scholarly journals Variable Neighborhood Search with Cost Function Networks To Solve Large Computational Protein Design Problems

2018 ◽  
Vol 59 (1) ◽  
pp. 127-136 ◽  
Author(s):  
Antoine Charpentier ◽  
David Mignon ◽  
Sophie Barbe ◽  
Juan Cortes ◽  
Thomas Schiex ◽  
...  
Keyword(s):  
Cost Function ◽  
Protein Design ◽  
Variable Neighborhood Search ◽  
Computational Protein Design ◽  
Neighborhood Search ◽  
Design Problems

scholarly journals Cost Function Networks to Solve Large Computational Protein Design Problems

2020 ◽  
pp. 81-102
Author(s):  
David Allouche ◽  
Sophie Barbe ◽  
Simon de Givry ◽  
George Katsirelos ◽  
Yahia Lebbah ◽  
...  
Keyword(s):  
Cost Function ◽  
Protein Design ◽  
Computational Protein Design ◽  
Design Problems

scholarly journals A new framework for computational protein design through cost function network optimization

2013 ◽  
Vol 29 (17) ◽  
pp. 2129-2136 ◽  
Author(s):  
Seydou Traoré ◽  
David Allouche ◽  
Isabelle André ◽  
Simon de Givry ◽  
George Katsirelos ◽  
...  
Keyword(s):  
Cost Function ◽  
Protein Design ◽  
Network Optimization ◽  
Computational Protein Design ◽  
Function Network ◽  
New Framework

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

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 168
Author(s):  
Manon Ruffini ◽  
Jelena Vucinic ◽  
Simon de de Givry ◽  
George Katsirelos ◽  
Sophie Barbe ◽  
...  
Keyword(s):  
Cost Function ◽  
Protein Design ◽  
Optimal Solution ◽  
Design Methods ◽  
Computational Protein Design ◽  
Large Set ◽  
Network Algorithms ◽  
Native Sequence ◽  
Application Requirements ◽  
Function Network

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.

scholarly journals Guaranted Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods

2021 ◽  
Author(s):  
Manon Ruffini ◽  
Jelena Vucinic ◽  
Simon de Givry ◽  
George Katsirelos ◽  
Sophie Barbe ◽  
...  
Keyword(s):  
Cost Function ◽  
Protein Design ◽  
Optimal Solution ◽  
Design Methods ◽  
Computational Protein Design ◽  
Large Set ◽  
Network Algorithms ◽  
Native Sequence ◽  
Application Requirements ◽  
Function Network

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.

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