TY - JOUR T1 - SURPASS Low-Resolution Coarse-Grained Protein Modeling JF - Journal of Chemical Theory and Computation Y1 - 2017 A1 - Aleksandra Dawid A1 - Dominik Gront A1 - Andrzej KoliƄski KW - coarse-grained models KW - de novo protein folding KW - empirical force field KW - knowledge-based potential KW - protein modeling KW - reduced models AB - Coarse-grained modeling of biomolecules has a very important role in molecular biology. In this work we present a novel SURPASS (Single United Residue per Pre-Averaged Secondary Structure fragment) model of proteins that can be an interesting alternative for existing coarse-grained models. The design of the model is unique and strongly supported by the statistical analysis of structural regularities characteristic for protein systems. Coarse-graining of protein chain structures assumes a single center of interactions per residue and accounts for preaveraged effects of four adjacent residue fragments. Knowledge-based statistical potentials encode complex patterns of these fragments. Using the Replica Exchange Monte Carlo sampling scheme and a generic version of the SURPASS force field we performed test simulations of a representative set of single-domain globular proteins. The method samples a significant part of conformational space and reproduces protein structures, including native-like, with surprisingly good accuracy. Future extension of the SURPASS model on large biomacromolecular systems is briefly discussed. VL - 13(11) UR - https://pubs.acs.org/doi/10.1021/acs.jctc.7b00642 ER -