AllesTM: predicting multiple structural features of transmembrane proteins

Abstract

Background 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. Results 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. Conclusions 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.

Evaluation of Transmembrane Protein Structural Models Using HPMScore

“…Initially, the addition of hydrophobicity scales to the prediction of secondary structures gave better results [79,80]. An impressive number of methods were proposed, such as MEMSAT [81,82], HTP [83], DAS [83], SOSUI [84], HMMTOP [85,86], TMHMM 1.0 [87], PRED-TMR [88], OCTOPUS [89], TOPCONS [90,91], MINNOU [92], SVMtm [93], TUPS [94], Localizome [95], MemBrain [96], AllesTM [97], TMPSS [98], and TMbed [99]. The most recent approaches also take into account other features, such as the regions of the protein that actually face the membrane, the cytosolic or extracellular sides, and the motifs responsible for the interactions [97,[100][101][102].…”

Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications

“…Rather than focusing on single property prediction, a few studies have sought to predict a number of properties in combination, such as solvent accessibility, secondary structures, and torsion angles. These methods include AllesTM [242] , MASSP [243] , and TopProperty [244] , which all use deep learning methods to keep abreast of any possible advances in prediction performance ( Table 6 ). For example, in the AllesTM work, the ensemble of conventional machine learning methods (random forest) and deep learning methods (CNNs and bidirectional LSTM NNs) leads to superior performance in predicting Z-coordinates, flexibility, and topology, and its performance in predicting torsion angles, secondary structures, and monomer relative solvent accessibility is roughly similar to that of SPOT-1D.…”