Denis Znamenskiy, Khan Le Tuan, Jean-Paul Mornon, Jacques Chomilier
{"title":"A new protein folding algorithm based on hydrophobic compactness: Rigid Unconnected Secondary Structure Iterative Assembly (RUSSIA). II: Applications.","authors":"Denis Znamenskiy, Khan Le Tuan, Jean-Paul Mornon, Jacques Chomilier","doi":"10.1093/protein/gzg141","DOIUrl":null,"url":null,"abstract":"<p><p>The RUSSIA procedure (Rigid Unconnected Secondary Structure Iterative Assembly) produces structural models of cores of small- and medium-sized proteins. Loops are omitted from this treatment and regular secondary structures are reduced to points, the centers of their hydrophobic faces. This methodology relies on the maximum compactness of the hydrophobic residues, as described in detail in Part I. Starting data are the sequence and the predicted limits and natures of regular secondary structures (alpha or beta). Helices are treated as rigid cylinders, whereas beta-strands are collectively taken into account within beta-sheets modeled by helicoid surfaces. Strands are allowed to shift along their mean axis to allow some flexibility and the alpha-helices can be placed on either side of beta-sheets. Numerous initial conformations are produced by discrete rotations of the helices and sheets around the direction going from the center of their hydrophobic face to the global center of the protein. Selection of proposed models is based upon a criterion lying on the minimization of distances separating hydrophobic residues belonging to different regular secondary structures. The procedure is rapid and appears to be robust relative to the quality of starting data (nature and length of regular secondary structures). This dependence of the quality of the model on secondary structure prediction and in particular the beta-sheet topology, is one of the limits of the present algorithm. We present here some results for a set of 12 proteins (alpha, beta and alpha/beta classes) of lengths 40-166 amino acids. The r.m.s. deviations for core models with respect to the native proteins are in the range 1.4-3.7 A.</p>","PeriodicalId":20902,"journal":{"name":"Protein engineering","volume":"16 12","pages":"937-48"},"PeriodicalIF":0.0000,"publicationDate":"2003-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/protein/gzg141","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Protein engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/protein/gzg141","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The RUSSIA procedure (Rigid Unconnected Secondary Structure Iterative Assembly) produces structural models of cores of small- and medium-sized proteins. Loops are omitted from this treatment and regular secondary structures are reduced to points, the centers of their hydrophobic faces. This methodology relies on the maximum compactness of the hydrophobic residues, as described in detail in Part I. Starting data are the sequence and the predicted limits and natures of regular secondary structures (alpha or beta). Helices are treated as rigid cylinders, whereas beta-strands are collectively taken into account within beta-sheets modeled by helicoid surfaces. Strands are allowed to shift along their mean axis to allow some flexibility and the alpha-helices can be placed on either side of beta-sheets. Numerous initial conformations are produced by discrete rotations of the helices and sheets around the direction going from the center of their hydrophobic face to the global center of the protein. Selection of proposed models is based upon a criterion lying on the minimization of distances separating hydrophobic residues belonging to different regular secondary structures. The procedure is rapid and appears to be robust relative to the quality of starting data (nature and length of regular secondary structures). This dependence of the quality of the model on secondary structure prediction and in particular the beta-sheet topology, is one of the limits of the present algorithm. We present here some results for a set of 12 proteins (alpha, beta and alpha/beta classes) of lengths 40-166 amino acids. The r.m.s. deviations for core models with respect to the native proteins are in the range 1.4-3.7 A.