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Design of Protein Multi-specificity Using an Independent Sequence Search Reduces the Barrier to Low Energy Sequences.

Sevy AM, Jacobs TM, Crowe JE, Meiler J - PLoS Comput. Biol. (2015)

Bottom Line: Computational protein design has found great success in engineering proteins for thermodynamic stability, binding specificity, or enzymatic activity in a 'single state' design (SSD) paradigm.As a result, RECON can readily be used in simulations with a flexible protein backbone.We show that RECON is able to efficiently recover native-like, biologically relevant sequences in this diverse set of protein complexes.

View Article: PubMed Central - PubMed

Affiliation: Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America.

ABSTRACT
Computational protein design has found great success in engineering proteins for thermodynamic stability, binding specificity, or enzymatic activity in a 'single state' design (SSD) paradigm. Multi-specificity design (MSD), on the other hand, involves considering the stability of multiple protein states simultaneously. We have developed a novel MSD algorithm, which we refer to as REstrained CONvergence in multi-specificity design (RECON). The algorithm allows each state to adopt its own sequence throughout the design process rather than enforcing a single sequence on all states. Convergence to a single sequence is encouraged through an incrementally increasing convergence restraint for corresponding positions. Compared to MSD algorithms that enforce (constrain) an identical sequence on all states the energy landscape is simplified, which accelerates the search drastically. As a result, RECON can readily be used in simulations with a flexible protein backbone. We have benchmarked RECON on two design tasks. First, we designed antibodies derived from a common germline gene against their diverse targets to assess recovery of the germline, polyspecific sequence. Second, we design "promiscuous", polyspecific proteins against all binding partners and measure recovery of the native sequence. We show that RECON is able to efficiently recover native-like, biologically relevant sequences in this diverse set of protein complexes.

No MeSH data available.


Schematic showing proposed energy landscape of forced vs. encouraged sequence convergence in MSD.By allowing each state to maintain its own sequence and explore sequence space independently, RECON is able to provide an intermediate solution in a MSD problem, enabling more rapid determination of a low energy solution. Dashed lines represent forced convergence, where both states must adopt the same sequence (either AB or BA), whereas the solid line represents encouraged convergence, where state 1 can adopt sequence AB while state 2 adopts BA. This creates a lower energy intermediate state leading to more rapid adoption of the optimal solution, sequence BB.
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pcbi.1004300.g002: Schematic showing proposed energy landscape of forced vs. encouraged sequence convergence in MSD.By allowing each state to maintain its own sequence and explore sequence space independently, RECON is able to provide an intermediate solution in a MSD problem, enabling more rapid determination of a low energy solution. Dashed lines represent forced convergence, where both states must adopt the same sequence (either AB or BA), whereas the solid line represents encouraged convergence, where state 1 can adopt sequence AB while state 2 adopts BA. This creates a lower energy intermediate state leading to more rapid adoption of the optimal solution, sequence BB.

Mentions: By allowing each state to determine its optimal sequence independently, we can collapse the energy barrier to reaching a “compromised” sequence that results in low energy in all states. We propose a scenario in which encouraging sequencing convergence in this way can reduce the energetic barrier and enable convergence on a low energy solution (Fig 2). In this scenario, two separate mutations from residue identity A to B are needed for the lowest fitness over both states. Each single mutation will encounter a high energy penalty and rarely selected by a genetic algorithm–only when both mutations are stochastically placed together will the solution emerge, which may take a large number of evaluations. However, when sequence convergence is encouraged rather than enforced, each state will identify an intermediate solution in early rounds, and in later rounds the most favorable solution will be selected from the differing states. This collapses the barrier on the pathway to a favorable solution and reduces the steps necessary to find that solution.


Design of Protein Multi-specificity Using an Independent Sequence Search Reduces the Barrier to Low Energy Sequences.

Sevy AM, Jacobs TM, Crowe JE, Meiler J - PLoS Comput. Biol. (2015)

Schematic showing proposed energy landscape of forced vs. encouraged sequence convergence in MSD.By allowing each state to maintain its own sequence and explore sequence space independently, RECON is able to provide an intermediate solution in a MSD problem, enabling more rapid determination of a low energy solution. Dashed lines represent forced convergence, where both states must adopt the same sequence (either AB or BA), whereas the solid line represents encouraged convergence, where state 1 can adopt sequence AB while state 2 adopts BA. This creates a lower energy intermediate state leading to more rapid adoption of the optimal solution, sequence BB.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493036&req=5

pcbi.1004300.g002: Schematic showing proposed energy landscape of forced vs. encouraged sequence convergence in MSD.By allowing each state to maintain its own sequence and explore sequence space independently, RECON is able to provide an intermediate solution in a MSD problem, enabling more rapid determination of a low energy solution. Dashed lines represent forced convergence, where both states must adopt the same sequence (either AB or BA), whereas the solid line represents encouraged convergence, where state 1 can adopt sequence AB while state 2 adopts BA. This creates a lower energy intermediate state leading to more rapid adoption of the optimal solution, sequence BB.
Mentions: By allowing each state to determine its optimal sequence independently, we can collapse the energy barrier to reaching a “compromised” sequence that results in low energy in all states. We propose a scenario in which encouraging sequencing convergence in this way can reduce the energetic barrier and enable convergence on a low energy solution (Fig 2). In this scenario, two separate mutations from residue identity A to B are needed for the lowest fitness over both states. Each single mutation will encounter a high energy penalty and rarely selected by a genetic algorithm–only when both mutations are stochastically placed together will the solution emerge, which may take a large number of evaluations. However, when sequence convergence is encouraged rather than enforced, each state will identify an intermediate solution in early rounds, and in later rounds the most favorable solution will be selected from the differing states. This collapses the barrier on the pathway to a favorable solution and reduces the steps necessary to find that solution.

Bottom Line: Computational protein design has found great success in engineering proteins for thermodynamic stability, binding specificity, or enzymatic activity in a 'single state' design (SSD) paradigm.As a result, RECON can readily be used in simulations with a flexible protein backbone.We show that RECON is able to efficiently recover native-like, biologically relevant sequences in this diverse set of protein complexes.

View Article: PubMed Central - PubMed

Affiliation: Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America.

ABSTRACT
Computational protein design has found great success in engineering proteins for thermodynamic stability, binding specificity, or enzymatic activity in a 'single state' design (SSD) paradigm. Multi-specificity design (MSD), on the other hand, involves considering the stability of multiple protein states simultaneously. We have developed a novel MSD algorithm, which we refer to as REstrained CONvergence in multi-specificity design (RECON). The algorithm allows each state to adopt its own sequence throughout the design process rather than enforcing a single sequence on all states. Convergence to a single sequence is encouraged through an incrementally increasing convergence restraint for corresponding positions. Compared to MSD algorithms that enforce (constrain) an identical sequence on all states the energy landscape is simplified, which accelerates the search drastically. As a result, RECON can readily be used in simulations with a flexible protein backbone. We have benchmarked RECON on two design tasks. First, we designed antibodies derived from a common germline gene against their diverse targets to assess recovery of the germline, polyspecific sequence. Second, we design "promiscuous", polyspecific proteins against all binding partners and measure recovery of the native sequence. We show that RECON is able to efficiently recover native-like, biologically relevant sequences in this diverse set of protein complexes.

No MeSH data available.