Bayesian segmentation of protein secondary structure

被引:93
作者
Schmidler, SC
Liu, JS
Brutlag, DL
机构
[1] Stanford Univ, Sch Med, Sect Med Informat, Stanford, CA 94305 USA
[2] Stanford Univ, Dept Stat, Stanford, CA 94305 USA
[3] Stanford Univ, Sch Med, Dept Biochem, Stanford, CA 94305 USA
关键词
protein secondary structure prediction; Bayesian methods; probabilistic modeling;
D O I
10.1089/10665270050081496
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
We present a novel method for predicting the secondary structure of a protein from its amino acid sequence, Most existing methods predict each position in turn based on a local window of residues, sliding this window along the length of the sequence. In contrast, we develop a probabilistic model of protein sequence/structure relationships in terms of structural segments, and formulate secondary structure prediction as a general Bayesian inference problem, A distinctive feature of our approach is the ability to develop explicit probabilistic models for alpha-helices, beta-strands, and other classes of secondary structure, incorporating experimentally and empirically observed aspects of protein structure such as helical capping signals, side chain correlations, and segment length distributions. Our model is Markovian in the segments, permitting efficient exact calculation of the posterior probability distribution over all possible segmentations of the sequence using dynamic programming. The optimal segmentation is computed and compared to a predictor based on marginal posterior modes, and the latter is shown to provide significant improvement in predictive accuracy. The marginalization procedure provides exact secondary structure probabilities at each sequence position, which are shown to be reliable estimates of prediction uncertainty. We apply this model to a database of 452 nonhomologous structures, achieving accuracies as high as the best currently available methods. We conclude by discussing an extension of this framework to model nonlocal interactions in protein structures, providing a possible direction for future improvements in secondary structure prediction accuracy.
引用
收藏
页码:233 / 248
页数:16
相关论文
共 66 条
[21]   ANALYSIS OF ACCURACY AND IMPLICATIONS OF SIMPLE METHODS FOR PREDICTING SECONDARY STRUCTURE OF GLOBULAR PROTEINS [J].
GARNIER, J ;
OSGUTHORPE, DJ ;
ROBSON, B .
JOURNAL OF MOLECULAR BIOLOGY, 1978, 120 (01) :97-120
[22]  
Garnier J, 1996, METHOD ENZYMOL, V266, P540
[23]  
HERINGA J, 1992, COMPUT APPL BIOSCI, V8, P599
[24]  
HOBOHM U, 1994, PROTEIN SCI, V3, P522
[25]  
HOLLEY HL, 1989, P NATL ACAD SCI USA, V86, P152
[26]   A MODEL RECOGNITION APPROACH TO THE PREDICTION OF ALL-HELICAL MEMBRANE-PROTEIN STRUCTURE AND TOPOLOGY [J].
JONES, DT ;
TAYLOR, WR ;
THORTON, JM .
BIOCHEMISTRY, 1994, 33 (10) :3038-3049
[27]   DICTIONARY OF PROTEIN SECONDARY STRUCTURE - PATTERN-RECOGNITION OF HYDROGEN-BONDED AND GEOMETRICAL FEATURES [J].
KABSCH, W ;
SANDER, C .
BIOPOLYMERS, 1983, 22 (12) :2577-2637
[28]   DISCOVERING STRUCTURAL CORRELATIONS IN ALPHA-HELICES [J].
KLINGLER, TM ;
BRUTLAG, DL .
PROTEIN SCIENCE, 1994, 3 (10) :1847-1857
[29]   A THERMODYNAMIC SCALE FOR LEUCINE-ZIPPER STABILITY AND DIMERIZATION SPECIFICITY - E-INTERHELICAL AND G-INTERHELICAL INTERACTIONS [J].
KRYLOV, D ;
MIKHAILENKO, I ;
VINSON, C .
EMBO JOURNAL, 1994, 13 (12) :2849-2861
[30]  
KULP D, 1996, 4 INTNTL C INT SYS M