the boceprevir drug efficacy against the 2019-nCoV MPRO.

Virtual screening and molecular dynamics investigations using natural compounds against autotaxin for the treatment of chronic pain

Chronic pain is a common and debilitating condition with a huge social and economic burden worldwide. Currently, available drugs in clinics are not adequately effective and possess a variety of severe side effects leading to treatment withdrawal and poor quality of life. Recent findings highlight the potential role of autotaxin (ATX) as a promising novel target for chronic pain management, extending beyond its previously established involvement in arthritis and other neurological disorders, such as Alzheimer's disease. In the present study, we used a virtual screening strategy by targeting ATX against commercially available natural compounds (enamine- phenotypic screening library) to identify the potential inhibitors for the treatment of chronic pain. After initial identification using molecular docking based virtual screening, molecular mechanics (MM/GBSA), ADMET profiling and molecular dynamics simulation were performed to verify top hits. The computational screening resulted in the identification of fifteen top scoring structurally diverse hits that have free energy of binding (ΔG) values in the range of -25.792 (for compound Enamine_1850) to -74.722 Kcal/mol (for compound Enamine_1687). Moreover, the top-scoring hits have favourable ADME properties as calculated using in-silico algorithms. Additionally, the molecular dynamics simulation revealed the stable nature of protein-ligand interaction and provided information about amino acid residues involved in binding. This study led to the identification of potential autotaxin inhibitors with favourable pharmacokinetic properties. Identified hits may further be investigated for their safety and efficacy potential using in-vitro and in-vivo models of chronic pain.Communicated by Ramaswamy H. Sarma.

Integrating computational methods guided the discovery of phytochemicals as potential Pin1 inhibitors for cancer: pharmacophore modeling, molecular docking, MM-GBSA calculations and molecular dynamics studies

Pin1 is a pivotal player in interactions with a diverse array of phosphorylated proteins closely linked to critical processes such as carcinogenesis and tumor suppression. Its axial role in cancer initiation and progression, coupled with its overexpression and activation in various cancers render it a potential candidate for the development of targeted therapeutics. While several known Pin1 inhibitors possess favorable enzymatic profiles, their cellular efficacy often falls short. Consequently, the pursuit of novel Pin1 inhibitors has gained considerable attention in the field of medicinal chemistry. In this study, we employed the Phase tool from Schrödinger to construct a structure-based pharmacophore model. Subsequently, 449,008 natural products (NPs) from the SN3 database underwent screening to identify compounds sharing pharmacophoric features with the native ligand. This resulted in 650 compounds, which then underwent molecular docking and binding free energy calculations. Among them, SN0021307, SN0449787 and SN0079231 showed better docking scores with values of -9.891, -7.579 and -7.097 kcal/mol, respectively than the reference compound (-6.064 kcal/mol). Also, SN0021307, SN0449787 and SN0079231 exhibited lower free binding energies (-57.12, -49.81 and -46.05 kcal/mol, respectively) than the reference ligand (-37.75 kcal/mol). Based on these studies, SN0021307, SN0449787, and SN0079231 showed better binding affinity that the reference compound. Further the validation of these findings, molecular dynamics simulations confirmed the stability of the ligand-receptor complex for 100 ns with RMSD ranging from 0.6 to 1.8 Å. Based on these promising results, these three phytochemicals emerge as promising lead compounds warranting comprehensive biological screening in future investigations. These compounds hold great potential for further exploration regarding their efficacy and safety as Pin1 inhibitors, which could usher in new avenues for combating cancer.

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.

Entropy driven cooperativity effect in multi-site drug optimization targeting SARS-CoV-2 papain-like protease

Papain-like protease (PLpro), a non-structural protein encoded by SARS-CoV-2, is an important therapeutic target. Regions 1 and 5 of an existing drug, GRL0617, can be optimized to produce cooperativity with PLpro binding, resulting in stronger binding affinity. This work investigated the origin of the cooperativity using molecular dynamics simulations combined with the interaction entropy (IE) method. The regions' improvement exhibits cooperativity by calculating the binding free energies between the complex of PLpro-inhibitor. The thermodynamic integration method further verified the cooperativity generated in the drug improvement. To further determine the specific source of cooperativity, enthalpy and entropy in the complexes were calculated using molecular mechanics/generalized Born surface area and IE. The results show that the entropic change is an important contributor to the cooperativity. Our study also identified residues P248, Q269, and T301 that play a significant role in cooperativity. The optimization of the inhibitor stabilizes these residues and minimizes the entropic loss, and the cooperativity observed in the binding free energy can be attributed to the change in the entropic contribution of these residues. Based on our research, the application of cooperativity can facilitate drug optimization, and provide theoretical ideas for drug development that leverage cooperativity by reducing the contribution of entropy through multi-locus binding.

Leveraging decagonal in-silico strategies for uncovering IL-6 inhibitors with precision

Interleukin-6 upregulation leads to various acute phase reactions such as local inflammation and systemic inflammation in many diseases like cancer, multiple sclerosis, rheumatoid arthritis, anemia, and Alzheimer's disease stimulating JAK/STAT3, Ras/MAPK, PI3K-PKB/Akt pathogenic pathways. Since no small molecules are available in the market against IL-6 till now, we have designed a class of small bioactive 1,3 - indanedione (IDC) molecules for inhibiting IL-6 using a decagonal approach computational studies. The IL-6 mutations were mapped in the IL-6 protein (PDB ID: 1ALU) from thorough pharmacogenomic and proteomics studies. The protein-drug interaction networking analysis for 2637 FFDA-approved drugs with IL-6 protein using Cytoscape software showed that 14 drugs have prominent interactions with IL-6. Molecular docking studies showed that the designed compound IDC-24 (-11.8 kcal/mol) and methotrexate (-5.20) bound most strongly to the 1ALU south asian population mutated protein. MMGBSA results indicated that IDC-24 (-41.78 kcal/mol) and methotrexate (-36.81 kcal/mol) had the highest binding energy when compared to the standard molecules LMT-28 (-35.87 kcal/mol) and MDL-A (-26.18 kcal/mol). These results we substantiated by the molecular dynamic studies in which the compound IDC-24 and the methotrexate had the highest stability. Further, the MMPBSA computations produced energies of -28 kcal/mol and -14.69 kcal/mol for IDC-24 and LMT-28. KDeep absolute binding affinity computations revealed energies of -5.81 kcal/mol and -4.74 kcal/mol for IDC-24 and LMT-28 respectively. Finally, our decagonal approach established the compound IDC-24 from the designed 1,3-indanedione library and methotrexate from protein drug interaction networking as suitable HITs against IL-6.

Exploring the potential of approved drugs for triple-negative breast cancer treatment by targeting casein kinase 2: Insights from computational studies

Triple-negative breast cancer (TNBC) is an aggressive malignancy that requires effective targeted drug therapy. In this study, we employed in silico methods to evaluate the efficacy of seven approved drugs against human ck2 alpha kinase, a significant modulator of TNBC metastasis and invasiveness. Molecular docking revealed that the co-crystallized reference inhibitor 108600 achieved a docking score of (-7.390 kcal/mol). Notably, among the seven approved drugs tested, sunitinib, bazedoxifene, and etravirine exhibited superior docking scores compared to the reference inhibitor. Specifically, their respective docking scores were -10.401, -7.937, and -7.743 kcal/mol. Further analysis using MM/GBSA demonstrated that these three top-ranked drugs possessed better binding energies than the reference ligand. Subsequent molecular dynamics simulations identified etravirine, an FDA-approved antiviral drug, as the only repurposed drug that demonstrated a stable and reliable binding mode with the human ck2 alpha protein, based on various analysis measures including RMSD, RMSF, and radius of gyration. Principal component analysis indicated that etravirine exhibited comparable stability of motion as a complex with human ck2 alpha protein, similar to the co-crystallized inhibitor. Additionally, Density functional theory (DFT) calculations were performed on a complex of etravirine and a representative gold atom positioned at different sites relative to the heteroatoms of etravirine. The results of the DFT calculations revealed low-energy complexes that could potentially serve as guides for experimental trials involving gold nanocarriers of etravirine, enhancing its delivery to malignant cells and introducing a new drug delivery route. Based on the results obtained in this research study, etravirine shows promise as a potential antitumor agent targeting TNBC, warranting further investigation through experimental and clinical assessments.

Discovery of a Selective and Orally Bioavailable FGFR2 Degrader for Treating Gastric Cancer

Abnormal activation of fibroblast growth factor receptors (FGFRs) results in the development and progression of human cancers. FGFR2 is frequently amplified or mutated in cancers; therefore, it is an attractive target for tumor therapy. Despite the development of several pan-FGFR inhibitors, their long-term therapeutic efficacy is hindered by acquired mutations and low isoform selectivity. Herein, we report the discovery of an efficient and selective FGFR2 proteolysis-targeting chimeric molecule, LC-MB12, that incorporates an essential rigid linker. LC-MB12 preferentially internalizes and degrades membrane-bound FGFR2 among the four FGFR isoforms; this may promote greater clinical benefits. LC-MB12 exhibits superior potency in FGFR signaling suppression and anti-proliferative activity compared to the parental inhibitor. Furthermore, LC-MB12 is orally bioavailable and shows significant antitumor effects in FGFR2-dependent gastric cancer in vivo. Taken together, LC-MB12 is a candidate FGFR2 degrader for alternative FGFR2-targeting strategies and offers a promising starting point for drug development.

Screening, characterization and specific binding mechanism of aptamers against human plasminogen Kringle 5

Plasminogen Kringle 5 is one of the most potent cytokines identified to inhibit the proliferation and migration of vascular endothelial cells. Herein, six aptamer candidates that specifically bind to Kringle 5 were generated by the systematic evolution of ligands by exponential enrichment (SELEX). After 10 rounds of screening against Kringle 5, a highly enriched ssDNA pool was sequenced and the representative aptamers were subjected to binding assays to evaluate their affinity and specificity. The preferred aptamer KG-4, which demonstrated a low dissociation constant (K_d) of ∼ 432 nM and excellent selectivity for Kringle 5. A conserved "motif" of eight bases located at the stem-loop intersection, common to the aptamer, was further confirmed as the recognition element for binding with Kringle 5. The bulge formed by the motif and depression on the lysine binding site of Kringle 5 were both located at the binding interface, and the "induced fit" between their structures played a central role in the recognition process. Kringle 5 interacts KG-4 primarily through enthalpy-driven van der Waals forces and hydrogen bond. The key nucleotides A34 and C35 at motif on KG-4 and the positively charged amino acids in the loop 1 and loop 4 regions on Kringle 5 play a major role in the interaction. Furthermore, KG-4 dose-dependently reduced the proliferation inhibition of vascular endothelial cells by Kringle 5 and had a blocking effect on the function of Kringle 5 in inhibiting migration and promoting apoptosis of vascular endothelial cells in vitro. This study put a new light on protein-aptamer binding mechanism and may provide insight into the treatment of ischemic diseases by target depletion of Kringle 5.

Discovery of a potent and selective allosteric inhibitor targeting the SHP2 tunnel site for RTK-driven cancer treatment

Src homology 2 domain-containing phosphatase 2 (SHP2) is a cytoplasmic protein tyrosine phosphatase (PTP) that regulates signal transduction of receptor tyrosine kinases (RTKs). Abnormal SHP2 activity is associated with tumorigenesis and metastasis. Because SHP2 contains multiple allosteric sites, identifying inhibitors at specific allosteric binding sites remains challenging. Here, we used structure-based virtual screening to directly search for the SHP2 "tunnel site" allosteric inhibitor. A novel hit (70) was identified as the SHP2 allosteric inhibitor with an IC₅₀ of 10.2 μM against full-length SHP2. Derivatization of hit compound 70 using molecular modeling-guided structure-based modification allowed the discovery of an effective and selective SHP2 inhibitor, compound 129, with 122-fold improved potency compared to the hit. Further studies revealed that 129 effectively inhibited signaling in multiple RTK-driven cancers and RTK inhibitor-resistant cancer cells. Remarkably, 129 was orally bioavailable (F = 55%) and significantly inhibited tumor growth in haematological malignancy. Taken together, compound 129 developed in this study may serve as a promising lead or candidate for cancers bearing RTK oncogenic drivers and SHP2-related diseases.

Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations

BRD9 and TAF1(2) have been regarded as significant targets of drug design for clinically treating acute myeloid leukemia, malignancies, and inflammatory diseases. In this study, multiple short molecular dynamics simulations combined with the molecular mechanics generalized Born surface area method were employed to investigate the binding selectivity of three ligands, 67B, 67C, and 69G, to BRD9/TAF1(2) with IC50 values of 230/59 nM, 1400/46 nM, and 160/410 nM, respectively. The computed binding free energies from the MM-GBSA method displayed good correlations with that provided by the experimental data. The results indicate that the enthalpic contributions played a critical factor in the selectivity recognition of inhibitors toward BRD9 and TAF1(2), indicating that 67B and 67C could more favorably bind to TAF1(2) than BRD9, while 69G had better selectivity toward BRD9 over TAF1(2). In addition, the residue-based free energy decomposition approach was adopted to calculate the inhibitor–residue interaction spectrum, and the results determined the gatekeeper (Y106 in BRD9 and Y1589 in TAF1(2)) and lipophilic shelf (G43, F44, and F45 in BRD9 and W1526, P1527, and F1528 in TAF1(2)), which could be identified as hotspots for designing efficient selective inhibitors toward BRD9 and TAF1(2). This work is also expected to provide significant theoretical guidance and insightful molecular mechanisms for the rational designs of efficient selective inhibitors targeting BRD9 and TAF1(2).

Structure-based discovery of novel α-aminoketone derivatives as dual p53-MDM2/MDMX inhibitors for the treatment of cancer

The function of the p53 protein is impaired by the overexpression of its negative regulator murine double minute 2 protein (MDM2) and homologous protein MDMX. Disruption of the p53-MDM2/MDMX interaction to restore the transcriptional function of p53 is considered a promising strategy for cancer therapy. To design dual MDM2/MDMX inhibitors, the binding modes of MDM2 or MDMX with their inhibitors are elucidated. Several hot-spot residues of MDM2 or MDMX are identified by molecular dynamics simulations, alanine scanning and MM-GBSA calculations. Then, focusing on the interaction with hot-spot residues, two series of derivatives bearing 1,3-diketone and α-aminoketone scaffolds are designed and synthesized. Among these compounds, C16 is identified as the most potent compound with low micromolar binding affinities with MDM2 and MDMX. C16 also displays moderate antiproliferative activities against MDM2-overexpressing and MDMX-overexpressing cells, with IC₅₀ values of 0.68 μM in HCT116 cells and 0.54 μM in SH-SY5Y cells. Furthermore, C16 inhibits cell migration and invasion, reactivates the function of p53, arrests the cell cycle and induces cellular apoptosis in HCT116 and SH-SY5Y cells. Collectively, C16 can be developed as a dual MDM2 and MDMX inhibitor for cancer therapy.

Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method

Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson-Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1-BCC-GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1-BCC (Austin Model 1-bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types, on, oi, hn1, hn2, hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein-ligand binding free energies using the MM-PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM-PBSA method.

Structure-based virtual screening and molecular dynamics simulation for the identification of sphingosine kinase-2 inhibitors as potential analgesics

Neuropathic pain is due to an injury or disease of the somatosensory nervous system, which accounts for a significant economical and health burden to society. Due to poor understanding of their underlying mechanisms, the available treatments merely provide symptomatic relief and precipitates a variety of adverse effects. This suggests that there is an unmet medical need that must be addressed with effective strategies for the development of novel therapeutics. Sphingosine kinase 2 (SphK2) is an oncogenic lipid kinase that has emerged as a promising target for chronic pain and other diseases. In the present study, we have explored the structure-based virtual high-throughput screening of the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE) to identify potent natural products as inhibitors of SphK2. A molecular docking study was performed to calculate binding affinities and specificity to identify potential leads against SphK2. Initially, hits were selected by the implementation of absorption, distribution, metabolism, excretion and toxicity properties, Lipinski rule, and PAINS filters. The top-scoring hits also exhibiting an optimal ADMET profile were subjected to MM/GBSA free binding free energy calculation and molecular dynamics simulation. The results from molecular dynamics simulation revealed a stable ligand -SphK2 complex with protein and ligand RMSD within reasonable limits. Overall, we identified compounds, NuBBE_972 and NuBBE_1107 as potential inhibitors of SphK2 with optimal pharmacokinetic properties which have the potential to be developed as novel therapeutics for the management of chronic pain.Communicated by Ramaswamy H. Sarma.

Molecular docking and molecular dynamics study Lianhua Qingwen granules (LHQW) treats COVID-19 by inhibiting inflammatory response and regulating cell survival

Purpose

2019 Coronavirus disease (COVID-19) is endangering health of populations worldwide. Latest research has proved that Lianhua Qingwen granules (LHQW) can reduce tissue damage caused by inflammatory reactions and relieve patients' clinical symptoms. However, the mechanism of LHQW treats COVID-19 is currently lacking. Therefore, we employed computer simulations to investigate the mechanism of LHQW treats COVID-19 by modulating inflammatory response.

Methods

We employed bioinformatics to screen active ingredients in LHQW and intersection gene targets. PPI, GO and KEGG was used to analyze relationship of intersection gene targets. Molecular dynamics simulations validated the binding stability of active ingredients and target proteins. Binding free energy, radius of gyration and the solvent accessible surface area were analyzed by supercomputer platform.

Results

COVID-19 had 4628 gene targets, LHQW had 1409 gene targets, intersection gene targets were 415. Bioinformatics analysis showed that intersection targets were closely related to inflammation and immunomodulatory. Molecular docking suggested that active ingredients (including: licopyranocoumarin, Glycyrol and 3-3-Oxopropanoic acid) in LHQW played a role in treating COVID-19 by acting on CSF2, CXCL8, CCR5, NLRP3, IFNG and TNF. Molecular dynamics was used to prove the binding stability of active ingredients and protein targets.

Conclusion

The mechanism of active ingredients in LHQW treats COVID-19 was investigated by computer simulations. We found that active ingredients in LHQW not only reduce cell damage and tissue destruction by inhibiting the inflammatory response through CSF2, CXCL8, CCR5 and IFNG, but also regulate cell survival and growth through NLRP3 and TNF thereby reducing apoptosis.

Elucidation of Binding Features and Dissociation Pathways of Inhibitors and Modulators in SARS-CoV-2 Main Protease by Multiple Molecular Dynamics Simulations

COVID-19 can cause different neurological symptoms in some people, including smell, inability to taste, dizziness, confusion, delirium, seizures, stroke, etc. Owing to the issue of vaccine effectiveness, update and coverage, we still need one or more diversified strategies as the backstop to manage illness. Characterizing the structural basis of ligand recognition in the main protease (Mpro) of SARS-CoV-2 will facilitate its rational design and development of potential drug candidates with high affinity and selectivity against COVID-19. Up to date, covalent-, non-covalent inhibitors and allosteric modulators have been reported to bind to different active sites of Mpro. In the present work, we applied the molecular dynamics (MD) simulations to systematically characterize the potential binding features of catalytic active site and allosteric binding sites in Mpro using a dataset of 163 3D structures of Mpro-inhibitor complexes, in which our results are consistent with the current studies. In addition, umbrella sampling (US) simulations were used to explore the dissociation processes of substrate pathway and allosteric pathway. All the information provided new insights into the protein features of Mpro and will facilitate its rational drug design for COVID-19.

Molecular Docking and Dynamics Simulation of Natural Compounds from Betel Leaves (Piper betle L.) for Investigating the Potential Inhibition of Alpha-Amylase and Alpha-Glucosidase of Type 2 Diabetes

Piper betle L. is widely distributed and commonly used medicinally important herb. It can also be used as a medication for type 2 diabetes patients. In this study, compounds of P. betle were screened to investigate the inhibitory action of alpha-amylase and alpha-glucosidase against type 2 diabetes through molecular docking, molecular dynamics simulation, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis. The molecule apigenin-7-O-glucoside showed the highest binding affinity among 123 (one hundred twenty-three) tested compounds. This compound simultaneously bound with the two-target proteins alpha-amylase and alpha-glucosidase, with high molecular mechanics-generalized born surface area (MM/GBSA) values (ΔG Bind = -45.02 kcal mol^-1 for alpha-amylase and -38.288 for alpha-glucosidase) compared with control inhibitor acarbose, which had binding affinities of -36.796 kcal mol^-1 for alpha-amylase and -29.622 kcal mol^-1 for alpha-glucosidase. The apigenin-7-O-glucoside was revealed to be the most stable molecule with the highest binding free energy through molecular dynamics simulation, indicating that it could compete with the inhibitors' native ligand. Based on ADMET analysis, this phytochemical exhibited a wide range of physicochemical, pharmacokinetic, and drug-like qualities and had no significant side effects, making them prospective drug candidates for type 2 diabetes. Additional in vitro, in vivo, and clinical investigations are needed to determine the precise efficacy of drugs.

XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein-Ligand Scoring and Ranking

Prediction of protein-ligand binding affinities is a central issue in structure-based computer-aided drug design. In recent years, much effort has been devoted to the prediction of the binding affinity in protein-ligand complexes using machine learning (ML). Due to the remarkable ability of ML methods in nonlinear fitting, ML-based scoring functions (SFs) can deliver much improved performance on a selected test set, such as the comparative assessment of scoring functions (CASF), when compared to the classical SFs. However, the performance of ML-based SFs heavily relies on the overall similarity of the training set and the test set. To improve the performance and transferability of an SF, we have tried to combine various features including energy terms from X-score and AutoDock Vina, the properties of ligands, and the statistical sequence-related information from either the binding site or the full protein. In conjunction with extreme trees (ET), an ML model, we have developed XLPFE, a new SF. Compared with other tested methods such as X-score, AutoDock Vina, ΔvinaXGB, PSH-ML, or CNN-score, XLPFE achieves consistently better scoring and ranking power for various types of protein-ligand complex structures beyond the CASF, suggesting that XLPFE has superior transferability. In particular, XLPFE performs better with metalloenzymes. With its faster speed, improved accuracy, and better transferability, XLPFE could be usefully applied to a diverse range of protein-ligand complexes.

EViS: An Enhanced Virtual Screening Approach Based on Pocket-Ligand Similarity

Virtual screening (VS) is a popular technology in drug discovery to identify a new scaffold of actives for a specific drug target, which can be classified into ligand-based and structure-based approaches. As the number of protein-ligand complex structures available in public databases increases, it would be possible to develop a template searching-based VS approach that utilizes such information. In this work, we proposed an enhanced VS approach, which is termed EViS, to integrate ligand docking, protein pocket template searching, and ligand template shape similarity calculation. A novel and simple PL-score to characterize local pocket-ligand template similarity was used to evaluate the screening compounds. Benchmark tests were performed on three datasets including DUDE, LIT-PCBA, and DEKOIS. EViS achieved the average enrichment factors (EFs) of 27.8 and 23.4 at a 1% cutoff for experimental and predicted structures on the widely used DUDE dataset, respectively. Detailed data analysis shows that EViS benefits from obtaining favorable ligand poses from docking and using such ligand geometric information to perform three-dimensional (3D) ligand similarity calculations, and the PL-score is efficient to screen compounds based on template searching in the protein-ligand structure database.

Theoretical study of myriocin-binding mechanism targeting serine palmitoyltransferase

Sphingolipids (SLs) are vital for cells as forming membrane and transducing signals. The first step for de novo biosynthesis of SLs is catalyzed by the pyridoxal-5'-phosphate (PLP)-dependent enzyme serine palmitoyltransferase (SPT), which has been proven to be a promising drug target for treating various diseases. However, there are few SPT-specific inhibitors have been identified so far. Myriocin, a natural fungal product, is confirmed as the most potent inhibitor of SPT and has been widely used, but studies of its molecular mechanism are still underway. Besides, there is no intact co-crystal structure of SPT-binding myriocin until now. Aiming to uncover the interaction mechanism between SPT- and PLP-binding myriocin at the molecular level, a systematic computational strategy was performed in this present study. Firstly, covalent docking was implemented to preliminarily predict the binding pose SPT/PLP-myriocin aldimine and its structurally similar intermediate SPT/PLP-β-ketoacid aldimine. Secondly, two binding complexes were treated as initial structures to perform molecular dynamics simulations and binding free energy calculations. The calculated docking scores and predicted binding energies were consistent with the reported bioactivities. Finally, the binding mechanism of myriocin binding with SPT was meticulously described, and the key residues making favorable contributions were highlighted. Taken together, the current study could provide some important information and valuable guidance for further rational screening, design, and modification of potent specific SPT inhibitors.