Wan Hao, Liu Qing, Ju Ying
Institute of Advanced Cross-field Science, College of Life Science, Qingdao University, Qingdao, China.
Department of Anesthesiology, Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou, China.
Comput Biol Med. 2023 Jan;152:106380. doi: 10.1016/j.compbiomed.2022.106380. Epub 2022 Nov 30.
Neurotoxins are a class of proteins that have a significant damaging effect on nerve tissue. Neurotoxins are classified into presynaptic neurotoxins and postsynaptic neurotoxins, and accurate identification of neurotoxins plays a key role in drug development. In this study, 90 presynaptic neurotoxins and 165 postsynaptic neurotoxins were classified. The features of the presynaptic and postsynaptic neurotoxin sequences were extracted using the AutoProp feature extraction method and feature selection was performed using the maximum relevance maximum distance (MRMD) program, Finally, only two features were retained to achieve 84.7% classification accuracy. Moreover, it was found that the two retained features were present in the conserved sites and motifs of presynaptic neurotoxins and could represent the critical structures of presynaptic neurotoxins. This method demonstrates that using a few key features to classify proteins can effectively identify critical protein structures.
神经毒素是一类对神经组织具有显著破坏作用的蛋白质。神经毒素可分为突触前神经毒素和突触后神经毒素,准确识别神经毒素在药物开发中起着关键作用。在本研究中,对90种突触前神经毒素和165种突触后神经毒素进行了分类。使用自动属性特征提取方法提取突触前和突触后神经毒素序列的特征,并使用最大相关性最大距离(MRMD)程序进行特征选择,最后仅保留两个特征,实现了84.7%的分类准确率。此外,发现保留的这两个特征存在于突触前神经毒素的保守位点和基序中,并且可以代表突触前神经毒素的关键结构。该方法表明,使用一些关键特征对蛋白质进行分类可以有效地识别关键的蛋白质结构。