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Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques

We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in protein tertiary structure prediction, followed by structure refinement based on singular value decomposition (SVD) and PSO. The algorithm presen...

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Autores principales: Álvarez-Machancoses, Óscar, Fernández-Martínez, Juan Luis, Kloczkowski, Andrzej
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7321371/
https://www.ncbi.nlm.nih.gov/pubmed/32466409
http://dx.doi.org/10.3390/molecules25112467
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author Álvarez-Machancoses, Óscar
Fernández-Martínez, Juan Luis
Kloczkowski, Andrzej
author_facet Álvarez-Machancoses, Óscar
Fernández-Martínez, Juan Luis
Kloczkowski, Andrzej
author_sort Álvarez-Machancoses, Óscar
collection PubMed
description We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in protein tertiary structure prediction, followed by structure refinement based on singular value decomposition (SVD) and PSO. The algorithm presented in this paper corresponds to the category of template-based modeling. The algorithm performs a preselection of protein templates before constructing a lower dimensional subspace via a regularized LDA. The protein coordinates in the reduced spaced are sampled using a highly explorative optimization algorithm, regressive–regressive PSO (RR-PSO). The obtained structure is then projected onto a reduced space via singular value decomposition and further optimized via RR-PSO to carry out a structure refinement. The final structures are similar to those predicted by best structure prediction tools, such as Rossetta and Zhang servers. The main advantage of our methodology is that alleviates the ill-posed character of protein structure prediction problems related to high dimensional optimization. It is also capable of sampling a wide range of conformational space due to the application of a regularized linear discriminant analysis, which allows us to expand the differences over a reduced basis set.
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spelling pubmed-73213712020-06-29 Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques Álvarez-Machancoses, Óscar Fernández-Martínez, Juan Luis Kloczkowski, Andrzej Molecules Article We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in protein tertiary structure prediction, followed by structure refinement based on singular value decomposition (SVD) and PSO. The algorithm presented in this paper corresponds to the category of template-based modeling. The algorithm performs a preselection of protein templates before constructing a lower dimensional subspace via a regularized LDA. The protein coordinates in the reduced spaced are sampled using a highly explorative optimization algorithm, regressive–regressive PSO (RR-PSO). The obtained structure is then projected onto a reduced space via singular value decomposition and further optimized via RR-PSO to carry out a structure refinement. The final structures are similar to those predicted by best structure prediction tools, such as Rossetta and Zhang servers. The main advantage of our methodology is that alleviates the ill-posed character of protein structure prediction problems related to high dimensional optimization. It is also capable of sampling a wide range of conformational space due to the application of a regularized linear discriminant analysis, which allows us to expand the differences over a reduced basis set. MDPI 2020-05-26 /pmc/articles/PMC7321371/ /pubmed/32466409 http://dx.doi.org/10.3390/molecules25112467 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Álvarez-Machancoses, Óscar
Fernández-Martínez, Juan Luis
Kloczkowski, Andrzej
Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques
title Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques
title_full Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques
title_fullStr Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques
title_full_unstemmed Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques
title_short Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques
title_sort prediction of protein tertiary structure via regularized template classification techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7321371/
https://www.ncbi.nlm.nih.gov/pubmed/32466409
http://dx.doi.org/10.3390/molecules25112467
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