DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning.
DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning.
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DOI：
10.1186/1471-2105-12-43
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发表时间：
2011-02-01
影响因子：
3
通讯作者：
Cheng J
中科院分区：
文献类型：
--
作者：
Eickholt J;Deng X;Cheng J
准确识别蛋白质结构域边界有助于蛋白质结构的确定和预测。然而，从序列中预测蛋白质结构域边界仍然非常具有挑战性，并且在很大程度上没有得到解决。我们开发了一种新的方法，将机器学习的分类能力与嵌入在蛋白质家族中的进化信号相结合，以提高蛋白质结构域边界的预测。该方法首先从查询序列与其同源序列之间的多序列比对中提取假定的域边界信号。然后由支持向量机结合序列剖面、二级结构、位点周围溶剂可及性及其位置等输入特征对假定位点进行分类和评分。在10倍交叉验证的领域基准上对该方法进行了评估，以60%的精度召回了60%的真实领域边界。通过在支持向量机分配的域边界分数上使用不同的决策阈值，可以根据具体需要调整精度和召回率之间的权衡。该方法具有良好的预测精度和在不同精度和召回率下选择结构域边界位置的灵活性，使其成为蛋白质结构确定和建模的有用工具。该方法可在http://sysbio.rnet.missouri.edu/dobo/上获得。
Accurate identification of protein domain boundaries is useful for protein structure determination and prediction. However, predicting protein domain boundaries from a sequence is still very challenging and largely unsolved. We developed a new method to integrate the classification power of machine learning with evolutionary signals embedded in protein families in order to improve protein domain boundary prediction. The method first extracts putative domain boundary signals from a multiple sequence alignment between a query sequence and its homologs. The putative sites are then classified and scored by support vector machines in conjunction with input features such as sequence profiles, secondary structures, solvent accessibilities around the sites and their positions. The method was evaluated on a domain benchmark by 10-fold cross-validation and 60% of true domain boundaries can be recalled at a precision of 60%. The trade-off between the precision and recall can be adjusted according to specific needs by using different decision thresholds on the domain boundary scores assigned by the support vector machines. The good prediction accuracy and the flexibility of selecting domain boundary sites at different precision and recall values make our method a useful tool for protein structure determination and modelling. The method is available at http://sysbio.rnet.missouri.edu/dobo/.
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影响因子：
14.9
作者：
Cheng, J;Randall, AZ;Sweredoski, MJ;Baldi, P
通讯作者：
Baldi, P
影响因子：
14.9
作者：
Holm, L;Sander, C
通讯作者：
Sander, C
影响因子：
2.9
作者：
Kim, DE;Chivian, D;Baker, D
通讯作者：
Baker, D
DOI：
10.1093/protein/15.11.871
发表时间：
2002-11-01
期刊：
PROTEIN ENGINEERING
影响因子：
--
作者：
George, RA;Heringa, J
通讯作者：
Heringa, J
影响因子：
3.5
作者：
BORK, P
通讯作者：
BORK, P