Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes

Theoretical Studies on the Selectivity Mechanisms of Glycogen Synthase Kinase 3β (GSK3β) with Pyrazine ATP-competitive Inhibitors by 3DQSAR, Molecular Docking, Molecular Dynamics Simulation and Free Energy Calculations

Background

Glycogen synthase kinase-3 (GSK3) is associated with various key biological processes and has been considered as an important therapeutic target for the treatment of many diseases. Great efforts have been made on the development of GSK3 inhibitors, especially ATP-competitive GSK3β inhibitor, but it is still a great challenge to develop selective GSK3β inhibitors because of the high sequence homology with other kinases.

Objective

In order to reveal the selectivity mechanisms of GSK3β inhibition at the molecular level, a series of ATP-competitive GSK3β inhibitor was analyzed by a systematic computational method, combining 3D-QSAR, molecular docking, molecular dynamic simulations and free energy calculations.

Methods

Firstly, 3D-QSAR with CoMFA was built to explore the general structure activity relationships. Secondly, CDOCKER and Flexible docking were employed to predicted the reasonable docking poses of all studied inhibitors. And then, both GSK3β and CDK2 complexes were selected to conduct molecular dynamics simulations. Finally, the free energy calculations were employed to find the key selective-residues.

Results

CoMFA model suggested the steric, hydrophobic fields play key roles in the bioactivities of inhibitors, and the binding mechanisms were well analyzed through molecular docking. The binding free energies predicted are in good agreement with the experimental bioactivities and the free energy calculations showed that the binding of GSK3β/inhibitors was mainly contributed from hydrogen bonding and hydrophobic interaction.

Conclusion

Some key residues for selective binding were highlighted, which may afford important guidance for the rational design of novel ATP-competitive GSK3β inhibitors.

Simulation Study of the Plasticity of k-Turn Motif in Different Environments

The k-turn is a widespread and important motif in RNA. According to the internal hydrogen bond network, it has two stable states, called N1 and N3. The relative stability between the states changes with the environment. It is able to accept different conformations in different environments. This is called the "plasticity" of a molecule. In this work, we study the plasticity of k-turn by the mixing REMD method in explicit solvent. The results are concluded as follows. First, N1 and N3 are almost equally stable when k-turn is in the solvent alone. The molecule is quite flexible as a hinge. However, after binding to different proteins, such as the proteins L7Ae and L24e, k-turn falls into one global minimum. The preferred state could be either N1 or N3. On the contrary, the other nonpreferred state becomes unstable with a weaker binding affinity to the protein. It reveals that RNA-binding protein is able to modulate the representative state of k-turn at equilibrium. This is in agreement with the findings in experiments. Moreover, free energy calculations show that the free energy barrier between the N1 and N3 states of k-turn increases in the complexes. The state-to-state transition is greatly impeded. We also give a deep discussion on the mechanism of the high plasticity of k-turn in different environments.

Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models

MOTIVATION

Given the importance of protein-ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble-RNA docking strategy to explicitly consider the protein flexibility in protein-RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB).

RESULTS

Our approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound-bound and 18 unbound-unbound protein-RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/mprdock/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Fast Identification of Possible Drug Treatment of Coronavirus Disease-19 (COVID-19) through Computational Drug Repurposing Study

The recent outbreak of novel coronavirus disease-19 (COVID-19) calls for and welcomes possible treatment strategies using drugs on the market. It is very efficient to apply computer-aided drug design techniques to quickly identify promising drug repurposing candidates, especially after the detailed 3D structures of key viral proteins are resolved. The virus causing COVID-19 is SARS-CoV-2. Taking advantage of a recently released crystal structure of SARS-CoV-2 main protease in complex with a covalently bonded inhibitor, N3 (Liu et al., 10.2210/pdb6LU7/pdb), I conducted virtual docking screening of approved drugs and drug candidates in clinical trials. For the top docking hits, I then performed molecular dynamics simulations followed by binding free energy calculations using an end point method called MM-PBSA-WSAS (molecular mechanics/Poisson-Boltzmann surface area/weighted solvent-accessible surface area; Wang, Chem. Rev. 2019, 119, 9478; Wang, Curr. Comput.-Aided Drug Des. 2006, 2, 287; Wang; ; Hou J. Chem. Inf. Model., 2012, 52, 1199). Several promising known drugs stand out as potential inhibitors of SARS-CoV-2 main protease, including carfilzomib, eravacycline, valrubicin, lopinavir, and elbasvir. Carfilzomib, an approved anticancer drug acting as a proteasome inhibitor, has the best MM-PBSA-WSAS binding free energy, -13.8 kcal/mol. The second-best repurposing drug candidate, eravacycline, is synthetic halogenated tetracycline class antibiotic. Streptomycin, another antibiotic and a charged molecule, also demonstrates some inhibitory effect, even though the predicted binding free energy of the charged form (-3.8 kcal/mol) is not nearly as low as that of the neutral form (-7.9 kcal/mol). One bioactive, PubChem 23727975, has a binding free energy of -12.9 kcal/mol. Detailed receptor-ligand interactions were analyzed and hot spots for the receptor-ligand binding were identified. I found that one hot spot residue, His41, is a conserved residue across many viruses including SARS-CoV, SARS-CoV-2, MERS-CoV, and hepatitis C virus (HCV). The findings of this study can facilitate rational drug design targeting the SARS-CoV-2 main protease.

A series of molecular modeling techniques to reveal selective mechanisms of inhibitors to β-Site amyloid precursor protein cleaving enzyme 1 (BACE1) and β-site amyloid precursor protein cleaving enzyme 2 (BACE2)

Inhibition of β-Site amyloid precursor protein cleaving enzyme 1 (BACE1) has been shown to be an effective treatment for Alzheimer's disease. A wealth of research has focused on finding highly selective small-molecule inhibitors targeting the BACE1 over its close homologue BACE2 to avoid potential side effects. However, given the highly structural similarities of BACE1 and BACE2, designing highly selective BACE1 inhibitors remains a huge challenge. Recently, it has been reported that a potential BACE1 inhibitor named C28 (∼52-fold selectivity) exhibited greater selectivity to BACE1 over BACE2 than the previously reported inhibitors AZD3293 and AZD3839 (∼1.5-fold and 14-fold selectivity). However, few computational studies have been performed to reveal its underlying mechanisms. In this study, a series of molecular modeling techniques were performed to reveal the selective mechanisms. Classical molecular dynamics (cMD) simulations indicated that the major variations appeared to be controlled by overall protein dynamics. Free energy calculations further suggested that the binding affinities of AZD3293 to BACE1 and BACE2 are similar, but the binding affinity of AZD3839 and C28 to BACE1 is much higher than to BACE2, and that the major variations are electrostatic interactions. The protein dynamics and energy differences were further observed in accelerated molecular dynamics (aMD) simulations. In addition, the umbrella sampling simulations revealed the inhibitors' different patterns of dissociation from the binding pockets of BACE1 and BACE2, and that different energy barriers were responsible for the selectivity. The physical principles revealed by this study may facilitate the rational design of more potent BACE1 selective inhibitors. Communicated by Ramaswamy H. Sarma.

Residue-Residue Mutual Work Analysis of Retinal-Opsin Interaction in Rhodopsin: Implications for Protein-Ligand Binding

Energetic contributions at the single-residue level for retinal-opsin interactions in rhodopsin were studied by combining molecular dynamics simulations, transition path sampling, and a newly developed energy decomposition approach. The virtual work at an infinitesimal time interval was decomposed into the work components on one residue due to its interaction with another residue, which were then averaged over the transition path ensemble along a proposed reaction coordinate. Such residue-residue mutual work analysis on 62 residues within the active center of rhodopsin resulted in a very sparse interaction matrix, which is generally not symmetric but antisymmetric to some extent. Fourteen residues were identified to be major players in retinal relaxation along a plausible pathway from bathorhodopsin to the blue-shifted intermediate, which is in good agreement with an existing NMR study. Based on the matrix of mutual work, a comprehensive network was constructed to provide detailed insights into the chromophore-protein interaction from a viewpoint of energy flow.

Fast Identification of Possible Drug Treatment of Coronavirus Disease -19 (COVID-19) Through Computational Drug Repurposing Study

The recent outbreak of novel coronavirus disease -19 (COVID-19) calls for and welcomes possible treatment strategies using drugs on the market. It is very efficient to apply computer-aided drug design techniques to quickly identify promising drug repurposing candidates, especially after the detailed 3D-structures of key virous proteins are resolved. Taking the advantage of a recently released crystal structure of COVID-19 protease in complex with a covalently-bonded inhibitor, N3,1 I conducted virtual docking screening of approved drugs and drug candidates in clinical trials. For the top docking hits, I then performed molecular dynamics simulations followed by binding free energy calculations using an endpoint method called MM-PBSA-WSAS.2-4 Several promising known drugs stand out as potential inhibitors of COVID-19 protease, including Carfilzomib, Eravacycline, Valrubicin, Lopinavir and Elbasvir. Carfilzomib, an approved anti-cancer drug acting as a proteasome inhibitor, has the best MM-PBSA-WSAS binding free energy, -13.82 kcal/mol. Streptomycin, an antibiotic and a charged molecule, also demonstrates some inhibitory effect, even though the predicted binding free energy of the charged form (-3.82 kcal/mol) is not nearly as low as that of the neutral form (-7.92 kcal/mol). One bioactive, PubChem 23727975, has a binding free energy of -12.86 kcal/mol. Detailed receptor-ligand interactions were analyzed and hot spots for the receptor-ligand binding were identified. I found that one hotspot residue HIS41, is a conserved residue across many viruses including COVID-19, SARS, MERS, and HCV. The findings of this study can facilitate rational drug design targeting the COVID-19 protease.

In silico design of novel benzohydroxamate-based compounds as inhibitors of histone deacetylase 6 based on 3D-QSAR, molecular docking, and molecular dynamics simulations

In silico design of benzohydroxamate-based selective HDAC6 inhibitors.

Computational Insight Into the Small Molecule Intervening PD-L1 Dimerization and the Potential Structure-Activity Relationship

Recently, small-molecule compounds have been reported to block the PD-1/PD-L1 interaction by inducing the dimerization of PD-L1. All these inhibitors had a common scaffold and interacted with the cavity formed by two PD-L1 monomers. This special interactive mode provided clues for the structure-based drug design, however, also showed limitations for the discovery of small-molecule inhibitors with new scaffolds. In this study, we revealed the structure-activity relationship of the current small-molecule inhibitors targeting dimerization of PD-L1 by predicting their binding and unbinding mechanism via conventional molecular dynamics and metadynamics simulation. During the binding process, the representative inhibitors (BMS-8 and BMS-1166) tended to have a more stable binding mode with one PD-L1 monomer than the other and the small-molecule inducing PD-L1 dimerization was further stabilized by the non-polar interaction of Ile54, Tyr56, Met115, Ala121, and Tyr123 on both monomers and the water bridges involved in ALys124. The unbinding process prediction showed that the PD-L1 dimerization kept stable upon the dissociation of ligands. It's indicated that the formation and stability of the small-molecule inducing PD-L1 dimerization was the key factor for the inhibitory activities of these ligands. The contact analysis, R-group based quantitative structure-activity relationship (QSAR) analysis and molecular docking further suggested that each attachment point on the core scaffold of ligands had a specific preference for pharmacophore elements when improving the inhibitory activities by structural modifications. Taken together, the results in this study could guide the structural optimization and the further discovery of novel small-molecule inhibitors targeting PD-L1.

Exploration of the selective binding mechanism of protein kinase Aurora A selectivity via a comprehensive molecular modeling study

Background

The kinase of Aurora A has been regarded as a promising therapeutic target due to its altered expression in various human cancers. However, given the high similarity of the active binding site of Aurora A to other kinases, designing highly selective inhibitors towards Aurora A remains a challenge. Recently, two potential small-molecule inhibitors named AT9283 and Danusertib were reported to exhibit significant selectivity to Aurora A, but not to Gleevec. It was argued that protein dynamics is crucial for drug selectivity to Aurora A. However, little computational research has been conducted to shed light on the underlying mechanisms.

Methods

In this study, MM/GBSA calculations based on conventional molecular dynamics (cMD) simulations and enhanced sampling simulations including Gaussian accelerated MD (GaMD) simulations and umbrella sampling were carried out to illustrate the selectivity of inhibitors to Aurora A.

Results

The calculation results from cMD simulation showed that the binding specificity is primarily controlled by conformational change of the kinase hinge. The protein dynamics and energetic differences were further supported by the GaMD simulations. Umbrella sampling further proved that AT9283 and Danusertib have similar potential of mean force (PMF) profiles toward Aurora A in terms of PMF depth. Compared with AT9283 and Danusertib, Gleevec has much lower PMF depth, indicating that Gleevec is more easily dissociated from Aurora A than AT9283 and Danusertib. These results not only show the selective determinants of Aurora A, but also provide valuable clues for the further development of novel potent Aurora A selective inhibitors.

New application of in silico methods in identifying mechanisms of action and key components of anti-cancer herbal formulation YIV-906 (PHY906)

YIV-906 (formally PHY906, KD018) is a four-herb formulation that is currently being developed to improve the therapeutic index and ameliorate the side effects of many chemotherapeutic drugs including sorafenib, irinotecan, and capecitabine. However, as a promising anti-cancer adjuvant, the molecular mechanism of action of YIV-906 remains unrevealed due to its multi-component and multi-target features. Since YIV-906 has been shown to induce apoptosis and autophagy in cancer cells through modulating the negative regulators of ERK1/2, namely DUSPs, it is of great interest to elucidate the key components that cause the therapeutic effect of YIV-906. In this work, we investigated the mechanism of YIV-906 inhibiting DUSPs, using a broad spectrum of molecular modelling techniques, including molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations. In total, MD simulations and binding free energy calculations were performed for 99 DUSP-ligand complexes. We found that some herbal components or their metabolites could inhibit DUSPs. Based on the docking scores and binding free energies, the sulfation and glucuronidation metabolites of the S ingredient in YIV-906 play a leading role in inhibiting DUSPs, although several original herbal chemicals with carboxyl groups from the P and Z ingredients also make contributions to this inhibitory effect. It is not a surprise that the electrostatic interaction plays the dominant role in the ligand binding process, given the fact that several charged residues reside in the binding pockets of DUSPs. Our MD simulation results demonstrate that the sulfate moieties and carboxyl moieties of the advantageous ligands from YIV-906 can occupy the enzymes' catalytic sites, mimicking the endogenous phosphate substrates of DUSPs. As such, the ligand binding can inhibit the association of DUSPs and ERK1/2, which in turn reduces the dephosphorylation of ERK1/2 and causes cell cycle arrest in the tumor. Our modelling study provides useful insights into the rational design of highly potent anti-cancer drugs targeting DUSPs. Finally, we have demonstrated that multi-scale molecular modelling techniques are able to elucidate molecular mechanisms involving complex molecular systems.

Aptamer-peptide conjugates as a new strategy to modulate human α-thrombin binding affinity

Aptamers are single-stranded RNA or DNA molecules that specifically recognize their targets and have proven valuable for functionalizing sensitive biosensors. α-thrombin is a trypsin-like serine proteinase which plays a crucial role in haemostasis and thrombosis. An abnormal activity or overexpression of this protein is associated with a variety of diseases. A great deal of attention was devoted to the construction of high-throughput biosensors for accurately detect thrombin for the early diagnosis and treatment of related diseases. Herein, we propose a new approach to modulate the interaction between α-thrombin and the aptamer TBA₁₅. To this end, TBA₁₅ was chemically conjugated to two peptide sequences (TBA-G₃FIE-Ac and TBA-G₃EIF-Ac) corresponding to a short fragment of the acidic region of the human factor V, which is known to interact directly with exosite I. Surface Plasmon Resonance (SPR) results showed enhanced analytical performances of thrombin with TBA-G₃EIF-Ac than with TBA wild-type, reaching a limit of detection as low as 44.9 pM. Electrophoresis mobility shift assay (EMSA) corroborated the SPR results. Molecular dynamics (MD) simulations support experimental evidences and provided further insight into thrombin/TBA-peptide interaction. Our findings demonstrate that the combination of TBA₁₅ with key interacting peptides offers good opportunities to produce sensitive devices for thrombin detection and potential candidates to block thrombin activity.

Rational Design of Novel Phosphoinositide 3-Kinase Gamma (PI3Kγ) Selective Inhibitors: A Computational Investigation Integrating 3D-QSAR, Molecular Docking and Molecular Dynamics Simulation

Phosphoinositide 3-kinase gamma (PI3Kγ) draws an increasing attention due to its link with deadly cancer, chronic inflammation and allergy. But the development of PI3Kγ selective inhibitors is still a challenging endeavor because of the high sequence homology with the other PI3K isoforms. In order to acquire valuable information about the interaction mechanism between potent inhibitors and PI3Kγ, a series of PI3Kγ isoform-selective inhibitors were analyzed by a systematic computational method, combining 3D-QSAR, molecular docking, molecular dynamic (MD) simulations, free energy calculations and decomposition. The general structure-activity relationships were revealed and some key residues relating to selectivity and high activity were highlighted. It provides precious guidance for rational virtual screening, modification and design of selective PI3Kγ inhibitors. Finally, ten novel inhibitors were optimized and P10 showed satisfactory predicted bioactivity, demonstrating the feasibility to develop potent PI3Kγ inhibitors through this computational modeling and optimization.

Insight into the selective binding mechanism of DNMT1 and DNMT3A inhibitors: a molecular simulation study

DNA methyltransferases (DNMTs), responsible for the regulation of DNA methylation, have been regarded as promising drug targets for cancer therapy. However, high structural conservation of the catalytic domains of DNMTs poses a big challenge to design selective inhibitors for a specific DNMT isoform. In this study, molecular dynamics (MD) simulations, end-point free energy calculations and umbrella sampling (US) simulations were performed to reveal the molecular basis of the binding selectivity of three representative DNMT inhibitors towards DNMT1 and DNMT3A, including SFG (DNMT1 and DNMT3A dual inhibitors), DC-05 (DNMT1 selective inhibitor) and GSKex1 (DNMT3A selective inhibitor). The binding selectivity of the studied inhibitors reported in previous experiments is reproduced by the MD simulation and binding free energy prediction. The simulation results also suggest that the driving force to determine the binding selectivity of the studied inhibitors stems from the difference in the protein-inhibitor van der Waals interactions. Meanwhile, the per-residue free energy decomposition reveals that the contributions from several non-conserved residues in the binding pocket of DNMT1/DNMT3A, especially Val1580/Trp893, Asn1578/Arg891 and Met1169/Val665, are the key factors responsible for the binding selectivity of DNMT inhibitors. In addition, the binding preference of the studied inhibitors was further validated by the potentials of mean force predicted by the US simulations. This study will provide valuable information for the rational design of novel selective inhibitors targeting DNMT1 and DNMT3A.

Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein-peptide complexes

A significant number of protein-protein interactions (PPIs) are mediated through the interactions between proteins and peptide segments, and therefore determination of protein-peptide interactions (PpIs) is critical to gain an in-depth understanding of the PPI network and even design peptides or small molecules capable of modulating PPIs. Computational approaches, especially molecular docking, provide an efficient way to model PpIs, and a reliable scoring function that can recognize the correct binding conformations for protein-peptide complexes is one of the most important components in protein-peptide docking. The end-point binding free energy calculation methods, such as MM/GBSA and MM/PBSA, are theoretically more rigorous than most empirical and semi-empirical scoring functions designed for protein-peptide docking, but their performance in predicting binding affinities and binding poses for protein-peptide systems has not been systematically assessed. In this study, we first evaluated the capability of MM/GBSA and MM/PBSA with different solvation models, interior dielectric constants (εin) and force fields to predict the binding affinities for 53 protein-peptide complexes. For the 19 short peptides with 5-12 residues, MM/PBSA based on the minimized structures in explicit solvent with the ff99 force field and εin = 2 yields the best correlation between the predicted binding affinities and the experimental data (rp = 0.748), while for the 34 medium-size peptides with 20-25 residues, MM/GBSA based on 1 ns of molecular dynamics (MD) simulations in implicit solvent with the ff03 force field, the GBOBC1 model and a low interior dielectric constant (εin = 1) yields the best accuracy (rp = 0.735). Then, we assessed the rescoring capability of MM/PBSA and MM/GBSA to distinguish the correct binding conformations from the decoys for 112 protein-peptide systems. The results illustrate that MM/PBSA based on the minimized structures with the ff99 or ff14SB force field and MM/GBSA based on the minimized structures with the ff03 force field show excellent capability to recognize the near-native binding poses for the short and medium-size peptides, respectively, and they outperform the predictions given by two popular protein-peptide docking algorithms (pepATTRACT and HPEPDOCK). Therefore, MM/PBSA and MM/GBSA are powerful tools to predict the binding affinities and identify the correct binding poses for protein-peptide systems.

Insights into the substrate binding specificity of quorum-quenching acylase PvdQ

Quorum sensing is a cell to cell signaling mechanism that enables them to coordinate their behaviors in a density-dependent manner mediated by small diffusible signaling molecules, which can control the virulence and biofilm gene expression in many Gram-negative and positive bacteria. N-acyl homoserine lactone acylase PvdQ from human opportunistic pathogen Pseudomonas aeruginosa is a quorum-quenching enzyme that can hydrolyze the amide bond of the quorum signaling N-acyl homoserine lactones (AHLs) thereby degrading the signaling molecules, turning off the biofilm phenotype and resulting in a reduction of bacterial virulence. Previous studies demonstrated that PvdQ has different preferences for N-acyl substrates with different acyl chain lengths and substituents. However, the substrate binding specificity determinants of the quorum-quenching enzyme PvdQ with the different bacterial ligands are unknown and unintuitive. Further, elucidation of these determinants can lead to mutants with efficiency and broader substrate promiscuity. To investigate this question, a computational study was carried out combining multiple molecular docking methods, molecular dynamics simulations, residue interaction network analysis, and binding free energy calculations. The main findings are: firstly, the results from pKa predictions support that the pKa of the N-terminus of Serβ1 was depressed due to the surrounding residues. Multiple molecular docking studies provide useful information about the detailed binding modes and binding affinities. Secondly, 300 ns molecular dynamics simulations were carried out to analyze the overall molecular motions of substrate-bound and substrate-free PvdQ. The specific interactions between the active site of PvdQ and different ligands revealed the determinants for the preference among the ligands. A systematic comparison and analysis of the protein dynamic fingerprint of each complex demonstrated that binding of the most favorable ligand, C12-homoserine lactone (C12-HSL), reduced the global motions of the complex and maintained the correct arrangement of the catalytic site. Further, the residue interaction network analysis of each system illustrated that there are more communication contacts and pathways between the residues in the C12-HSL complex as compared to complexes with the other ligands. The binding of the C12-HSL ligand facilitates structural communication between the two knobs and the active site. While the binding of the other ligands tend to impair specific communication pathways between the two knobs and the active site, and lead to a catalytically inefficient state. Finally, simulation results from free energy landscape and binding free energy analysis revealed that the C12-HSL ligand has the lowest binding free energy and greater stability than the less favored ligands. Each of the following residues: Serβ1, Hisβ23, Pheβ24, Metβ30, Pheβ32, Leuβ50, Asnβ57, Thrβ69, Valβ70, Trpβ162, Trpβ186, Asnβ269, Argβ297 and Leuα146, play different roles in substrate binding specificity. This is the first computational study that provides molecular information for structure-dynamic-function relationships of PvdQ with different ligands and demonstrates determinants of bacterial substrate binding specificity.

Insight into the selective mechanism of phosphoinositide 3-kinase γ with benzothiazole and thiazolopiperidine γ-specific inhibitors by in silico approaches

The phosphoinositide 3-kinase γ (PI3Kγ) has been verified to be a potential drug target for the treatments of various human physical disorders. Although received lots of attention, the development of PI3Kγ-selective inhibitors is still a challenging subject because of its unique protein structural features. Aiming to uncover the interaction mechanism between the selective inhibitors and PI3Kγ, a series of benzothiazole and thiazolopiperidine PI3Kγ isoform-selective inhibitors were studied with an integrated in silico strategy by combining molecular docking, molecular dynamic simulations, binding free energy calculations, and decomposition analysis. Firstly, three molecular docking models, including rigid receptor docking, induced fit docking (IFD), and quantum mechanical-polarized ligand docking, were respectively, built, and the IFD preliminarily predicted the docking poses of all studied inhibitors and roughly analyzed the binding mechanism. Secondly, four binding complexes with representative inhibitors were selected to perform molecular dynamic simulations and free energy calculations. The predicted binding energies were consistent with the experimental bioactivities and different binding patterns between potent and weak inhibitors were uncovered. Finally, through the Molecular Mechanics/Generalized Born Surface Area binding free energy decomposition, residue-inhibitor interactions spectra were obtained and several key residues contributing to favorable binding were highlighted, which provides valuable information for rational PI3Kγ inhibitor design and modification.

Importance of a crystalline water network in docking-based virtual screening: a case study of BRD4

As a member of the bromodomain and extra terminal domain (BET) protein family, bromodomain-containing protein 4 (BRD4) is an epigenetic reader and can recognize acetylated lysine residues in histones.