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Exploring symmetry as an avenue to the computational design of large protein domains.

ABSTRACT: It has been demonstrated previously that symmetric, homodimeric proteins are energetically favored, which explains their abundance in nature. It has been proposed that such symmetric homodimers underwent gene duplication and fusion to evolve into protein topologies that have a symmetric arrangement of secondary structure elements--"symmetric superfolds". Here, the ROSETTA protein design software was used to computationally engineer a perfectly symmetric variant of imidazole glycerol phosphate synthase and its corresponding symmetric homodimer. The new protein, termed FLR, adopts the symmetric (??)(8) TIM-barrel superfold. The protein is soluble and monomeric and exhibits two-fold symmetry not only in the arrangement of secondary structure elements but also in sequence and at atomic detail, as verified by crystallography. When cut in half, FLR dimerizes readily to form the symmetric homodimer. The successful computational design of FLR demonstrates progress in our understanding of the underlying principles of protein stability and presents an attractive strategy for the in silico construction of larger protein domains from smaller pieces.

SUBMITTER: Fortenberry C

PROVIDER: S-EPMC3781211 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Exploring symmetry as an avenue to the computational design of large protein domains.

Fortenberry Carie C Bowman Elizabeth Anne EA Proffitt Will W Dorr Brent B Combs Steven S Harp Joel J Mizoue Laura L Meiler Jens J

Journal of the American Chemical Society 20111021 45

It has been demonstrated previously that symmetric, homodimeric proteins are energetically favored, which explains their abundance in nature. It has been proposed that such symmetric homodimers underwent gene duplication and fusion to evolve into protein topologies that have a symmetric arrangement of secondary structure elements--"symmetric superfolds". Here, the ROSETTA protein design software was used to computationally engineer a perfectly symmetric variant of imidazole glycerol phosphate sy ...[more]

PMID: 21978247

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Project description:UnlabelledThe de novo engineering of new proteins will allow the design of complex systems in synthetic biology. But the design of large proteins is very challenging due to the large combinatorial sequence space to be explored and the lack of a suitable selection system to guide the evolution and optimization. One way to approach this challenge is to use computational design methods based on the current crystallographic data and on molecular mechanics. We have used a laccase protein fold as a scaffold to design a new protein sequence that would adopt a 3D conformation in solution similar to a wild-type protein, the Trametes versicolor (TvL) fungal laccase. Laccases are multi-copper oxidases that find utility in a variety of industrial applications. The laccases with highest activity and redox potential are generally secreted fungal glycoproteins. Prokaryotic laccases have been identified with some desirable features, but they often exhibit low redox potentials. The designed sequence (DLac) shares a 50% sequence identity to the original TvL protein. The new DLac gene was overexpressed in E. coli and the majority of the protein was found in inclusion bodies. Both soluble protein and refolded insoluble protein were purified, and their identity was verified by mass spectrometry. Neither protein exhibited the characteristic T1 copper absorbance, neither bound copper by atomic absorption, and neither was active using a variety of laccase substrates over a range of pH values. Circular dichroism spectroscopy studies suggest that the DLac protein adopts a molten globule structure that is similar to the denatured and refolded native fungal TvL protein, which is significantly different from the natively secreted fungal protein. Taken together, these results indicate that the computationally designed DLac expressed in E. coli is unable to utilize the same folding pathway that is used in the expression of the parent TvL protein or the prokaryotic laccases. This sequence can be used going forward to help elucidate the sequence requirements needed for prokaryotic multi-copper oxidase expression.Electronic supplementary materialThe online version of this article (doi:10.1007/s11693-011-9080-9) contains supplementary material, which is available to authorized users.

Project description:BackgroundLarge-scale datasets of protein structures and sequences are becoming ubiquitous in many domains of biological research. Experimental approaches and computational modelling methods are generating biological data at an unprecedented rate. The detailed analysis of structure-sequence relationships is critical to unveil governing principles of protein folding, stability and function. Computational protein design (CPD) has emerged as an important structure-based approach to engineer proteins for novel functions. Generally, CPD workflows rely on the generation of large numbers of structural models to search for the optimal structure-sequence configurations. As such, an important step of the CPD process is the selection of a small subset of sequences to be experimentally characterized. Given the limitations of current CPD scoring functions, multi-step design protocols and elaborated analysis of the decoy populations have become essential for the selection of sequences for experimental characterization and the success of CPD strategies.ResultsHere, we present the rstoolbox, a Python library for the analysis of large-scale structural data tailored for CPD applications. rstoolbox is oriented towards both CPD software users and developers, being easily integrated in analysis workflows. For users, it offers the ability to profile and select decoy sets, which may guide multi-step design protocols or for follow-up experimental characterization. rstoolbox provides intuitive solutions for the visualization of large sequence/structure datasets (e.g. logo plots and heatmaps) and facilitates the analysis of experimental data obtained through traditional biochemical techniques (e.g. circular dichroism and surface plasmon resonance) and high-throughput sequencing. For CPD software developers, it provides a framework to easily benchmark and compare different CPD approaches. Here, we showcase the rstoolbox in both types of applications.Conclusionsrstoolbox is a library for the evaluation of protein structures datasets tailored for CPD data. It provides interactive access through seamless integration with IPython, while still being suitable for high-performance computing. In addition to its functionalities for data analysis and graphical representation, the inclusion of rstoolbox in protein design pipelines will allow to easily standardize the selection of design candidates, as well as, to improve the overall reproducibility and robustness of CPD selection processes.

Dataset Information

Exploring symmetry as an avenue to the computational design of large protein domains.

Publications

Exploring symmetry as an avenue to the computational design of large protein domains.

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