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DrugScorePPI knowledge-based potentials used as scoring and objective function in protein-protein docking.

ABSTRACT: The distance-dependent knowledge-based DrugScore(PPI) potentials, previously developed for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes, are evaluated as a scoring and objective function for the structure prediction of protein-protein complexes. When applied for ranking "unbound perturbation" ("unbound docking") decoys generated by Baker and coworkers a 4-fold (1.5-fold) enrichment of acceptable docking solutions in the top ranks compared to a random selection is found. When applied as an objective function in FRODOCK for bound protein-protein docking on 97 complexes of the ZDOCK benchmark 3.0, DrugScore(PPI)/FRODOCK finds up to 10% (15%) more high accuracy solutions in the top 1 (top 10) predictions than the original FRODOCK implementation. When used as an objective function for global unbound protein-protein docking, fair docking success rates are obtained, which improve by ? 2-fold to 18% (58%) for an at least acceptable solution in the top 10 (top 100) predictions when performing knowledge-driven unbound docking. This suggests that DrugScore(PPI) balances well several different types of interactions important for protein-protein recognition. The results are discussed in view of the influence of crystal packing and the type of protein-protein complex docked. Finally, a simple criterion is provided with which to estimate a priori if unbound docking with DrugScore(PPI)/FRODOCK will be successful.

SUBMITTER: Kruger DM

PROVIDER: S-EPMC3931789 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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DrugScorePPI knowledge-based potentials used as scoring and objective function in protein-protein docking.

Krüger Dennis M DM Ignacio Garzón José J Chacón Pablo P Gohlke Holger H

PloS one 20140221 2

The distance-dependent knowledge-based DrugScore(PPI) potentials, previously developed for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes, are evaluated as a scoring and objective function for the structure prediction of protein-protein complexes. When applied for ranking "unbound perturbation" ("unbound docking") decoys generated by Baker and coworkers a 4-fold (1.5-fold) enrichment of acceptable docking solutions in the top ranks compared to ...[more]

PMID: 24586799

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Project description:MOTIVATION:Protein complexes play critical roles in many aspects of biological functions. Three-dimensional (3D) structures of protein complexes are critical for gaining insights into structural bases of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determinations of 3D protein complex structures, computational docking has evolved as a valuable tool to predict 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge. RESULTS:Here we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein-protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to, that of state-of-the-art scoring functions on two independent datasets: (i) Docking software-specific models and (ii) the CAPRI score set generated by a wide variety of docking approaches (i.e. docking software-non-specific). iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary, topological and energetic information for scoring docked conformations. This work represents the first successful demonstration of graph kernels to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes. AVAILABILITY AND IMPLEMENTATION:The iScore code is freely available from Github: https://github.com/DeepRank/iScore (DOI: 10.5281/zenodo.2630567). And the docking models used are available from SBGrid: https://data.sbgrid.org/dataset/684). SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Project description:Elastic ellipsoidal functions defined by the observed hydration patterns around the DNA bases provide a new basis for measuring the recognition of ligands in the grooves of double-helical structures. Here a set of knowledge-based potentials suitable for quantitative description of such behavior is extracted from the observed positions of water molecules and amino acid atoms that form hydrogen bonds with the nitrogenous bases in high resolution crystal structures. Energies based on the displacement of hydrogen-bonding sites on drugs in DNA-crystal complexes relative to the preferred locations of water binding around the heterocyclic bases are low, pointing to the reliability of the potentials and the apparent displacement of water molecules by drug atoms in these structures. The validity of the energy functions has been further examined in a series of sequence substitution studies based on the structures of DNA bound to polyamides that have been designed to recognize the minor-groove edges of Watson-Crick basepairs. The higher energies of binding to incorrect sequences superimposed (without conformational adjustment or displacement of polyamide ligands) on observed high resolution structures confirm the hypothesis that the drug subunits associate with specific DNA bases. The knowledge-based functions also account satisfactorily for the measured free energies of DNA-polyamide association in solution and the observed sites of polyamide binding on nucleosomal DNA. The computations are generally consistent with mechanisms by which minor-groove binding ligands are thought to recognize DNA basepairs. The calculations suggest that the asymmetric distributions of hydrogen-bond-forming atoms on the minor-groove edge of the basepairs may underlie ligand discrimination of G.C from C.G pairs, in addition to the commonly believed role of steric hindrance. The analysis of polyamide-bound nucleosomal structures reveals other discrepancies in the expected chemical design, including unexpected contacts to DNA and modified basepair targets of some ligands. The ellipsoidal potentials thus appear promising as a mathematical tool for the study of drug- and protein-DNA interactions and for gaining new insights into DNA-binding mechanisms.

Dataset Information

DrugScorePPI knowledge-based potentials used as scoring and objective function in protein-protein docking.

Publications

DrugScorePPI knowledge-based potentials used as scoring and objective function in protein-protein docking.

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