Premium
β Edge strands in protein structure prediction and aggregation
Author(s) -
Siepen Jennifer A.,
Radford Sheena E.,
Westhead David R.
Publication year - 2003
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.03234503
Subject(s) - beta sheet , protein secondary structure , protein folding , protein structure prediction , support vector machine , protein structure , folding (dsp implementation) , enhanced data rates for gsm evolution , computer science , computational biology , biological system , chemistry , pattern recognition (psychology) , biophysics , artificial intelligence , biology , biochemistry , engineering , electrical engineering
It is well established that recognition between exposed edges of β‐sheets is an important mode of protein–protein interaction and can have pathological consequences; for instance, it has been linked to the aggregation of proteins into a fibrillar structure, which is associated with a number of predominantly neurodegenerative disorders. A number of protective mechanisms have evolved in the edge strands of β‐sheets, preventing the aggregation and insolubility of most natural β‐sheet proteins. Such mechanisms are unfavorable in the interior of a β‐sheet. The problem of distinguishing edge strands from central strands based on sequence information alone is important in predicting residues and mutations likely to be involved in aggregation, and is also a first step in predicting folding topology. Here we report support vector machine (SVM) and decision tree methods developed to classify edge strands from central strands in a representative set of protein domains. Interestingly, rules generated by the decision tree method are in close agreement with our knowledge of protein structure and are potentially useful in a number of different biological applications. When trained on strands from proteins of known structure, using structure‐based (Dictionary of Secondary Structure in Proteins) strand assignments, both methods achieved mean cross‐validated, prediction accuracies of ∼78%. These accuracies were reduced when strand assignments from secondary structure prediction were used. Further investigation of this effect revealed that it could be explained by a significant reduction in the accuracy of standard secondary structure prediction methods for edge strands, in comparison with central strands.