ENSEMBL |
a software system which produces and maintains
automatic annotation on metazoan genomes: |
| |
|
| ENSEBL |
Annotation on the genomes of human, chicken, chimp,mouse,rat,
zebrafish, fubu, mosquito, fruitfly, C.elegans and C. briggsae.
|
| AtENSEMBL |
Annotation on the genomes of Arabidopsis thaliana. |
| |
|
| |
|
HOMOLOGY |
To identify a previously recorded
protein and its homologues from its sequence: |
| |
|
| BLASTn |
|
| FastA |
|
| |
|
| |
|
| MULIPLE ALIGNMENT |
To align several entered sequences so that anyhomologous
regions are parallel: |
| |
|
| clustal
W |
Also draws a phylogenetic tree |
| DBClustal |
Aligns all to a consensus sequence |
| HMMer |
Uses hidden marcov model, complicated but more reliable |
| SAM |
Uses hidden marcov model, complicated but more reliable |
| SeqLogo
|
visualisation |
| |
|
| |
|
| SECONDARY STRUCTURE |
To predict a variety of structural features from
an entered protein sequence. |
| |
|
| COILS |
Prediction of coiled coil regions. |
| CPHmodels |
CPHmodels - CPHmodels is a collection of methods and databases
developed to predict protein structures. It currently consists
of the following tools: |
| GenTHREADER
|
Folds (if homologs of known folds) |
| GOR |
|
| nnpredict |
|
| PSI-Pred |
An excellent tool for prediction of secondary structure, with
access to GenTHREADER for protein fold recognition and MEMSAT-2
transmembrane topology prediction |
| EVA |
EValuation of Automatic protein structure prediction; provides
a continuous, automated, statistical analysis of structure prediction
servers |
| PSA |
Prediction of probable secondary structures and fold-class;
good for visualizing amphipathic helices, where present |
| |
|
| |
|
| MOTIFS AND DOMANS |
To predict domains and/or motifs from protein
sequence: |
| |
|
| Block
Searcher |
Block Searcher - Search your protein or DNA sequence against
a Blocks Database. BLOCKS are multiply aligned ungapped segments
corresponding to the most highly conserved regions of proteins.
|
| PRODOM
|
PRODOM - The ProDom Protein Domain database consists of an
automatic compilation of homologous domains detected in the
SWISS-PROT database |
| InterPro |
InterPro - Integrated Resources of Proteins Domains and Functional
Sites |
| PFAM
|
Functional domains, transmembrane (tmhmm), coiled-coils (ncoils),
low-complexity (seg). |
| ProDom |
ProDom - Protein domain db (Automatically generated) |
| SBASE |
SBASE - SBASE domain db |
| SMART |
Simple Modular Architecture Research Tool - Functional domains,
Outlier homologues and homologues of known structure, signal
peptides, internal repeats |
| |
|
| |
To predict proteins from motif sequence: |
| |
|
| OWL |
predicts all proteins containing an entered motif. OWL - Protein
database search |
| |
|
| |
To predict fingerpint sequences within a protein
which characterise a specific family: |
| |
|
| PRINTS |
PRINTS - Protein Motif Fingerprint Database. PRINTS is a compendium
of protein fingerprints. A fingerprint is a group of conserved
motifscharacteristic of members |
| PROTOMAP |
PROTOMAP - An automatic hierarchical classification of Swiss-Prot
proteins |
| TIGRFAMs |
|
| |
|
| |
To predict signal peptides within protein sequence: |
| |
|
signalP
 |
SignalP - The SignalP World Wide Web server predicts the presence
and location of signal peptide cleavage sites in amino acid
sequences from different organisms. |
| |
|
| |
To predict sites of post translational modification
peptides within protein sequence: |
| |
|
| |
|
| PROSITE |
dictionary of protein sites and patterns. PROSITE is a method
of determining the function of uncharacterized proteins translated
from genomic or cDNA sequences. |
DictyOGlyc
|
|
| |
|
| |
|
| TRANSMEMBRANE DOMAINS |
To predict transmembrane domains within protein
sequence: |
| |
|
| Ikeda
et al.
|
Paper: Transmembrane Topology
Prediction Methods: A Re-assessment and Improvement by a Consensus
Method Using a Dataset of Experimentally-Characterized Transmembrane
Topologies |
| |
| DAS |
DAS is based on the low-stringency dot-plots of the query
sequence against a collection of non-homologous TM proteins
using a previously derived scoring matrix. |
| pred-tmr2 |
PRED-TMR2 is an extension of PRED-TMR [Pasquier et al., 1999],
which incorporates a pre-processing stage by using a simple
hierarchical feed-forward artificial neural network to classify
proteins into either membrane or non-membrane proteins |
| SOSUI |
SOSUI utilizes physicochemical properties of amino acid sequences
such as hydrophobicity, charges, and sequence length. |
| TMAP |
TMAP utilizes the extra information coming from multiple sequence
alignments of homologous proteins. |
| |
|
| |
To predict transmembrane domains within protein
sequence and orientation within membrane: |
| |
|
| conpred
II |
a prediction server of transmembrane (TM) topology (i.e.,
the number of TM segments (TMSs), TMS positions and N-tail location)
based on a consensus approach by combining the results of several
proposed methods. |
| TMHMM
2.0 |
based on a hidden Markov model that is cyclic with seven types
of states for helix core, helix caps on either side, loop on
the cytplasmic side, two loops for the non-cytoplasmic side,
and a globular domain state in the middle of each loop. |
| TMpred |
TMpred employs a combination of several weight-matrices based
on the statistical analysis of TMbase, a database of TM proteins
from SWISS-PROT (Release 25), for scoring |
| TopPred
II |
applies the "positive-inside rule" to evaluate the validity
of topology models derived from the hydropathy analysis. (after
first using one of 3 methods of tm prediction: KD, GVH, GES) |
| HMMTOP
2.0 |
determines five structural parts of TM proteins using a HMM
formalism |
| MEMSAT
2 |
uses a set of statistical tables, a dynamic programming algorithm
to recognize TM topology models by expectation maximization,
and making use of multiple sequences alignment generated by
PSI-BLAST |
| PHDhtm |
|
