Predicting protein tertiary structure and its uncertainty analysis via particle swarm sampling

Álvarez, Óscar; Fernández-Martínez, Juan Luis; Corbeanu, Ana Cernea; Fernández-Muñiz, Zulima; Kloczkowski, Andrzej

doi:10.1007/s00894-019-3956-0

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…In general, the algorithm uses LDA in combination with SVD as mathematical techniques to perform model reduction in a template-based modelling general methodology. The main idea is to obtain a different perspective with respect to other similar methods such as Alvarez-Machancoses et al [43], which uses PCA in combination with PSO, or Baker et al, which uses PCA and a simplex and Powell method optimization [68].…”

Section: Discussionmentioning

confidence: 99%

“…In this research paper, we propose the utilization of a regularized linear discriminant analysis in order to classify a set of protein templates based on its dipolar Distance-scale Finite Ideal Gas Reference Equation (dDFIRE) energy score in combination with a particle swarm optimizer (PSO). PSO has been successfully utilized in the prediction of both secondary and tertiary protein structures, and it is a good alternative to reconstruct the protein model and sample the full conformational space of the protein family at the same time [9,42,43]. After this, an additional refinement step is performed utilizing a simple and fast SVD model reduction with a further PSO optimization.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques

2020

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques

2020

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“…BioShell was used for crmsd calculations and hierarchical clustering of hormone conformations. In two other works, Alvarez et al explored the applicability of novel methods for protein structure prediction [11,12]. They implemented their methodology as BioShell scripts and concluded that "BioShell combined with the methodology presented in this paper, is crucial in order to predict protein structures while avoiding structural clashes".…”

Section: Introductionmentioning

confidence: 99%

BioShell 3.0: Library for Processing Structural Biology Data

et al. 2020

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BioShell is an open-source package for processing biological data, particularly focused on structural applications. The package provides parsers, data structures and algorithms for handling and analyzing macromolecular sequences, structures and sequence profiles. The most frequently used routines are accessible by a set of easy-to-use command line utilities for a Linux environment. The full functionality of the package assumes knowledge of C++ or Python to assemble an application using this software library. Since the last publication that announced the version 2.0, the package has been greatly expanded and rewritten in C++ standard 11 (C++11) to improve its modularity and efficiency. A new testing platform has been implemented to continuously test the correctness and integrity of the package. More than two hundred test programs have been published to provide simple examples that can be used as templates. This makes BioShell an easy to use library that greatly speeds up development of bioinformatics applications and web services without compromising computational efficiency.

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