In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method.

Accumulated evidence suggests that binding kinetic properties-especially dissociation rate constant or drug-target residence time-are crucial factors affecting drug potency. However, quantitative prediction of kinetic properties has always been a challenging task in drug discovery. In this study, the VolSurf method was successfully applied to quantitatively predict the koff values of the small ligands of heat shock protein 90α (HSP90α), adenosine receptor (AR) and p38 mitogen-activated protein kinase (p38 MAPK). The results showed that few VolSurf descriptors can efficiently capture the key ligand surface properties related to dissociation rate; the resulting models demonstrated to be extremely simple, robust and predictive in comparison with available prediction methods. Therefore, it can be concluded that the VolSurf-based prediction method can be widely applied in the ligand-receptor binding kinetics and de novo drug design researches.

Computational Insights into Allosteric Conformational Modulation of P-Glycoprotein by Substrate and Inhibitor Binding.

The ATP-binding cassette (ABC) transporter P-glycoprotein (P-gp) is a physiologically essential membrane protein that protects many tissues against xenobiotic molecules, but limits the access of chemotherapeutics into tumor cells, thus contributing to multidrug resistance. The atomic-level mechanism of how substrates and inhibitors differentially affect the ATP hydrolysis by P-gp remains to be elucidated. In this work, atomistic molecular dynamics simulations in an explicit membrane/water environment were performed to explore the effects of substrate and inhibitor binding on the conformational dynamics of P-gp. Distinct differences in conformational changes that mainly occurred in the nucleotide-binding domains (NBDs) were observed from the substrate- and inhibitor-bound simulations. The binding of rhodamine-123 can increase the probability of the formation of an intermediate conformation, in which the NBDs were closer and better aligned, suggesting that substrate binding may prime the transporter for ATP hydrolysis. By contrast, the inhibitor QZ-Leu stabilized NBDs in a much more separated and misaligned conformation, which may result in the deficiency of ATP hydrolysis. The significant differences in conformational modulation of P-gp by substrate and inhibitor binding provided a molecular explanation of how these small molecules exert opposite effects on the ATPase activity. A further structural analysis suggested that the allosteric communication between transmembrane domains (TMDs) and NBDs was primarily mediated by two intracellular coupling helices. Our computational simulations provide not only valuable insights into the transport mechanism of P-gp substrates, but also for the molecular design of P-gp inhibitors.

SMD-Based Interaction-Energy Fingerprints Can Predict Accurately the Dissociation Rate Constants of HIV-1 Protease Inhibitors.

Recent research has increasingly suggested that the crucial factors affecting drug potencies are related not only to the thermodynamic properties but also to the kinetic properties. Therefore, in silico prediction of ligand-binding kinetic properties, especially the dissociation rate constant ( koff), has aroused more and more attention. However, there are still a lot of challenges that need to be addressed. In this paper, steered molecular dynamics (SMD) combined with residue-based energy decomposition was employed to predict the dissociation rate constants of 37 HIV-1 protease inhibitors (HIV-1 PIs). For the first time, a predictive model of the dissociation rate constant was established by using the interaction-energy fingerprints sampled along the ligand dissociation pathway. On the basis of the key fingerprints extracted it can be inferred that the dissociation rates of 37 HIV-1 PIs are basically determined in the first half of the dissociation processes and that the H-bond interactions with active-site Asp25 and van der Waals interactions with flap-region Ile47 and Ile50 have important influences on the dissociation processes. In general, the strategy established in this paper can provide an efficient way for the prediction of dissociation rate constants as well as the unbinding mechanism research.

Enhanced Turnover for the P450 119 Peroxygenase-Catalyzed Asymmetric Epoxidation of Styrenes by Random Mutagenesis.

A randomized library is constructed based on pET30a-CYP119-T214V plasmid. This library of random mutants of CYP119-T214V was screened by means of the reduced CO difference spectra and epoxidation of styrene. By using directed evolution, a new CYP119 quadruple mutant S148P/I161T/K199E/T214V is constructed, expressed, and purified. This quadruple mutant significantly increases the turnover rate and conversion for the asymmetric epoxidation of styrene and its derivatives. The kcat. value of cis-ß-methylstyrene epoxidation catalyzed by the quadruple mutant exhibits an approximately 10-fold increase, relative to the previously reported T213M mutant under the same conditions. This is the first engineered CYP119 peroxygenase for the epoxidation of cis-ß-methylstyrene with a high turnover rate. The proposed mechanism, on the basis of a molecular docking study, for the asymmetric epoxidation suggests that the introduction of an acidic amino acid side chain into the active site and a hydrophobic amino acid into the substrate channels of CYP119 peroxygenase might result in high efficiency for the formation of compound I, and its subsequent peroxygenation by reconstructing the hydrogen-bonding interaction and increasing the substrate affinity and access.

Constructing Interconsistent, Reasonable, and Predictive Models for Both the Kinetic and Thermodynamic Properties of HIV-1 Protease Inhibitors.

Accumulated evidence suggests that the in vivo biological potency of a ligand is more strongly correlated with the binding/unbinding kinetics than the equilibrium thermodynamics of the protein-ligand interaction (PLI). However, the existing experimental and computational techniques are largely insufficient and limited in large-scale measurements or accurate predictions of the kinetic properties of PLI. In this work, elaborate efforts have been made to develop interconsistent, reasonable, and predictive models of the association rate constant (kon), dissociation rate constant (koff), and equilibrium dissociation constant (KD) of a series of HIV protease inhibitors with different structural skeletons. The results showed that nine Volsurf descriptors derived from water (OH2) and hydrophobic (DRY) probes are key molecular determinants for the kinetic and thermodynamic properties of HIV-1 protease inhibitors. To the best of our knowledge, this is the first time that interconsistent and reasonable models with strong prediction power have been established for both the kinetic and thermodynamic properties of HIV protease inhibitors.

Subangstrom accuracy in pHLA-I modeling by Rosetta FlexPepDock refinement protocol.

Flexible peptides binding to human leukocyte antigen (HLA) play a key role in mediating human immune responses and are also involved in idiosyncratic adverse drug reactions according to recent research. However, the structural determinations of pHLA complexes remain challenging under the present conditions. In this paper, the performance of a new peptide docking method, namely FlexPepDock, was systematically investigated by a benchmark of 30 crystallized structures of peptide-HLA class I complexes. The docking results showed that the near-native pHLA-I models with peptide bb-RMSD less than 2 Å were ranked in the top 1 model for 100% (70/70) docking cases, and the subangstrom models with peptide bb-RMSD less than 1 Å were ranked in the top 5 lowest-energy models for 65.7% (46/70) docking cases. Furthermore, 10 out of 70 docking cases ranked the subangstrom all-atom models in the top 5 lowest-energy models. The results showed that the FlexPepDock can generate high-quality models of pHLA-I complexes and can be widely applied to pHLA-I modeling and mechanism research of peptide-mediated immune responses.

Membrane-assisted tariquidar access and binding mechanisms of human ATP-binding cassette transporter P-glycoprotein.

The human multidrug transporter P-glycoprotein (P-gp) is physiologically essential and of key relevance to biomedicine. Recent structural studies have shed light on the mode of inhibition of the third-generation inhibitors for human P-gp, but the molecular mechanism by which these inhibitors enter the transmembrane sites remains poorly understood. In this study, we utilized all-atom molecular dynamics (MD) simulations to characterize human P-gp dynamics under a potent inhibitor, tariquidar, bound condition, as well as the atomic-level binding pathways in an explicit membrane/water environment. Extensive unbiased simulations show that human P-gp remains relatively stable in tariquidar-free and bound states, while exhibiting a high dynamic binding mode at either the drug-binding pocket or the regulatory site. Free energy estimations by partial nudged elastic band (PNEB) simulations and Molecular Mechanics Generalized Born Surface Area (MM/GBSA) method identify two energetically favorable binding pathways originating from the cytoplasmic gate with an extended tariquidar conformation. Interestingly, free tariquidar in the lipid membrane predominantly adopts extended conformations similar to those observed at the regulatory site. These results suggest that membrane lipids may preconfigure tariquidar into an active ligand conformation for efficient binding to the regulatory site. However, due to its conformational plasticity, tariquidar ultimately moves toward the drug-binding pocket in both pathways, explaining how it acts as a substrate at low concentrations. Our molecular findings propose a membrane-assisted mechanism for the access and binding of the third-generation inhibitors to the binding sites of human P-gp, and offer deeper insights into the molecule design of more potent inhibitors against P-gp-mediated drug resistance.

Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors.

P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter. The over expression of P-gp leads to the development of multidrug resistance (MDR), which is a major obstacle to effective treatment of cancer. Thus, designing effective P-gp inhibitors has an extremely important role in the overcoming MDR. In this paper, both ligand-based quantitative structure-activity relationship (QSAR) and receptor-based molecular docking are used to predict P-gp inhibitors. The results show that each method achieves good prediction performance. According to the results of tenfold cross-validation, an optimal linear SVM model with only three descriptors is established on 857 training samples, of which the overall accuracy (Acc), sensitivity, specificity, and Matthews correlation coefficient are 0.840, 0.873, 0.813, and 0.683, respectively. The SVM model is further validated by 418 test samples with the overall Acc of 0.868. Based on a homology model of human P-gp established, Surflex-dock is also performed to give binding free energy-based evaluations with the overall accuracies of 0.823 for the test set. Furthermore, a consensus evaluation is also performed by using these two methods. Both QSAR and molecular docking studies indicate that molecular volume, hydrophobicity and aromaticity are three dominant factors influencing the inhibitory activities.

[The associations between idiosyncratic adverse drug reactions and HLA alleles and their underlying mechanism].

With the advent of Twenty-First century, more and more genome-wide association studies (GWAS) showed that idiosyncratic adverse drug reactions (ADRs) were closely related with human leukocyte antigen (HLA) alleles, such as the associations of abacavir-HLA-B*5701, allopurinol-HLA-B*5801, and carbamazepine-HLA-B*1502, etc. To explore the mechanisms of these idiosyncratic drug reactions, hapten hypothesis, danger signal hypothesis, pharmacological interaction (P-I) concept and autoimmune mechanism are proposed. In this paper, recent GWAS studies on the HLA-mediated adverse drug reactions and underlying mechanism are reviewed in detail.

NIR squaraine dyes for dual colorimetric and fluorescent determination of Fe3+, Cu2+, and Hg2+ ions.

Four benzoindolenine-based squaraine dyes (SQs), which have the advantages of intense visible and near-infrared (NIR) absorption and emission (λabs/max 663-695 nm, λem/max 686-730 nm) were synthesized and characterized by UV-vis absorption, fluorescent emission spectrophotometry, FTIR, NMR and HRMS analysis. Among them, BBSQ showed excellent performance, which exhibited high selectivity to Fe3+, Cu2+, and Hg2+ in acetonitrile solution even in the presence of other competitive metal ions, accompanied by obvious color change easily detected by the naked eye. The detection limit was 14.17 µM for Fe3+ and 6.06 µM for Cu2+. Most importantly, the response mechanism of BBSQ to Fe3+, Cu2+, and Hg2+ involves the coordination of BBSQ and metal ions through the O atom on the central squarate ring, N atom, and olefin π bond of BBSQ and has been demonstrated by Job's plot, FTIR, and 1H NMR titration analyses. Furthermore, BBSQ was applied successfully to detect Fe3+, Cu2+, and Hg2+ in thin-layer chromatography (TLC) plates with good precision and is quite promising for the quantitative detection of Fe3+ and Cu2+ ions in water samples.

Development of Simple and Accurate in Silico Ligand-Based Models for Predicting ABCG2 Inhibition.

The ATP binding cassette transporter ABCG2 is a physiologically important drug transporter that has a central role in determining the ADMET (absorption, distribution, metabolism, elimination, and toxicity) profile of therapeutics, and contributes to multidrug resistance. Thus, development of predictive in silico models for the identification of ABCG2 inhibitors is of great interest in the early stage of drug discovery. In this work, by exploiting a large public dataset, a number of ligand-based classification models were developed using partial least squares-discriminant analysis (PLS-DA) with molecular interaction field- and fingerprint-based structural description methods, regarding physicochemical and fragmental properties related to ABCG2 inhibition. An in-house dataset compiled from recently experimental studies was used to rigorously validated the model performance. The key molecular properties and fragments favored to inhibitor binding were discussed in detail, which was further explored by docking simulations. A highly informative chemical property was identified as the principal determinant of ABCG2 inhibition, which was utilized to derive a simple rule that had a strong capability for differentiating inhibitors from non-inhibitors. Furthermore, the incorporation of the rule into the best PLS-DA model significantly improved the classification performance, particularly achieving a high prediction accuracy on the independent in-house set. The integrative model is simple and accurate, which could be applied to the evaluation of drug-transporter interactions in drug development. Also, the dominant molecular features derived from the models may help medicinal chemists in the molecular design of novel inhibitors to circumvent ABCG2-mediated drug resistance.

A Charcot-Marie-Tooth-Causing Mutation in HSPB1 Decreases Cell Adaptation to Repeated Stress by Disrupting Autophagic Clearance of Misfolded Proteins.

Charcot-Marie-Tooth (CMT) disease is the most common inherited neurodegenerative disorder with selective degeneration of peripheral nerves. Despite advances in identifying CMT-causing genes, the underlying molecular mechanism, particularly of selective degeneration of peripheral neurons remains to be elucidated. Since peripheral neurons are sensitive to multiple stresses, we hypothesized that daily repeated stress might be an essential contributor to the selective degeneration of peripheral neurons induced by CMT-causing mutations. Here, we mainly focused on the biological effects of the dominant missense mutation (S135F) in the 27-kDa small heat-shock protein HSPB1 under repeated heat shock. HSPB1S135F presented hyperactive binding to both α-tubulin and acetylated α-tubulin during repeated heat shock when compared with the wild type. The aberrant interactions with tubulin prevented microtubule-based transport of heat shock-induced misfolded proteins for the formation of perinuclear aggresomes. Furthermore, the transport of autophagosomes along microtubules was also blocked. These results indicate that the autophagy pathway was disrupted, leading to an accumulation of ubiquitinated protein aggregates and a significant decrease in cell adaptation to repeated stress. Our findings provide novel insights into the molecular mechanisms of HSPB1S135F-induced selective degeneration of peripheral neurons and perspectives for targeting autophagy as a promising therapeutic strategy for CMT neuropathy.

An evaluation of combined strategies for improving the performance of molecular docking.

Molecular docking is a fast and efficient computational method for the prediction of the binding mode and binding affinity between a ligand and a target protein at the atomic level. However, the performance of current docking programs is less than satisfactory. Herein, with a focus on free programs and scoring functions, the performances of LeDock and three standalone scoring functions were tested by 195 high-quality protein-ligand complexes. Results showed that the success rate for the best pose of the free available docking program LeDock achieved 89.20%, indicative of a strong sampling power. Based on the poses generated by LeDock, a comparative evaluation on other three non-commercial scoring functions, including DSX (DrugScore X), PoseScore and X-score was performed. Among all the evaluated scoring functions, DSX and X-score exhibited the best scoring power and ranking power, respectively. The performances of LeDock, DSX and X-score were similar in docking power test, which was much better than the PoseScore. Accordingly, it was suggested that the combination of pose sampling by LeDock with rescoring by DSX or X-score could improve the prediction accuracy of molecular docking and applied in the lead discovery.

Mechanism Prediction of Astragalus membranaceus against Cisplatin-Induced Kidney Damage by Network Pharmacology and Molecular Docking.

BACKGROUND: Cisplatin is a frequently used and effective chemotherapy drug in clinical practice, but severe side effects limit its use, among which nephrotoxicity is considered the most serious and prolonged damage to the body. Astragalus membranaceus (AM) is a well-known herbal medicine, and modern pharmacological studies have confirmed its antioxidant, immunomodulatory, and antiapoptotic effects. Clinical studies have shown that AM and its active components can attenuate cisplatin-induced kidney damage, but the molecular mechanism has not been fully expounded. MATERIALS AND METHODS: First, the components and targets information of AM were collected from the TCMSP, and the relevant targets of cisplatin-induced kidney damage were accessed from the GeneCards and OMIM databases. Then, the core targets were selected by the Venn diagram and network topology analysis, which was followed by GO and KEGG pathway enrichment analysis. Finally, we construct a component-target-pathway network. Furthermore, molecular docking was carried out to identify the binding activity between active components and key targets. RESULTS: A total of 20 active components and 200 targets of AM and 646 targets related to cisplatin-induced kidney damage were obtained. 91 intersection targets were found between AM and cisplatin-induced kidney damage. Then, 16 core targets were identified, such as MAPK1, TNF-α, and p53. Furthermore, GO and KEGG pathway enrichment analysis suggested that MAPK, Toll-like receptor, and PI3K-Akt signaling pathways may be of significance in the treatment of cisplatin-induced kidney damage by AM. Molecular docking indicated that quercetin and kaempferol had high binding affinities with many core targets. CONCLUSION: In summary, the active components, key targets, and signaling pathways of AM in the treatment of cisplatin-induced kidney damage were predicted in this study, which contributed to the development and application of AM.

Enhancement of Activity and Thermostability of Keratinase From Pseudomonas aeruginosa CCTCC AB2013184 by Directed Evolution With Noncanonical Amino Acids.

A keratinase from Pseudomonas aeruginosa (KerPA), which belongs to the M4 family of metallopeptidases, was characterised in this study. This enzyme was engineered with non-canonical amino acids (ncAAs) using genetic code expansion. Several variants with enhanced activity and thermostability were identified and the most prominent, Y21pBpF/Y70pBpF/Y114pBpF, showed an increase in enzyme activity and half-life of approximately 1.3-fold and 8.2-fold, respectively. Considering that keratinases usually require reducing agents to efficiently degrade keratin, the Y21pBpF/Y70pBpF/Y114pBpF variant with enhanced activity and stability under reducing conditions may have great significance for practical applications. Molecular Dynamics (MD) was performed to identify the potential mechanisms underlying these improvements. The results showed that mutation with pBpF at specific sites of the enzyme could fill voids, form new interactions, and reshape the local structure of the active site of the enzyme.

A simple pan-specific RNN model for predicting HLA-II binding peptides.

The prediction of human leukocyte antigen (HLA) class II binding peptides plays important roles in understanding the mechanism of immune recognition and developing effective epitope-based vaccines. In this work, gated recurrent unit (GRU)-based recurrent neural network (RNN) was successfully employed to establish a pan-specific prediction model of HLA-II-binding peptides by using only the HLA and peptide sequence information. In comparison with the existing pan-specific models of HLA-II-binding peptides, the GRU-based RNN model covered a broad spectrum of HLA-II molecules including 50 HLA-DR, 47 HLA-DQ, and 19 HLA-DP molecules with peptide lengths varying from 8 to 43 mers. The results demonstrated strong discriminant capabilities of the GRU-based RNN model, of which the AUC values were 0.92, 0.88, and 0.88 for the training, validation, and test sets, respectively. Also, the GRU-based model showed state-of-the-art performances in predicting the binding peptides with the length ranging from 8-32 mers, which provides an efficient method for predicting HLA-II-binding peptides of longer lengths in comparison with the available methods. Overall, taking the advantages of the RNN architecture, the established pan-specific GRU model can be used for predicting accurately the HLA-II-binding peptides in a simple and direct manner.

De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach.

Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an urgent need to develop novel caspase-6 candidate inhibitors. Herein, a gated recurrent unit (GRU)-based recurrent neural network (RNN) combined with transfer learning was used to build a molecular generative model of caspase-6 inhibitors. The results showed that the GRU-based RNN model can accurately learn the SMILES grammars of about 2.4 million chemical molecules including ionic and isomeric compounds and can generate potential caspase-6 inhibitors after transfer learning of the known 433 caspase-6 inhibitors. Based on the novel molecules derived from the molecular generative model, an optimal logistic regression model and Surflex-dock were employed for predicting and ranking the inhibitory activities. According to the prediction results, three potential caspase-6 inhibitors with different scaffolds were selected as the promising candidates for further research. In general, this paper provides an efficient combinational strategy for de novo molecular design of caspase-6 inhibitors.

Conformational transitions of caspase-6 in substrate-induced activation process explored by perturbation-response scanning combined with targeted molecular dynamics.

Caspase-6 participates in a series of neurodegenerative pathways, and has aroused widespread attentions as a promising molecular target for the treatment of neurodegeneration. Caspase-6 is a homodimer with 6 central-stranded ß-sheets and 5 α-helices in each monomer. Previous crystallographic studies suggested that the 60's, 90's and 130's helices of caspase-6 undergo a distinctive conformational transition upon substrate binding. Although the caspase-6 structures in apo and active states have been determined, the conformational transition process between the two states remains poorly understood. In this work, perturbation-response scanning (PRS) combined with targeted molecular dynamics (TMD) simulations was employed to unravel the atomistic mechanism of the dynamic conformational transitions underlying the substrate-induced activation process of caspase-6. The results showed that the conformational transition of caspase-6 from apo to active states is mainly characterized by structural rearrangements of the substrate-binding site as well as the conformational changes of 60's and 130's extended helices. The H-bond interactions between L1, 130's helix and 90's helix are proved to be key determinant factors for substrate-induced conformational transition. These findings provide valuable insights into the activation mechanism of caspase-6 as well as the molecular design of caspase-6 inhibitors.

Enhancement of keratin-degradation ability of the keratinase KerBL from Bacillus licheniformis WHU by proximity-triggered chemical crosslinking.

Keratinases are valuable enzymes, given their application in keratin-rich waste recycling. Considering that keratinases usually require reducing agents to efficiently degrade keratin, improving the stability of keratinases under reducing conditions is highly desirable for practical applications. Here, we show that the introduction of several tyrosine derivatives containing para-substituted long-chain haloalkanes into the keratinase KerBL, which enabled proximity-triggered covalent crosslinking by rational design, could improve both the thermostability and autolytic resistance of the enzyme. After screening a series of noncanonical amino acid (ncAA)-based variants generated by rational design, two variants, N159C/Y260BprY and N159C/Y260BbtY, with enhanced keratinolytic activity were obtained. Both variants increased the Tm of the enzyme by approximately 10 °C. The potential mechanism underlying these improvements was investigated by molecular dynamics (MD) analysis. The results indicated that BprY-Cys and BbtY-Cys covalent bonds in the N159C/Y260TAG variant could significantly decrease the flexibility and fluctuations of the long loop (residues 151-162).

An Energetically Favorable Ligand Entrance Gate of a Multidrug Transporter Revealed by Partial Nudged Elastic Band Simulations.

P-glycoprotein (P-gp) is a multidrug transporter, which harnesses the chemical energy of ATP to power the efflux of diverse chemotherapeutics out of cells and thus contributes to the development of multidrug resistance (MDR) in cancer. It has been proved that the ligand-binding pocket of P-gp is located at the transmembrane domains (TMDs). However, the access of ligands into the binding pocket remains to be elucidated, which definitely hinder the development of P-gp inhibitors. Herein, the access pathways of a well-known substrate rhodamine-123 and a cyclopeptide inhibitor QZ-Leu were characterized by time-independent partial nudged elastic band (PNEB) simulations. The decreasing free energies along the PNEB-optimized access pathway indicated that TM4/6 cleft may be an energetically favorable entrance gate for ligand entry into the binding pocket of P-gp. The results can be reconciled with a range of experimental studies, further corroborating the reliability of the gate revealed by computational simulations. Our atomic level description of the ligand access pathway provides valuable insights into the gating mechanism for drug uptake and transport by P-gp and other multidrug transporters.