Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore 54770, Pakistan.
Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore 54770, Pakistan.
J Theor Biol. 2019 Feb 21;463:47-55. doi: 10.1016/j.jtbi.2018.12.015. Epub 2018 Dec 12.
The structure of protein gains additional stability against various detrimental effects by the presence of disulfide bonds. The formation of correct disulfide bonds between cysteine residues ensures proper in vivo and in vitro folding of the protein. Many cysteine residues can be present in the polypeptide chain of a protein, however, not all cysteine residues are involved in the formation of a disulfide bond, and therefore, accurate prediction of these bonds is crucial for identifying biophysical characteristics of a protein. In the present study, a novel method is proposed for the prediction of intramolecular disulfide bonds accurately using statistical moments and PseAAC. The pSSbond-PseAAC uses PseAAC along with position and composition relative features to calculate statistical moments. Statistical moments are important as they are very sensitive regarding the position of data sequences and for prediction of intramolecular disulfide bonds, moments are combined together to train neural networks. The overall accuracy of the pSSbond-PseAAC is 98.97% to sensitivity value 98.92%, specificity 98.99% and 0.98 MCC; and it outperforms various previously reported studies.
蛋白质的结构通过二硫键的存在获得了额外的稳定性，以抵抗各种不利影响。半胱氨酸残基之间形成正确的二硫键可以确保蛋白质在体内和体外的正确折叠。许多半胱氨酸残基可以存在于蛋白质的多肽链中，但是并非所有的半胱氨酸残基都参与二硫键的形成，因此，准确预测这些键对于识别蛋白质的生物物理特性至关重要。在本研究中，提出了一种使用统计矩和 PseAAC 准确预测分子内二硫键的新方法。pSSbond-PseAAC 使用 PseAAC 以及位置和组成相对特征来计算统计矩。统计矩很重要，因为它们对于数据序列的位置非常敏感，并且对于预测分子内二硫键，矩会组合在一起以训练神经网络。pSSbond-PseAAC 的总体准确性为 98.97%，灵敏度值为 98.92%，特异性为 98.99%和 0.98 MCC；并且优于各种先前报道的研究。
