Cutoff lensing: predicting catalytic sites in enzymes.

Simon Aubailly,Francesco Piazza

doi:10.1038/srep14874

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.

Highlights

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
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
The ENM30 and its CG versions[31,32] are light and computationally inexpensive tools that have proved tremendously effective in dissecting function-related vibrational patterns in proteins, both embodied in low-frequency collective normal modes[33,34,35,36,37] and, more subtly, related to high-frequency localized vibrations[28,38,39,40,41]

Summary

Methods

We model a given protein consisting of N amino acids as an ensemble of N fictitious particles occupying the equilibrium positions of the corresponding α-carbons, as found in the experimental structure. A series of recent studies has demonstrated a rather surprising agreement between the location of catalytic sites in enzymes and the localization patterns of nonlinear vibrational modes known as discrete breathers (DB)[38,39] Such observations have been rationalized in terms of a spectral measure of local stiffness, based on the localization properties of high-frequency normal modes[62]. Our goal is to extract from such patterns the most relevant peaks as flags for potentially functional sites To this aim, we apply a high-pass filtering procedure, by keeping for a given indicator pattern only the values above a specified number of standard deviations (computed over the whole sequence).

Amino acid number

Residue number

Normalized number of peaks

Connectivity Closeness Stiffness

Findings

Additional Information