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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.


Pseudocode describing the implementation of the RECON algorithm.
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pcbi.1004300.g001: Pseudocode describing the implementation of the RECON algorithm.

Mentions: The RECON algorithm allows separate states to explore their own local sequence and conformational space to optimize free energy, while restraining corresponding residues in different states with a convergence restraint to encourage sequence convergence. Convergence restraints are kept small in early rounds, to allow each state to explore its own lowest energy sequence, and ramped up in later rounds to encourage sequence convergence between different states. This is followed by a greedy selection step, which evaluates all candidate amino acids at positions that fail to converge, and selects the one that results in the lowest fitness when applied over all states. This greedy selection is included in order to ensure that one multi-specific sequence is generated from each design trajectory. Backbone minimization steps can be included between design rounds to relieve slight clashes between side chains. Pseudocode describing the implementation of the algorithm is shown in Fig 1. Individual states optimize rotamer placement using a simulated annealing Monte Carlo search, sampling from a predefined rotamer library [17,18]. However, we emphasize that this method can be applied to any multi-specificity problem using an arbitrary optimization method and scoring function.


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)

Pseudocode describing the implementation of the RECON algorithm.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004300.g001: Pseudocode describing the implementation of the RECON algorithm.
Mentions: The RECON algorithm allows separate states to explore their own local sequence and conformational space to optimize free energy, while restraining corresponding residues in different states with a convergence restraint to encourage sequence convergence. Convergence restraints are kept small in early rounds, to allow each state to explore its own lowest energy sequence, and ramped up in later rounds to encourage sequence convergence between different states. This is followed by a greedy selection step, which evaluates all candidate amino acids at positions that fail to converge, and selects the one that results in the lowest fitness when applied over all states. This greedy selection is included in order to ensure that one multi-specific sequence is generated from each design trajectory. Backbone minimization steps can be included between design rounds to relieve slight clashes between side chains. Pseudocode describing the implementation of the algorithm is shown in Fig 1. Individual states optimize rotamer placement using a simulated annealing Monte Carlo search, sampling from a predefined rotamer library [17,18]. However, we emphasize that this method can be applied to any multi-specificity problem using an arbitrary optimization method and scoring function.

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.