Combination of scoring schemes for protein docking.
BACKGROUND
Docking algorithms are developed to predict in which orientation two proteins are likely to bind under natural conditions. The currently used methods usually consist of a sampling step followed by a scoring step. We developed a weighted geometric correlation based on optimised atom specific weighting factors and combined them with our previously published amino acid specific scoring and with a comprehensive SVM-based scoring function.


RESULTS
The scoring with the atom specific weighting factors yields better results than the amino acid specific scoring. In combination with SVM-based scoring functions the percentage of complexes for which a near native structure can be predicted within the top 100 ranks increased from 14% with the geometric scoring to 54% with the combination of all scoring functions. Especially for the enzyme-inhibitor complexes the results of the ranking are excellent. For half of these complexes a near-native structure can be predicted within the first 10 proposed structures and for more than 86% of all enzyme-inhibitor complexes within the first 50 predicted structures.


CONCLUSIONS
We were able to develop a combination of different scoring schemes which considers a series of previously described and some new scoring criteria yielding a remarkable improvement of prediction quality.
Cologne University Bioinformatics Center (CUBIC), University of Cologne, Zuelpicher Str, 47, 50674 Koeln, Germany.
Philipp
P
Heuser
Dietmar
D
Schomburg
D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*