accurate protein structure annotation through competitive diffusion of enzymatic functions over a network of local evolutionary similarities准确的蛋白质结构注释通过竞争酶功能通过网络扩散的地方进化的相似之处.pdfVIP

Accurate Protein Structure Annotation through Competitive Diffusion of Enzymatic Functions over a Network of Local Evolutionary Similarities 1,2,3. 1. 1,3 1,2,3 1 Eric Venner , Andreas Martin Lisewski , Serkan Erdin , R. Matthew Ward , Shivas R. Amin , Olivier Lichtarge1,2,3* 1 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America, 2 Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas, United States of America, 3 W. M. Keck Center for Interdisciplinary Bioscience Training, Houston, Texas, United States of America Abstract High-throughput Structural Genomics yields many new protein structures without known molecular function. This study aims to uncover these missing annotations by globally comparing select functional residues across the structural proteome. First, Evolutionary Trace Annotation, or ETA, identifies which proteins have local evolutionary and structural features in common; next, these proteins are linked together into a proteomic network of ETA similarities; then, starting from proteins with known functions, competing functional labels diffuse link-by-link over the entire network. Every node is thus assigned a likelihood z-score for every function, and the most significant one at each node wins and defines its annotation. In high- throughput controls, this competitive diffusion process recovered enzyme activity annotations with 99% and 97% accuracy at half-coverage for the third and fourth Enzyme Commission (EC) levels, respectively. This corresponds to false positive rates 4-fold lower than nearest-neighbor