Protein interactions constitute a crucial part of the cell metabolsim. In particular, protein-protein, DNA-protein and RNA-protein interactions play a critical role in the whole cellular system of transcription-expression-regulation. Machine Learning is utilized to predict protein interactions through empirical models: simulations are viable tools to experiments that const time and resources, and also require expensive equipment and trained biologists to be performed. In this work, we provide novel Machine Learning tools for protein-protein interaction, prediction DNA- and RNA-protein interaction prediction and quantitative protein-protein affinity prediction.

The proposed protein-protein interaction classifier is based on an ensemble of classifiers. The algorithm extends the c...[ Read more ]

Protein interactions constitute a crucial part of the cell metabolsim. In particular, protein-protein, DNA-protein and RNA-protein interactions play a critical role in the whole cellular system of transcription-expression-regulation. Machine Learning is utilized to predict protein interactions through empirical models: simulations are viable tools to experiments that const time and resources, and also require expensive equipment and trained biologists to be performed. In this work, we provide novel Machine Learning tools for protein-protein interaction, prediction DNA- and RNA-protein interaction prediction and quantitative protein-protein affinity prediction.

The proposed protein-protein interaction classifier is based on an ensemble of classifiers. The algorithm extends the concept of similarity from protein pairs to protein pairs of pairs, therefore allowing the application of a k-Nearest Neighbor machinery to instances composed of protein pairs. By combining Support Vector Machines and this novel pairwise approach, our classifier overcomes the flaws of the single algorithms composing it. It represents a novel method for protein-protein interaction prediction. Compared to previously published works trained on the very same data, it provides more accurate results.

The proposed approach for the DNA- and RNA-protein interaction classication overcomes the limitations of other techinques by combining a pair perspective and the presence both of sequential and structural elements in feature encoding. Thanks to the pair perspective multiple targets can be embedded in the models. As consequence our method is capable of generalization: it predicts the interaction of any given DNA- and RNA-protein pair. In fact, it is utilized also to infer new putative interactions in model organisms that are not used for training. In order to get a better insight of the predicting mechanisms, we explore the effects of four different negative sample assembling techniques and the how different features influences the predictions.

A quantitative affinity prediction method is presented as well. The method is tailored on the Dscam protein-protein Affinity prediction problem. It represents the first attempt to model the Dscam protein self-binding machinery, whose mechanisms have been described in Molecular Biology before but never mathematically modeled. Based on a restricted data sample of 89 instances, the model provides predictions about all the possible 19008 self-binding Dscam isoforms, while feature ranking allows to investigate the evolution of Drosophile Dscam binding machinery. By comparing predicted affinities on actual and ancient Dscam proteins, we demonstrate that Dscam evolved more efficient self-binding isoforms.