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An improved hidden Markov model for transmembrane protein detection and topology prediction and its applications to complete genomes

R. Y. Kahsay, G. Gao, L. Liao
2005 Bioinformatics  
Results: We present an improved hidden Markov model, TMMOD, for the identification and topology prediction of transmembrane proteins.  ...  In cross-validation experiments using a set of 83 transmembrane proteins with known topology, TMMOD outperformed TMHMM and other existing methods, with an accuracy of 89% for both topology and locations  ...  ACKNOWLEDGEMENTS We thank the anonymous reviewers for the useful comments.  ... 
doi:10.1093/bioinformatics/bti303 pmid:15691854 fatcat:tv6ul7g34vhwrfrwltxgjlyjbe

An improved hidden Markov model for transmembrane topology prediction

R.Y. Kahsay, Li Liao, G. Gao
16th IEEE International Conference on Tools with Artificial Intelligence  
In this work, we proposed a hidden Markov model for transmembrane protein sequences.  ...  The prediction accuracy for membrane domain location and topology are %89 and %84 respectively, both surpassing significantly these of the best existing model TMHMM (%83 and %77).  ...  Acknowledgement This publication was made possible by NIH Grant Number P20 RR-15588 from the COBRE Program of the National Center for Research Resources, and by a DuPont Science & Engineering grant.  ... 
doi:10.1109/ictai.2004.30 dblp:conf/ictai/KahsayLG04 fatcat:aw5rmwumo5a6vg34wx26sdpcre

HMMpTM: Improving transmembrane protein topology prediction using phosphorylation and glycosylation site prediction

Georgios N. Tsaousis, Pantelis G. Bagos, Stavros J. Hamodrakas
2014 Biochimica et Biophysica Acta - Proteins and Proteomics  
We report the development of a Hidden Markov Model based method, capable of predicting the topology of transmembrane proteins and the existence of kinase specific phosphorylation and N/O-linked glycosylation  ...  Our method integrates a novel feature in transmembrane protein topology prediction, which results in improved performance for topology prediction and reliable prediction of phosphorylation and glycosylation  ...  Acknowledgements The authors would like to thank the handling editor for properly handling this manuscript and the anonymous reviewers for their useful and constructive criticism.  ... 
doi:10.1016/j.bbapap.2013.11.001 pmid:24225132 fatcat:6e5oqea6ubh3bcayc5eaghtrmy

ZPRED: Predicting the distance to the membrane center for residues in -helical membrane proteins

E. Granseth, H. Viklund, A. Elofsson
2006 Bioinformatics  
Results: We show that the Z-coordinate can be predicted using either artificial neural networks, hidden Markov models or combinations of both.  ...  The best method, ZPRED, uses the output from a hidden Markov model together with a neural network.  ...  The improved topology predictions result in improved Z-coordinate predictions. It can be noted that the hidden Markov models have particular problems at predicting the 5-15 Å region, Figure 2 .  ... 
doi:10.1093/bioinformatics/btl206 pmid:16873471 fatcat:hx3mvsmmnrdbxpo7raqq57wcyy

The HMMTOP transmembrane topology prediction server

G. E. Tusnady, I. Simon
2001 Bioinformatics  
The HMMTOP transmembrane topology prediction server predicts both the localization of helical transmembrane segments and the topology of transmembrane proteins.  ...  This option improves the prediction accuracy as well as helps the interpretation of experimental results, i.e. in epitope insertion experiments.  ...  HMMTOP (Hidden Markov Model for TOpology Prediction) method is based on the principle, that topology of the transmembrane proteins are determined by the maximum divergence of amino acid composition of  ... 
doi:10.1093/bioinformatics/17.9.849 pmid:11590105 fatcat:h7qo3zlaujddxickn5naf3y5di

CCTOP: a Consensus Constrained TOPology prediction web server

László Dobson, István Reményi, Gábor E. Tusnády
2015 Nucleic Acids Research  
the PDBTM, TOPDB and TOP-DOM databases using the probabilistic framework of hidden Markov model.  ...  The Consensus Constrained TOPology prediction (CCTOP; server is a web-based application providing transmembrane topology prediction.  ...  ACKNOWLEDGEMENTS We thank András Fiser and Dániel Kozma for discussion of the manuscript.  ... 
doi:10.1093/nar/gkv451 pmid:25943549 pmcid:PMC4489262 fatcat:zr35czb4lvdwddnjo46ygqnree

PRED-TMBB: a web server for predicting the topology of -barrel outer membrane proteins

P. G. Bagos, T. D. Liakopoulos, I. C. Spyropoulos, S. J. Hamodrakas
2004 Nucleic Acids Research  
The method is based on a Hidden Markov Model, trained according to the Conditional Maximum Likelihood criterion.  ...  The server reports the predicted topology of a given protein, a score indicating the probability of the protein being an outer membrane b-barrel protein, posterior probabilities for the transmembrane strand  ...  ACKNOWLEDGEMENTS The authors would like to thank the anonymous referees for their valuable comments and constructive criticism.  ... 
doi:10.1093/nar/gkh417 pmid:15215419 pmcid:PMC441555 fatcat:roej5xoq6nee3adfbb273c75tu

A Hidden Markov Model method, capable of predicting and discriminating beta-barrel outer membrane proteins

Pantelis G Bagos, Theodore D Liakopoulos, Ioannis C Spyropoulos, Stavros J Hamodrakas
2004 BMC Bioinformatics  
We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane beta-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble  ...  While transmembrane segment prediction for the alpha-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the beta-barrel membrane proteins.  ...  Acknowledgements The authors would like to thank the two anonymous referees for their constructive criticism.  ... 
doi:10.1186/1471-2105-5-29 pmid:15070403 pmcid:PMC385222 fatcat:pmln47bqrngpxo7eo2ey7lzlbu

PONGO: a web server for multiple predictions of all-alpha transmembrane proteins

M. Amico, M. Finelli, I. Rossi, A. Zauli, A. Elofsson, H. Viklund, G. von Heijne, D. Jones, A. Krogh, P. Fariselli, P. Luigi Martelli, R. Casadio
2006 Nucleic Acids Research  
However the web service allows the prediction of the topology of any kind of putative membrane proteins, regardless of the organism and more importantly with the same sequence profile for a given sequence  ...  The stored and pre-computed predictions for the human proteins can be searched and displayed in a graphical view.  ...  Funding to pay the Open Access publication charges for this article was provided by the BioSapiens project. Conflict of interest statement. None declared  ... 
doi:10.1093/nar/gkl208 pmid:16844984 pmcid:PMC1538841 fatcat:jt2zpuvj2vfddp6luli4xr66ja

A new algorithm to train hidden Markov models for biological sequences with partial labels

Jiefu Li, Jung-Youn Lee, Li Liao
2021 BMC Bioinformatics  
Conclusions A novel training method is developed to improve the training of hidden Markov models by utilizing partial labelled data.  ...  Background Hidden Markov models (HMM) are a powerful tool for analyzing biological sequences in a wide variety of applications, from profiling functional protein families to identifying functional domains  ...  Acknowledgements The authors are grateful for the anonymous reviewers' valuable comments and suggestions, in particular for bringing the posterior-Viterbi decoding to their attention.  ... 
doi:10.1186/s12859-021-04080-0 pmid:33771095 fatcat:7kcdk3tiorbstcy5llbiuhwqwm

Advantages of combined transmembrane topology and signal peptide prediction--the Phobius web server

L. Kall, A. Krogh, E. L.L. Sonnhammer
2007 Nucleic Acids Research  
To address this problem, we previously designed a hidden Markov model, Phobius, that combines transmembrane topology and signal peptide predictions.  ...  Applying these methods to five complete proteomes, we found that 30-65% of all predicted signal peptides and 25-35% of all predicted transmembrane topologies overlap.  ...  ACKNOWLEDGEMENTS Funding to pay the Open Access publication charges for this article was provided by Pharmacia corp. Conflict of interest statement. None declared.  ... 
doi:10.1093/nar/gkm256 pmid:17483518 pmcid:PMC1933244 fatcat:aotyv65vjjaehjq5rsx2yw7mci

Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks

Sheila M. Reynolds, Lukas Käll, Michael E. Riffle, Jeff A. Bilmes, William Stafford Noble, Burkhard Rost
2008 PLoS Computational Biology  
Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction.  ...  We report a relative improvement of 13% over Phobius in full-topology prediction accuracy on transmembrane proteins, and a sensitivity and specificity of 0.96 in detecting signal peptides.  ...  Acknowledgments The authors thank Henrik Nielsen and Håkan Viklund for providing datasets. Author Contributions  ... 
doi:10.1371/journal.pcbi.1000213 pmid:18989393 pmcid:PMC2570248 fatcat:glwgwqufnbgmpa77iydzgpfery

A Generalized Hidden Markov Model Approach to Transmembrane Region Prediction with Poisson Distribution as State Duration Probabilities

Takashi Kaburagi, Takashi Matsumoto
2008 IPSJ Digital Courier  
The proposed algorithm utilizes a Generalized Hidden Markov Model (GHMM), an extension of the HMM, to cope with this problem.  ...  Previous studies have shown that the Hidden Markov Model (HMM) is one of the powerful tools known to predict transmembrane regions; however, one of the conceptual drawbacks of the standard HMM is the fact  ...  Introduction The Hidden Markov Model (HMM) is one of the most successful tools for modeling timeseries data sequences.  ... 
doi:10.2197/ipsjdc.4.193 fatcat:a6kskgbt6rcupl55hdzndmq45a

Improving the accuracy of transmembrane protein topology prediction using evolutionary information

D. T. Jones
2007 Bioinformatics  
The method is found to predict both the correct topology and the locations of transmembrane segments for 80% of the test set.  ...  In order to improve transmembrane topology prediction, we evaluate the combined use of both integrated signal peptide prediction and evolutionary information in a single algorithm.  ...  work was partly supported by the Biosapiens Network of Excellence, which is funded by the European Commission within its FP6 Programme, under the thematic area 'Life sciences, genomics and biotechnology for  ... 
doi:10.1093/bioinformatics/btl677 pmid:17237066 fatcat:y5hv2wmy75dedfpkiv5ommicii

Efficient and accurate prediction of transmembrane topology from amino acid sequence only [article]

Qing Wang, Chongming Ni, Zhen Li, Xiufeng Li, Renmin Han, Feng Zhao, Jinbo Xu, Xin Gao, Sheng Wang
2019 biorxiv/medrxiv   pre-print
Fast and accurate identification of transmembrane (TM) topology is well suited for the annotation of whole membrane proteome, and in turn the initial step to predict the structure and function of membrane  ...  Compared to previous pureseq approaches that based on Hidden Markov Models (HMM) or Dynamic Bayesian Network (DBN), DeepCNF can accommodate a lot more context information by a hierarchical deep neural  ...  We also use 20 hidden Markov model (HMM) profile generated by HHpred [45] , which is complementary to PSSM to some degree.  ... 
doi:10.1101/627307 fatcat:6pebog7jx5curizx5cme35qiuq
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