Protein metal binding residue prediction based on neural networks

Chin Teng Lin; Ken Li Lin; Chih Hsien Yang; I. Fang Chung; Chuen Der Huang; Yuh Shyong Yang

doi:10.1007/978-3-540-30499-9_205

Protein metal binding residue prediction based on neural networks

Chin Teng Lin^*, Ken Li Lin, Chih Hsien Yang, I. Fang Chung, Chuen Der Huang, Yuh Shyong Yang

^*Corresponding author for this work

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

1 Scopus citations

Abstract

It is known that over one-third of protein structures contain metal ions, and they are the necessary elements in life system. Traditionally, structural biologists used to investigate properties of metalloproteins (proteins which bind with metal ions) by physical means and interpret the function formation and reaction mechanism of enzyme by their structures and observation from experiments in vitro. Most of proteins have primary structures (amino acid sequence information) only; however, the 3-dimension structures are not always available. In this paper, a direct analysis method is proposed to predict protein metalbinding amino acid residues only from its sequence information by neural network with sliding window-based feature extraction and biological feature encoding techniques and it can successfully detect 15 binding elements in protein, and 6 binding elements in enzyme.

Original language	English
Pages (from-to)	1316-1321
Number of pages	6
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3316
DOIs	https://doi.org/10.1007/978-3-540-30499-9_205
State	Published - 1 Dec 2004

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title = "Protein metal binding residue prediction based on neural networks",

abstract = "It is known that over one-third of protein structures contain metal ions, and they are the necessary elements in life system. Traditionally, structural biologists used to investigate properties of metalloproteins (proteins which bind with metal ions) by physical means and interpret the function formation and reaction mechanism of enzyme by their structures and observation from experiments in vitro. Most of proteins have primary structures (amino acid sequence information) only; however, the 3-dimension structures are not always available. In this paper, a direct analysis method is proposed to predict protein metalbinding amino acid residues only from its sequence information by neural network with sliding window-based feature extraction and biological feature encoding techniques and it can successfully detect 15 binding elements in protein, and 6 binding elements in enzyme.",

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AU - Lin, Chin Teng

AU - Lin, Ken Li

AU - Yang, Chih Hsien

AU - Chung, I. Fang

AU - Huang, Chuen Der

AU - Yang, Yuh Shyong

PY - 2004/12/1

Y1 - 2004/12/1

N2 - It is known that over one-third of protein structures contain metal ions, and they are the necessary elements in life system. Traditionally, structural biologists used to investigate properties of metalloproteins (proteins which bind with metal ions) by physical means and interpret the function formation and reaction mechanism of enzyme by their structures and observation from experiments in vitro. Most of proteins have primary structures (amino acid sequence information) only; however, the 3-dimension structures are not always available. In this paper, a direct analysis method is proposed to predict protein metalbinding amino acid residues only from its sequence information by neural network with sliding window-based feature extraction and biological feature encoding techniques and it can successfully detect 15 binding elements in protein, and 6 binding elements in enzyme.

AB - It is known that over one-third of protein structures contain metal ions, and they are the necessary elements in life system. Traditionally, structural biologists used to investigate properties of metalloproteins (proteins which bind with metal ions) by physical means and interpret the function formation and reaction mechanism of enzyme by their structures and observation from experiments in vitro. Most of proteins have primary structures (amino acid sequence information) only; however, the 3-dimension structures are not always available. In this paper, a direct analysis method is proposed to predict protein metalbinding amino acid residues only from its sequence information by neural network with sliding window-based feature extraction and biological feature encoding techniques and it can successfully detect 15 binding elements in protein, and 6 binding elements in enzyme.

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SN - 0302-9743

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Protein metal binding residue prediction based on neural networks

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