Graphical models of residue coupling in protein families
Proceedings of the 5th international workshop on Bioinformatics
Linear predictive coding representation of correlated mutation for protein sequence alignment
Proceedings of the third international workshop on Data and text mining in bioinformatics
Co-evolution and information signals in biological sequences
Theoretical Computer Science
| Hi-index | 3.84 |
Motivation: The constituent amino acids of a protein work together to define its structure and to facilitate its function. Their interdependence should be apparent in the evolutionary record of each protein family: positions in the sequence of a protein family that are intimately associated in space or in function should co-vary in evolution. A recent approach by Ranganathan and colleagues proposes to look at subsets of a protein family, selected for their sequence at one position, to see how this affects variation at other positions. Results: We present a quantitative algorithm for assessing covariation with this approach, based on explicit likelihood calculations. By applying our algorithm to 138 Pfam families with at least one member of known structure, we demonstrate that our method has improved power in finding physically close residues in crystal structures, compared to that of Ranganathan and colleagues. Supplementary information: www.afodor.net/bioinfosup.html