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. Algorithms2021, 14, 168.
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.
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. Algorithms2021, 14, 168.
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.
Abstract
Proteins are the main active molecules of Life. While 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.
Keywords
Computational Protein Design; Graphical Models; Automata; Cost Function Networks; Structural Biology; Diversity.
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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