Journal of Computer-Aided Molecular Design最新文献

Systematic optimization of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead-optimization using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical candidate. We show how conformational restraints can be derived by exploiting NMR data to identify low-energy solution ensembles of a lead compound. Such restraints can be used to focus conformational search for analogs in order to accurately predict bound ligand poses through molecular docking and thereby estimate ligand strain and protein-ligand intermolecular binding energy. We also describe an analogous ligand-based approach that employs molecular similarity optimization to predict bound poses. Both approaches are shown to be effective for prioritizing lead-compound analogs. Surprisingly, relatively small ligand modifications, which may have minimal effects on predicted bound pose or intermolecular interactions, often lead to large changes in estimated strain that have dominating effects on overall binding energy estimates. Effective macrocyclic conformational search is crucial, whether in the context of NMR-based restraints, X-ray ligand refinement, partial torsional restraint for docking/ligand-similarity calculations or agnostic search for nominal global minima. Lead optimization for peptidic macrocycles can be made more productive using a multi-disciplinary approach that combines biophysical data with practical and efficient computational methods.

In this article, we present PoseEdit, a new, interactive frontend of the popular pose visualization tool PoseView. PoseEdit automatically produces high-quality 2D diagrams of intermolecular interactions in 3D binding sites calculated from ligands in complex with protein, DNA, and RNA. The PoseView diagrams have been improved in several aspects, most notably in their interactivity. Thanks to the easy-to-use 2D editor of PoseEdit, the diagrams are extensively editable and extendible by the user, can be merged with other diagrams, and even be created from scratch. A large variety of graphical objects in the diagram can be moved, rotated, selected and highlighted, mirrored, removed, or even newly added. Furthermore, PoseEdit enables a synchronized 2D-3D view of macromolecule-ligand complexes simplifying the analysis of structural features and interactions. The representation of individual diagram objects regarding their visualized chemical properties, like stereochemistry, and general graphical styles, like the color of interactions, can additionally be edited. The primary objective of PoseEdit is to support scientists with an enhanced way to communicate ligand binding mode information through graphical 2D representations optimized with the scientist’s input in accordance with objective criteria and individual needs. PoseEdit is freely available on the ProteinsPlus web server (https://proteins.plus).

When designing peptide ligands based on the structure of a protein receptor, it can be very useful to narrow down the possible binding positions and bound conformations of the ligand without the need to choose its amino acid sequence in advance. Here, we construct and benchmark a tool for this purpose based on a recently reported statistical energy model named SCUBA (Sidechain-Unknown Backbone Arrangement) for designing protein backbones without considering specific amino acid sequences. With this tool, backbone fragments of different local conformation types are generated and optimized with SCUBA-driven stochastic simulations and simulated annealing, and then ranked and clustered to obtain representative backbone fragment poses of strong SCUBA interaction energies with the receptor. We computationally benchmarked the tool on 111 known protein-peptide complex structures. When the bound ligands are in the strand conformation, the method is able to generate backbone fragments of both low SCUBA energies and low root mean square deviations from experimental structures of peptide ligands. When the bound ligands are helices or coils, low-energy backbone fragments with binding poses similar to experimental structures have been generated for approximately 50% of benchmark cases. We have examined a number of predicted ligand-receptor complexes by atomistic molecular dynamics simulations, in which the peptide ligands have been found to stay at the predicted binding sites and to maintain their local conformations. These results suggest that promising backbone structures of peptides bound to protein receptors can be designed by identifying outstanding minima on the SCUBA-modeled backbone energy landscape.

Owing to the emergence of antibiotic resistance, the polymyxin colistin has been recently revived to treat acute, multidrug-resistant Gram-negative bacterial infections. Positively charged colistin binds to negatively charged lipids and damages the outer membrane of Gram-negative bacteria. However, the MCR-1 protein, encoded by the mobile colistin resistance (mcr) gene, is involved in bacterial colistin resistance by catalysing phosphoethanolamine (PEA) transfer onto lipid A, neutralising its negative charge, and thereby reducing its interaction with colistin. Our preliminary results showed that treatment with a reference pyrazolone compound significantly reduced colistin minimal inhibitory concentrations in Escherichia coli expressing mcr-1 mediated colistin resistance (Hanpaibool et al. in ACS Omega, 2023). A docking-MD combination was used in an ensemble-based docking approach to identify further pyrazolone compounds as candidate MCR-1 inhibitors. Docking simulations revealed that 13/28 of the pyrazolone compounds tested are predicted to have lower binding free energies than the reference compound. Four of these were chosen for in vitro testing, with the results demonstrating that all the compounds tested could lower colistin MICs in an E. coli strain carrying the mcr-1 gene. Docking of pyrazolones into the MCR-1 active site reveals residues that are implicated in ligand–protein interactions, particularly E246, T285, H395, H466, and H478, which are located in the MCR-1 active site and which participate in interactions with MCR-1 in ≥ 8/10 of the lowest energy complexes. This study establishes pyrazolone-induced colistin susceptibility in E. coli carrying the mcr-1 gene, providing a method for the development of novel treatments against colistin-resistant bacteria.

African swine fever virus (ASFV), an extremely contagious virus with high mortality rates, causes severe hemorrhagic viral disease in both domestic and wild pigs. Fortunately, ASFV cannot be transmitted from pigs to humans. However, ongoing ASFV outbreaks could have severe economic consequences for global food security. Although ASFV was discovered several years ago, no vaccines or treatments are commercially available yet; therefore, the identification of new anti-ASFV drugs is urgently warranted. Using molecular docking and machine learning, we have previously identified pentagastrin, cangrelor, and fostamatinib as potential antiviral drugs against ASFV. Here, using machine learning combined with docking simulations, we identified natural products with a high affinity for AsfvPolX proteins. We selected five natural products (NPs) that are located close in chemical space to the six known natural flavonoids that possess anti-ASFV activity. Polygalic acid markedly reduced AsfvPolX polymerase activity in a dose-dependent manner. We propose an efficient protocol for identifying NPs as potential antiviral drugs by identifying chemical spaces containing high-affinity binders against ASFV in NP databases.

Drug candidates identified by the pharmaceutical industry typically have unique structural characteristics to ensure they interact strongly and specifically with their biological targets. Identifying these characteristics is a key challenge for developing new drugs, and quantitative structure-activity relationship (QSAR) analysis has generally been used to perform this task. QSAR models with good predictive power improve the cost and time efficiencies invested in compound development. Generating these good models depends on how well differences between “active” and “inactive” compound groups can be conveyed to the model to be learned. Efforts to solve this difference issue have been made, including generating a “molecular descriptor” that compressively expresses the structural characteristics of compounds. From the same perspective, we succeeded in developing the Activity Differences-Quantitative Structure-Activity Relationship (ADis-QSAR) model by generating molecular descriptors that more explicitly convey features of the group through a pair system that performs direct connections between active and inactive groups. We used popular machine learning algorithms, such as Support Vector Machine, Random Forest, XGBoost and Multi-Layer Perceptron for model learning and evaluated the model using scores such as accuracy, area under curve, precision and specificity. The results showed that the Support Vector Machine performed better than the others. Notably, the ADis-QSAR model showed significant improvements in meaningful scores such as precision and specificity compared to the baseline model, even in datasets with dissimilar chemical spaces. This model reduces the risk of selecting false positive compounds, improving the efficiency of drug development.

Kallikrein 6 (KLK6) is an attractive drug target for the treatment of neurological diseases and for various cancers. Herein, we explore the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding (ΔG_bind) for a series of 49 inhibitors of KLK6. We found that the performance of the methods varied strongly with the tested system. For only one of the three KLK6 datasets, the docking scores obtained with rDock were in good agreement (R² ≥ 0.5) with experimental values of ΔG_bind. A similar result was obtained with MM/GBSA (using the ff14SB force field) calculations based on single minimized structures. Improved binding affinity predictions were obtained with the free energy perturbation (FEP) method, with an overall MUE and RMSE of 0.53 and 0.68 kcal/mol, respectively. Furthermore, in a simulation of a real-world drug discovery project, FEP was able to rank the most potent compounds at the top of the list. These results indicate that FEP can be a promising tool for the structure-based optimization of KLK6 inhibitors.

The SAMPL8 blind prediction challenge, which addresses the acid/base dissociation constants (pKa) and the distribution coefficients (logD), was addressed by the Conductor like Screening Model for Realistic Solvation (COSMO-RS). Using the COSMOtherm implementation of COSMO-RS together with a rigorous conformational sampling, yielded logD predictions with a root mean square deviation (RMSD) of 1.36 log units over all 11 compounds and seven bi-phasic systems of the data set, which was the most accurate of all contest submissions (logD).

For the SAMPL8 pKa competition, participants were asked to report the standard state free energies of all microstates, which were then used to calculate the macroscopic pKa. We have used COSMO-RS based linear free energy fit models to calculate the requested energies. The assignment of the calculated and experimental pKa values was made on the basis of the popular transitions, i.e. the transition hat was predicted by the majority of the submissions. With this assignment and a model that covers both, pKa and base pKa, we achieved an RMSD of 3.44 log units (18 pKa values of 14 molecules), which is the second place of the six ranked submissions. By changing to an assignment that is based on the experimental transition curves, the RMSD reduces to 1.65. In addition to the ranked contribution, we submitted two more data sets, one for the standard pKa model and one or the standard base pKa model of COSMOtherm. Using the experiment based assignment with the predictions of the two sets we received a RMSD of 1.42 log units (25 pKa values of 20 molecules). The deviation mainly arises from a single outlier compound, the omission of which leads to an RMSD of 0.89 log units.

As an alternative to antibiotics, Antimicrobial Peptides (AMPs) possess unique properties including cationic, amphipathic and their abundance in nature, but the exact characteristics of AMPs against bacterial membranes are still undetermined. To estimate the structural stability and functional activity of AMPs, the Pseudin AMPs (Pse-1, Pse-2, Pse-3, and Pse-4) from Hylid frog species, Pseudis paradoxa, an abundantly discovered source for AMPs were examined. We studied the intra-peptide interactions and thermal denaturation stability of peptides, as well as the geometrical parameters and secondary structure profiles of their conformational trajectories. On this basis, the peptides were screened out and the highly stable peptide, Pse-4 was subjected to membrane simulation in order to observe the changes in membrane curvature formed by Pse-4 insertion. Monomeric Pse-4 was found to initiate the membrane disruption; however, a stable multimeric form of Pse-4 might be competent to counterbalance the helix-coil transition and to resist the hydrophobic membrane environment. Eventually, hexameric Pse-4 on membrane simulation exhibited the hydrogen bond formation with E. coli bacterial membrane and thereby, leading to the formation of membrane spanning pore that allowed the entry of excess water molecules into the membrane shell, thus causing membrane deformation. Our report points out the mechanism of Pse-4 peptide against the bacterial membrane for the first time. Relatively, Pse-4 works on the barrel stave model against E. coli bacterial membrane; hence it might act as a good therapeutic scaffold in the treatment of multi-drug resistant bacterial strains.