AllesTM: predicting multiple structural features of transmembrane proteins

BMC Bioinformatics
Peter HönigschmidDmitrij Frishman

Abstract

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 predictio...Continue Reading

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Methods Mentioned

BETA
RSA
X-ray

Software Mentioned

SPINE X
RF
PolyPhobius
PredyFlexy
HHblits
DSSP
ReLu
learn
ANGLOR
PROFbval

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