Comparing three stochastic search algorithms for computational protein design: Monte Carlo, replica exchange Monte Carlo, and a multistart, steepest-descent heuristic

Abstract : Computational protein design depends on an energy function and an algorithm to search the sequence/conformation space. We compare three stochastic search algorithms: a heuristic, Monte Carlo (MC), and a Replica Exchange Monte Carlo method (REMC). The heuristic performs a steepest-descent minimization starting from thousands of random starting points. The methods are applied to nine test proteins from three structural families, with a fixed backbone structure, a molecular mechanics energy function, and with 1, 5, 10, 20, 30, or all amino acids allowed to mutate. Results are compared to an exact, Cost Function Network method that identifies the global minimum energy conformation (GMEC) in favorable cases. The designed sequences accurately reproduce experimental sequences in the hydrophobic core. The heuristic and REMC agree closely and reproduce the GMEC when it is known, with a few exceptions. Plain MC performs well for most cases, occasionally departing from the GMEC by 3-4 kcal/mol. With REMC, the diversity of the sequences sampled agrees with exact enumeration where the latter is possible: up to 2 kcal/mol above the GMEC. Beyond, room temperature replicas sample sequences up to 10 kcal/mol above the GMEC, providing thermal averages and a solution to the inverse protein folding problem.
Type de document :
Article dans une revue
Journal of Computational Chemistry, Wiley, 2016, 37 (19), pp.1781 - 1793. 〈10.1002/jcc.24393〉
Liste complète des métadonnées

https://hal-polytechnique.archives-ouvertes.fr/hal-01445473
Contributeur : Denis Roura <>
Soumis le : mardi 24 janvier 2017 - 21:28:10
Dernière modification le : jeudi 10 mai 2018 - 02:08:11

Identifiants

Citation

David Mignon, Thomas Simonson. Comparing three stochastic search algorithms for computational protein design: Monte Carlo, replica exchange Monte Carlo, and a multistart, steepest-descent heuristic. Journal of Computational Chemistry, Wiley, 2016, 37 (19), pp.1781 - 1793. 〈10.1002/jcc.24393〉. 〈hal-01445473〉

Partager

Métriques

Consultations de la notice

132