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Article

Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods †

1
Université Fédérale de Toulouse, ANITI, INRAE, UR 875, 31326 Toulouse, France
2
TBI, Université de Toulouse, CNRS, INRAE, INSA, ANITI, 31077 Toulouse, France
3
MIA-Paris-Mathématiques et Informatique Appliquées, INRAE, 75231 Paris, France
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the Proceedings of the 2019 IEEE 31st International Conference on Tools with Artificial Intelligence, Portland, OR, USA, 4–6 November 2019.
Academic Editors: Hélène Touzet and Aïda Ouangraoua
Algorithms 2021, 14(6), 168; https://doi.org/10.3390/a14060168
Received: 7 April 2021 / Revised: 21 May 2021 / Accepted: 26 May 2021 / Published: 28 May 2021
(This article belongs to the Special Issue Algorithms in Computational Biology)
Proteins are the main active molecules of life. Although natural proteins play many roles, as enzymes or antibodies for example, there is a need to go beyond the repertoire of natural proteins to produce engineered proteins that precisely meet application requirements, in terms of function, stability, activity or other protein capacities. Computational Protein Design aims at designing new proteins from first principles, using full-atom molecular models. However, the size and complexity of proteins require approximations to make them amenable to energetic optimization queries. These approximations make the design process less reliable, and a provable optimal solution may fail. In practice, expensive libraries of solutions are therefore generated and tested. In this paper, we explore the idea of generating libraries of provably diverse low-energy solutions by extending cost function network algorithms with dedicated automaton-based diversity constraints on a large set of realistic full protein redesign problems. We observe that it is possible to generate provably diverse libraries in reasonable time and that the produced libraries do enhance the Native Sequence Recovery, a traditional measure of design methods reliability. View Full-Text
Keywords: computational protein design; graphical models; automata; cost function networks; structural biology; diversity computational protein design; graphical models; automata; cost function networks; structural biology; diversity
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MDPI and ACS Style

Ruffini, M.; Vucinic, J.; de Givry, S.; Katsirelos, G.; Barbe, S.; Schiex, T. Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods. Algorithms 2021, 14, 168. https://doi.org/10.3390/a14060168

AMA Style

Ruffini M, Vucinic J, de Givry S, Katsirelos G, Barbe S, Schiex T. Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods. Algorithms. 2021; 14(6):168. https://doi.org/10.3390/a14060168

Chicago/Turabian Style

Ruffini, Manon, Jelena Vucinic, Simon de Givry, George Katsirelos, Sophie Barbe, and Thomas Schiex. 2021. "Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods" Algorithms 14, no. 6: 168. https://doi.org/10.3390/a14060168

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