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PSIpred - Prediction of secondary structure from multiple sequences
SAM-T99
BCM Protein Secondary Structure Prediction
Jnet - a neural network protein secondary structure prediction method
Jpred - A consensus method for protein secondary structure prediction
Predator
Nnpredict server
SOPMA
PredictProtein Server (MaxHom / PHD)
HMMTOP - predict transmembrane helices and topology
SPLIT (Membrane Protein Secondary Structure Prediction)
DAS (Prediction of transmembrane alpha-helices in prokaryotic membrane proteins)
SOSUI - Secondary Structure Prediction of Membrane Proteins
TopPred - Topology prediction of membrane proteins
TMpred - prediction of transmembrane regions and orientation
Coils - prediction of coiled coil regions
Paircoil
SignalP - predicts signal peptides of secretory proteins
ChloroP - Chloroplast Transit Peptide Prediction
Helix-Turn-Helix (HTH)
PSIpred - Prediction of secondary structure from multiple sequences
PSIpred - Prediction of secondary structure from multiple sequences
MEMSAT 2 - Prediction of transmembrane topology from multiple sequences
GenTHREADER - Fast and reliable protein fold recognition
SAM-T99
The best 2ry structure predictor at CASP3 was clearly Jones's PSIPRED.
A close second was this predictor. They have since improved thier predictor considerably. They hope to beat PSIPRED at CASP4 with this predictor. Currently, this predictor is about 77-78% correct, and does a good job of knowing when it is inaccurate.
BCM Protein Secondary Structure Prediction
This provides a rich set of programs for protein secondary structure
determination.
Jnet - a neural network protein secondary structure prediction method
Jnet is a neural network prediction algorithm that works by applying
multiple sequence alignments, alongside PSIBLAST and HMM profiles.
Consensus techniques are applied that predict the final secondary
structure more accurately. It was written as part of a continuing study
to improve protein secondary structure prediction. Jnet can also
predict 2 state solvent exposure at 25, 5 and 0% relative exposure.
Positions where the different prediction methods do not agree are marked
as no jury positions. A separate network is applied for these
positions, which improves the cross-validated accuracy. A reliability
index indicates which residues are predicted with a high confidence.
Jpred - A consensus method for protein secondary structure prediction
Jpred takes either a protein sequence or mulitply aligned
protein sequences, and predicts secondary structure. It works by combining a number of
modern, high quality prediction methods to form a consensus.
Jpred runs DSC, PHD, PREDATOR and NNSSP to build it's consensus prediction, but predictions from older algorithms Mulpred and Zpred are also included in the final output.
The consensus method has been shown, to be on average more accurate than any of the component methods, by ca. 1%. However the strength of this server lies in the fact that it leaves the final decision to the user who can use the supplied coloured HTML and Java viewer to decide where the best or most sensible consensus may be.
Predator
Protein secondary structure prediction from single sequence
or from a set of sequences.
PREDATOR takes as input a sequence file in FASTA, MSF or CLUSTAL format containing one or many protein sequences. By default, the prediction will be made for the first sequence in the set.
Nnpredict server
nnpredict is a program that predicts the secondary structure type for
each residue in an amino acid sequence. The basis of the prediction is
a two-layer, feed-forward neural network.
nnpredict takes as input a sequence consisting of one-letter amino acid codes (A C D E F G H I K L M N P Q R S T V W Y) (NOTE: B and Z are not recognized as valid amino acid codes) or three-letter amino acid codes separated by spaces (ALA CYS ASP GLU PHE GLY HIS ILE LYS LEU MET ASN PRO GLN ARG SER THR VAL TRP TYR). The output is a secondary structure prediction for each position in the sequence. Multiple-chain proteins can be predicted either in pieces, or as a single sequence, with a '!' character between chains.
SOPMA
SOPMA (Self Optimized Prediction Method from Alignment) is a
package to make secondary structure predictions of proteins.
PredictProtein Server (MaxHom / PHD)
PredictProtein is an automatic service for the prediction of aspects of
protein structure. You send an amino acid sequence and PredictProtein
returns a multiple sequence alignment, and a prediction of the secondary
structure, residue solvent accessibility, and helical transmembrane
regions.
HMMTOP - predict transmembrane helices and topology
HMMTOP is an automatic server for predicting topology of transmembrane
proteins. The method is based on the hypothesis that topology is
determined by the maximum divergence of the amino acid distributions of
the various structural parts in membrane proteins.
SPLIT (Membrane Protein Secondary Structure Prediction)
The purpose of this server is to predict the transmembrane (TM) secondary structures of membrane proteins, using the method of
preference functions.
DAS (Prediction of transmembrane alpha-helices in prokaryotic membrane proteins)
The so-called Dense Alignment Surface (DAS) method was
introduced in an attempt to improve sequence alignments in the G-protein
coupled receptor family of transmembrane proteins. We have now
generalized this method to predict transmembrane segments in any
integral membrane protein. DAS is based on low-stringency dot-plots of
the query sequence against a collection of non-homologous membrane
proteins using a previously derived, special scoring matrix.
SOSUI - Secondary Structure Prediction of Membrane Proteins
The SOSUI system is a useful tool for secondary structure prediction of
membrane proteins from a protein sequence. The basic idea of prediction
in this system is based on the physicochemical properties of amino acid
sequences such as hydrophobicity and charges. The system deals with
three types of prediction: discrimination of membrane proteins from
soluble one, prediction of existence of transmembrane helices and
determination of transmembrane helical regions. The accuracy of this
system, discrimination of membrane proteins, existence of transmembrane
helices and transmembrane helical regions, are about 99%, 96% and 85%
respectively.
TopPred - Topology prediction of membrane proteins
A new, simple method for predicting transmembrane segments in integral membrane proteins.
It is based on low-stringency dot-plots of the query sequence against a collection of
non-homologous membrane proteins using a previously derived scoring matrix.
This so-called dense alignment surface (DAS) method is shown to perform on
par with earlier methods that require extra information in the form of multiple sequence alignments or
the distribution of positively charged residues outside the transmembrane segments, and thus improves
prediction abilities when only single-sequence information is available or for classes of membrane
proteins that do not follow the 'positive inside' rule.
TMpred - prediction of transmembrane regions and orientation
This program tries to find putative transmembrane domains in proteins
and also speculates on the possible orientation of these segments. For
its scoring, it uses a combination of multiple weight-matrices that have
been extracted from a statistical analysis of TMbase, a collection of
all annotated transmembrane proteins present in SwissProt.
Coils - prediction of coiled coil regions
This program predicts (2 stranded) coiled coil regions in proteins by
the Lupas-algorithm.
Paircoil
The Paircoil program predicts the location of coiled-coil regions in
amino acid sequences.
SignalP - predicts signal peptides of secretory proteins
SignalP predicts signal peptides of secretory proteins. For cleaved signal
peptides, the precise location of the cleavage site in the amino acid
sequence is predicted. The prediciton is optimised for three different
types of organisms: Gram-positive prokaryotes, Gram-negative
prokaryotes, and eukaryotes. The method incorporates a prediction of
cleavage sites and a signal peptide/non-signal peptide prediction based
on a combination of several artificial neural networks.
ChloroP - Chloroplast Transit Peptide Prediction
The ChloroP www-server is able to predict two things:
1. cTP or no cTP
Helix-Turn-Helix (HTH)
This predicts Helix-turn-helix motifs.
Any Comments, Questions? Support@hgmp.mrc.ac.uk
