Mismatch string kernels for discriminative protein classification.
BACKGROUND
Classification of proteins sequences into functional and structural families based on sequence homology is a central problem in computational biology. Discriminative supervised machine learning approaches provide good performance, but simplicity and computational efficiency of training and prediction are also important concerns.


RESULTS
We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the problem of protein classification and remote homology detection. These kernels measure sequence similarity based on shared occurrences of fixed-length patterns in the data, allowing for mutations between patterns. Thus, the kernels provide a biologically well-motivated way to compare protein sequences without relying on family-based generative models such as hidden Markov models. We compute the kernels efficiently using a mismatch tree data structure, allowing us to calculate the contributions of all patterns occurring in the data in one pass while traversing the tree. When used with an SVM, the kernels enable fast prediction on test sequences. We report experiments on two benchmark SCOP datasets, where we show that the mismatch kernel used with an SVM classifier performs competitively with state-of-the-art methods for homology detection, particularly when very few training examples are available. Examination of the highest-weighted patterns learned by the SVM classifier recovers biologically important motifs in protein families and superfamilies.
Leslie
Christina S
CS
Department of Computer Science, Columbia University, 1214 Amsterdam Avenue, Mail Code 0401, New York, NY 10027, USA. cleslie@cs.columbia.edu
Eskin
Eleazar
E
Cohen
Adiel
A
Weston
Jason
J
Noble
William Stafford
WS
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D008969:Molecular Sequence Data; D009687:Nuclear Proteins / Q000737:chemistry / Q000145:classification; D010363:Pattern Recognition, Automated; D010749:Phosphoprotein Phosphatases; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
D009687:Nuclear Proteins; D011506:Proteins; C400601:PHLPP1 protein, human; D010749:Phosphoprotein Phosphatases