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Factors limiting the performance of prediction‐based fold recognition methods
Author(s) -
Cruz Xavier De La,
Thornton Janet M.
Publication year - 1999
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1110/ps.8.4.750
Subject(s) - limiting , normalization (sociology) , threading (protein sequence) , ramachandran plot , computer science , fold (higher order function) , pattern recognition (psychology) , artificial intelligence , data mining , algorithm , machine learning , protein structure , chemistry , engineering , mechanical engineering , biochemistry , sociology , anthropology , programming language
In the past few years, a new generation of fold recognition methods has been developed, in which the classical sequence information is combined with information obtained from secondary structure and, sometimes, accessibility predictions. The results are promising, indicating that this approach may compete with potential‐based methods (Rost B et al., 1997, J Mol Biol 270 :471–480). Here we present a systematic study of the different factors contributing to the performance of these methods, in particular when applied to the problem of fold recognition of remote homologues. Our results indicate that secondary structure and accessibility prediction methods have reached an accuracy level where they are not the major factor limiting the accuracy of fold recognition. The pattern degeneracy problem is confirmed as the major source of error of these methods. On the basis of these results, we study three different options to overcome these limitations: normalization schemes, mapping of the coil state into the different zones of the Ramachandran plot, and post‐threading graphical analysis.