Using Support Vector Machine Combined with Post-processing Procedure to Improve Prediction of Interface Residues in Transient Complexes

Rong Liu; Yanhong Zhou

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Using Support Vector Machine Combined with Post-processing Procedure to Improve Prediction of Interface Residues in Transient Complexes

机译：使用支持向量机与后处理程序相结合来改进瞬态复合物中界面残留的预测

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Reliable prediction of interface residues in transient complexes remains challenging, yet is highly desirable for the design of new drugs. The existing computational methods mainly rely on evolutionary information to identify these key residues, but evolutionary information may not be effective for the interface residues in all types of transient complexes, such as antigen-antibody complexes. Herein we combined B-factor with sequence profile and accessible surface area to predict these important residues using support vector machine (SVM). Furthermore, a postprocessing method was developed to reduce the number of false positives recognized by SVM. The prediction results show that B-factor is an effective indicator for the interface residues in antigen-antibody complexes as well as those in other types of transient complexes. In addition, we found that the post-processing procedure made an important contribution to further improve the prediction performance. Consequently, the proposed approach could provide new insight into accurately predicting interface residues in different types of transient complexes.

机译：可靠预测瞬态复合物中的界面残基仍然具有挑战性，但对于新药的设计却非常需要。现有的计算方法主要依靠进化信息来识别这些关键残基，但是进化信息可能对于所有类型的瞬时复合物（例如抗原-抗体复合物）中的界面残基均无效。本文中，我们将B因子与序列图谱和可及表面积结合起来，以使用支持向量机（SVM）预测这些重要的残基。此外，开发了一种后处理方法来减少SVM识别的误报的数量。预测结果表明，B-因子是抗原-抗体复合物中以及其他类型的瞬时复合物中的界面残基的有效指示剂。此外，我们发现后处理程序对进一步提高预测性能做出了重要贡献。因此，提出的方法可以为准确预测不同类型的瞬态复合物中的界面残基提供新的见解。

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《The protein journal》 |2009年第8期|共6页
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Rong Liu; Yanhong Zhou;
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正文语种 eng
中图分类生物化学;
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1. Using Support Vector Machine Combined with Post-processing Procedure to Improve Prediction of Interface Residues in Transient Complexes [J] . Rong Liu, Yanhong Zhou The protein journal . 2009,第7a8期

机译：使用支持向量机与后处理程序相结合来改进瞬态复合物中界面残留的预测
2. Identification of interface residues in protease-inhibitor and antigen-antibody complexes: a support vector machine approach [J] . Changhui Yan, Vasant Honavar, Drena Dobbs Neural computing & applications . 2004,第2期

机译：蛋白酶抑制剂和抗原抗体复合物中界面残基的鉴定：一种支持向量机方法
3. Prediction of protein-protein binding site by using core interface residue and support vector machine [J] . Nan Li, Zhonghua Sun, Fan Jiang BMC Bioinformatics . 2008,第1期

机译：用芯界面残留物预测蛋白质 - 蛋白质结合位点，支持矢量机
4. Amino Acid Features for Prediction of Protein-Protein Interface Residues with Support Vector Machines [C] . Minh N. Nguyen, Jagath C. Rajapakse, Kai-Bo Duan Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; Lecture Notes in Computer Science; 4447 . 2007

机译：支持向量机的蛋白质-蛋白质界面残基预测氨基酸特征
5. Combining biomarkers and clinicopathologic factors for prediction of response to adjuvant chemotherapy for breast cancer: Cox model and Support Vector Machine (SVM) methods. [D] . Liu, Xudong. 2010

机译：结合生物标志物和临床病理因素预测乳腺癌对辅助化疗的反应：Cox模型和支持向量机（SVM）方法。
6. Predictions of Hot Spot Residues at Protein-Protein Interfaces Using Support Vector Machines [O] . Stefano Lise, Daniel Buchan, Massimiliano Pontil, 2009

机译：使用支持向量机预测蛋白质-蛋白质界面上的热点残留
7. Predictions of Hot Spot Residues at Protein-Protein Interfaces Using Support Vector Machines [O] . Lise, Stefano, Buchan, Daniel, Pontil, Massimiliano, 2011

机译：使用支持向量机预测蛋白质-蛋白质界面上的热点残留

Using Support Vector Machine Combined with Post-processing Procedure to Improve Prediction of Interface Residues in Transient Complexes

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