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• W1985402094 abstract "Recent improvements in the protein-structure prediction method developed in our laboratory, based on the thermodynamic hypothesis, are described. The conformational space is searched extensively at the united-residue level by using our physics-based UNRES energy function and the conformational space annealing method of global optimization. The lowest-energy coarse-grained structures are then converted to an all-atom representation and energy-minimized with the ECEPP/3 force field. The procedure was assessed in two recent blind tests of protein-structure prediction. During the first blind test, we predicted large fragments of α and α+β proteins [60–70 residues with C α rms deviation (rmsd) <6 Å]. However, for α+β proteins, significant topological errors occurred despite low rmsd values. In the second exercise, we predicted whole structures of five proteins (two α and three α+β, with sizes of 53–235 residues) with remarkably good accuracy. In particular, for the genomic target TM0487 (a 102-residue α+β protein from Thermotoga maritima ), we predicted the complete, topologically correct structure with 7.3-Å C α rmsd. So far this protein is the largest α+β protein predicted based solely on the amino acid sequence and a physics-based potential-energy function and search procedure. For target T0198, a phosphate transport system regulator PhoU from T. maritima (a 235-residue mainly α-helical protein), we predicted the topology of the whole six-helix bundle correctly within 8 Å rmsd, except the 32 C-terminal residues, most of which form a β-hairpin. These and other examples described in this work demonstrate significant progress in physics-based protein-structure prediction." @default.
• W1985402094 created "2016-06-24" @default.
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• W1985402094 date "2005-05-13" @default.
• W1985402094 modified "2023-10-10" @default.
• W1985402094 title "Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: Assessment in two blind tests" @default.
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• W1985402094 doi "https://doi.org/10.1073/pnas.0502655102" @default.
• W1985402094 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/1140449" @default.
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