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Engineering carboxypeptidase G2 circular permutations for the design of an autoinhibited enzyme.

ABSTRACT: Carboxypeptidase G2 (CPG2) is an Food and Drug Administration (FDA)-approved enzyme drug used to treat methotrexate (MTX) toxicity in cancer patients receiving MTX treatment. It has also been used in directed enzyme-prodrug chemotherapy, but this strategy has been hampered by off-site activation of the prodrug by the circulating enzyme. The development of a tumor protease activatable CPG2, which could be achieved using a circular permutation of CPG2 fused to an inactivating 'prodomain', would aid in these applications. We report the development of a protease accessibility-based screen to identify candidate sites for circular permutation in proximity of the CPG2 active site. The resulting six circular permutants showed similar expression, structure, thermal stability, and, in four cases, activity levels compared to the wild-type enzyme. We rationalize these results based on structural models of the permutants obtained using the Rosetta software. We developed a cell growth-based selection system, and demonstrated that when fused to periplasm-directing signal peptides, one of our circular permutants confers MTX resistance in Escherichia coli with equal efficiency as the wild-type enzyme. As the permutants have similar properties to wild-type CPG2, these enzymes are promising starting points for the development of autoinhibited, protease-activatable zymogen forms of CPG2 for use in therapeutic contexts.

SUBMITTER: Yachnin BJ

PROVIDER: S-EPMC6283397 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Engineering carboxypeptidase G2 circular permutations for the design of an autoinhibited enzyme.

Yachnin Brahm J BJ Khare Sagar D SD

Protein engineering, design & selection : PEDS 20170401 4

Carboxypeptidase G2 (CPG2) is an Food and Drug Administration (FDA)-approved enzyme drug used to treat methotrexate (MTX) toxicity in cancer patients receiving MTX treatment. It has also been used in directed enzyme-prodrug chemotherapy, but this strategy has been hampered by off-site activation of the prodrug by the circulating enzyme. The development of a tumor protease activatable CPG2, which could be achieved using a circular permutation of CPG2 fused to an inactivating 'prodomain', would ai ...[more]

PMID: 28160000

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Project description:Circular permutation (CP) refers to situations in which the termini of a protein are relocated to other positions in the structure. CP occurs naturally and has been artificially created to study protein function, stability and folding. Recently CP is increasingly applied to engineer enzyme structure and function, and to create bifunctional fusion proteins unachievable by tandem fusion. CP is a complicated and expensive technique. An intrinsic difficulty in its application lies in the fact that not every position in a protein is amenable for creating a viable permutant. To examine the preferences of CP and develop CP viability prediction methods, we carried out comprehensive analyses of the sequence, structural, and dynamical properties of known CP sites using a variety of statistics and simulation methods, such as the bootstrap aggregating, permutation test and molecular dynamics simulations. CP particularly favors Gly, Pro, Asp and Asn. Positions preferred by CP lie within coils, loops, turns, and at residues that are exposed to solvent, weakly hydrogen-bonded, environmentally unpacked, or flexible. Disfavored positions include Cys, bulky hydrophobic residues, and residues located within helices or near the protein's core. These results fostered the development of an effective viable CP site prediction system, which combined four machine learning methods, e.g., artificial neural networks, the support vector machine, a random forest, and a hierarchical feature integration procedure developed in this work. As assessed by using the hydrofolate reductase dataset as the independent evaluation dataset, this prediction system achieved an AUC of 0.9. Large-scale predictions have been performed for nine thousand representative protein structures; several new potential applications of CP were thus identified. Many unreported preferences of CP are revealed in this study. The developed system is the best CP viability prediction method currently available. This work will facilitate the application of CP in research and biotechnology.

Dataset Information

Engineering carboxypeptidase G2 circular permutations for the design of an autoinhibited enzyme.

Publications

Engineering carboxypeptidase G2 circular permutations for the design of an autoinhibited enzyme.

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