Predicting protein function and annotating complex pathways with machine learning

Abstract

BIOINFORMATICS FOR PREDICTING PROTEIN FUNCTION Biologists can build such hypotheses of gene function with computers. As genome sequencing becomes routine in experimental laboratories, computational gene function prediction has also become increasingly important. Computational methods are very suitable for function prediction because function information of a gene can be inferred from a database search that identifies similarity between the gene and known proteins or experimental data. Sequence similarity tools like the Basic Local Alignment Search Tool (BLAST) is one such method that searches against all previously recorded sequences and suggests a scored list of possible roles for it. PROBLEMS WITH PREVIOUS COMPUTATIONAL METHODS However, existing bioinformatic tools can’t always predict protein function accurately, and often end up incorrectly annotating proteins within a biological system. Traditional protein function prediction tools like BLAST are usually reliable when a high sequence similarity is detected, but their accuracy falls quickly for sequences with lower similarities. For example, enzyme functions differ immensely when similarity scores fall below a certain level. Moreover, in many cases traditional methods do not annotate any function if highly similar sequences are not found, leaving many genes unannotated. In addition, other metrics such as similarity in three-dimensional structure, gene expression, or interaction data could be used. However, each of these metrics are often missing for many proteins under investigation, and so have limited applicability in reliable research.