School of Information Science and Technology, Northeast Normal University, Changchun 130117, China.
Institute of Computational Biology, Northeast Normal University, Changchun 130117, China.
Biomed Res Int. 2020 Mar 25;2020:4569037. doi: 10.1155/2020/4569037. eCollection 2020.
As an important category of proteins, alpha-helix transmembrane proteins (TMPs) play an important role in various biological activities. Because the solved αTMP structures are inadequate, predicting the residue contacts among the transmembrane segments of an TMP exhibits the basis of protein fold, which can be used to further discover more protein functions. A few efforts have been devoted to predict the interhelical residue contact using machine learning methods based on the prior knowledge of transmembrane protein structure. However, it is still a challenge to improve the prediction accuracy, while the deep learning method provides an opportunity to utilize the structural knowledge in a different insight. For this purpose, we proposed a novel TMP residue-residue contact prediction method IMPContact, in which a convolutional neural network (CNN) was applied to recognize those interhelical contacts in a TMP using its specific structural features. There were four sequence-based TMP-specific features selected to descript a pair of residues, namely, evolutionary covariation, predicted topology structure, residue relative position, and evolutionary conservation. An up-to-date dataset was used to train and test the IMPContact; our method achieved better performance compared to peer methods. In the case studies, IHRCs in the regular transmembrane helixes were better predicted than in the irregular ones.
作为蛋白质的重要类别之一，α-螺旋跨膜蛋白（TMP）在各种生物活性中发挥着重要作用。由于已解决的αTMP 结构不足，预测 TMP 跨膜片段之间的残基接触展示了蛋白质折叠的基础，可以用于进一步发现更多的蛋白质功能。已经有一些努力致力于使用基于跨膜蛋白结构先验知识的机器学习方法来预测螺旋间残基接触。然而，提高预测准确性仍然是一个挑战，而深度学习方法为利用不同视角的结构知识提供了机会。为此，我们提出了一种新的 TMP 残基-残基接触预测方法 IMPContact，该方法使用卷积神经网络（CNN）利用 TMP 的特定结构特征识别其跨膜螺旋中的那些接触。选择了四个基于序列的 TMP 特异性特征来描述一对残基，即进化共变、预测拓扑结构、残基相对位置和进化保守性。使用最新的数据集来训练和测试 IMPContact；与同类方法相比，我们的方法取得了更好的性能。在案例研究中，规则跨膜螺旋中的 IHRC 比不规则跨膜螺旋中的 IHRC 预测得更好。
