SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

BMC Bioinformatics
Iain MelvinChristina Leslie


Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence...Continue Reading


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Related Concepts

Knowledge Representation (Computer)
Pattern Recognition System
Gene Products, Protein
Computer Programs and Programming
Discriminant Analysis
Determination, Sequence Homology
Homologous Sequences, Amino Acid
Protein Folding, Globular
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Sequence Analysis, Protein

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