Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine

Abstract The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubility. Although previous efforts to identify...

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Autores principales:	Sam Giannakoulias, Sumant R. Shringari, John J. Ferrie, E. James Petersson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c1284061dfd245adaed36a4cf18dc1a1
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spelling	oai:doaj.org-article:c1284061dfd245adaed36a4cf18dc1a12021-12-02T18:34:01ZBiomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine10.1038/s41598-021-97965-22045-2322https://doaj.org/article/c1284061dfd245adaed36a4cf18dc1a12021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97965-2https://doaj.org/toc/2045-2322Abstract The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubility. Although previous efforts to identify features which accurately capture these site-specific effects have been unsuccessful, we have developed a set of novel Rosetta Custom Score Functions and alternative Empirical Score Functions that accurately predict the effects of acridon-2-yl-alanine (Acd) incorporation on protein yield and solubility. Acd-containing mutants were simulated in PyRosetta, and machine learning (ML) was performed using either the decomposed values of the Rosetta energy function, or changes in residue contacts and bioinformatics. Using these feature sets, which represent Rosetta score function specific and bioinformatics-derived terms, ML models were trained to predict highly abstract experimental parameters such as mutant protein yield and solubility and displayed robust performance on well-balanced holdouts. Model feature importance analyses demonstrated that terms corresponding to hydrophobic interactions, desolvation, and amino acid angle preferences played a pivotal role in predicting tolerance of mutation to Acd. Overall, this work provides evidence that the application of ML to features extracted from simulated structural models allow for the accurate prediction of diverse and abstract biological phenomena, beyond the predictivity of traditional modeling and simulation approaches.Sam GiannakouliasSumant R. ShringariJohn J. FerrieE. James PeterssonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sam Giannakoulias Sumant R. Shringari John J. Ferrie E. James Petersson Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine
description	Abstract The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubility. Although previous efforts to identify features which accurately capture these site-specific effects have been unsuccessful, we have developed a set of novel Rosetta Custom Score Functions and alternative Empirical Score Functions that accurately predict the effects of acridon-2-yl-alanine (Acd) incorporation on protein yield and solubility. Acd-containing mutants were simulated in PyRosetta, and machine learning (ML) was performed using either the decomposed values of the Rosetta energy function, or changes in residue contacts and bioinformatics. Using these feature sets, which represent Rosetta score function specific and bioinformatics-derived terms, ML models were trained to predict highly abstract experimental parameters such as mutant protein yield and solubility and displayed robust performance on well-balanced holdouts. Model feature importance analyses demonstrated that terms corresponding to hydrophobic interactions, desolvation, and amino acid angle preferences played a pivotal role in predicting tolerance of mutation to Acd. Overall, this work provides evidence that the application of ML to features extracted from simulated structural models allow for the accurate prediction of diverse and abstract biological phenomena, beyond the predictivity of traditional modeling and simulation approaches.
format	article
author	Sam Giannakoulias Sumant R. Shringari John J. Ferrie E. James Petersson
author_facet	Sam Giannakoulias Sumant R. Shringari John J. Ferrie E. James Petersson
author_sort	Sam Giannakoulias
title	Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine
title_short	Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine
title_full	Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine
title_fullStr	Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine
title_full_unstemmed	Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine
title_sort	biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c1284061dfd245adaed36a4cf18dc1a1
work_keys_str_mv	AT samgiannakoulias biomolecularsimulationbasedmachinelearningmodelsaccuratelypredictsitesoftolerabilitytotheunnaturalaminoacidacridonylalanine AT sumantrshringari biomolecularsimulationbasedmachinelearningmodelsaccuratelypredictsitesoftolerabilitytotheunnaturalaminoacidacridonylalanine AT johnjferrie biomolecularsimulationbasedmachinelearningmodelsaccuratelypredictsitesoftolerabilitytotheunnaturalaminoacidacridonylalanine AT ejamespetersson biomolecularsimulationbasedmachinelearningmodelsaccuratelypredictsitesoftolerabilitytotheunnaturalaminoacidacridonylalanine
_version_	1718377943672029184

Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine

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