title: DECK: Distance and environment-dependent, coarse-grained, knowledge-based potentials for protein-protein docking.
abstract: BACKGROUND
  Computational approaches to protein-protein docking typically include scoring aimed at improving the rank of the near-native structure relative to the false-positive matches. Knowledge-based potentials improve modeling of protein complexes by taking advantage of the rapidly increasing amount of experimentally derived information on protein-protein association. An essential element of knowledge-based potentials is defining the reference state for an optimal description of the residue-residue (or atom-atom) pairs in the non-interaction state.
  
  
  RESULTS
  The study presents a new Distance- and Environment-dependent, Coarse-grained, Knowledge-based (DECK) potential for scoring of protein-protein docking predictions. Training sets of protein-protein matches were generated based on bound and unbound forms of proteins taken from the DOCKGROUND resource. Each residue was represented by a pseudo-atom in the geometric center of the side chain. To capture the long-range and the multi-body interactions, residues in different secondary structure elements at protein-protein interfaces were considered as different residue types. Five reference states for the potentials were defined and tested. The optimal reference state was selected and the cutoff effect on the distance-dependent potentials investigated. The potentials were validated on the docking decoys sets, showing better performance than the existing potentials used in scoring of protein-protein docking results.
  
  
  CONCLUSIONS
  A novel residue-based statistical potential for protein-protein docking was developed and validated on docking decoy sets. The results show that the scoring function DECK can successfully identify near-native protein-protein matches and thus is useful in protein docking. In addition to the practical application of the potentials, the study provides insights into the relative utility of the reference states, the scope of the distance dependence, and the coarse-graining of the potentials.
authors: Biomolecular Physics and Modeling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
authors: Shiyong
authors: S
authors: Liu
authors: Ilya A
authors: IA
authors: Vakser
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D051188:Knowledge Bases; D008958:Models, Molecular; D011485:Protein Binding; D054730:Protein Interaction Domains and Motifs; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012984:Software
chemical_list: D011506:Proteins