SCHEMA-Designed Variants of Human Arginase I and II Reveal Sequence Elements Important to Stability and Catalysis

Abstract

Arginases catalyze the divalent cation-dependent hydrolysis of L-arginine to urea and L-ornithine. There is significant interest in using arginase as a therapeutic anti-neogenic agent against L-arginine auxotrophic tumors and in enzyme replacement therapy for treating hyperargininemia. Both therapeutic applications require enzymes with sufficient stability under physiological conditions. To explore sequence elements that contribute to arginase stability we used SCHEMA-guided recombination to design a library of chimeric enzymes composed of sequence fragments from the two human isozymes Arginase I and II. We then developed a novel active learning algorithm that selects sequences from this library that are both highly informative and functional. Using high-throughput gene synthesis and our two-step active learning algorithm, we were able to rapidly create a small but highly informative set of seven enzymatically active chimeras that had an average variant distance of 40 mutations from the closest parent arginase. Within this set of sequences, linear regression was used to identify the sequence elements that contribute to the long-term stability of human arginase under physiological conditions. This approach revealed a striking correlation between the isoelectric point and the long-term stability of the enzyme to deactivation under physiological conditions.

Structure-guided SCHEMA recombination generates diverse chimeric channelrhodopsins

“…This suggests that each sequence/structural block behaves differently in different contexts. For certain soluble protein properties (e.g., thermostability), it has been shown that block contributions are additive, that is, context independent, and that chimera stability can be predicted using linear regression (28,29,49,50). Our data suggest that ChR localization and photocurrent properties, however, require a more complex model to account for the nonlinear dependence of function on block sequence.…”

“…These chimeras consist of nine blocks of one parent and a single block from one of the other two parents. An additional 103 sequences were designed to maximize mutual information (46) between chosen chimeras and the remainder of the chimeric library, using the rationale described by Romero et al (29). Seventeen of these sequences were designed with a constraint on the number of mutations from the nearest parent (<40 mutations).…”

“…SCHEMA has enabled successful recombination of parental sequences with as low as 34% identity (25), which is not possible using random DNA recombination methods such as DNA shuffling (26). SCHEMA recombination has been used to create a variety of functionally diverse soluble proteins (25,(27)(28)(29)(30), but it has not yet been applied to MP engineering. Our goals in this study were to (i) test whether structureguided recombination produces chimeric MPs that express and localize; (ii) measure the fraction of chimeric sequences in a SCHEMA library that express and localize; and (iii) assess the functional diversity of the MPs that successfully localize to the membrane.…”

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“…This study provided greater understanding about the stability in the long term of the arginase 1. The arginase 1 deactivation in physiological conditions was better understood and its possible therapeutic use in the hyperargininemia patients [44].…”

Directed evolution 2.0: improving and deciphering enzyme properties

“…Nowadays, protein engineering by directed evolution has become a standard method to tailor enzyme properties in academia and industry (mainly for biocatalysis 8 purposes). Novel application fields in material science 9 and medical science [10][11][12] will likely lead to a further growth in directed evolution reports despite that the development time for improving enzymes by directed evolution are often still too long to be implemented as a standard operation in process development. Till today most directed evolution campaigns are performed in a traditional manner, in which usually random mutagenesis libraries are generated through epPCR with low mutations frequencies (1 to 3 mutations per 1000 bp) and screened (MTP format; often 1000-2000 variants) in order to obtained improved variants.…”