MOESM1 of Efficient conformational ensemble generation of protein-bound peptides

Additional file 1. The average accuracies and standard deviations of MODPEP for the peptides of 3–30 amino acids on ten randomly splitted training/test sets

New Knowledge-Based Scoring Function with Inclusion of Backbone Conformational Entropies from Protein Structures

Accurate prediction of a protein’s structure requires a reliable free energy function that consists of both enthalpic and entropic contributions. Although considerable progresses have been made in the calculation of potential energies in protein structure prediction, the computation for entropies of protein has lagged far behind, due to the challenge that estimation of entropies often requires expensive conformational sampling. In this study, we have used a knowledge-based approach to estimate the backbone conformational entropies from experimentally determined structures. Instead of conducting computationally expensive MD/MC simulations, we obtained the entropies of protein structures based on the normalized probability distributions of back dihedral angles observed in the native structures. Our new knowledge-based scoring function with inclusion of the backbone entropies, which is referred to as ITScoreDA or ITDA, was extensively evaluated on 16 commonly used decoy sets and compared with 50 other published scoring functions. It was shown that ITDA is significantly superior to the other tested scoring functions in selecting native structures from decoys. The present study suggests the role of backbone conformational entropies in protein structures and provides a way for fast estimation of the entropic effect

HybridDock: A Hybrid Protein–Ligand Docking Protocol Integrating Protein- and Ligand-Based Approaches

Structure-based molecular docking and ligand-based similarity search are two commonly used computational methods in computer-aided drug design. Structure-based docking tries to utilize the structural information on a drug target like protein, and ligand-based screening takes advantage of the information on known ligands for a target. Given their different advantages, it would be desirable to use both protein- and ligand-based approaches in drug discovery when information for both the protein and known ligands is available. Here, we have presented a general hybrid docking protocol, referred to as HybridDock, to utilize both the protein structures and known ligands by combining the molecular docking program MDock and the ligand-based similarity search method SHAFTS, and evaluated our hybrid docking protocol on the CSAR 2013 and 2014 exercises. The results showed that overall our hybrid docking protocol significantly improved the performance in both binding affinity and binding mode predictions, compared to the sole MDock program. The efficacy of the hybrid docking protocol was further confirmed using the combination of DOCK and SHAFTS, suggesting an alternative docking approach for modern drug design/discovery

Automated Large-Scale File Preparation, Docking, and Scoring: Evaluation of ITScore and STScore Using the 2012 Community Structure–Activity Resource Benchmark

In this study, we use the recently released 2012 Community Structure–Activity Resource (CSAR) data set to evaluate two knowledge-based scoring functions, ITScore and STScore, and a simple force-field-based potential (VDWScore). The CSAR data set contains 757 compounds, most with known affinities, and 57 crystal structures. With the help of the script files for docking preparation, we use the full CSAR data set to evaluate the performances of the scoring functions on binding affinity prediction and active/inactive compound discrimination. The CSAR subset that includes crystal structures is used as well, to evaluate the performances of the scoring functions on binding mode and affinity predictions. Within this structure subset, we investigate the importance of accurate ligand and protein conformational sampling and find that the binding affinity predictions are less sensitive to non-native ligand and protein conformations than the binding mode predictions. We also find the full CSAR data set to be more challenging in making binding mode predictions than the subset with structures. The script files used for preparing the CSAR data set for docking, including scripts for canonicalization of the ligand atoms, are offered freely to the academic community

Hierarchical Flexible Peptide Docking by Conformer Generation and Ensemble Docking of Peptides

Given the importance of peptide-mediated protein interactions in cellular processes, protein–peptide docking has received increasing attention. Here, we have developed a <b>H</b>ierarchical flexible <b>Pep</b>tide <b>Dock</b>ing approach through fast generation and ensemble docking of peptide conformations, which is referred to as <b>HPepDock</b>. Tested on the LEADS-PEP benchmark data set of 53 diverse complexes with peptides of 3–12 residues, HPepDock performed significantly better than the 11 docking protocols of five small-molecule docking programs (DOCK, AutoDock, AutoDock Vina, Surflex, and GOLD) in predicting near-native binding conformations. HPepDock was also evaluated on the 19 bound/unbound and 10 unbound/unbound protein–peptide complexes of the Glide SP-PEP benchmark and showed an overall better performance than Glide SP-PEP+MM-GBSA and FlexPepDock in both bound and unbound docking. HPepDock is computationally efficient, and the average running time for docking a peptide is ∼15 min with the range from about 1 min for short peptides to around 40 min for long peptides