Cysteine separations profiles on protein sequences infer disulfide connectivity
Cysteine separations profiles on protein sequences infer disulfide connectivity
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DOI：
10.1093/bioinformatics/bti179
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发表时间：
2005-04-15
期刊：
影响因子：
5.8
通讯作者：
Kao, CY
中科院分区：
文献类型：
--
作者：
Zhao, E;Liu, HL;Kao, CY
动机：二硫键在蛋白质折叠中起着重要作用。二硫键连接性的精确预测可以大大减少构象搜索空间，提高蛋白质结构预测的准确性。传统的二硫键连接预测使用序列信息，预测精度有限。在这里，通过使用一个替代方案与全球信息二硫键连接预测，更高的性能相对于其他approachs.Result：半胱氨酸分离配置文件已被用来预测蛋白质的二硫键连接。半胱氨酸分离谱（CSPs）是将蛋白质序列上氧化半胱氨酸残基之间的分离进行编码的载体。通过比较它们的CSP，从非冗余模板组推断测试蛋白的二硫键连接性。对于SwissProt 39（SP39）中共享小于30%序列同一性的非冗余蛋白质，四重交叉验证的预测准确度为49%。SwissProt 43（SP43）中蛋白质二硫键连接的预测准确度甚至更高（53%）。还讨论了关键支持点相似度与预测精度之间的关系。在这项工作中提出的方法是相对简单的，可以产生更高的精度相比，传统的方法。它也可以与其他算法相结合，以进一步改善蛋白质结构预测。
Motivation: Disulfide bonds play an important role in protein folding. A precise prediction of disulfide connectivity can strongly reduce the conformational search space and increase the accuracy in protein structure prediction. Conventional disulfide connectivity predictions use sequence information, and prediction accuracy is limited. Here, by using an alternative scheme with global information for disulfide connectivity prediction, higher performance is obtained with respect to other approaches.Result: Cysteine separation profiles have been used to predict the disulfide connectivity of proteins. The separations among oxidized cysteine residues on a protein sequence have been encoded into vectors named cysteine separation profiles (CSPs). Through comparisons of their CSPs, the disulfide connectivity of a test protein is inferred from a non-redundant template set. For non-redundant proteins in SwissProt 39 (SP39) sharing less than 30% sequence identity, the prediction accuracy of a fourfold cross-validation is 49%. The prediction accuracy of disulfide connectivity for proteins in SwissProt 43 (SP43) is even higher (53%). The relationship between the similarity of CSPs and the prediction accuracy is also discussed. The method proposed in this work is relatively simple and can generate higher accuracies compared to conventional methods. It may be also combined with other algorithms for further improvements in protein structure prediction.