engleski

Prediction of Protein-Protein Interaction Sites in Sequences and 3D Structures by Random Forests

Identifying interaction sites in proteins provides important clues to the function of a protein, and is becoming increasingly relevant in topics like systems biology and drug discovery. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using i) a combination of sequence and structure-derived parameters ; and ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and the F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with the F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modelling the Ras-Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.

protein – protein interactions ; sequence ; residues ; 3D protein structure ; Radnom Forest ; Classification ; protein docking

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