Insights on protein thermal stability: a graph representation of molecular interactions

Author:

Miotto Mattia123,Olimpieri Pier Paolo1,Di Rienzo Lorenzo1,Ambrosetti Francesco14,Corsi Pietro5,Lepore Rosalba67,Tartaglia Gian Gaetano8910,Milanetti Edoardo12

Affiliation:

1. Department of Physics, Sapienza University, Piazzale Aldo Moro 5, Rome, Italy

2. Center for Life Nano Science@Sapienza, Instituto Italiano di Tecnologia, Viale Regina Elena, 291 Roma (RM), Italy

3. Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Rome, Italy

4. Bijvoet Center for Biomolecular Research, Faculty of Science – Chemistry, Utrecht University, Padualaan 8, Utrecht, the Netherlands

5. Department of Science, Università degli Studi “Roma Tre”, via della Vasca Navale 84, Rome, Italy

6. Biozentrum, University of Basel, Klingelbergstrasse 50–70, CH-4056 Basel, Switzerland

7. SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50–70, CH-4056 Basel, Switzerland

8. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader St. 88, Barcelona, Spain

9. Institucio’ Catalana de Recerca i Estudis Avancats (ICREA), 23 Passeig Lluìs Companys, Barcelona, Spain

10. Department of Biology and Biotechnology, Sapienza University of Rome, Piazzale Aldo Moro 5, Rome, Italy

Abstract

Abstract Motivation Understanding the molecular mechanisms of thermal stability is a challenge in protein biology. Indeed, knowing the temperature at which proteins are stable has important theoretical implications, which are intimately linked with properties of the native fold, and a wide range of potential applications from drug design to the optimization of enzyme activity. Results Here, we present a novel graph-theoretical framework to assess thermal stability based on the structure without any a priori information. In this approach we describe proteins as energy-weighted graphs and compare them using ensembles of interaction networks. Investigating the position of specific interactions within the 3D native structure, we developed a parameter-free network descriptor that permits to distinguish thermostable and mesostable proteins with an accuracy of 76% and area under the receiver operating characteristic curve of 78%. Availability and implementation Code is available upon request to edoardo.milanetti@uniroma1.it Supplementary information Supplementary data are available at Bioinformatics online.

Funder

EPIGEN

European Research Council

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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