CorNet: Assigning function to networks of co-evolving residues by automated literature mining

Tom Van Den Bergh, Giorgio Tamo, Alberto Nobili, Yifeng Tao, Tianwei Tan, Uwe T. Bornscheuer, Remko K.P. Kuipers, Bas Vroling, René M. De Jong, Kalyanasundaram Subramanian, Peter J. Schaap, Tom Desmet, Bernd Nidetzky, Gert Vriend, Henk-Jan Joosten*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)


CorNet is a web-based tool for the analysis of co-evolving residue positions in protein super-family sequence alignments. CorNet projects external information such as mutation data extracted from literature on interactively displayed groups of co-evolving residue positions to shed light on the functions associated with these groups and the residues in them. We used CorNet to analyse six enzyme super-families and found that groups of strongly co-evolving residues tend to consist of residues involved in a same function such as activity, specificity, co-factor binding, or enantioselectivity. This finding allows to assign a function to residues for which no data is available yet in the literature. A mutant library was designed to mutate residues observed in a group of co-evolving residues predicted to be involved in enantioselectivity, but for which no literature data is available yet. The resulting set of mutations indeed showed many instances of increased enantioselectivity.
Original languageEnglish
Pages (from-to)e0176427
JournalPLoS ONE
Issue number5
Publication statusPublished - 18 May 2017


Dive into the research topics of 'CorNet: Assigning function to networks of co-evolving residues by automated literature mining'. Together they form a unique fingerprint.

Cite this