Motivation: A number of statistical phylogenetic methods have been proposed to identify type-I functional divergence in duplicate genes by detecting heterogeneous substitution rates in phylogenetic trees. A common disadvantage of the existing methods is that autocorrelation of substitution rates along sequences is not modeled. This reduces the power of existing methods to identify regions under functional divergence. Results: We design a phylogenetic hidden Markov model to identify protein regions relevant to type-I functional divergence. A C++ program, HMMDiverge, has been developed to estimate model parameters and to identify regions under type-I functional divergence. Simulations demonstrate that HMMDiverge can successfully identify protein regions under type-I functional divergence unless the discrepancy of substitution rates between subfamilies is very limited or the regions under functional divergence are very short. Applying HMMDiverge to G protein α subunits in animals, we identify a candidate region longer than 20 amino acids, which overlaps with the α-4 helix and the α4-β6 loop in the GTPase domain with divergent rates of substitutions. These sites are different from those reported by an existing program, DIVERGE2. Interestingly, previous biochemical studies suggest the α-4 helix and the α4-β6 loop are important to the specificity of the receptor-G protein interaction. Therefore, the candidate region reported by HMMDiverge highlights that the type-I functional divergence in G protein α subunits may be relevant to the change of receptor-G protein specificity after gene duplication. From these results, we conclude that HMMDiverge is a useful tool to identify regions under type-I functional divergence after gene duplication.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics