Machine learning methods to study sequence-ensemble-function relationships in disordered proteins
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910846107516928 |
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| author | von Bülow, Sören Tesei, Giulio Lindorff-Larsen, Kresten |
| author_facet | von Bülow, Sören Tesei, Giulio Lindorff-Larsen, Kresten |
| contents | Recent years have seen tremendous developments in the use of machine learning models to link amino acid sequence, structure and function of folded proteins. These methods are, however, rarely applicable to the wide range of proteins and sequences that comprise intrinsically disordered regions. We here review developments in the study of sequence-ensemble-function relationships of disordered proteins that exploit or are used to train machine learning models. These include methods for generating conformational ensembles and designing new sequences, and for linking sequences to biophysical properties and biological functions. We highlight how these developments are built on a tight integration between experiment, theory and simulations, and account for evolutionary constraints, which operate on sequences of disordered regions differently than on those of folded domains. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_15940 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning methods to study sequence-ensemble-function relationships in disordered proteins von Bülow, Sören Tesei, Giulio Lindorff-Larsen, Kresten Biomolecules Recent years have seen tremendous developments in the use of machine learning models to link amino acid sequence, structure and function of folded proteins. These methods are, however, rarely applicable to the wide range of proteins and sequences that comprise intrinsically disordered regions. We here review developments in the study of sequence-ensemble-function relationships of disordered proteins that exploit or are used to train machine learning models. These include methods for generating conformational ensembles and designing new sequences, and for linking sequences to biophysical properties and biological functions. We highlight how these developments are built on a tight integration between experiment, theory and simulations, and account for evolutionary constraints, which operate on sequences of disordered regions differently than on those of folded domains. |
| title | Machine learning methods to study sequence-ensemble-function relationships in disordered proteins |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2410.15940 |