A reliable predictor of protein-protein interaction sites is necessary to investigate and model protein functional interaction networks. Hidden Markov Support Vector Machines (HM-SVM) have been shown to be among the best performing methods on this task. Furthermore, it has been noted that the performance of a predictor improves when its input takes advantage of the difference between observed and predicted residue solvent accessibility. In this paper, for first time, we combine these elements and we present ISPRED2, a new HM-SVM-based method that overpasses the state of the art performance (Q2=0.71 and correlation=0.43). ISPRED2 consists of a sets of Python scripts aimed at integrating the different third-party software to obtain the final prediction.
Machine-Learning Methods to Predict Protein Interaction Sites in Folded Proteins
Piero Fariselli;
2012-01-01
Abstract
A reliable predictor of protein-protein interaction sites is necessary to investigate and model protein functional interaction networks. Hidden Markov Support Vector Machines (HM-SVM) have been shown to be among the best performing methods on this task. Furthermore, it has been noted that the performance of a predictor improves when its input takes advantage of the difference between observed and predicted residue solvent accessibility. In this paper, for first time, we combine these elements and we present ISPRED2, a new HM-SVM-based method that overpasses the state of the art performance (Q2=0.71 and correlation=0.43). ISPRED2 consists of a sets of Python scripts aimed at integrating the different third-party software to obtain the final prediction.
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