Diversification of Catalytic Function in a Synthetic Family of Chimeric Cytochrome P450s

Landwehr, Marco; Carbone, Martina; Otey, Christopher R.; Li, Yougen; Arnold, Frances H.

doi:10.1016/j.chembiol.2007.01.009

Cited by 67 publications

(49 citation statements)

References 40 publications

(53 reference statements)

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“…Chimeric protein libraries are particularly desirable training sets because they uniformly sample a massive combinatorial space of mutations. In addition, the sequences within chimera libraries have a high probability of functioning (35) and display significant functional diversity (16,36).…”

Section: Discussionmentioning

confidence: 99%

Navigating the protein fitness landscape with Gaussian processes

Romero

Krause

Arnold

2012

Proc. Natl. Acad. Sci. U.S.A.

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Knowing how protein sequence maps to function (the "fitness landscape") is critical for understanding protein evolution as well as for engineering proteins with new and useful properties. We demonstrate that the protein fitness landscape can be inferred from experimental data, using Gaussian processes, a Bayesian learning technique. Gaussian process landscapes can model various protein sequence properties, including functional status, thermostability, enzyme activity, and ligand binding affinity. Trained on experimental data, these models achieve unrivaled quantitative accuracy. Furthermore, the explicit representation of model uncertainty allows for efficient searches through the vast space of possible sequences. We develop and test two protein sequence design algorithms motivated by Bayesian decision theory. The first one identifies small sets of sequences that are informative about the landscape; the second one identifies optimized sequences by iteratively improving the Gaussian process model in regions of the landscape that are predicted to be optimized. We demonstrate the ability of Gaussian processes to guide the search through protein sequence space by designing, constructing, and testing chimeric cytochrome P450s. These algorithms allowed us to engineer active P450 enzymes that are more thermostable than any previously made by chimeragenesis, rational design, or directed evolution.protein engineering | recombination | machine learning | experimental design | active learning I n the mapping of protein sequence to protein behavior, the phenotype can be envisioned as a surface, or landscape, over the high-dimensional space of possible sequences (1). This "fitness landscape" could describe how the protein contributes to organismal fitness, or it may represent a biophysical property, such as stability, enzyme activity, or ligand binding affinity. The structure of this surface describes the spectrum of possible phenotypes as well as the mutational accessibility among them and therefore strongly influences protein evolution. This surface is also the objective function for protein engineering, which seeks to identify protein sequences that are highly optimized for a given property or set of properties.Identifying such optimized sequences is extremely challenging for several reasons. First, the space of possible protein sequences is incomprehensibly large and will never be searched exhaustively by any means, naturally, in the laboratory, or computationally (2, 3). Second, within this vast space, functional proteins are extremely scarce, with estimates that range from a high of 1 in 10 11 to as little as 1 in 10 77 (4,5). Of the sequences that are functional, most have poor fitness and their numbers decrease exponentially with higher levels of fitness (6, 7). Thus, highly fit sequences are vanishingly rare and overwhelmed by nonfunctional and mediocre sequences.Computational protein engineering uses models of protein function to guide a search for optimized sequences. These models typically contain an atomic struc...

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Section: Discussionmentioning

confidence: 99%

Navigating the protein fitness landscape with Gaussian processes

Romero

Krause

Arnold

2012

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

295

311

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“…Because recombination is more conservative than mutagenesis, several research groups have tried to introduce variability in a sequence by constructing chimeras with genes for homologous proteins. This can be done either randomly (Crameri et al, 1998;Minshull & Stemmer, 1999) or in a site-directed fashion (Landwehr et al, 2007;Li et al, 2007;Pantazes et al, 2007). By substituting a homologous segment, some interactions may be perturbed; and the protein might not be functional.…”

Section: Site-directed Homologous Recombinationmentioning

confidence: 99%

Site-Directed Mutagenesis as Applied to Biocatalysts

Damián-Almazo¹,

Saab‐Rincón²

2013

Genetic Manipulation of DNA and Protein - Examples From Current Research

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“…In contrast to mutagenesis, the mutations introduced by site-directed recombination are known to be compatible with each other in parent proteins with a similar structural context (due to homology), and are thus expected to be less disruptive. Without requiring precise modeling or prediction of the effects of mutation, sitedirected recombination can produce variant proteins with improved properties and activities [2,3,11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Models of residue correlation have been shown to capture important information in a number of applications, including prediction of free energy changes caused by hydrophobic core mutations [16], prediction and recognition of native-like protein structure [17], and functional classification of members of protein families [18]. Pairwise [1] and higher-order [9] models have been used in algorithms to plan site-directed recombination experiments minimizing perturbation (and thereby maximizing expected stability), and have led to the creation of variant proteins with improved or novel activities [2,3,11,12].…”

Section: Introductionmentioning

confidence: 99%

Algorithms for Joint Optimization of Stability and Diversity in Planning Combinatorial Libraries of Chimeric Proteins

Zheng

Friedman²,

Bailey‐Kellogg³

2009

Journal of Computational Biology

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Abstract. In engineering protein variants by constructing and screening combinatorial libraries of chimeric proteins, two complementary and competing goals are desired: the new proteins must be similar enough to the evolutionarily-selected wild-type proteins to be stably folded, and they must be different enough to display functional variation. We present here the first method, Staversity, to simultaneously optimize stability and diversity in selecting sets of breakpoint locations for site-directed recombination. Our goal is to uncover all "undominated" breakpoint sets, for which no other breakpoint set is better in both factors. Our first algorithm finds the undominated sets serving as the vertices of the lower envelope of the two-dimensional (stability and diversity) convex hull containing all possible breakpoint sets. Our second algorithm identifies additional breakpoint sets in the concavities that are either undominated or dominated only by undiscovered breakpoint sets within a distance bound computed by the algorithm. Both algorithms are efficient, requiring only time polynomial in the numbers of residues and breakpoints, while characterizing a space defined by an exponential number of possible breakpoint sets. We applied Staversity to identify 2-10 breakpoint sets for three different sets of parent proteins from the purE family of biosynthetic enzymes. The average normalized distance between our plans and the lower bound for optimal plans is around 1 percent. Our plans dominate most (60-90% on average for each parent set) of the plans found by other possible approaches, random sampling or explicit optimization for stability with implicit optimization for diversity. The identified breakpoint sets provide a compact representation of good plans, enabling a protein engineer to understand and account for the trade-offs between two key considerations in combinatorial chimeragenesis.

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Diversification of Catalytic Function in a Synthetic Family of Chimeric Cytochrome P450s

Cited by 67 publications

References 40 publications

Navigating the protein fitness landscape with Gaussian processes

Navigating the protein fitness landscape with Gaussian processes

Site-Directed Mutagenesis as Applied to Biocatalysts

Algorithms for Joint Optimization of Stability and Diversity in Planning Combinatorial Libraries of Chimeric Proteins

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