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Knowledge-Based Unfolded State Model for Protein Design

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Computational Peptide Science

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2405))

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Abstract

The design of proteins and miniproteins is an important challenge. Designed variants should be stable, meaning the folded/unfolded free energy difference should be large enough. Thus, the unfolded state plays a central role. An extended peptide model is often used, where side chains interact with solvent and nearby backbone, but not each other. The unfolded energy is then a function of sequence composition only and can be empirically parametrized. If the space of sequences is explored with a Monte Carlo procedure, protein variants will be sampled according to a well-defined Boltzmann probability distribution. We can then choose unfolded model parameters to maximize the probability of sampling native-like sequences. This leads to a well-defined maximum likelihood framework. We present an iterative algorithm that follows the likelihood gradient. The method is presented in the context of our Proteus software, as a detailed downloadable tutorial. The unfolded model is combined with a folded model that uses molecular mechanics and a Generalized Born solvent. It was optimized for three PDZ domains and then used to redesign them. The sequences sampled are native-like and similar to a recent PDZ design study that was experimentally validated.

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Authors and Affiliations

  1. Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France

    Vaitea Opuu, David Mignon & Thomas Simonson

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  1. Vaitea Opuu
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  2. David Mignon
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  3. Thomas Simonson
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Correspondence to Thomas Simonson .

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Editors and Affiliations

  1. Lab de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France

    Thomas Simonson

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Opuu, V., Mignon, D., Simonson, T. (2022). Knowledge-Based Unfolded State Model for Protein Design. In: Simonson, T. (eds) Computational Peptide Science. Methods in Molecular Biology, vol 2405. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1855-4_19

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  • DOI: https://doi.org/10.1007/978-1-0716-1855-4_19

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1854-7

  • Online ISBN: 978-1-0716-1855-4

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