Predicting reliable regions in protein sequence alignments.
BACKGROUND
Protein sequence alignments have a myriad of applications in bioinformatics, including secondary and tertiary structure prediction, homology modeling, and phylogeny. Unfortunately, all alignment methods make mistakes, and mistakes in alignments often yield mistakes in their application. Thus, a method to identify and remove suspect alignment positions could benefit many areas in protein sequence analysis.


RESULTS
We tested four predictors of alignment position reliability, including near-optimal alignment information, column score, and secondary structural information. We validated each predictor against a large library of alignments, removing positions predicted as unreliable. Near-optimal alignment information was the best predictor, removing 70% of the substantially-misaligned positions and 58% of the over-aligned positions, while retaining 86% of those aligned accurately.
Cline
Melissa
M
Center for Biomolecular Science and Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA. cline@soe.ucsc.edu
Hughey
Richard
R
Karplus
Kevin
K
D000465:Algorithms; D019295:Computational Biology; D016571:Neural Networks, Computer; D011506:Proteins / Q000235:genetics*; D016415:Sequence Alignment / Q000706:statistics & numerical data*; D012984:Software
D011506:Proteins