Characterization of protein structure and function at genome scale with a computational prediction pipeline.

Abstract

Recent advances in high-throughput production capabilities for biological data such as genomic sequence (1,2), large-scale gene expression data (3-5), genomescale protein-protein interactions (6,7), and protein structures (8), arc revolutionizing the biological sciences. Essential to this new revolution are capabilities to computationally interpret large quantities of biological data generated under various experimental conditions and build mathematical models that fit these data. The combination of on-line bioinformatics tools and easy access to the high-speed Internet has made it generally possible to facilitate such computational steps and make biological discoveriesin silicoin a highly efficient manner. By utilizing various bioinformatics prediction, analysis and modeling tools, one can quickly generate hypotheses and theoretical models, which could then guide the design of experiments for further validation. The paradigm that links and integrates systematic data generation, computational data interpretation, and experimental validation is clearly providing a new and powerful way for conducting biological research. The focus of this paper is on (a) development of new computational tools for interpretation of large quantity of genomic sequence data for structural and functional inference and (b) example applications of these tools to studies of microbial genomes, particularlycyanobacterialgenomes.KeywordsStructure PredictionProtein Structure PredictionQuery ProteinFunctional AssignmentComputational PipelineThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.