Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.

@article{Krogh2001PredictingTP,
  title={Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.},
  author={Anders Krogh and B. Larsson and Gunnar von Heijne and Erik L. L. Sonnhammer},
  journal={Journal of molecular biology},
  year={2001},
  volume={305 3},
  pages={
          567-80
        }
}
We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. [] Key Result We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present.

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

Application of TMMOD to a collection of complete genomes shows that the number of predicted membrane proteins accounts for approximately 20-30% of all genes in those genomes, and that the topology where both the N- and C-termini are in the cytoplasm is dominant in these organisms except for Caenorhabditis elegans.

Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks

This large-scale study provides an overall picture of the relative numbers of proteins that include a signal-peptide and/or one or more transmembrane segments as well as a valuable resource for the scientific community.

Reliability measures for membrane protein topology prediction algorithms.

MetaTM - a consensus method for transmembrane protein topology prediction

A novel TM consensus method, named MetaTM, which is based on support vector machine models and combines the results of six TM topology predictors and two signal peptide predictors, and has higher accuracy than a previous consensus predictor.

Protein Topology Prediction Algorithms Systematically Investigated in the Yeast Saccharomyces cerevisiae

It is found that the predictions of nine algorithms on the yeast proteome have little agreement when predicting TMD number and termini orientation, and suggests that more systematic data on protein topology are required to increase the training sets for prediction algorithms and to have accurate knowledge of membraneprotein topology.

Best α‐helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information

A novel method is introduced, PRODIV‐TMHMM, which is a profile‐based hidden Markov model (HMM) that also incorporates the best features of earlier HMM methods, and outperforms earlier methods both when evaluated on “low‐resolution” topology data and on high‐resolution 3D structures.

MemBrain: Improving the Accuracy of Predicting Transmembrane Helices

To improve the accuracy of TMH detection, a machine-learning based predictor is developed, MemBrain, which integrates a number of modern bioinformatics approaches including sequence representation by multiple sequence alignment matrix, the optimized evidence-theoretic K-nearest neighbor prediction algorithm, fusion of multiple prediction window sizes, and classification by dynamic threshold.

consensus approach Prediction of partial membrane protein topologies using a data

A method to reliably identify partial membrane protein topologies using the consensus of five topology prediction methods is developed and it is found that approximately 90% of the partial consensus topologies are correctly predicted in membrane proteins from prokaryotic as well as eukaryotic organisms.

Prediction of partial membrane protein topologies using a consensus approach

A method to reliably identify partial membrane protein topologies using the consensus of five topology prediction methods is developed and it is found that approximately 90% of the partial consensus topologies are correctly predicted in membrane proteins from prokaryotic as well as eukaryotic organisms.
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