Predicting Protein Functions From Interactions Using Neural Networks and Ontologies

Abstract

To understand the process of life, it is crucial for us to study proteins and their functions. Proteins execute (almost) all cellular activities, and their functions are standardized by Gene Ontology (GO). The amount of discovered protein sequences grows rapidly as a consequence of the fast rate of development of technologies in gene sequencing. In UniProtKB, there are more than 200 million proteins. Still, less than 1% of the proteins in the UniProtKB database are experimentally GO-annotated, which is the result of the exorbitant cost of biological experiments. To minimize the large gap, developing an efficient and effective method for automatic protein function prediction (AFP) is essential. Many approaches have been proposed to solve the AFP problem. Still, these methods suffer from limitations in the way the knowledge of the domain is presented and what type of knowledge is included. In this work, we formulate the task of AFP as an entailment problem and exploit the structure of the related knowledge in a set and reusable framework. To achieve this goal, we construct a knowledge base of formal GO axioms and protein-protein interactions to use as background knowledge for AFP. Our experiments show that the approach proposed here, which allows for ontology awareness, improves results for AFP of proteins; they also show the importance of including protein-protein interactions for predicting the functions of proteins.