Data-driven models for predicting intrinsically disordered protein polymer physics directly from composition or sequence-Reference-Cited by-同舟云学术

Data-driven models for predicting intrinsically disordered protein polymer physics directly from composition or sequence

Published:2023 Issue:9 Volume:8 Page:1146-1155
ISSN:2058-9689
Container-title:Molecular Systems Design & Engineering
language:en
Short-container-title:Mol. Syst. Des. Eng.

Author:

Chao Tzu-Hsuan¹^ORCID,Rekhi Shiv¹²^ORCID,Mittal Jeetain¹²³^ORCID,Tabor Daniel P.¹^ORCID

Affiliation:

1. Department of Chemistry, Texas A&M University, PO Box 30012, College Station, TX 77842-3012, USA

2. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA

3. Interdisciplinary Graduate Program in Genetics and Genomics, Texas A&M University, College Station, TX, USA

Abstract

A new representation for intrinsically disordered protein sequences is developed for usage in both classical and deep learning machine learning models.

Funder

National Institute of General Medical Sciences

Welch Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,Industrial and Manufacturing Engineering,Process Chemistry and Technology,Energy Engineering and Power Technology,Biomedical Engineering,Chemical Engineering (miscellaneous),Chemistry (miscellaneous)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/ME/D3ME00053B

Reference50 articles.

1. Classification of Intrinsically Disordered Regions and Proteins

2. NMR Provides Unique Insight into the Functional Dynamics and Interactions of Intrinsically Disordered Proteins

3. Single-Molecule Studies of Intrinsically Disordered Proteins

4. Proteins with weakly funneled energy landscapes challenge the classical structure-function paradigm

5. Exploring Energy Landscapes of Intrinsically Disordered Proteins: Insights into Functional Mechanisms

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1. Design of intrinsically disordered protein variants with diverse structural properties;Science Advances;2024-08-30

2. Accelerated Missense Mutation Identification in Intrinsically Disordered Proteins using Deep Learning;2024-07-10

3. Conformational ensembles of the human intrinsically disordered proteome;Nature;2024-01-31

4. Machine-learning-based methods to generate conformational ensembles of disordered proteins;Biophysical Journal;2024-01