• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.595-602
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• 2007

This paper proposes a method that discriminates signal peptide and predicts the cleavage site of the secretory proteins cleaved by the signal peptidase I. The preprocessing stage uses hydrophobicity scales of amino acids in order to predict the presence of signal sequence and the cleavage site. The preprocessing enhances the performance of the prediction method by eliminating the non-secretory proteins in the early stage of prediction. for the effective use of support vector machine for the signal sequence prediction, the biologically relevant distance between the amino acid sequences is defined by using the hydrophobicity and substitution matrix; the hydrophobicity can be used to Predict the location of amino acid in a cell and the substitution matrix represents the evolutionary relationships of amino acids. The proposed method showed 98.9% discrimination rates from signal sequences and 88% correct rate of the cleavage site prediction on Swiss-Prot release 50 protein database using the 5-fold-cross-validation. In the comparison tests, the proposed method has performed significantly better than other prediction methods.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.247-249
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• 2005

Cleavage Site 분석 및 예측은 바이러스 증식에 필요한 핵심 단백질인 Protease$(3CL^{pro})$를 예측하게 하고, 예측한 Protease의 활성을 억제함으로써 바이러스 중식을 저지하게 된다. 본 연구에서는 신경망과 결정트리, 유전자 알고리즘을 이용하여 SARS-CoV의 cleavage site를 분석하고, 학습 결과에서 추출된 규칙(Rule)에 의해 cleavage site를 예측한다. 또한 신경망에서 학습된 지식(Knowledge)을 이용하여 유전자 알고리즘의 성능을 향상시키는 지식기반 유전자 알고리즘 (KBGA: Knowledge-Based Genetic Algorithm)을 제안한다.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.786-792
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• 2009

A kernel in support vector machines can be described as a similarity measure between data, and this measure is used to find an optimal hyperplane that classifies patterns. It is therefore important to effectively incorporate the characteristics of data into the similarity measure. To find an optimal similarity between amino acid sequences, we propose a real exponent exponential form of the two metrices, which are derived from the evolutionary relationships of amino acids and the hydrophobicity of amino acids. We prove that the proposed metric satisfies the conditions to be a metric, and we find a relation between the proposed metric and the metrics in the string kernels which are widely used for the processing of amino acid sequences and DNA sequences. In the prediction experiments on the cleavage site of the signal peptide, the optimal metric can be found in the proposed metrics.

• YAKHAK HOEJI
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• v.53 no.4
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• pp.222-227
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• 2009

Human topoisomerase I (Topo I) plays a pivotal role in cell replication, transcription and repair and, therefore, is an important anti-cancer target. 20S-camptothecin (CPT) is a representative Topo I inhibitor. Compounds belonging to CPT family inhibit the religation step of Topo I-DNA by binding to the DNA cleavage site. Computational docking studies with Surflex-Dock were carried out to investigate the binding modes between Topo I-DNA binary complex structure and the ligand such as 20S-CPT and 9,10-substituted 20S-CPT analogues. The docking results demonstrated that most of the compounds with $IC_{50}$ value under $0.5{\mu}M$ intercalated exactly between the -1 and +1 DNA bases, deeply toward the cleavage site. The complex was stabilized by hydrogen-bonding and hydrophobic interactions with both the enzyme and the DNA. The compounds with $IC_{50}$ value above $0.5{\mu}M$ were poorly docked and did not intercalate. In addition, the docking results confirmed the overall correlation between the $IC_{50}$ values and docking scores, indicating the possible use of the modeling for the prediction of biological activity and design of potential inhibitors.