title: Incorporating post-translational modifications and unnatural amino acids into high-throughput modeling of protein structures.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.affiliation: 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.
authors.forename: Ken
authors.initials: K
authors.lastname: Nagata
authors.affiliation: 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.
authors.forename: Arlo
authors.initials: A
authors.lastname: Randall
authors.affiliation: 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.
authors.forename: Pierre
authors.initials: P
authors.lastname: Baldi
mesh_terms: D000596:Amino Acids / Q000737:chemistry* / Q000378:metabolism; D001185:Artificial Intelligence; D008958:Models, Molecular; D016571:Neural Networks, Computer; D010766:Phosphorylation; D011487:Protein Conformation; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry; D012984:Software
chemical_list: D000596:Amino Acids; D011506:Proteins