A new multiple classifier system for the prediction of protein's contacts map

Santiesteban-Toca, Cosme E.; Aguilar–Ruiz, Jesús S.

doi:10.1016/j.ipl.2015.06.008

Cited by 3 publications

(2 citation statements)

References 26 publications

(36 reference statements)

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“…These methods have been successful because of their ability to learn, given a labeled data set, the underlying relationships present in sequence-based features; and they have been especially effective in predicting proteins with few homologues. Machine learning methods include support vector machines (SVM) [24,21,25] and deep artificial neural network (DNN) [26,27] in various forms. For example, in ResPRE [8], DNCON2 [28], MapPred [7], DeepCov [29], RaptorX-Contact, [30], DMP [31], SPOT-Contact [3], among others [32,21].…”

Section: Current Approaches For Protein Contact Predictionmentioning

confidence: 99%

Performance Analysis of Deep Learning Methods for Protein Contact Prediction in CASP13

Valdez¹,

Roig²,

Pinto-Roa³

et al. 2021

CLEIej

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Protein structure prediction is one of the most important problems in Computational Biology; and consists of determining the 3D structure of a protein given its amino acid sequence. A key component that has allowed considerable improvements in recent decades is the prediction of contacts in a protein, since it provides fundamental information about its three-dimensional structure. In the 13th edition of the CASP (Critical Assessment of protein Structure Prediction), a notable progress has been evidenced for both problems with the use of deep learning algorithms. For the contact prediction category, the best methods in CASP13 achieved an average precision of 70%. In the present work, the performance of these methods is analyzed using a larger data set, with 483 proteins from four families according to the structural classification of the SCOP database (Structural Classification of Proteins). The selected methods were evaluated using the CASP metrics, and their results indicate an average contact prediction precision greater than 90%. SPOT-Contact was the method with the best overall performance, and one of the methods with the best performance for each SCOP class. The set of proteins used for the experiments and the implementations made for the analysis are publicly available.

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Section: Current Approaches For Protein Contact Predictionmentioning

confidence: 99%

Performance Analysis of Deep Learning Methods for Protein Contact Prediction in CASP13

Valdez¹,

Roig²,

Pinto-Roa³

et al. 2021

CLEIej

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“…TABLA En la Tabla 1 se muestra una base de datos con diferentes distribuciones de itemsets (clases estructurales de proteínas): hélices (Alpha), hélices+láminas (Alpha+Beta), hélices/láminas (Alpha/Beta), láminas (Beta) [53]. Adicionalmente, fue agregada una base de datos más, "Hepatitis C" [54], con el objetivo de realizar la evaluación externa del algoritmo en un caso real.…”

Section: A Selección De Los Datosunclassified

Minería de Patrones Secuenciales aplicada a la Predicción del Plegamiento de Proteínas

Quintana-Zaez¹,

Calderón-Ruiz²,

Santisteban-Toca³

et al. 2019

Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation

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Sequence mining consists of finding statistically relevant patterns in data collections represented sequentially. These, are an important type of data, where it matters the order that occupy the elements in the set and that finds a wide range of applications in Bioinformatics and Computational Biology. The prediction of protein structures is one of these applications. Where, a protein is no more than a sequence of amino acids forming patterns known as alpha helices, beta sheets and turns. For purposes of our investigation, these collections or secondary structures would be the itemsets, while the amino acids that make up the entire sequence, the items. Despite multiple attempts to predict protein folding, the algorithms developed to date only reach a 35% effectiveness. That is why we propose SPMCcm, an algorithm based on the prediction of frequent sequences and a scheme of classifiers. Which uses the information provided by the amino acid sequence, in two stages. Where, the first stage learns of the interactions between the secondary structures of the proteins, which it extracts as frequent sequences or itemsets. Meanwhile, the second stage learns of the interaction between the amino acids present in the interacting structures or items.The experimental evaluation showed that SPMCcm behaves in a similar way, independently of the base classifier used, reaching accuracies in the prediction of up to 48%, higher than the 35% reported by the literature, without using large computational resources and possessing explanatory capacity.

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Protein contact maps: A binary depiction of protein 3D structures

Emerson

Amala

2017

Physica A: Statistical Mechanics and its Applications

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A new multiple classifier system for the prediction of protein's contacts map

Cited by 3 publications

References 26 publications

Performance Analysis of Deep Learning Methods for Protein Contact Prediction in CASP13

Performance Analysis of Deep Learning Methods for Protein Contact Prediction in CASP13

Minería de Patrones Secuenciales aplicada a la Predicción del Plegamiento de Proteínas

Protein contact maps: A binary depiction of protein 3D structures

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