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Journal Articles Bioinformatics Year : 2020

Positive multistate protein design

Abstract

MOTIVATION:Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. The usual approach considers a single rigid backbone as a target, which ignores backbone flexibility. Multistate design (MSD) allows instead to consider several backbone states simultaneously, defining challenging computational problems. RESULTS:We introduce efficient reductions of positive MSD problems to Cost Function Networks with two different fitness definitions and implement them in the Pompd (Positive Multistate Protein design) software. Pompd is able to identify guaranteed optimal sequences of positive multistate full protein redesign problems and exhaustively enumerate suboptimal sequences close to the MSD optimum. Applied to NMR and back-rubbed X-ray structures, we observe that the average energy fitness provides the best sequence recovery. Our method outperforms state-of-the-art guaranteed computational design approaches by orders of magnitudes and can solve MSD problems with sizes previously unreachable with guaranteed algorithms. AVAILABILITY:https://forgemia.inra.fr/thomas.schiex/pompd as documented Open Source.
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Dates and versions

hal-02625007 , version 1 (25-03-2021)

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Jelena Vucinic, David Simoncini, Manon Ruffini, Sophie Barbe, Thomas Schiex. Positive multistate protein design. Bioinformatics, 2020, ⟨10.1093/bioinformatics/btz497⟩. ⟨hal-02625007⟩
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