Paper Info
Title
Connect the dots: exposing hidden protein family connections from the entire sequence tree.
Abstract
Motivation: Mapping of remote evolutionary links is a classic computational problem of much interest. Relating protein families allows for functional and structural inference on uncharacterized families. Since sequences have diverged beyond reliable alignment, these are too remote to identify by conventional methods. Approach: We present a method to systematically identify remote evolutionary relations between protein families, leveraging a novel evolutionary-driven tree of all protein sequences and families. A global approach which considers the entire volume of similarities while clustering sequences, leads to a robust tree that allows tracing of very faint evolutionary links. The method systematically scans the tree for clusters which partition exceptionally well into extant protein families, thus suggesting an evolutionary breakpoint in a putative ancient superfamily. Our method does not require family pro.les (or HMMs), or multiple alignment. Results: Considering the entire Pfam database, we are able to suggest 710 links between protein families, 125 of which are con.rmed by existence of Pfam clans. The quality of our predictions is also validated by structural assignments. We further provide an intrinsic characterization of the validity of our results and provide examples for new biological.ndings, from our systematic scan. For example, we are able to relate several bacterial pore-forming toxin families, and then link them with a novel family of eukaryotic toxins expressed in plants,.sh venom and notably also uncharacterized proteins from human pathogens.
Year
DOI
Venue
2008
10.1093/bioinformatics/btn301
BIOINFORMATICS
Keywords
Field
DocType
protein family,protein sequence,multiple alignment
Protein family,Computational problem,SUPERFAMILY,Computer science,Inference,Breakpoint,Extant taxon,Bioinformatics,Multiple sequence alignment,Cluster analysis,Genetics
Conference
Volume
Issue
ISSN
24
16
1367-4803
Citations 
PageRank 
References 
2
0.45
14
Authors
2
Name
Order
Citations
PageRank
Yaniv Loewenstein1231.64
Michal Linial21502149.92