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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.


Native/germline sequence recovery of designed complexes.100 designs were generated using RECON, with both fixed backbone (FBB) and backbone minimized (BBM) protocols, and MPI_MSD. Sequences of the top 10% of models were compared to either the native sequence or, in the case of common germline-derived antibodies, to the germline sequence. See methods for details of native sequence recovery calculations.
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pcbi.1004300.g003: Native/germline sequence recovery of designed complexes.100 designs were generated using RECON, with both fixed backbone (FBB) and backbone minimized (BBM) protocols, and MPI_MSD. Sequences of the top 10% of models were compared to either the native sequence or, in the case of common germline-derived antibodies, to the germline sequence. See methods for details of native sequence recovery calculations.

Mentions: For common germline gene-derived antibodies, RECON was consistently able to recover the germline sequence at a higher rate than MPI_MSD (Table 3 and Fig 3). Germline sequence recovery for RECON ranged from 55–94% using fixed backbone and 51–95% using backbone minimization, while recovery for designs using MPI_MSD ranged from 32–64%. When comparing RECON fixed backbone to MPI_MSD, it appeared that designs created by RECON, although higher in native sequence content, were also energetically less favorable. We therefore subjected all fixed backbone designs to a single round of Rosetta relax energy minimization to relieve frustrations and allow for direct comparison of fitness of RECON incorporating backbone minimization to fixed backbone designs (S1 Table). These post-minimization fitness values show that the energetic gap between RECON- and MPI_MSD-generated designs was substantially closed, and that designs generated by any method occupied similar ranges of fitness. We observed that MPI_MSD tended to produce designs with the lowest fitness—however, it is important to note that rotamer optimization within Rosetta is a stochastic process, with no guarantee of reaching the global minimum. Therefore a protocol that performs hundreds of rounds of rotamer optimization, such as MPI_MSD, would be expected to produce better energies than one performing four rounds of optimization, such as RECON, independent of the sequence identity of structures being optimized.


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)

Native/germline sequence recovery of designed complexes.100 designs were generated using RECON, with both fixed backbone (FBB) and backbone minimized (BBM) protocols, and MPI_MSD. Sequences of the top 10% of models were compared to either the native sequence or, in the case of common germline-derived antibodies, to the germline sequence. See methods for details of native sequence recovery calculations.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004300.g003: Native/germline sequence recovery of designed complexes.100 designs were generated using RECON, with both fixed backbone (FBB) and backbone minimized (BBM) protocols, and MPI_MSD. Sequences of the top 10% of models were compared to either the native sequence or, in the case of common germline-derived antibodies, to the germline sequence. See methods for details of native sequence recovery calculations.
Mentions: For common germline gene-derived antibodies, RECON was consistently able to recover the germline sequence at a higher rate than MPI_MSD (Table 3 and Fig 3). Germline sequence recovery for RECON ranged from 55–94% using fixed backbone and 51–95% using backbone minimization, while recovery for designs using MPI_MSD ranged from 32–64%. When comparing RECON fixed backbone to MPI_MSD, it appeared that designs created by RECON, although higher in native sequence content, were also energetically less favorable. We therefore subjected all fixed backbone designs to a single round of Rosetta relax energy minimization to relieve frustrations and allow for direct comparison of fitness of RECON incorporating backbone minimization to fixed backbone designs (S1 Table). These post-minimization fitness values show that the energetic gap between RECON- and MPI_MSD-generated designs was substantially closed, and that designs generated by any method occupied similar ranges of fitness. We observed that MPI_MSD tended to produce designs with the lowest fitness—however, it is important to note that rotamer optimization within Rosetta is a stochastic process, with no guarantee of reaching the global minimum. Therefore a protocol that performs hundreds of rounds of rotamer optimization, such as MPI_MSD, would be expected to produce better energies than one performing four rounds of optimization, such as RECON, independent of the sequence identity of structures being optimized.

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.