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`Our results show that TMHMM is currently the best performing transmembrane prediction program.' TMHMM is described in A. Krogh, B. Larsson, G. von Heijne, and E. L. L. Sonnhammer. Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes. Journal of Molecular Biology, 305(3):567-580, January 2001.
TMHMM-2.0 - redirect. NOTE: TMHMM-2.0 is outdated. A more recent and better transmembrane predictor, DeepTMHMM, has been released and is available at https://services.healthtech.dtu.dk/service.php?DeepTMHMM .
Nov 27, 2006 · TMHMM is a membrane protein topology prediction method based on a hidden Markov model. It predicts transmembrane helices and discriminate between soluble and membrane proteins with high degree of accuracy. Users can submit as many as 4000 protein sequences in FASTA format each time.
Jan 19, 2001 · We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices.
Transmembrane Helix Prediction. TMHMM is a method for prediction transmembrane helices based on a hidden Markov model and developed by Anders Krogh and Erik Sonnhammer.
TMHMM (TransMembrane prediction using Hidden Markov Models) is a program for predicting transmembrane helices based on a hidden Markov model. It reads a FASTA formatted protein sequence and predicts locations of transmembrane, intracellular and extracellular regions.
In particular, we provide detailed step-by-step instructions for the coupled use of the amino-acid sequence-based predictors TargetP, SignalP, ChloroP and TMHMM, which are all hosted at the Center for Biological Sequence Analysis, Technical University of Denmark.
TMHMM2.0 User's guide. This program is for prediction of transmembrane helices in proteins. July 2001: TMHMM has been rated best in an independent comparison of programs for prediction of TM helices: S. Moller, M.D.R. Croning, R. Apweiler. Evaluation of methods for the prediction of membrane spanning regions.
Apr 10, 2022 · Abstract. Transmembrane proteins span the lipid bilayer and are divided into two major structural classes, namely alpha helical and beta barrels. We introduce DeepTMHMM, a deep learning protein language model-based algorithm that can detect and predict the topology of both alpha helical and beta barrels proteins with unprecedented accuracy.
Jan 21, 2021 · Functional domains were predicted by NCBI conserved domain search, PFAM v32.0 and SMART programs, and the number and location of transmembrane helices are based on the TMHMM, TMpred, and Phobius suites.
