Ken Nagata1, Arlo Randall1, Pierre Baldi1. 1. Department of Computer Science and Institute for Genomics and Bioinformatics, University of California, Irvine, CA, USADepartment of Computer Science and Institute for Genomics and Bioinformatics, University of California, Irvine, CA, USA.
Abstract
MOTIVATION: Accurately predicting protein side-chain conformations is an important subproblem of the broader protein structure prediction problem. Several methods exist for generating fairly accurate models for moderate-size proteins in seconds or less. However, a major limitation of these methods is their inability to model post-translational modifications (PTMs) and unnatural amino acids. In natural living systems, the chemical groups added following translation are often critical for the function of the protein. In engineered systems, unnatural amino acids are incorporated into proteins to explore structure-function relationships and create novel proteins. RESULTS: We present a new version of SIDEpro to predict the side chains of proteins containing non-standard amino acids, including 15 of the most frequently observed PTMs in the Protein Data Bank and all types of phosphorylation. SIDEpro uses energy functions that are parameterized by neural networks trained from available data. For PTMs, the [Formula: see text] and [Formula: see text] accuracies are comparable with those obtained for the precursor amino acid, and so are the RMSD values for the atoms shared with the precursor amino acid. In addition, SIDEpro can accommodate any PTM or unnatural amino acid, thus providing a flexible prediction system for high-throughput modeling of proteins beyond the standard amino acids. AVAILABILITY AND IMPLEMENTATION: SIDEpro programs and Web server, rotamer libraries and data are available through the SCRATCH suite of protein structure predictors at http://scratch.proteomics.ics.uci.edu/
MOTIVATION: Accurately predicting protein side-chain conformations is an important subproblem of the broader protein structure prediction problem. Several methods exist for generating fairly accurate models for moderate-size proteins in seconds or less. However, a major limitation of these methods is their inability to model post-translational modifications (PTMs) and unnatural amino acids. In natural living systems, the chemical groups added following translation are often critical for the function of the protein. In engineered systems, unnatural amino acids are incorporated into proteins to explore structure-function relationships and create novel proteins. RESULTS: We present a new version of SIDEpro to predict the side chains of proteins containing non-standard amino acids, including 15 of the most frequently observed PTMs in the Protein Data Bank and all types of phosphorylation. SIDEpro uses energy functions that are parameterized by neural networks trained from available data. For PTMs, the [Formula: see text] and [Formula: see text] accuracies are comparable with those obtained for the precursor amino acid, and so are the RMSD values for the atoms shared with the precursor amino acid. In addition, SIDEpro can accommodate any PTM or unnatural amino acid, thus providing a flexible prediction system for high-throughput modeling of proteins beyond the standard amino acids. AVAILABILITY AND IMPLEMENTATION: SIDEpro programs and Web server, rotamer libraries and data are available through the SCRATCH suite of protein structure predictors at http://scratch.proteomics.ics.uci.edu/
Authors: Helen M Berman; Tammy Battistuz; T N Bhat; Wolfgang F Bluhm; Philip E Bourne; Kyle Burkhardt; Zukang Feng; Gary L Gilliland; Lisa Iype; Shri Jain; Phoebe Fagan; Jessica Marvin; David Padilla; Veerasamy Ravichandran; Bohdan Schneider; Narmada Thanki; Helge Weissig; John D Westbrook; Christine Zardecki Journal: Acta Crystallogr D Biol Crystallogr Date: 2002-05-29
Authors: Wan Ning Vanessa Chow; Jacky Chi Ki Ngo; Wen Li; Yu Wai Chen; Ka Ming Vincent Tam; Ho Yin Edwin Chan; Christopher C J Miller; Kwok-Fai Lau Journal: Biochem J Date: 2015-07-17 Impact factor: 3.857