Cutoff lensing: predicting catalytic sites in enzymes

Abstract : Predicting function-related amino acids in proteins with unknown function or unknown allosteric binding sites in drug-targeted proteins is a task of paramount importance in molecular biomedicine. In this paper we introduce a simple, light and computationally inexpensive structure-based method to identify catalytic sites in enzymes. Our method, termed cutoff lensing, is a general procedure consisting in letting the cutoff used to build an elastic network model increase to large values. A validation of our method against a large database of annotated enzymes shows that optimal values of the cutoff exist such that three different structure-based indicators allow one to recover a maximum of the known catalytic sites. Interestingly, we find that the larger the structures the greater the predictive power afforded by our method. Possible ways to combine the three indicators into a single figure of merit and into a specific sequential analysis are suggested and discussed with reference to the classic case of HIV-protease. Our method could be used as a complement to other sequence- and/or structure-based methods to narrow the results of large-scale screenings.
Document type :
Journal articles
Complete list of metadatas
Contributor : Laëtitia Legoupil <>
Submitted on : Tuesday, March 19, 2019 - 8:49:31 AM
Last modification on : Wednesday, March 20, 2019 - 1:20:21 AM

Links full text




Simon Aubailly, Francesco Piazza. Cutoff lensing: predicting catalytic sites in enzymes. Scientific Reports, Nature Publishing Group, 2015, 5 (1), ⟨10.1038/srep14874⟩. ⟨hal-02072226⟩



Record views