Predicting contact map using Radial Basis Function Neural Network with Conformational Energy Function

Peng Chen; De Shuang Huang; Xing Ming Zhao; Xueling Li

doi:10.1504/IJBRA.2008.018340

Predicting contact map using Radial Basis Function Neural Network with Conformational Energy Function

Peng Chen, De Shuang Huang, Xing Ming Zhao, Xueling Li

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

Contact map, which is important to understand and reconstruct protein's three-dimensional (3D) structure, may be helpful to solve the protein's 3D structure. This paper presents a novel approach to predict the contact map using Radial Basis Function Neural Network (RBFNN) optimised by Conformational Energy Function (CEF) based on chemico-physical knowledge of amino acids. Finally, the results are trimmed by Short-Range Contact Function (SRCF). Consequently, it can be found that our proposed method is better than the existing methods such as PROFcon and the PE-based method. Particularly, this method can accurately predict 35% of contacts at a distance cutoff of 8 Å.

Original language	English (US)
Pages (from-to)	123-136
Number of pages	14
Journal	International Journal of Bioinformatics Research and Applications
Volume	4
Issue number	2
DOIs	https://doi.org/10.1504/IJBRA.2008.018340
State	Published - May 2008
Externally published	Yes

Keywords

Bioinformatics
CEF
Conformational energy function
Contact map
PCA
Principal component analysis
RBFNN
Radial basis function neural network
SRCF
Short-range contact function

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Clinical Biochemistry
Health Information Management

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10.1504/IJBRA.2008.018340

Cite this

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title = "Predicting contact map using Radial Basis Function Neural Network with Conformational Energy Function",

abstract = "Contact map, which is important to understand and reconstruct protein's three-dimensional (3D) structure, may be helpful to solve the protein's 3D structure. This paper presents a novel approach to predict the contact map using Radial Basis Function Neural Network (RBFNN) optimised by Conformational Energy Function (CEF) based on chemico-physical knowledge of amino acids. Finally, the results are trimmed by Short-Range Contact Function (SRCF). Consequently, it can be found that our proposed method is better than the existing methods such as PROFcon and the PE-based method. Particularly, this method can accurately predict 35% of contacts at a distance cutoff of 8 {\AA}.",

keywords = "Bioinformatics, CEF, Conformational energy function, Contact map, PCA, Principal component analysis, RBFNN, Radial basis function neural network, SRCF, Short-range contact function",

author = "Peng Chen and Huang, {De Shuang} and Zhao, {Xing Ming} and Xueling Li",

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T1 - Predicting contact map using Radial Basis Function Neural Network with Conformational Energy Function

AU - Chen, Peng

AU - Huang, De Shuang

AU - Zhao, Xing Ming

AU - Li, Xueling

PY - 2008/5

Y1 - 2008/5

N2 - Contact map, which is important to understand and reconstruct protein's three-dimensional (3D) structure, may be helpful to solve the protein's 3D structure. This paper presents a novel approach to predict the contact map using Radial Basis Function Neural Network (RBFNN) optimised by Conformational Energy Function (CEF) based on chemico-physical knowledge of amino acids. Finally, the results are trimmed by Short-Range Contact Function (SRCF). Consequently, it can be found that our proposed method is better than the existing methods such as PROFcon and the PE-based method. Particularly, this method can accurately predict 35% of contacts at a distance cutoff of 8 Å.

AB - Contact map, which is important to understand and reconstruct protein's three-dimensional (3D) structure, may be helpful to solve the protein's 3D structure. This paper presents a novel approach to predict the contact map using Radial Basis Function Neural Network (RBFNN) optimised by Conformational Energy Function (CEF) based on chemico-physical knowledge of amino acids. Finally, the results are trimmed by Short-Range Contact Function (SRCF). Consequently, it can be found that our proposed method is better than the existing methods such as PROFcon and the PE-based method. Particularly, this method can accurately predict 35% of contacts at a distance cutoff of 8 Å.

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KW - CEF

KW - Conformational energy function

KW - Contact map

KW - PCA

KW - Principal component analysis

KW - RBFNN

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KW - Short-range contact function

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Predicting contact map using Radial Basis Function Neural Network with Conformational Energy Function

Abstract

Keywords

ASJC Scopus subject areas

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