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New method for accurate prediction of solvent accessibility from protein sequence
Author(s) -
Li Xia,
Pan XianMing
Publication year - 2000
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/1097-0134(20010101)42:1<1::aid-prot10>3.0.co;2-n
Subject(s) - jackknife resampling , sequence (biology) , correlation coefficient , solvent , monomer , matthews correlation coefficient , chemistry , correlation , computer science , mathematics , biological system , statistics , artificial intelligence , biology , biochemistry , organic chemistry , polymer , estimator , support vector machine , geometry
A novel method was developed for predicting the solvent accessibility. Based on single sequence data, this method achieved 71.5% accuracy with a correlation coefficient of 0.42 in a database of 704 proteins with threshold of 20% for a two‐state‐defining solvent accessibility. Prediction in a data subset of 341 monomeric proteins achieved 72.7% accuracy with a correlation coefficient of 0.43. On the average, prediction over short chains gives better results than that over long chains. With a solvent accessibility threshold of 20%, prediction over 236 monomeric proteins with chain length < 300 amino acid residues achieved 75.3% accuracy with a correlation coefficient of 0.44 by jackknife analysis, which is higher than that obtained by previous methods using multiple sequence alignments. Proteins 2001;42:1–5. © 2000 Wiley‐Liss, Inc.