Adenosine triphosphate binding site predicting method for protein

A technology for adenosine triphosphate and binding sites, which is applied in the field of high-precision prediction of protein adenosine triphosphate binding sites, can solve problems such as large gaps in prediction accuracy, achieve the effects of reducing scale, solving unbalanced phenomena, and improving prediction accuracy

Inactive Publication Date: 2012-10-31
CHANGSHU RES INSTITUE OF NANJING UNIV OF SCI & TECH +2
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Problems solved by technology

[0005] However, a comprehensive analysis of the only two prediction models shows that there is still a larg

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  • Adenosine triphosphate binding site predicting method for protein

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[0052] On the database (K. Chen, M. J. Mizianty, and L. Kurgan, "ATPsite: sequence-based prediction of ATP-binding residues," Proteome Sci, vol. 9 Suppl 1, pp. S4, 2011.) using this method a predictor. The predictor is obtained by ensemble of 5 SVMs using Dempster-Shafer evidence theory.

[0053] Taking the prediction of the ATP binding site of protein 3CQD_A as an example, the prediction results are shown in Table 1.

[0054] The amino acid sequence of protein 3CQD_A is as follows:

[0055] >3CQD_A

[0056] MVRIYTLTLAPSLDSATITPQIYPEGKLRCTAPVFEPGGGGINVARAIAHLGGSATAIFPAGGATGEHLVSLLADENVPVATVEAKDWTRQNLHVHVEASGEQYRFVMPGAALNEDEFRQLEEQVLEIESGAILVISGSLPPGVKLEKLTQLISAAQKQGIRCIVDSSGEALSAALAIGNIELVKPNQKELSALVNRELTQPDDVRKAAQEIVNSGKAKRVVVSLGPQGALGVDSENCIQVVPPPVKSQSTVGAGDSMVGAMTLKLAENASLEEMVRFGVAAGSAATLNQGTRLCSHDDTQKIYAYLSR

[0057] The protein has a total of 17 ATP binding sites.

[0058] As can be seen from Table 1, using this method, 12 ATP binding sites were correctly predicted, 5...

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Abstract

The invention discloses an adenosine triphosphate binding site predicting method for protein. The adenosine triphosphate binding site predicting method for the protein comprises the following steps: firstly, acquiring evolution information and secondary structural information of the protein by using an IPSI-BLAST and PSIPRED program, and extracting characteristics of each amino acid residue by a sliding window technology; secondly, performing random downsampling on non-binding site samples for multiple times by a random downsampling technology, sample of several; thirdly, training an SVM (support vector machine) based on a non-binding site sample subset obtained in each random downsampling and a binding site sample set, and performing random downsampling on all the sample sets to obtain a plurality of SVMs; and finally, integrating the trained SVMs through Dempster-Shafer theoretic evidence. The adenosine triphosphate binding site predicting method for the protein has the advantages as follows: by a random downsampling technology, the scale of a training set can be effectively reduced and the model training speed can be effectively increased; and by an SVM integrating technology, information loss caused by downsampling can be effectively reduced and the model predicting precision can be effectively improved.

Description

technical field [0001] The present invention relates to a protein adenosine triphosphate binding site prediction method, in particular to a high-precision protein adenosine triphosphate binding site prediction method based on random down sampling and support vector machine integration. Background technique [0002] Adenosine triphosphate (ATP) is an important biological macromolecule, which is of great significance to membrane transport, muscle contraction, cell movement, signal transmission, DNA replication and transcription and other life activities in organisms. ATP interacts with proteins through protein adenosine triphosphate binding sites, and provides chemical energy to proteins through hydrolysis, so that proteins can perform various biochemical functions. In addition, the protein ATP is also the target of important antibacterial and anticancer drugs. Therefore, it is of great significance to rapidly and accurately locate protein ATP binding sites in protein sequenc...

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Application Information

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IPC IPC(8): G06F19/18
Inventor 於东军吴小伟胡俊沈红斌戚湧唐振民杨静宇
Owner CHANGSHU RES INSTITUE OF NANJING UNIV OF SCI & TECH
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