TMMOD: Hidden Markov Model for Transmembrane Protein Topology Prediction

tmmod description

Submit a job

Submit amino acid sequence for analysis by TMMOD

Description

TMMOD is a server for transmembrane proteins topology prediction using a hidden Markov model. TMMOD uses TMHMM (Sonnhammer et al., 1998) as a prototype, but differs from TMHMM by the architecture of the submodels for loops on both sides of the membrane and also by the model training procedure. In cross-validation experiments, TMMOD has outperformed existing methods. In an experiment using a set of 83 transmembrane proteins with known topology, TMMOD has an accuracy of 89% for both topology and locations. In another experiment using a separate set of 160 transmembrane proteins, TMMOD has 84% for topology and 89% for locations. When used to identify transmembrane proteins from non-transmembrane proteins, particularly signal peptides, TMMOD has consistently fewer false positives than TMHMM does (same number of false negatives as TMHMM).

TMMOD is described in

Kahsay, R., Liao, L., Gao, G. (Nov 2004). An improved hidden Markov model for transmembrane protein topology prediction.
ICTAI'04 (16th IEEE International Conference on Tools with Artificial Intelligence,Boca Raton, FL, USA).
Kahsay, R., Liao, L., Gao, G. (2005). An Improved Hidden Markov Model for Transmembrane Protein Topology Prediction and Its Applications to Complete Genomes Bioinformatics vol. 21, pp. 1853-1858, 2005.

Download supplementary results.

Input

The program takes proteins in FASTA format. Example is given below.

>5H2A_CRIGR you can have comments after the ID
MEILCEDNTSLSSIPNSLMQVDGDSGLYRNDFNSRDANSSDASNWTIDGENRTNLSFEGYLPPTCLSILHLQEKNWSALLTAVVI
ILTIAGNILVIMAVSLEKKLQNATNYFLMSLAIADMLLGFLVMPVSMLTILYGYRWPLPSKLCAVWIYLDVLFSTASIMHLCAIS
LDRYVAIQNPIHHSRFNSRTKAFLKIIAVWTISVGVSMPIPVFGLQDDSKVFKQGSCLLADDNFVLIGSFVAFFIPLTIMVITYFLTIK
SLQKEATLCVSDLSTRAKLASFSFLPQSSLSSEKLFQRSIHREPGSYTGRRTMQSISNEQKACKVLGIVFFLFVVMWCPFFITNIMA
VICKESCNEHVIGALLNVFVWIGYLSSAVNPLVYTLFNKTYRSAFSRYIQCQYKENRKPLQLILVNTIPALAYKSSQLQAGQNKDS
KEDAEPTDNDCSMVTLGKQQSEETCTDNINTVNEKVSCV

Output

TMMOD gives some statistics and a list of the locations for predicted regions.

Here is an example:

# Name	FTSH_ECOLI	show posterior probabilities
# Length	644
# Number of predicted TMHs	2
# Exp number of AAs in TMHs	37.326473
# Exp number, first 60 AAs	17.506702
# Total prob of N-in	0.906959
inside	1	4
tmhelix	5	23
outside	24	98
tmhelix	99	119
inside	120	644

The first few lines gives some statistics:

Length : The length of the protein sequence.

Number of predicted TMHs : The number of predicted transmembrane helices.

Exp number of AAs in TMHs : The expected number of amino acids intransmembrane helices. If this number is larger than 18 it is very likely to be a transmembrane protein (OR have a signal peptide).

Exp number, first 60 AAs : The expected number of amino acids in transmembrane helices in the first 60 amino acids of the protein. If this number more than a few, you should be warned that a predicted transmembrane helix in the N-term could be a signal peptide.

Total prob of N-in : The total probability that the N-term is on the cytoplasmic side of the membrane.

Plot of probabilities

User can also see the posterior probabilities by clicking on the link "show posterior probabilities".

This website was designed and developed by Robel Kahsay, Ph.D.,
and is maintained by Roger Craig