A probabilistic view of protein stability, conformational specificity, and design.

Autor: Stern JA; Department of Computer Science, Brigham Young University, Provo, UT, USA., Free TJ; Department of Chemical Engineering, Brigham Young University, Provo, UT, USA., Stern KL; Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA., Gardiner S; Department of Physics and Astronomy, Brigham Young University, Provo, UT, USA., Dalley NA; Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA., Bundy BC; Department of Chemical Engineering, Brigham Young University, Provo, UT, USA., Price JL; Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA., Wingate D; Department of Computer Science, Brigham Young University, Provo, UT, USA., Della Corte D; Department of Physics and Astronomy, Brigham Young University, Provo, UT, USA. dennis.dellacorte@byu.edu.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Sep 19; Vol. 13 (1), pp. 15493. Date of Electronic Publication: 2023 Sep 19.
DOI: 10.1038/s41598-023-42032-1
Abstrakt: Various approaches have used neural networks as probabilistic models for the design of protein sequences. These "inverse folding" models employ different objective functions, which come with trade-offs that have not been assessed in detail before. This study introduces probabilistic definitions of protein stability and conformational specificity and demonstrates the relationship between these chemical properties and the [Formula: see text] Boltzmann probability objective. This links the Boltzmann probability objective function to experimentally verifiable outcomes. We propose a novel sequence decoding algorithm, referred to as "BayesDesign", that leverages Bayes' Rule to maximize the [Formula: see text] objective instead of the [Formula: see text] objective common in inverse folding models. The efficacy of BayesDesign is evaluated in the context of two protein model systems, the NanoLuc enzyme and the WW structural motif. Both BayesDesign and the baseline ProteinMPNN algorithm increase the thermostability of NanoLuc and increase the conformational specificity of WW. The possible sources of error in the model are analyzed.
(© 2023. Springer Nature Limited.)
Databáze: MEDLINE
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