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Computationally mapping sequence space to understand evolutionary protein engineering.

ABSTRACT: Evolutionary protein engineering has been dramatically successful, producing a wide variety of new proteins with altered stability, binding affinity, and enzymatic activity. However, the success of such procedures is often unreliable, and the impact of the choice of protein, engineering goal, and evolutionary procedure is not well understood. We have created a framework for understanding aspects of the protein engineering process by computationally mapping regions of feasible sequence space for three small proteins using structure-based design protocols. We then tested the ability of different evolutionary search strategies to explore these sequence spaces. The results point to a non-intuitive relationship between the error-prone PCR mutation rate and the number of rounds of replication. The evolutionary relationships among feasible sequences reveal hub-like sequences that serve as particularly fruitful starting sequences for evolutionary search. Moreover, genetic recombination procedures were examined, and tradeoffs relating sequence diversity and search efficiency were identified. This framework allows us to consider the impact of protein structure on the allowed sequence space and therefore on the challenges that each protein presents to error-prone PCR and genetic recombination procedures.

SUBMITTER: Armstrong KA

PROVIDER: S-EPMC3495008 | biostudies-literature | 2008 Jan-Feb

REPOSITORIES: biostudies-literature

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Computationally mapping sequence space to understand evolutionary protein engineering.

Armstrong Kathryn A KA Tidor Bruce B

Biotechnology progress 20071117 1

Evolutionary protein engineering has been dramatically successful, producing a wide variety of new proteins with altered stability, binding affinity, and enzymatic activity. However, the success of such procedures is often unreliable, and the impact of the choice of protein, engineering goal, and evolutionary procedure is not well understood. We have created a framework for understanding aspects of the protein engineering process by computationally mapping regions of feasible sequence space for ...[more]

PMID: 18020358

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Project description:The specificity of the kinase-regulator interaction is driven by a limited set of interfacial residues in each protein that strongly. To identify combinations of these interface residues that are functional and potentially insulated from existing two-component signaling pathways in E. coli, we constructed a dual library of mutants in which the key, coevolving interface residues of a canonical two-component system, PhoQ and PhoP, were randomized. We used NNS codons to randomize six residues in PhoQ and five residues in PhoP, all of which lie at the interface formed by the two proteins in complex and that are critical to determining partner specificity in all two-component signaling pathways. To identify functional combinations of residues, we first grew the library of PhoQ-PhoP variants overnight in medium with low Mg2+, which activates PhoQ. Because cells must phosphorylate PhoP to grow when extracellular Mg2+ is limiting, this step enriches for functional PhoQ-PhoP variants. Variants that survived selection in limiting Mg2+ were then subjected to Sort-Seq, using fluorescence-activated cell sorting (FACS) and deep sequencing to quantify the signal responsiveness of variants in the library. To gauge the phosphorylation of PhoP in vivo, we used a fluorescent transcriptional reporter, PmgrB-yfp. In the presence of low extracellular Mg2+, functional PhoQ promotes the phosphorylation of PhoP and the production of YFP, whereas in the presence of high concentrations of Mg2+, PhoQ drives the dephosphorylation of PhoP, limiting the accumulation of YFP). The library was grown in each condition for 6 hours before sorting and sequencing. To identify variants that are signal responsive and drive YFP production specifically in low Mg2+, we sorted cells from each condition into 8 separate bins and deep sequenced the randomized regions of variants collected in each bin. We then calculated the frequency of each variant in each bin to yield the distributions of individual variants in low and high Mg2+, which were fit to Gaussians. From these fits, we assessed the mean level of YFP in each condition and the fold-induction, or signal responsiveness, of each variant detected in the library. The 11 codons / amino acids listed in this dataset refer to codons 12, 14, 15, 18, and 19 in PhoP and codons 284, 288, 289, 292, 302, and 303 in PhoQ, in that order.

Dataset Information

Computationally mapping sequence space to understand evolutionary protein engineering.

Publications

Computationally mapping sequence space to understand evolutionary protein engineering.

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