| Title: |
Sequence determinants of protein phase separation and recognition by protein phase-separated condensates through molecular dynamics and active learning |
| Authors: |
Changiarath, Arya; Arya, Aayush; Xenidis, Vasileios A.; Padeken, Jan; Stelzl, Lukas S. |
| Source: |
Faraday discussions. Version of Record (VoR). -. 2024. -. -. - |
| Publisher Information: |
Johannes Gutenberg-Universität Mainz |
| Publication Year: |
2024 |
| Collection: |
Gutenberg Open (Johannes Gutenberg Universität Mainz - JGU) |
| Subject Terms: |
ddc:530; ddc:570 |
| Description: |
Elucidating how protein sequence determines the properties of disordered proteins and their phase-separated condensates is a great challenge in computational chemistry, biology, and biophysics. Quantitative molecular dynamics simulations and derived free energy values can in principle capture how a sequence encodes the chemical and biological properties of a protein. These calculations are, however, computationally demanding, even after reducing the representation by coarse-graining; exploring the large spaces of potentially relevant sequences remains a formidable task. We employ an “active learning” scheme introduced by Yang et al. (bioRxiv, 2022, https://doi.org/10.1101/2022.08.05.502972) to reduce the number of labelled examples needed from simulations, where a neural network-based model suggests the most useful examples for the next training cycle. Applying this Bayesian optimisation framework, we determine properties of protein sequences with coarse-grained molecular dynamics, which enables the network t |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.25358/openscience-11209 |
| Availability: |
https://openscience.ub.uni-mainz.de/handle/20.500.12030/11230; https://hdl.handle.net/20.500.12030/11230; https://doi.org/10.25358/openscience-11209 |
| Rights: |
CC-BY-4.0 ; https://creativecommons.org/licenses/by/4.0/ ; openAccess |
| Accession Number: |
edsbas.32B0F590 |
| Database: |
BASE |