Prediction of Protein Dispensability through Integrated Analysis of Multiple-Source High-Throughput Data

Protein dispensability is fundamental to understanding of gene function and evolution. It is usually studied at the individual gene phenotype level. Recent advances in generating high-throughput data such as genomic sequence data, protein-protein interaction data, gene-expression data, and growth-rate data of mutants allow us to investigate protein dispensability systematically at the genome scale. In our studies, protein dispensability was represented as a fitness score that was measured by the growth rate of gene-deletion mutants. Through analyses of high-throughput data in yeast Saccharomyces cerevisia, we found that a protein’s dispensability had significant correlations with its evolutionary rate and duplication rate, as well as its connectivity in protein-protein interaction network and gene-expression correlation network. Correspondence analysis also showed such significant dependencies, which imply that the integration of high-throughput data can provide substantial information on protein dispensability. Thus neural network and support vector machines were applied to predict protein dispensability. Our study provides a “proof-of-principle” for a global understanding of protein dispensability through computational analyses of high-throughput data.