Reconstruction of ancestral protein sequences and its applications.
BACKGROUND
Modern-day proteins were selected during long evolutionary history as descendants of ancient life forms. In silico reconstruction of such ancestral protein sequences facilitates our understanding of evolutionary processes, protein classification and biological function. Additionally, reconstructed ancestral protein sequences could serve to fill in sequence space thus aiding remote homology inference.


RESULTS
We developed ANCESCON, a package for distance-based phylogenetic inference and reconstruction of ancestral protein sequences that takes into account the observed variation of evolutionary rates between positions that more precisely describes the evolution of protein families. To improve the accuracy of evolutionary distance estimation and ancestral sequence reconstruction, two approaches are proposed to estimate position-specific evolutionary rates. Comparisons show that at large evolutionary distances our method gives more accurate ancestral sequence reconstruction than PAML, PHYLIP and PAUP*. We apply the reconstructed ancestral sequences to homology inference and functional site prediction. We show that the usage of hypothetical ancestors together with the present day sequences improves profile-based sequence similarity searches; and that ancestral sequence reconstruction methods can be used to predict positions with functional specificity.


CONCLUSIONS
As a computational tool to reconstruct ancestral protein sequences from a given multiple sequence alignment, ANCESCON shows high accuracy in tests and helps detection of remote homologs and prediction of functional sites. ANCESCON is freely available for non-commercial use. Pre-compiled versions for several platforms can be downloaded from ftp://iole.swmed.edu/pub/ANCESCON/.
Cai
Wei
W
Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, 5323 Harry Hines Blvd., Dallas, TX 75390-9050, USA. wcai@biochem.swmed.edu
Pei
Jimin
J
Grishin
Nick V
NV
D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D003198:Computer Simulation; D030562:Databases, Protein; D019143:Evolution, Molecular; D016013:Likelihood Functions; D008969:Molecular Sequence Data; D010802:Phylogeny; D011237:Predictive Value of Tests; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012986:Software Validation
D011506:Proteins