This study is motivated by the following three considerations: a) the physico-chemical properties of transmembrane (TM) proteins are distinctly different from those of globular proteins, necessitating the development of specialized structure prediction techniques, b) for many structural features no specialized predictors for TM proteins are available at all, and c) deep learning algorithms allow to automate the feature engineering process and thus facilitate the development of multi-target methods for predicting several protein properties at once. We present AllesTM, an integrated tool to predict almost all structural features of transmembrane proteins that can be extracted from atomic coordinate data. It blends several machine learning algorithms: random forests and gradient boosting machines, convolutional neural networks in their original form as well as those enhanced by dilated convolutions and residual connections, and, finally, long short-term memory architectures. AllesTM outperforms other available methods in predicting residue depth in the membrane, flexibility, topology, relative solvent accessibility in its bound state, while in torsion angles, secondary structure and monomer relative solvent accessibility prediction it lags only slightly behind the currently leading technique SPOT-1D. High accuracy on a multitude of prediction targets and easy installation make AllesTM a one-stop shop for many typical problems in the structural bioinformatics of transmembrane proteins. In addition to presenting a highly accurate prediction method and eliminating the need to install and maintain many different software tools, we also provide a comprehensive overview of the impact of different machine learning algorithms and parameter choices on the prediction performance. AllesTM is freely available at https://github.com/phngs/allestm.

中文翻译：

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AllesTM：预测跨膜蛋白的多种结构特征。

这项研究是出于以下三个考虑：a）跨膜（TM）蛋白质的物理化学性质与球状蛋白质明显不同，因此需要开发专门的结构预测技术，b）对于许多结构特征没有专门的预测因子完全可以使用TM蛋白，并且c）深度学习算法可以使特征工程过程自动化，从而有助于开发可同时预测几种蛋白特性的多目标方法。我们提出了AllesTM，这是一种预测几乎可以从原子坐标数据中提取的跨膜蛋白结构特征的集成工具。它融合了多种机器学习算法：随机森林和梯度增强机器，卷积神经网络的原始形式以及通过扩张卷积和残差连接以及最终的长短期记忆体系结构增强的形式。AllesTM在预测膜中残留深度，柔韧性，拓扑结构，结合状态下的相对溶剂可及性方面优于其他可用方法，而在扭转角，二级结构和单体相对溶剂可及性方面的预测仅略微落后于目前领先的技术SPOT-1D 。多种预测目标的高精度和易于安装的特点，使AllesTM成为一站式服务，解决了跨膜蛋白结构生物信息学中的许多典型问题。除了提供一种高度准确的预测方法并消除安装和维护许多不同软件工具的需要之外，我们还提供了有关不同机器学习算法和参数选择对预测性能的影响的全面概述。可在https://github.com/phngs/allestm免费获得AllesTM。