Kim Hyunsoo, Park Haesun
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Protein Eng. 2003 Aug;16(8):553-60. doi: 10.1093/protein/gzg072.
The prediction of protein secondary structure is an important step in the prediction of protein tertiary structure. A new protein secondary structure prediction method, SVMpsi, was developed to improve the current level of prediction by incorporating new tertiary classifiers and their jury decision system, and the PSI-BLAST PSSM profiles. Additionally, efficient methods to handle unbalanced data and a new optimization strategy for maximizing the Q(3) measure were developed. The SVMpsi produces the highest published Q(3) and SOV94 scores on both the RS126 and CB513 data sets to date. For a new KP480 set, the prediction accuracy of SVMpsi was Q(3) = 78.5% and SOV94 = 82.8%. Moreover, the blind test results for 136 non-redundant protein sequences which do not contain homologues of training data sets were Q(3) = 77.2% and SOV94 = 81.8%. The SVMpsi results in CASP5 illustrate that it is another competitive method to predict protein secondary structure.
蛋白质二级结构预测是蛋白质三级结构预测中的重要一步。一种新的蛋白质二级结构预测方法SVMpsi被开发出来，通过纳入新的三级分类器及其评判决策系统以及PSI-BLAST PSSM图谱来提高当前的预测水平。此外，还开发了处理不平衡数据的有效方法以及用于最大化Q(3)度量的新优化策略。到目前为止，SVMpsi在RS126和CB513数据集上产生了已发表的最高Q(3)和SOV94分数。对于新的KP480数据集，SVMpsi的预测准确率为Q(3)=78.5%，SOV94=82.8%。此外，对136个不包含训练数据集同源物的非冗余蛋白质序列的盲测结果为Q(3)=77.2%，SOV94=81.8%。SVMpsi在CASP5中的结果表明它是预测蛋白质二级结构的另一种有竞争力的方法。
