artificial neural networks trained to detect viral and phage structural proteins人工神经网络训练来检测病毒和噬菌体结构蛋白.pdf

Artificial Neural Networks Trained to Detect Viral and Phage Structural Proteins 1 2. 3. 4 4 Victor Seguritan , Nelson Alves Jr. , Michael Arnoult , Amy Raymond , Don Lorimer , 4 5 1,3 Alex B. Burgin Jr. , Peter Salamon , Anca M. Segall * 1 Program of Computational Science, San Diego State University, San Diego, California, United States of America, 2 Department of Genetics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil, 3 Department of Biology, San Diego State University, San Diego, California, United States of America, 4 Emerald BioStructures, Seattle, Washington, United States of America, 5 Department of Mathematics and Statistics, San Diego State University, San Diego, California, United States of America Abstract Phages play critical roles in the survival and pathogenicity of their hosts, via lysogenic conversion factors, and in nutrient redistribution, via cell lysis. Analyses of phage- and viral-encoded genes in environmental samples provide insights into the physiological impact of viruses on microbial communities and human health. However, phage ORFs are extremely diverse of which over 70% of them are dissimilar to any genes with annotated functions in GenBank. Better identification of viruses would also aid in better detection and diagnosis of disease, in vaccine development, and generally in better understanding the physiological potential of any environment. In contrast to enzymes, viral structural protein function can be much more challenging to detect from sequence data because of low sequence conservation, few known conserved catalytic sites or sequence domains, and relat