PROTEIN SECONDARY
STRUCTURE
Precautionary Quote: "We should be quite remiss not to emphasize that
despite the popularity of secondary structural prediction schemes, and the
almost ritual performance of these calculations, the information available from
this is of limited reliability. This is true even of the best methods now known,
and much more so of the less successful methods commonly available in sequence
analysis packages. Running a secondary structure prediction on a
newly-determined sequence just because everyone else does so, is to be deplored,
and the fact that the results of such predictions are generally ignored is
insufficient justification for doing and publishing them." Arthur Lesk, 1988.
My favourite site is
The Protein
Sequence Analysis (PSA) Protein Structure Prediction Server (BMERC
at Boston University
(BioMolecular Engineering Research Center) predicts probable secondary structures and folding classes
for a given amino acid sequence. Results are available in postscript requiring
a viewer such as Ghostview. A self-extracting version of the later for Windows
can be obtained free from the University of Wisconsin. In addition, a new private Web format is available.
YASPIN
secondary structure prediction - is
a HNN (Hidden Neural Network) secondary structure prediction program that uses
the PSI-BLAST algorithm to produce a PSSM for the input sequence, which it then
uses to perform its prediction. (Reference: K. Lin et al. 2005. Bioinformatics
21:152-9).
For a metasite linked to a wide range of protein
sequence analysis and structure predictions online programs, I recommend PredictProtein
(ROSTLAB, Technische Universität
München). Also see:
SCRATCH Protein Predictor
(Institute for
Genomics & Bioinformatics, University of California, Irvine,
U.S.A.)
TMpred - Prediction of Trans-membrane Regions and Orientation
- ISREC (Swiss Institute for Experimental Cancer
Research) TMHMM
- Prediction of transmembrane
helices in proteins (Center for
Biological Sequence Analysis, The Technical University of
Denmark) DAS - Transmembrane Prediction Server (Stockholm University, Sweden)
SPLIT (D. Juretic, Univ. Split ,
Croatia) - the Transmembrane Protein
Topology Prediction Server provides clear and colourful output including beta
preference and modified hydrophobic moment index.
OCTOPUS - Using a novel combination
of hidden Markov models and artificial neural networks, OCTOPUS predicts the correct topology for 94% of the a
dataset of 124 sequences with known structures.
(Reference: Viklund, H. &
Elofsson, A. 2008. Bioinformatics 24: 1662-1668)
Phobius - is a
combined transmembrane topology and signal peptide predictor (Reference:
L. Käll et al. 2004.
J. Mol. Biol. 338: 1027-1036) This tool can also be accessed here and here.
SPOCTOPUS will also do this.
RHYTHM -
predicts the
orientation of transmembrane helices in channels and membrane-coils,
specifically buried versus exposed residues. (Reference: A. Rose et al. 2009. Nucl. Acids Res.
37(Web Server
issue):W575-W580)
TMMOD - Hidden Markov Model for Transmembrane Protein
Topology Prediction (Dept. Computer & Information Sciences, University of
Delaware, U.S.A.) - on the results page click on
"show posterior probabilities" to see a TMHMM-type
diagram
PRED-TMR2 (C. Pasquier & S.J.Hamodrakas,Dept. Cell Biology and
Biophysics, Univ. Athens, Greece) - when applied to several
test sets of transmembrane proteins the system gives a perfect prediction rating
of 100% by classifying all the sequences in the transmembrane class. Only 2.5%
error rate with nontransmembrane proteins.
TOPCONS - computes consensus predictions of membrane protein
topology using a Hidden Markov Model (HMM) and input from five state-of-the-art
topology prediction methods. (Reference: A. Bernsel et al. 2009. Nucleic Acids
Res. 37(Webserver issue), W465-8) . For a
batch server without BLAST runs use TOPCONSsingle.
MINNOU (Membrane protein
IdeNtificatioN withOUt explicit use of hydropathy
profiles and alignments) -
predicts alpha-helical
as well as beta-sheet transmembrane (TM) domains based on a compact
representation of an amino acid residue and its environment, which consists of
predicted solvent accessibility and secondary structure of each amino acid.
(Reference: Cao et al. 2006.
Bioinformatics 22: 303-309). A legend to help interpret the results in
here.
SuperLooper - provides
the first online interface for the automatic,
quick and interactive search and placement of loops in proteins. (Reference: P.W. Hildebrand et al. 2009. Nucl. Acids
Res. 37(Web Server
issue):W571-W574) )
For drawing the structure of transmembrane proteins two sites
are available:
TOPO2 (S. Johns, UCSF Sequence Analysis Consulting Service, U.S.A.)
- this is the best site providing
considerable control over the presentation. Extensive documentation is
provided here. RbDe
(F.Campagne, Inst. Computational Biomedicine,
Weill
Medical College of Cornell University, New
York, U.S.A.) - also permits one to prepare useful diagrams of
transmembrane proteins.
TMRPres2D (TransMembrane protein Re-Presentation in 2 Dimensions tool) -
this Java tool takes data from a variety of
protein folding servers and creates uniform, two-dimensional, high analysis
graphical images/ models of alpha-helical or beta-barrel transmembrane proteins.
(Reference: I.C. Spyropoulos et al. 2004. Bioinformatics
20: 3258-3260).
Signal
peptide recognition & subcellular localization:
A. Bacterial proteins
SLEP
(Surface Locationization Extracellular Protein) -
SLEP is a pipeline for predicting the localization of
bacterial proteins starting from genome sequences (Fasta formatted). It combines
the results of several tools: Glimmer, TMHMM, PRODIV-TMHMM, LipoP,
PSortB.
PSORTb (Brinkman Lab, Simon Fraser
Univ., Canada) -
provides probably
the most accurate bacterial protein subcellular localization predictor.
Alternatively use PSORT (Univ.
Tokyo, Japan)
- a series of
programs for the prediction of protein localization sites in cells. Choose
programs specific for for animal, yeast, plant or bacterial ( Gram-negative or
Gram- positive) proteins.
PSLpred - is a SVM based
method, predicts 5 major subcellular localization (cytoplasm, inner-membrane,
outer- membrane, extracellular, periplasm) of Gram-negative bacteria. This
method includes various SVM modules based on different features of the proteins.
The hybrid approach achieved an overall accuracy of 91%, which is best among
all the existing methods for the subcellular localization of prokaryotic
proteins. (Reference:
M. Bhasin et al. (2005)
Bioinformatics 21: 2522-2524.) CELLO subCELlular LOcalization predictive system -
assigns Gram-negative proteins to the
cytoplasm , inner membrane, periplasm, outer membrane or extracellular space
with overall prediction accuracy of ca. 89% . Also analyzes eukaryotic and
Gram-positive proteins. (Reference: C.S. Yu et al. 2004. Protein Sci. 13:1402-1406). SubLoc - based on
SOAP technology, this server/client suite offers a
user-friendly interface for searching and predicting
protein subcellular location. N.B. It does not does not predict membrane
proteins. Available here or here. (Reference: H. Chen
et al. 2006. Bioinformatics 22:
376-377).
SignalP
- predicts the presence and location
of signal peptide cleavage sites in Gram-positive, Gram-negative and eukaryotic
proteins (Center for Biological Sequence Analysis, The Technical
University of Denmark). For an
example of a periplasmic protein use test sequence MalE. Phobius - is a combined transmembrane topology and signal
peptide predictor (Reference:
L. Käll et al. 2004.
J. Mol. Biol. 338: 1027-1036). LipoP 1.0 (Center for Biological Sequence Analysis Technical University
of Denmark) - allows prediction of where signal peptidases I & II
cleavage sites from Gram negative bacteria will cleave a protein.
SecretomeP -
produces ab initio predictions of non-classical i.e. not signal peptide triggered protein secretion. The
method queries a large number of other feature prediction servers to obtain
information on various post-translational and localizational aspects of the
protein, which are integrated into the final secretion prediction (Reference: J.D. Bendtsen et al. 2005. BMC Microbiology 5: 58).
B. Eukaryotic proteins
SecretomeP -
produces ab initio predictions of non-classical i.e. not signal peptide triggered protein secretion. The
method queries a large number of other feature prediction servers to obtain
information on various post-translational and localizational aspects of the
protein, which are integrated into the final secretion prediction (Reference: J.D. Bendtsen et al. 2005. BMC Microbiology 5: 58).
Other sites
for secondary structure predictions include:
JPred - a
consensus method for protein secondary structure prediction based upon PHD,
Predator, DSC, NNSSP, Zpred and Mulpred programs (European Bioinformatics
Institute, Cambridge, United Kingdom) - My favourite site. N.B. Do not forget to deselect advanced
option 1 at bottom of page to obtain maximum information.
YASPIN secondary structure prediction - a Hidden Neural
Network secondary structure prediction program that uses the PSI-BLAST algorithm
to produce a Position Specific Scoring Matrix
for the input sequence,
which it then uses to perform its prediction. It was trained on 2896 structures
from the PDB40 database. (Reference: K. Lin et al. 2005. Bioinformatics
21:152-159).
For a different colourful approach try
PSIpred (UCL Bioinformatics Unit,
Department of Computer Science, University College London, United
Kingdom). For a full range of
properties of your protein including hydrophobicity, alpha helix, beta-sheet
plots see ProScale (ExPASy,
Switzerland)
Disordered states:
Many
proteins containing regions that do not form
well-defined structures and the following new programs help define these
regions:
RONN (Regional Order Neural Network) - (Reference:
Z.R. Yang et al. 2005. Bioinformatics 21: 3369-3376).
IUPred (Reference: Z. Dosztányi et al. 2005.
Bioinformatics 21: 3433-3434). DISOPRED2
(Reference: J.J. Ward et al. 2004. J. Molec. Biol. 337: 635-645).
metaPrDOS - is a meta server to predict natively
disordered regions of a protein chain from its amino acid sequence. metaPrDOS
returns disorder tendency of each residue as prediction results.(Reference: T. Ishida & K. Kinoshita.
2008. Bioinformatics 24:
1344-1348)
Scooby-domain (Sequence
hydrophobicity predicts domains) is a method to identify globular
regions in protein sequence that are suitable for structural studies. The
Scooby-domain JAVA applet can be used as a tool to visually identify 'foldable'
regions in protein sequence. Interesting graphics. (Reference: R.A. George et al. 2005. Nucl. Acids Res.
33: W160-W163).
For estimations on the antigenicity of regions of proteins
see:
Antigenicity Plot (JaMBW module) - Given a sequence of amino acids, this program computes and
plots the antigenicity along the polypeptide chain, as predicted by the
algorithm of Hopp & Woods (1981).
Antigenicity Prediction (Princeton
BioMolecules,Langhorne, PA, U.S.A.) - find antigenic sequences and also reviews them from the
point of hydrophobicity, aggregation, and steric hindrance.
EMBOSS Antigenic (EMBOSS package) - this program predicts potentially antigenic regions of a
protein sequence, using the method of Kolaskar & Tongaonkar (1990). Also
accessible here.
To screen for coiled-coil regions in proteins use:
Coils - Prediction of Coiled Coil Regions in Proteins (Swiss node of EMBnet,
Switzerland) - (Reference: A. Lupas et al. 1991 Science 252:
1162-1164). Paircoils (MIT Laboratory for Computer Science, U.S.A.) -
(Reference: B. Berger et al. 1995. Proc. Natl. Acad.
Sci. USA, 92: 8259-8263) or MultiCoil - is based on
the PairCoil algorithm and is used for locating dimeric and trimeric coiled
coils. (Reference: E. Wolf et al.
1997. Protein Sci. 6: 1179-1189).
REPPER
(REPeats and their PERiodicities) - detects and analyzes regions with short gapless repeats in
proteins. It finds periodicities by Fourier Transform (FTwin) and internal
similarity analysis (REPwin). FTwin assigns numerical values to amino acids that
reflect certain properties, for instance hydrophobicity, and gives information
on corresponding periodicities. REPwin uses self-alignments and displays repeats
that reveal significant internal similarities. They are complemented by PSIPRED
and coiled coil prediction (COILS), making the server a useful analytical tool
for fibrous proteins. (Reference:
M. Gruber et al. 2005. Nucl. Acids Res. 33:
W239-W243).
Domain linkers:
Armadillo Domain Linker Prediction (The Blueprint
Initiative, Toronto, Canada) - Proteins are often composed of multiple
structural/functional domains. Domain linkers link these domains together and
have been found to contain an amino acid signature that is distinct from the
structurally compact domains. Using a set of 211 two-domain contiguous proteins,
the sensitivity was 56%.
PRED-TMßß (Bagos, P. G., et al. Dept Cell Biology & Biophysics,
University of Athens, Greece) -
employs a Hidden Markov Model method, capable of
predicting and discriminating beta-barrel outer membrane proteins. Gives one the
opportunity to download a custom image plot or a 2D representation (see
below):
BetaTPred2 (Bioinformatics Center, Institute of Microbial Technology,
India) - predict ß turns in proteins
from multiple alignment by using neural network from the given amino acid
sequence. For ß turn prediction, it uses the position specific score matrices
generated by PSI-BLAST and secondary structure predicted by PSIPRED. For a
classification of the ß turn type use BetaTurns.
TMB-Hunt - amino acid composition based
TransMembrane Barrel-Hunt (A. Garrow, University of Leeds, England) - provides one with a color-coded score
(& Evalue) for an individual or a series of proteins. (Reference: A.G. Garrow et al. 2005.
Nucl. Acids Res. 33: W193-W197).
TMBETA-NET - Discrimination and Prediction of
Transmembrane Beta Strands in Outer Membrane Proteins from amino acid sequence.
Presents color-coded TM beta segments and their
probabilities (Reference: M.M. Gromiha & M. Suwa.
2005. Bioinformatics
21: 961 - 968).
Metasite:
Scratch Protein
Predictor - (Institute for Genomics and Bioinformatics, University
California, Irvine) - programs
include: ACCpro: the relative solvent accessibility of protein residues;
CMAPpro: Prediction of amino acid contact maps; COBEpro: Prediction of
continuous B-cell epitopes; CONpro: predicts whether the number of contacts of
each residue in a protein is above or below the average for that residue; DIpro:
Prediction of disulphide bridges; DISpro: Prediction of disordered regions;
DOMpro: Prediction of domains; SSpro: Prediction of protein secondary structure;
SVMcon: Prediction of amino acid contact maps using Support Vector Machines;
and, 3Dpro: Prediction of protein tertiary structure (Ab
Initio).
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