1D Characteristics
M. Tress, F. Pazos and L. Sanchez(CNB
Madrid).
1D charcteristics are those where each amino acid is associated
with a single value. For secondary structure these are: H -helix-, E
-strand-, L -loop-, ..., and they also exist for accessibility (buried
or exposed), for hydrophobicity,
etc. 1D characteristics are very useful in 3D structure prediction.
AA :
Sequence residues
OBSsec: Observed secondary structure (E: sheet, H: helix)
OBSacc: Observed accessibility (e: exposed, b: buried)
PHDsec: Predicted secondary structure
PHDacc: Predicted accessibility
In this link, a full description of Amino Acid Properties
Servers and Programs
-
PredictProtein :
This server provides variuos tools for sequence analysis and secondary structure prediction.
Once the sequence is sent to the server it will search for similar sequences and predict structural properties.
EXAMPLE
of server output.
-
Detection of functional motifs (SignalP, TMHMM, etc)
(CBS Prediction Servers)
-
-
Other 1D aspects:
-
SignalP: Prediction of
Signal Peptides (CBS, DK)
[Output]
-
TMHMM: Prediction of
trans-membrane helices (CBS, DK)
[Output]
-
COILS: Prediction
of "Coiled Coil" regions; (ISREC, CH)
[Output
]
-
Residue characteristics (hydrophobicity, ...): ExPASy
ProtScale
-
Post-translational modifications: Phosphorylation and glycosylation sites, etc. CBS
| PredictProtein |
PHDsec predicts secondary structure on the basis of multiple alignments using
neural networks. |
Example [Output] |
| JPred |
Jpred is a server that collects a protein sequence or an multiple alignment to predict secondary
structure. Works by combining the results of various methods to generate a consensus.
|
Example [Output] |
| PsiPred |
PSIPRED incorporates neural nets that analyse PSI-BLAST
output. Version 4 takes into account the the results of 4 independent neural
nets to improve the reliablility. |
Example [Output] |
Exercises
1. Send the sequences to various servers
(PHD,
JPred and PsiPred).
Compare the results.
>RPE_SCHPO O14105
MVQAKIAPSLLAGDFANLEKEVGRMLKYGSDWLHVDVMDAQFVPNLTIGPIVVKAMRNHY
TKEEAFFDCHLMVIEPERYIDQLADAGASLFCFHYEATEKHEEIISRAHEKGMLVGCALK
PKTPVEVILPFVEKLDMVLVMTVEPGKGGQSFMPECLPKVEFLRKKYPTLNVEVDGGLSL
KTVDAAADAGANVIVAGTAVFHAQSPEEVISGLRNSVMKAQETKPWFK
Results: PHD and PsiPred
2. Send these sequences to various trans-membrane prediction servers
(TMHMM,
PHD-TM,
THUMB-UP). Compare the results.
Example 1:
MYADM_HUMAN
Results: TMHMM,PHD-TM and THUMB-UP
Example 2:
FTSW_ECOLI
YWCF_BACSU
How many transmembrane regions have these proteins?
Compare the prediction with the information annotated in Swissprot/Uniprot Database.
FTSW_ECOLI and YWCF_BACSU are homologous sequences. How are their TMHMM predictions similar?
2. Signal Peptides and Signal Anchors
(SignalP server)
Which are the differeces among the N-terminal hydrophobic regions of these proteins:
FTSQ_ECOLI
GAS1_HUMAN
Take a look at:
Explain the SignalP output.
3. Send the sequences to the predictors of
phosphorylaytion and glyycoylation.
(NetOGly,
NetPhos)
>3_41 AA
ASYDGHKLVAGYDFTPPSTPSTDDPNVCREYSYKLGTYGAP
NetOGlyc
output
>4_153 AA
ASQKRPSQRHGSKYLATASTMDHARHGFLPRHRDTGILDSIGRFFGGDRGAPKNMYKDSHHPARTAHYGSLPQKSHGRTQ
DENPVVHFFKNIVTPRTPPPSQGKGRKSAHKGFKGVDAQGTLSKIFKLGGRDSRSGSPKPELVISALIVESRR
NetPhos
output
4. Coiled-Coil Regions
(COILS server)
The first step in sequence analysis - 1D Characteristics
A "nice" protein:
sptrembl.Q9VB11.Q9VB11_DROME
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