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Real value prediction of solvent accessibility from amino acid sequence
Author(s) -
Ahmad Shandar,
Gromiha M. Michael,
Sarai Akinori
Publication year - 2003
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/prot.10328
Subject(s) - a priori and a posteriori , mean absolute error , residue (chemistry) , mean squared prediction error , mathematics , statistics , accessible surface area , amino acid residue , correlation , approximation error , sequence (biology) , artificial neural network , pattern recognition (psychology) , artificial intelligence , chemistry , computer science , mean squared error , peptide sequence , geometry , computational chemistry , philosophy , biochemistry , epistemology , gene
The solvent accessibility of amino acid residues has been predicted in the past by classifying them into exposure states with varying thresholds. This classification provides a wide range of values for the accessible surface area (ASA) within which a residue may fall. Thus far, no attempt has been made to predict real values of ASA from the sequence information without a priori classification into exposure states. Here, we present a new method with which to predict real value ASAs for residues, based on neighborhood information. Our real value prediction neural network could estimate the ASA for four different nonhomologous, nonredundant data sets of varying size, with 18.0–19.5% mean absolute error, defined as per residue absolute difference between the predicted and experimental values of relative ASA. Correlation between the predicted and experimental values ranged from 0.47 to 0.50. It was observed that the ASA of a residue could be predicted within a 23.7% mean absolute error, even when no information about its neighbors is included. Prediction of real values answers the issue of arbitrary choice of ASA state thresholds, and carries more information than category prediction. Prediction error for each residue type strongly correlates with the variability in its experimental ASA values. Proteins 2003;50:629–635. © 2003 Wiley‐Liss, Inc.