Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Evolving cellular automata schemes for protein folding modeling using the Rosetta atomic representation

Varela, Daniel LU orcid and Santos, José (2022) In Genetic Programming and Evolvable Machines 23(2). p.225-252
Abstract

Protein folding is the dynamic process by which a protein folds into its final native structure. This is different to the traditional problem of the prediction of the final protein structure, since it requires a modeling of how protein components interact over time to obtain the final folded structure. In this study we test whether a model of the folding process can be obtained exclusively through machine learning. To this end, protein folding is considered as an emergent process and the cellular automata tool is used to model the folding process. A neural cellular automaton is defined, using a connectionist model that acts as a cellular automaton through the protein chain to define the dynamic folding. Differential evolution is used to... (More)

Protein folding is the dynamic process by which a protein folds into its final native structure. This is different to the traditional problem of the prediction of the final protein structure, since it requires a modeling of how protein components interact over time to obtain the final folded structure. In this study we test whether a model of the folding process can be obtained exclusively through machine learning. To this end, protein folding is considered as an emergent process and the cellular automata tool is used to model the folding process. A neural cellular automaton is defined, using a connectionist model that acts as a cellular automaton through the protein chain to define the dynamic folding. Differential evolution is used to automatically obtain the optimized neural cellular automata that provide protein folding. We tested the methods with the Rosetta coarse-grained atomic model of protein representation, using different proteins to analyze the modeling of folding and the structure refinement that the modeling can provide, showing the potential advantages that such methods offer, but also difficulties that arise.

(Less)
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Differential evolution, Neural cellular automata, Protein folding
in
Genetic Programming and Evolvable Machines
volume
23
issue
2
pages
225 - 252
publisher
Springer
external identifiers
  • scopus:85123112710
ISSN
1389-2576
DOI
10.1007/s10710-022-09427-x
language
English
LU publication?
yes
id
71fa33e3-080f-4593-ae08-83200b7cfaf0
date added to LUP
2022-03-25 10:22:12
date last changed
2022-06-29 20:16:36
@article{71fa33e3-080f-4593-ae08-83200b7cfaf0,
  abstract     = {{<p>Protein folding is the dynamic process by which a protein folds into its final native structure. This is different to the traditional problem of the prediction of the final protein structure, since it requires a modeling of how protein components interact over time to obtain the final folded structure. In this study we test whether a model of the folding process can be obtained exclusively through machine learning. To this end, protein folding is considered as an emergent process and the cellular automata tool is used to model the folding process. A neural cellular automaton is defined, using a connectionist model that acts as a cellular automaton through the protein chain to define the dynamic folding. Differential evolution is used to automatically obtain the optimized neural cellular automata that provide protein folding. We tested the methods with the Rosetta coarse-grained atomic model of protein representation, using different proteins to analyze the modeling of folding and the structure refinement that the modeling can provide, showing the potential advantages that such methods offer, but also difficulties that arise.</p>}},
  author       = {{Varela, Daniel and Santos, José}},
  issn         = {{1389-2576}},
  keywords     = {{Differential evolution; Neural cellular automata; Protein folding}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{225--252}},
  publisher    = {{Springer}},
  series       = {{Genetic Programming and Evolvable Machines}},
  title        = {{Evolving cellular automata schemes for protein folding modeling using the Rosetta atomic representation}},
  url          = {{http://dx.doi.org/10.1007/s10710-022-09427-x}},
  doi          = {{10.1007/s10710-022-09427-x}},
  volume       = {{23}},
  year         = {{2022}},
}