Cheng Jianlin, Baldi Pierre
School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816-2362, USA.
BMC Bioinformatics. 2007 Apr 2;8:113. doi: 10.1186/1471-2105-8-113.
Predicting protein residue-residue contacts is an important 2D prediction task. It is useful for ab initio structure prediction and understanding protein folding. In spite of steady progress over the past decade, contact prediction remains still largely unsolved.
Here we develop a new contact map predictor (SVMcon) that uses support vector machines to predict medium- and long-range contacts. SVMcon integrates profiles, secondary structure, relative solvent accessibility, contact potentials, and other useful features. On the same test data set, SVMcon's accuracy is 4% higher than the latest version of the CMAPpro contact map predictor. SVMcon recently participated in the seventh edition of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7) experiment and was evaluated along with seven other contact map predictors. SVMcon was ranked as one of the top predictors, yielding the second best coverage and accuracy for contacts with sequence separation > or = 12 on 13 de novo domains.
We describe SVMcon, a new contact map predictor that uses SVMs and a large set of informative features. SVMcon yields good performance on medium- to long-range contact predictions and can be modularly incorporated into a structure prediction pipeline.
预测蛋白质残基-残基接触是一项重要的二维预测任务。它对于从头开始的结构预测和理解蛋白质折叠很有用。尽管在过去十年中取得了稳步进展，但接触预测在很大程度上仍然未得到解决。
在此，我们开发了一种新的接触图预测器（SVMcon），它使用支持向量机来预测中程和远程接触。SVMcon整合了轮廓、二级结构、相对溶剂可及性、接触势和其他有用特征。在相同的测试数据集上，SVMcon的准确率比最新版本的CMAPpro接触图预测器高4%。SVMcon最近参加了第七届蛋白质结构预测技术关键评估（CASP7）实验，并与其他七个接触图预测器一起接受评估。SVMcon被列为顶级预测器之一，在13个从头开始的结构域上，对于序列间隔大于或等于12的接触，其覆盖率和准确率排名第二。
我们描述了SVMcon，一种使用支持向量机和大量信息特征的新接触图预测器。SVMcon在中程到远程接触预测方面表现良好，并且可以模块化地纳入结构预测流程中。
