Remote homology detection on alpha-structural proteins using simulated evolution
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
A Framework for Identifying Affinity Classes of Inorganic Materials Binding Peptide Sequences
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
A Privacy Preserving Markov Model for Sequence Classification
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Motivation: While profile hidden Markov models (HMMs) are successful and powerful methods to recognize homologous proteins, they can break down when homology becomes too distant due to lack of sufficient training data. We show that we can improve the performance of HMMs in this domain by using a simple simulated model of evolution to create an augmented training set. Results: We show, in two different remote protein homolog tasks, that HMMs whose training is augmented with simulated evolution outperform HMMs trained only on real data. We find that a mutation rate between 15 and 20% performs best for recognizing G-protein coupled receptor proteins in different classes, and for recognizing SCOP super-family proteins from different families. Contacts:anoop.kumar@tufts.edu;lenore.cowen@tufts.edu