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Natural Computing

11.08.2022

A self-adaptive evolutionary algorithm using Monte Carlo Fragment insertion and conformation clustering for the protein structure prediction problem

verfasst von: Rafael Stubs Parpinelli, Nilcimar Neitzel Will, Renan Samuel da Silva

Erschienen in: Natural Computing

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Abstract

The Protein Structure Prediction Problem is one of the most important and challenging open problems in Computer Science and Structural Bioinformatics. Accurately predicting protein conformations would significantly impact several fields, such as understanding proteinopathies and developing smart protein-based drugs. As such, this work has as its primary goal to improve the prediction power of ab initio methods by utilizing a self-adaptive evolutionary algorithm using Monte Carlo based fragment insertion and conformational clustering. A meta-heuristic is used as the core of the conformation sampling process with fragment insertion, feeding domain-specific information into the process. The online parameter control routines allow the method to adapt to a protein’s structure specificity and behave dynamically in different stages of the optimization process. The results obtained by the proposed method were compared to results obtained from several other algorithms found in the literature. It is possible to conclude that the proposed method is highly competitive in terms of free-energy and RMSD for the protein set used in the experiments.

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Fußnoten
1
 
3
Only physical cores were considered. No virtual (Hyper-threading) core was involved in the computations.
 
6
For the readers with a black and white copy, the predicted conformation is in a light shade of grey, while the native conformation is in dark grey.
 
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Metadaten
Titel
A self-adaptive evolutionary algorithm using Monte Carlo Fragment insertion and conformation clustering for the protein structure prediction problem
verfasst von
Rafael Stubs Parpinelli
Nilcimar Neitzel Will
Renan Samuel da Silva
Publikationsdatum
11.08.2022
Verlag
Springer Netherlands
Erschienen in
Natural Computing
Print ISSN: 1567-7818
Elektronische ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-022-09916-z

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