SHORT COMMUNICATION Fine-Grained Protein Fold Assignment by Support Vector Machines Using Generalized nPeptide Coding Schemes and Jury Voting From Multiple-Parameter Sets

ABSTRACT In the coarse-grained fold assign-ment ofmajor protein classes, such as all-, all-, , / proteins, one can easily achieve high predic-tion accuracy from primary amino acid sequences. However, the fine-grained assignment of folds, such as those defined in the Structural Classification of Proteins (SCOP) database, presents a challenge due to the larger amount of folds available. Recent study yielded reasonable prediction accuracy of 56.0 % on an independent set of 27 most populated folds. In this communication, we apply the support vector machine (SVM) method, using a combination of protein descriptors based on the properties derived from the composition of n-peptide and jury voting, to the fine-grained fold prediction, and are able to achieve an overall prediction accuracy of 69.6 % on the same independent set—significantly higher than the previous results. On 10-fold cross-validation, we obtained a prediction accuracy of 65.3%. Our results show that SVM coupled with suitable global se-quence-coding schemes can significantly improve the fine-grained fold prediction. Our approach should be useful in structure prediction and model-ing. Proteins 2003;50:531–536. © 2003 Wiley-Liss, Inc. Key words: support vector machines; fine-grained fold prediction; global sequence-coding scheme; n-peptid