How different from random are docking predictions when ranked by scoring functions?
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How different from random are docking predictions when ranked by scoring functions? / Feliu, Elisenda; Oliva, Baldomero.
I: Proteins: Structure, Function, and Bioinformatics, Bind 78, Nr. 16, 2010, s. 3376-3385.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - How different from random are docking predictions when ranked by scoring functions?
AU - Feliu, Elisenda
AU - Oliva, Baldomero
N1 - Copyright © 2010 Wiley-Liss, Inc.
PY - 2010
Y1 - 2010
N2 - Docking algorithms predict the structure of protein-protein interactions. They sample the orientation of two unbound proteins to produce various predictions about their interactions, followed by a scoring step to rank the predictions. We present a statistical assessment of scoring functions used to rank near-native orientations, applying our statistical analysis to a benchmark dataset of decoys of protein-protein complexes and assessing the statistical significance of the outcome in the Critical Assessment of PRedicted Interactions (CAPRI) scoring experiment. A P value was assigned that depended on the number of near-native structures in the sampling. We studied the effect of filtering out redundant structures and tested the use of pair-potentials derived using ZDock and ZRank. Our results show that for many targets, it is not possible to determine when a successful reranking performed by scoring functions results merely from random choice. This analysis reveals that changes should be made in the design of the CAPRI scoring experiment. We propose including the statistical assessment in this experiment either at the preprocessing or the evaluation step.
AB - Docking algorithms predict the structure of protein-protein interactions. They sample the orientation of two unbound proteins to produce various predictions about their interactions, followed by a scoring step to rank the predictions. We present a statistical assessment of scoring functions used to rank near-native orientations, applying our statistical analysis to a benchmark dataset of decoys of protein-protein complexes and assessing the statistical significance of the outcome in the Critical Assessment of PRedicted Interactions (CAPRI) scoring experiment. A P value was assigned that depended on the number of near-native structures in the sampling. We studied the effect of filtering out redundant structures and tested the use of pair-potentials derived using ZDock and ZRank. Our results show that for many targets, it is not possible to determine when a successful reranking performed by scoring functions results merely from random choice. This analysis reveals that changes should be made in the design of the CAPRI scoring experiment. We propose including the statistical assessment in this experiment either at the preprocessing or the evaluation step.
KW - Algorithms
KW - Cluster Analysis
KW - Databases, Protein
KW - Models, Molecular
KW - Protein Binding
KW - Protein Conformation
KW - Protein Interaction Mapping
KW - Proteins
U2 - 10.1002/prot.22844
DO - 10.1002/prot.22844
M3 - Journal article
C2 - 20848549
VL - 78
SP - 3376
EP - 3385
JO - Proteins: Structure, Function, and Bioinformatics
JF - Proteins: Structure, Function, and Bioinformatics
SN - 0887-3585
IS - 16
ER -
ID: 40285347