Predicting Protein Transmembrane Regionsby Using LSTM Model

Since about 10%-30% of all proteins contain transmembrane helices [1], explorations of protein transmembrane structures are critical for many fields of biology including pharmacy industry. In contrast to protein secondary structures, determining transmembrane protein structures requires a time-and-financeconsuming effort. To get over this problem, machine learning methods were proposed to capture information and relationship inside the structures of already explored transmembrane proteins and then, use that knowledge to predict transmembrane regions of new proteins without any experiments execution.

[1]  Marco Punta,et al.  Membrane protein prediction methods. , 2007, Methods.

[2]  B. Rost,et al.  Topology prediction for helical transmembrane proteins at 86% accuracy–Topology prediction at 86% accuracy , 1996, Protein science : a publication of the Protein Society.

[3]  R. Doolittle,et al.  A simple method for displaying the hydropathic character of a protein. , 1982, Journal of molecular biology.

[4]  G. von Heijne,et al.  Topogenic signals in integral membrane proteins. , 1988, European journal of biochemistry.

[5]  B. Rost,et al.  State-of-the-art in membrane protein prediction. , 2002, Applied bioinformatics.

[6]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[7]  Rolf Apweiler,et al.  A collection of well characterised integral membrane proteins , 2000, Bioinform..

[8]  Rolf Apweiler,et al.  Evaluation of methods for the prediction of membrane spanning regions , 2001, Bioinform..

[9]  Zheng Yuan,et al.  SVMtm: Support vector machines to predict transmembrane segments , 2004, J. Comput. Chem..

[10]  István Simon,et al.  The HMMTOP transmembrane topology prediction server , 2001, Bioinform..

[11]  A. Krogh,et al.  Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. , 2001, Journal of molecular biology.

[12]  C. Peters,et al.  Topology Prediction of α-Helical Transmembrane Proteins , 2016 .

[13]  S J Hamodrakas,et al.  An hierarchical artificial neural network system for the classification of transmembrane proteins. , 1999, Protein engineering.

[14]  S J Hamodrakas,et al.  A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: the PRED-TMR algorithm. , 1999, Protein engineering.