title: One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model.
abstract: Despite the significant increase in computational power, molecular modeling of protein structure using classical all-atom approaches remains inefficient, at least for most of the protein targets in the focus of biomedical research. Perhaps the most successful strategy to overcome the inefficiency problem is multiscale modeling to merge all-atom and coarse-grained models. This chapter describes a well-established CABS coarse-grained protein model. The CABS (C-Alpha, C-Beta, and Side chains) model assumes a 2-4 united-atom representation of amino acids, knowledge-based force field (derived from the statistical regularities seen in known protein sequences and structures) and efficient Monte Carlo sampling schemes (MC dynamics, MC replica-exchange, and combinations). A particular emphasis is given to the unique design of the CABS force-field, which is largely defined using one-dimensional structural properties of proteins, including protein secondary structure. This chapter also presents CABS-based modeling methods, including multiscale tools for de novo structure prediction, modeling of protein dynamics and prediction of protein-peptide complexes. CABS-based tools are freely available at http://biocomp.chem.uw.edu.pl/tools.
authors: Faculty of Chemistry, University of Warsaw, Pasteura 1, Warszawa, 02-093, Poland.
authors: Sebastian
authors: S
authors: Kmiecik
authors: Faculty of Chemistry, University of Warsaw, Pasteura 1, Warszawa, 02-093, Poland. kolinski@chem.uw.edu.pl.
authors: Andrzej
authors: A
authors: Kolinski
mesh_terms: D000595:Amino Acid Sequence / Q000235:genetics; D056004:Molecular Dynamics Simulation; D010455:Peptides / Q000737:chemistry; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry / Q000235:genetics*; D012984:Software
chemical_list: D010455:Peptides; D011506:Proteins
keywords: Force-field; Knowledge-based potentials; Protein modeling; Protein simulations; Statistical potentials