title: Scoring predictive models using a reduced representation of proteins: model and energy definition.
abstract: BACKGROUND
  Reduced representations of proteins have been playing a keyrole in the study of protein folding. Many such models are available, with different representation detail. Although the usefulness of many such models for structural bioinformatics applications has been demonstrated in recent years, there are few intermediate resolution models endowed with an energy model capable, for instance, of detecting native or native-like structures among decoy sets. The aim of the present work is to provide a discrete empirical potential for a reduced protein model termed here PC2CA, because it employs a PseudoCovalent structure with only 2 Centers of interactions per Amino acid, suitable for protein model quality assessment.
  
  
  RESULTS
  All protein structures in the set top500H have been converted in reduced form. The distribution of pseudobonds, pseudoangle, pseudodihedrals and distances between centers of interactions have been converted into potentials of mean force. A suitable reference distribution has been defined for non-bonded interactions which takes into account excluded volume effects and protein finite size. The correlation between adjacent main chain pseudodihedrals has been converted in an additional energetic term which is able to account for cooperative effects in secondary structure elements. Local energy surface exploration is performed in order to increase the robustness of the energy function.
  
  
  CONCLUSIONS
  The model and the energy definition proposed have been tested on all the multiple decoys' sets in the Decoys'R'us database. The energetic model is able to recognize, for almost all sets, native-like structures (RMSD less than 2.0 A). These results and those obtained in the blind CASP7 quality assessment experiment suggest that the model compares well with scoring potentials with finer granularity and could be useful for fast exploration of conformational space. Parameters are available at the url: http://www.dstb.uniud.it/~ffogolari/download/.
authors: Dipartimento di Scienze e Tecnologie Biomediche, Università di Udine, Udine, Italy. ffogolari@mail.dstb.uniud.it
authors: Federico
authors: F
authors: Fogolari
authors: Lidia
authors: L
authors: Pieri
authors: Agostino
authors: A
authors: Dovier
authors: Luca
authors: L
authors: Bortolussi
authors: Gilberto
authors: G
authors: Giugliarelli
authors: Alessandra
authors: A
authors: Corazza
authors: Gennaro
authors: G
authors: Esposito
authors: Paolo
authors: P
authors: Viglino
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D001665:Binding Sites; D053179:Caspase 7 / Q000737:chemistry; D003198:Computer Simulation; D018360:Crystallography, X-Ray; D030562:Databases, Protein; D008956:Models, Chemical; D015233:Models, Statistical; D009010:Monte Carlo Method; D011485:Protein Binding; D011487:Protein Conformation; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein; D012984:Software; D012997:Solvents; D013816:Thermodynamics
chemical_list: D000596:Amino Acids; D011506:Proteins; D012997:Solvents; D053179:Caspase 7