Prediction of protein-protein interactions based on amino acids sequences
Valeria Manna
ICAR/CNR
Italy

Abstract

Understanding protein-protein interaction (PPIs) is important for the investigation of intracellular signaling pathways, modelling of protein complex structures and for gaining insights into various biochemical processes. Experimentally, physical interactions between pairs of proteins can be inferred from a variety of experimental techniques (Y2H, TAP/MS, FREET, etc.). The limit of these high throughput methods is that they can generate false examples. Although efforts have beeen devoted to the development of computational methods for predicting PPIs and protein interactions network, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners.

In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a instance-based learning algortithm 1-Nearset Neighbor combined with a conjoint triad feature for describing amino acids, Pearson's Coefficient to generate the negative examples and Feature Selection to reduce vector space. More than 16 000 diverse PPI pairs were used to construct this model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity.

Jornada Matemática SPM/CIM "Mathematical Biology"