scholarly journals Comparison of Rosetta flexible‐backbone computational protein design methods on binding interactions

2019 ◽  
Vol 88 (1) ◽  
pp. 206-226 ◽  
Author(s):  
Amanda L. Loshbaugh ◽  
Tanja Kortemme
Keyword(s):  
Protein Design ◽  
Design Methods ◽  
Computational Protein Design ◽  
Binding Interactions

scholarly journals Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions

2019 ◽  
Author(s):  
Amanda L. Loshbaugh ◽  
Tanja Kortemme
Keyword(s):  
Protein Design ◽  
Computational Design ◽  
Design Methods ◽  
Backbone Flexibility ◽  
Functional Protein ◽  
Binding Interactions ◽  
Sequence Design ◽  
Sequence Profiles ◽  
Sampling Trajectory ◽  
Powerful Strategy

ABSTRACTComputational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible-backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible-backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.

scholarly journals Breakthroughs in computational design methods open up new frontiers for de novo protein engineering

2021 ◽  
Vol 34 ◽  
Author(s):  
Ben A Meinen ◽  
Christopher D Bahl
Keyword(s):  
Protein Engineering ◽  
Chemical Reactions ◽  
Protein Design ◽  
De Novo ◽  
Computational Design ◽  
Design Methods ◽  
Computational Protein Design ◽  
The Past ◽  
New Methods ◽  
De Novo Protein Design

Abstract Proteins catalyze the majority of chemical reactions in organisms, and harnessing this power has long been the focus of the protein engineering field. Computational protein design aims to create new proteins and functions in silico, and in doing so, accelerate the process, reduce costs and enable more sophisticated engineering goals to be accomplished. Challenges that very recently seemed impossible are now within reach thanks to several landmark advances in computational protein design methods. Here, we summarize these new methods, with a particular emphasis on de novo protein design advancements occurring within the past 5 years.

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.

scholarly journals The Role of Rigid Residues in Modulating TEM-1 β-Lactamase Function and Thermostability

2021 ◽  
Vol 22 (6) ◽  
pp. 2895
Author(s):  
Bethany Kolbaba-Kartchner ◽  
I. Can Kazan ◽  
Jeremy H. Mills ◽  
S. Banu Ozkan
Keyword(s):  
Protein Dynamics ◽  
Protein Design ◽  
Active Site ◽  
Design Methods ◽  
Computational Protein Design ◽  
Protein Motions ◽  
Enzymatic Function ◽  
Function Relationship ◽  
And Function ◽  
The Relationship

The relationship between protein motions (i.e., dynamics) and enzymatic function has begun to be explored in β-lactamases as a way to advance our understanding of these proteins. In a recent study, we analyzed the dynamic profiles of TEM-1 (a ubiquitous class A β-lactamase) and several ancestrally reconstructed homologues. A chief finding of this work was that rigid residues that were allosterically coupled to the active site appeared to have profound effects on enzyme function, even when separated from the active site by many angstroms. In the present work, our aim was to further explore the implications of protein dynamics on β-lactamase function by altering the dynamic profile of TEM-1 using computational protein design methods. The Rosetta software suite was used to mutate amino acids surrounding either rigid residues that are highly coupled to the active site or to flexible residues with no apparent communication with the active site. Experimental characterization of ten designed proteins indicated that alteration of residues surrounding rigid, highly coupled residues, substantially affected both enzymatic activity and stability; in contrast, native-like activities and stabilities were maintained when flexible, uncoupled residues, were targeted. Our results provide additional insight into the structure-function relationship present in the TEM family of β-lactamases. Furthermore, the integration of computational protein design methods with analyses of protein dynamics represents a general approach that could be used to extend our understanding of the relationship between dynamics and function in other enzyme classes.

scholarly journals cOSPREY: A Cloud-Based Distributed Algorithm for Large-Scale Computational Protein Design

2016 ◽  
Vol 23 (9) ◽  
pp. 737-749 ◽  
Author(s):  
Yuchao Pan ◽  
Yuxi Dong ◽  
Jingtian Zhou ◽  
Mark Hallen ◽  
Bruce R. Donald ◽  
...  
Keyword(s):  
Protein Design ◽  
Distributed Algorithm ◽  
Large Scale ◽  
Computational Protein Design

scholarly journals Discovery of Substrates for a SET Domain Lysine Methyltransferase Predicted by Multistate Computational Protein Design

Structure ◽  
2015 ◽  
Vol 23 (1) ◽  
pp. 206-215 ◽  
Author(s):  
Sylvain Lanouette ◽  
James A. Davey ◽  
Fred Elisma ◽  
Zhibin Ning ◽  
Daniel Figeys ◽  
...  
Keyword(s):  
Protein Design ◽  
Computational Protein Design ◽  
Set Domain ◽  
Lysine Methyltransferase
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