ArticleBioinformatics (Oxford, England)2018
Identifying functionally informative evolutionary sequence profiles.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Optimal selection of suitable templates in protein interface prediction.Bioinformatics (Oxford, England) · 2023Article
- Article
- The choice of sequence homologs included in multiple sequence alignments has a dramatic impact on evolutionary conservation analysis.Bioinformatics (Oxford, England) · 2019Article
- Protein-protein binding supersites.PLoS computational biology · 2019Article
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2 authors.
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Abstract
Motivation: Multiple sequence alignments (MSAs) can provide essential input to many bioinformatics applications, including protein structure prediction and functional annotation. However, the optimal selection of sequences to obtain biologically informative MSAs for such purposes is poorly explored, and has traditionally been performed manually. Results: We present Selection of Alignment by Maximal Mutual Information (SAMMI), an automated, sequence-based approach to objectively select an optimal MSA from a large set of alternatives sampled from a general sequence database search. The hypothesis of this approach is that the mutual information among MSA columns will be maximal for those MSAs that contain the most diverse set possible of the most structurally and functionally homogeneous protein sequences. SAMMI was tested to select MSAs for functional site residue prediction by analysis of conservation patterns on a set of 435 proteins obtained from protein-ligand (peptides, nucleic acids and small substrates) and protein-protein interaction databases. Availability and implementation: A freely accessible program, including source code, implementing SAMMI is available at https://github.com/nelsongil92/SAMMI.git. Contact: andras.fiser@einstein.yu.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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