Augmented training of hidden Markov models to recognize remote homologs via simulated evolution.
BACKGROUND
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.
Kumar
Anoop
A
Department of Computer Science, Tufts University, Medford, MA, USA. anoop.kumar@tufts.edu
Cowen
Lenore
L
D019295:Computational Biology / Q000379:methods*; D019277:Entropy; D008390:Markov Chains; D011506:Proteins / Q000737:chemistry; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
D011506:Proteins