Secondary Structure Prediction BCKDHA
Contents
1. Secondary structure prediction
General Information
The secondary structure of a protein bases on the primary structure and consists of alpha-helices, beta-sheets and coils.
alpha-helices
Alpha-helices are build by H-bounds between the NH-group of an amino acid and the CO-group of the amino acid which is placed four recidues earlier (i+4). This form of the alhpa-helix is the most common one. There are two other types of alpha-helices which are very rare. One is called 3,10-helices because the H-bound is between the NH-group and the CO-group three recidues earlier (i+3). And the other one is the Phi-helix and here the H-boung is between the NH-group and the CO-group five residues earlier (i+5). The different locations of the CO-group influence the width and the height of the helices.
beta-sheets
The H-bounds between the CO-group and the NH-group which build a beta-sheet can be located far away from each other in the sequence.
There are two different kinds of beta-sheets. The parallel one where the sheets all point in the same direction and the anti-parallel ones where the sheets point alternately in different directions.
coils
Coils are irregular formed elements like turns.
PSIPRED
Basic information
author: David T. Jones (University College London)
year:1998
version: 2
PSIPRED uses neuronal networks which has a single hidden layer and a feed-forward back-propagation architecture to predict the secondary structure.
To run PSIPRED local it requires the output of PSI-BLAST (Position Specific Iterated - BLAST) as input data.
For the online prediction on the server it is enough to enter a amino acid sequence.
Since PSIPRED uses a very stringent cross validation method to evaluate the performance it reaches an average Q3 score of 80.7%.
The predicition is splitted into three different steps. In the first step sequence profiles are generated by using a position specific scoring matrix from PSI-BLAST as input for the neuronal network. In the next step the secondary structure is predicted. In the last step the output of the secundary structure prediction is filtered.
There are three different options:
- Mask low complexity regions
- Mask transmembrane helices
- Mask coiled-coil regions
References
[PSIPRED Server]
[Overview of prediction methods]
[History of the PSIPRED]
Prediction
Seq MAVAIAAARVWRLNRGLSQAALLLLRQPGARGLARSHPPRQQQQFSSLDD Pred CHHHHHHHHHHHHHHHCHHHHHHHHCCCCCCCCCCCCCCCCCCCCCCCCC UniProt Seq KPQFPGASAEFIDKLEFIQPNVISGIPIYRVMDRQGQIINPSEDPHLPKE Pred CCCCCCCCCCCCCCCCCCCCCCCCCCCEEEEECCCCCCCCCCCCCCCCHH UniProt EEEE HHH HH Seq KVLKLYKSMTLLNTMDRILYESQRQGRISFYMTNYGEEGTHVGSAAALDN Pred HHHHHHHHHHHHHHHHHHHHHHHHCCCCCCCCCCCCHHHHHHHHHHCCCC UniProt HHHHHHHHHHHHHHHHHHHHHHHH EEE HHHHHHHH Seq TDLVFGQYREAGVLMYRDYPLELFMAQCYGNISDLGKGRQMPVHYGCKER Pred CCEEECCCCHHHHHHHCCCCHHHHHHHHCCCCCCCCCCCCCCCCCCCCCC UniProt EEE HHHHHH HHHHHHHHH CCCC CCC Seq HFVTISSPLATQIPQAVGAAYAAKRANANRVVICYFGEGAASEGDAHAGF Pred CCCCCCCCCCCCHHHHHHHHHHHHHCCCCCEEEEEECCCCCCHHHHHHHH UniProt C CCCHHHHHHHHHHHHHHH EEEEEE HHH HHHHHHH Seq NFAATLECPIIFFCRNNGYAISTPTSEQYRGDGIAARGPGYGIMSIRVDG Pred HHHHHHCCCEEEEEECCCCCCCCCCCHHCCCCHHHHHCCCCCCCCCEECC UniProt HHHHH EEEEEEE EEE HHH EEE HHH HHH EEEEEE Seq NDVFAVYNATKEARRRAVAENQPFLIEAMTYRIGHHSTSDDSSAYRSVDE Pred HHHHHHHHHHHHHHHHHHCCCCEEEEEECCCCCCCCCCCCCCCCCCHHHH UniProt EEEEEEEEEEEEEEEEEE EEEEEE Seq VNYWDKQDHPISRLRHYLLSQGWWDEEQEKAWRKQSRRKVMEAFEQAERK Pred HHHHHHCCCCHHHHHHHHHHCCCCC HHHHHHHHHHHHHHHHHHHHHHHC UniProt HHHHHHHHHCCCC HHHHHHHHHHHHHHHHHHHHHHHH Seq PKPNPNLLFSDVYQEMPAQLRKQQESLARHLQTYGEHYPLDHFDK Pred CCCCHHHHHHHHHCCCCHHHHHHHHHHHHHHHHHCCCCCCCCCCC UniProt HHHH EEEE HHHHHHHHHHHHHHHHHHHH HHH
PSIPRED has predicted 23 coils, 16 alpha helices and 6 beta sheets.
The alpha helices are quite good predicted by DSSP but it also predicts many beta-sheets but most of them are false-positives.
Jpred3
Basic information
author: Cole C, Barber JD & Barton GJ (Bioinformatics and Computational Biology Research, University of Dundee)
year: 1998
version: 3
Jpred is using a neuronal network to make the predictions. To predict the secondary structure of a protein sequence or of a multiple alignment of protein sequences the algorithm Jnet is used. The prediction accuracy for secondary strctures lies above 81%. Additionally Jpred makes predictions about the solvent
accessibility.
Jpred3 needs a protein sequence or multiple alignment of protein sequences as input.
It is important that the target sequence is the first sequence in the multiple alignment since the alignment is modified so that the first sequence do not have any gaps. The alignemt has to be in the MSF or in the BLC format.
References
Prediction
By predicting the secondary structure of BCKDHA with JPred it found many hits with very good e-values in other proteins.
e-value=0.0
2bew, 2bev, 2beu, 1x80, 1wci, 1u5b, 1olx, 1ols, 1dtw, 1x7y, 1x7z, 1x7x, 1x7w, 2j9f, 2bff, 1v1r, 1olu, 1v16, 1v11, 2bfc, 2bfb, 1v1m, 2bfd, 2bfe
e-value=6e-58
1umd, 1umc, 1umb, 1um9
e-value=1e-57
2bp7, 1qs0, 1w85, 3dva, 1w88
With these hits JPred run the prediction:
Seq MAVAIAAARVWRLNRGLSQAALLLLRQPGARGLARSHPPRQQQQFSSLDD Pred HHHHHHHHHHHHHH EEE Conf 10090009999980000000323546777770000303566666777777 UniProd Seq KPQFPGASAEFIDKLEFIQPNVISGIPIYRVMDRQGQIINPSEDPHLPKE Pred EEEEE HH Conf 77777777777777654567777777308885377740467787776368 UniProd EEEE HHH HH Seq KVLKLYKSMTLLNTMDRILYESQRQGRISFYMTNYGEEGTHVGSAAALDN Pred HHHHHHHHHHHHHHHHHHHHHHHH E HHHHHHHHHHH Conf 99999999999999999999875045000001677517899999885278 UniProt HHHHHHHHHHHHHHHHHHHHHHHH EEE HHHHHHHH Seq TDLVFGQYREAGVLMYRDYPLELFMAQCYGNISDLGKGRQMPVHYGCKER Pred EEEE HHHHHHHH HHHHHHHHH Conf 84465157745788885065689988740677754577777545677777 UniProt EEE HHHHHH HHHHHHHHH CCCC CCC Seq HFVTISSPLATQIPQAVGAAYAAKRANANRVVICYFGEGAASEGDAHAGF Pred HHHHHHHHHHHH EEEEEE HHHHHHHH Conf 64132147888770367889998750688558887407887468999999 UniProt C CCCHHHHHHHHHHHHHHH EEEEEE HHH HHHHHHH Seq NFAATLECPIIFFCRNNGYAISTPTSEQYRGDGIAARGPGYGIMSIRVDG Pred HHHH EEEEEEE HHHHHHH EEEEE Conf 87500888606888703677777777777764067777005725774078 UniProt HHHHH EEEEEEE EEE HHH EEE HHH HHH EEEEEE Seq NDVFAVYNATKEARRRAVAENQPFLIEAMTYRIGHHSTSDDSSAYRSVDE Pred HHHHHHHHHHHHHHHHH EEEEEEEEEE HHH Conf 74689999999999988507985588886354067777777765553688 UniProt EEEEEEEEEEEEEEEEEE EEEEEE Seq VNYWDKQDHPISRLRHYLLSQGWWDEEQEKAWRKQSRRKVMEAFEQAERK Pred HHHHHH HHHHHHHHHHH HHHHHHHHHHHHHHHHHHHHHHHH Conf 99998468758999999986068866899999999999999999988606 UniProt HHHHHHHHHCCCC HHHHHHHHHHHHHHHHHHHHHHHH Seq PKPNPNLLFSDVYQEMPAQLRKQQESLARHLQTYGEHYPLDHFDK Pred HHHHHHH HHHHHHHHHHHHHHHH Conf 887368777523688756899999999999875267777777888 UniProt HHHH EEEE HHHHHHHHHHHHHHHHHHHH HHH
By comparing the prediction of the secondary structure of Jpred and the secondary structure of BCKDHA in UniProt it is remarkable that in the beginning the prediction differs a lot from UniProt but in the middle and in the end it becomes much better. Jpred predicts more helices and less beta sheets than there are in the UniProt secondary structure.
In the first line the secondary structure prediction is shown. The red parts stand for the alpha-helices and the green parts stand for the beta-sheets. Under this line the confidence of the prediction is symbolized by the black bars. The higher a bar is the higher is the confidence for the prediction on this position. In the last line again the confidence is shown. The numbers reach from 0 to 9 where 0 means that the prediction is very uncertain and 9 means that this prediction is quite sure.
DSSP
Basic information
author: Wolfgang Kabsch and Chris Sander (Max-Planck-Institut fürmedizinische Forschung, Heidelberg)
year: 1983
whole name: Define Secondary Structure of Proteins
Based on atomic coordinates in Protein Data Bank format, DSSP defines the secondary structure of a protein.
With this method the secondary structure is not predicted but determined from the 3D coordinates.
Referencse
[Introduction]
[Explanation ]
Prediction
Seq KPQFPGASAEFIDKLEFIQPNVISGIPIYRVMDRQGQIINPSEDPHLPKEKVLKLYKSMT Pred TT T TT T T TTT T 333 HHHHHHHHHHHH UniProt EEEEE HH Seq LLNTMDRILYESQRQGRISFYMTNYGEEGTHVGSAAALDNTDLVFGQYREAGVLMYRDYP Pred HHHHHHHHHHHHHHTTTTT TT HHHHHHHHHTT TTTSSS TT HHHHHHTT UniProt HHHHHHHHHHHHHH E HHHHHHHHHHH EEEE HHHHHHHH Seq LELFMAQCYGNISDLGKGRQMPVHYGCKERHFVTISSPLATQIPQAVGAAYAAKRANANR Pred HHHHHHHHHT TT TTTT T TT TTTT TTTTTHHHHHHHHHHHHHHTT UniProt HHHHHHHHH HHHHHHHHHHHH Seq VVICYFGEGAASEGDAHAGFNFAATLECPIIFFCRNNGYAISTPTSEQYRGDGIAARGPG Pred SSSSSSTT333THHHHHHHHHHHHTT SSSSSSS TSSTTSS333T TTTTT333T33 UniProt EEEEEE HHHHHHHHHHHH EEEEEEE HHHHHHH Seq YGIMSIRVDGNDVFAVYNATKEARRRAVAENQPFLIEAMTYRIGHHSTSDDSSAYR Pred 3T SSSSSSTT HHHHHHHHHHHHHHHHHHT SSSSSS T TTTT 333T UniProt EEEEE HHHHHHHHHHHHHHHHH EEEEEEEEEE Seq VNYWDKQDHPISRLRHYLLSQGWWDEEQEKAWRKQSRRKVMEAFEQAERKPKPNPNLLFS Pred HHHHHHT HHHHHHHHHHHHTT HHHHHHHHHHHHHHHHHHHHHHHHT 3333TT UniProt HHHHHH HHHHHHHHHHH HHHHHHHHHHHHHHHHHHHHHHHH HHHHHH Seq DVYQEMPAQLRKQQESLARHLQTYGEHYPLDHFDK Pred TTTTT HHHHHHHHHHHHHHHHH333T 333 UniProt H HHHHHHHHHHHHHHHH
1. line: Sequence
2. line: structral elements
3. line: if a residue is involved in symmetrie contacts it is labeled with a star
4. line: if a residue is solvent accessible it is labeled with an "A"
Letter code for the secundary structure elements:
- H (blue): alpha
- 3 (yellow): residue in isolated beta-bridge
- T (red): hydrogen bonded turn
- S (green): bend
2. Prediction of disordered regions
General information
Disordered regions are long regions which do not have a regular secondary structure. They are dynamically flexible and have only a regular structure when they bind to another substrate or protein. In these regions polar and charged amino acid and especially proline are overrepresentated. The disordered regions are conserved and obtain mainly in regions which have a regulatory function. Since disordered regions have no clear secondary structure they also have no tertiary structure.
DISOPRED
Basic information
author: Jonathan J. Ward, Liam J. McGuffin, Kevin Bryson, Bernard F. Buxton and David T. Jones (University College London)
year: 2004
version: 2
DISOPRED2 identifies disordered regions by searching residues which appear in the sequence records but have no co-ordinates in the electron density map. This is a very simple method to find disordered regions because the absence of co-ordinates can also be explained with artifacts of the crystalization process.
References
Publication
DISOPRED server
Information
Prediction
In the first line the confidence of the prediction which is shown in the second line is denoted. The prediction of a disordered region is marked with an asterisk (*). All of the disordered regions are predicted with a very high confidence.
DISOPRED predicts disordered regions in the beginning and in the end of BCKDHA as it is shown in the left picture by the red fields.
Also the plot on the right side points out that the disordered regions are in the beginning and in the end since at these two sides there are the highest peaks.
POODLE
Basic information
author:
- POODLE-L S. Hirose, K. Shimizu, S. Kanai, Y. Kuroda and T. Noguchi
- POODLE-S K. Shimizu, Y. Muraoka, S. Hirose, and T. Noguchi
- POODLE-W K. Shimizu, Y. Muraoka, S. Hirose, K. Tomii and T. Noguchi
- POODLE-I S.Hirose, K.Shimizu, N.Inoue, S.Kanai and T.Noguchi
year:
- POODLE-L 2007
- POODLE-S 2007
- POODLE-W 2007
- POODLE-I 2008
POODLE uses machine learning approaches to predict the disordered regions of an amino acid sequence.
There are several different options which can be choosen:
POODLE-L: This tool searches for disordered regions which are longer than 40 consecutive amino acids.
POODLE-S: Here the focus lies on predicting short disordered regions. There are two different subtools: "Missing residues" and "High B-factor residues"
POODLE-W: With this option the proteins which are mostly disordered can be found.
POODLE-I: In this tool the other three tools are combined. POODLE-I also uses structural information to predict disordered regions. It bases on a work-flow approach.
References
[POODLE-L]
[POODLE-S]
[POODLE-W]
[POODLE-I]
[POODLE server]
[Help]
Prediction
POODLE-S
POODLE-S Missing residues |
POODLE-S High B-factor residues |
---|---|
POODLE-S (Missing residues):
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 |
M | A | V | A | I | A | A | A | R | V | W | R | L | N | R | G | L | S | Q | A | A | L | L | L | L | R | Q | P | G | A | R | G | L | A | R | S | H | P | P | R | Q | Q | Q | Q | F | S | S | L | D | D | K | P | Q | F | P | G |
0.585 | 0.601 | 0.624 | 0.69 | 0.753 | 0.809 | 0.798 | 0.748 | 0.679 | 0.595 | 0.526 | 0.55 | 0.59 | 0.604 | 0.679 | 0.754 | 0.783 | 0.817 | 0.849 | 0.826 | 0.799 | 0.779 | 0.782 | 0.763 | 0.748 | 0.722 | 0.714 | 0.668 | 0.661 | 0.691 | 0.724 | 0.754 | 0.799 | 0.841 | 0.862 | 0.88 | 0.885 | 0.892 | 0.89 | 0.892 | 0.897 | 0.892 | 0.91 | 0.926 | 0.913 | 0.908 | 0.888 | 0.829 | 0.77 | 0.715 | 0.691 | 0.652 | 0.616 | 0.586 | 0.577 | 0.512 |
341 | 342 | 343 | 344 | 345 | |||||||||||||||||||||||||||||||||||||||||||||||||||
D | S | S | A | Y | |||||||||||||||||||||||||||||||||||||||||||||||||||
0.562 | 0.6 | 0.615 | 0.597 | 0.501 | |||||||||||||||||||||||||||||||||||||||||||||||||||
420 | 421 | 422 | 423 | ||||||||||||||||||||||||||||||||||||||||||||||||||||
L | R | K | Q | ||||||||||||||||||||||||||||||||||||||||||||||||||||
0.565 | 0.594 | 0.557 | 0.525 |
POODLE-S (which predicts short disordered regions) with the option "Missing residues" predicted the disordered regions between the positions 1-56, 341-345 and 420-423. This is also shown in the plot above.
POODLE-S (High B-Factor residues):
6 | 7 | 8 | 9 | |||||||||||||||||||||||||||||||||||||||
A | A | A | R | |||||||||||||||||||||||||||||||||||||||
0.618 | 0.664 | 0.634 | 0.609 | |||||||||||||||||||||||||||||||||||||||
15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 |
R | G | L | S | Q | A | A | L | L | L | L | R | Q | P | G | A | R | G | L | A | R | S | H | P | P | R | Q | Q | Q | Q | F | S | S | L | D | D | K | P | Q | F | P | G | A |
0.544 | 0.647 | 0.669 | 0.716 | 0.762 | 0.791 | 0.777 | 0.801 | 0.8 | 0.799 | 0.786 | 0.782 | 0.744 | 0.738 | 0.753 | 0.797 | 0.812 | 0.875 | 0.898 | 0.907 | 0.907 | 0.889 | 0.865 | 0.849 | 0.816 | 0.811 | 0.843 | 0.867 | 0.889 | 0.916 | 0.909 | 0.894 | 0.858 | 0.805 | 0.745 | 0.689 | 0.634 | 0.619 | 0.583 | 0.594 | 0.588 | 0.552 | 0.525 |
93 | ||||||||||||||||||||||||||||||||||||||||||
E | ||||||||||||||||||||||||||||||||||||||||||
0.529 | ||||||||||||||||||||||||||||||||||||||||||
95 | 96 | |||||||||||||||||||||||||||||||||||||||||
P | H | |||||||||||||||||||||||||||||||||||||||||
0.542 | 0.549 | |||||||||||||||||||||||||||||||||||||||||
340 | 341 | 342 | 343 | 344 | 345 | 346 | 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | ||||||||||||||||||||||||||||
D | D | S | S | A | Y | R | S | V | D | E | V | N | Y | W | ||||||||||||||||||||||||||||
0.501 | 0.607 | 0.663 | 0.73 | 0.764 | 0.746 | 0.763 | 0.768 | 0.769 | 0.746 | 0.731 | 0.711 | 0.66 | 0.594 | 0.549 | ||||||||||||||||||||||||||||
379 | 380 | 381 | 382 | 383 | 384 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | 398 | 499 | 400 | 401 | 402 | |||||||||||||||||||
E | K | A | W | R | K | Q | S | R | R | K | V | M | E | A | F | E | Q | A | E | R | K | P | K | |||||||||||||||||||
0.546 | 0.577 | 0.559 | 0.571 | 0.63 | 0.601 | 0.502 | 0.517 | 0.536 | 0.518 | 0.504 | 0.577 | 0.572 | 0.568 | 0.574 | 0.607 | 0.622 | 0.658 | 0.719 | 0.74 | 0.706 | 0.668 | 0.642 | 0.548 |
POODLE-S (which predicts short disordered regions) with the option "High B-Factor residues" predicted the disordered regions between the positions 6-9, 15-57, 93, 95-96, 340-354 and 379-402. This is also shown in the plot above.
POODLE-L
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | ||||||||||||
M | A | V | A | I | A | A | A | R | V | W | R | L | N | R | G | L | S | Q | A | A | L | L | L | L | R | Q | P | G | A | R | G | L | A | R | S | H | P | P | R | Q | Q | Q | Q | F | S | S | L | ||||||||||||
0.516 | 0.518 | 0.517 | 0.521 | 0.526 | 0.538 | 0.543 | 0.55 | 0.562 | 0.574 | 0.58 | 0.587 | 0.594 | 0.606 | 0.613 | 0.618 | 0.622 | 0.626 | 0.632 | 0.642 | 0.652 | 0.666 | 0.674 | 0.68 | 0.682 | 0.684 | 0.685 | 0.683 | 0.679 | 0.675 | 0.672 | 0.668 | 0.663 | 0.657 | 0.648 | 0.642 | 0.637 | 0.634 | 0.628 | 0.619 | 0.61 | 0.601 | 0.588 | 0.575 | 0.558 | 0.542 | 0.521 | 0.598 | ||||||||||||
369 | 370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | 378 | 379 | 380 | 381 | 382 | 383 | 384 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 936 | 397 | 398 | 399 | 400 | 401 | 402 | 403 | 404 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 414 | 145 | 416 | 417 | 418 | 419 | 420 | 421 | 422 | 423 | 424 | 425 | 426 | 427 | 428 |
L | S | Q | G | W | W | D | E | E | Q | E | K | A | W | R | K | Q | S | R | R | K | V | M | E | A | F | E | Q | A | E | R | K | P | K | P | N | P | N | L | L | F | S | D | V | Y | Q | E | M | P | A | Q | L | R | K | Q | Q | E | S | L | A |
0.365 | 0.549 | 0.572 | 0.591 | 0.615 | 0.637 | 0.656 | 0.671 | 0.685 | 0.698 | 0.711 | 0.725 | 0.737 | 0.746 | 0.753 | 0.756 | 0.757 | 0.76 | 0.763 | 0.764 | 0.764 | 0.763 | 0.761 | 0.761 | 0.762 | 0.763 | 0.762 | 0.759 | 0.754 | 0.75 | 0.747 | 0.745 | 0.742 | 0.738 | 0.733 | 0.723 | 0.712 | 0.698 | 0.687 | 0.676 | 0.67 | 0.666 | 0.669 | 0.672 | 0.67 | 0.665 | 0.656 | 0.65 | 0.64 | 0.63 | 0.619 | 0.614 | 0.61 | 0.605 | 0.592 | 0.576 | 0.558 | 0.54 | 0.521 | 0.436 |
POODLE-L predicts two disordered regions which are longer than 40 amino acids.They are located between the positions 1-48 and 369-428.
POODLE-W
The regions which could be disordered regions but poodle is not sure are bordered by blue squares and the disordered regions are bordered by red squares.
0=ordered regions
5=perhaps disordered regions
9=disordered regions
POODLE-I
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | ||
M | A | V | A | I | A | A | A | R | V | W | R | L | N | R | G | L | S | Q | A | A | L | L | L | L | R | Q | P | G | A | R | G | L | A | R | S | H | P | P | R | Q | Q | Q | Q | F | S | S | L | D | D | K | P | Q | F | P | G | P | G |
0.516 | 0.518 | 0.517 | 0.521 | 0.526 | 0.538 | 0.543 | 0.55 | 0.562 | 0.574 | 0.58 | 0.587 | 0.594 | 0.606 | 0.613 | 0.618 | 0.622 | 0.626 | 0.632 | 0.642 | 0.652 | 0.666 | 0.674 | 0.68 | 0.682 | 0.684 | 0.685 | 0.683 | 0.679 | 0.675 | 0.672 | 0.668 | 0.663 | 0.657 | 0.648 | 0.642 | 0.637 | 0.634 | 0.628 | 0.619 | 0.61 | 0.601 | 0.588 | 0.575 | 0.558 | 0.542 | 0.521 | 0.598 | 0.661 | 0.725 | 0.686 | 0.637 | 0.602 | 0.577 | 0.57 | 0.534 | ||
341 | 342 | 343 | 344 | 345 | |||||||||||||||||||||||||||||||||||||||||||||||||||||
D | S | S | A | Y | |||||||||||||||||||||||||||||||||||||||||||||||||||||
0.544 | 0.592 | 0.604 | 0.571 | 0.503 | |||||||||||||||||||||||||||||||||||||||||||||||||||||
370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | 378 | 379 | 380 | 381 | 382 | 383 | 384 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 401 | 402 | 403 | 404 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 414 | 415 | 416 | 417 | 418 | 419 | 420 | 421 | 422 | 423 | 424 | 425 | 426 | 427 |
S | Q | G | W | W | D | E | E | Q | E | K | A | W | R | K | Q | S | R | R | K | V | M | E | A | F | E | Q | A | E | R | K | P | K | P | N | P | N | L | L | F | S | D | V | Y | Q | E | M | P | A | Q | L | R | K | Q | Q | E | S | L |
0.549 | 0.572 | 0.591 | 0.615 | 0.637 | 0.656 | 0.671 | 0.685 | 0.698 | 0.711 | 0.725 | 0.737 | 0.746 | 0.753 | 0.756 | 0.757 | 0.76 | 0.763 | 0.764 | 0.764 | 0.763 | 0.761 | 0.761 | 0.762 | 0.763 | 0.762 | 0.759 | 0.754 | 0.75 | 0.747 | 0.745 | 0.742 | 0.738 | 0.733 | 0.723 | 0.712 | 0.698 | 0.687 | 0.676 | 0.67 | 0.666 | 0.669 | 0.672 | 0.67 | 0.665 | 0.656 | 0.65 | 0.64 | 0.63 | 0.619 | 0.614 | 0.61 | 0.605 | 0.592 | 0.576 | 0.558 | 0.54 | 0.521 |
443 | 444 | 445 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
F | D | K | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
0.606 | 0.742 | 0.881 |
POODLE-I predicted the disordered regions between the positions 1-56, 341-345, 370-427 and 443-445.
Comparison
POODLE-S(Missing residues) | POODLE-S(High B-factor residues) | POODLE-L | POODLE-W | POODLE-I |
---|---|---|---|---|
1-56 | 6-9 | 1-48 | 325-345 | 1-56 |
341-345 | 15-57 | 369-428 | 341-345 | |
420-423 | 93 | 370-427 | ||
95-96 | 443-445 | |||
340-354 | ||||
379-402 |
IUPred
Basic information
author: Zsuzsanna Dosztányi, Veronika Csizmók, Péter Tompa and István Simon
year: 2005
IUPred predicts disordered regions by estimating the capacity of polypeptides to form stabilizing contacts. The potential to form these contacts depends on the surrounding sequence and on the chemical properties. This approach is based on the idea that disordered regions have no capacity to form sufficient interresidue interactions so that there is no stabilizing energy.
There are three different prediction types which can be chosen:
- long disorder
- short disorder
- structured regions
References
[IUPred server]
[Theory]
Prediction
Prediction type: long disorder
33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 |
L | A | R | S | H | P | P | R | Q | Q | Q | Q | F | S | S | L | D | D |
0.6043 | 0.5758 | 0.6851 | 0.7881 | 0.6851 | 0.6906 | 0.6661 | 0.6661 | 0.7415 | 0.7505 | 0.6136 | 0.7629 | 0.7982 | 0.7595 | 0.7595 | 0.7163 | 0.6948 | 0.5211 |
89 | 90 | 91 | 92 | 93 | |||||||||||||
I | N | P | S | E | |||||||||||||
0.5254 | 0.6427 | 0.5493 | 0.5382 | 0.5951 | |||||||||||||
385 | 386 | 387 | 388 | ||||||||||||||
Q | S | R | R | ||||||||||||||
0.5456 | 0.5176 | 0.5176 | 0.5017 | ||||||||||||||
390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | ||||||||||
V | M | E | A | F | E | Q | A | ||||||||||
0.5017 | 0.5017 | 0.5533 | 0.7209 | 0.7547 | 0.7755 | 0.6851 | 0.5992 | ||||||||||
399 | 400 | 401 | |||||||||||||||
R | K | P | |||||||||||||||
0.5017 | 0.5176 | 0.5211 | |||||||||||||||
404 | 405 | 406 | 4407 | 408 | 409 | 410 | 411 | 412 | 413 | ||||||||
N | P | N | L | L | F | S | D | V | Y | ||||||||
0.5055 | 0.5807 | 0.6089 | 0.5707 | 0.6136 | 0.5176 | 0.5176 | 0.5176 | 0.5017 | 0.5176 | ||||||||
420 | 421 | 422 | |||||||||||||||
L | R | K | |||||||||||||||
0.5098 | 0.5254 | 0.5176 | |||||||||||||||
424 | 425 | 426 | 427 | 428 | |||||||||||||
Q | E | S | L | A | |||||||||||||
0.5951 | 0.5854 | 0.5807 | 0.5296 | 0.5296 | |||||||||||||
431 | |||||||||||||||||
L | |||||||||||||||||
0.5533 |
When using the long disorder-tool of IUPred it predicts several disordered regions. They are located at the positions 33-50, 89-93, 385-388, 390-397, 399-401, 404-413, 420-422, 424-428 and on the position 431.
Detailed sequence with disordered region probability: File:LongSeqOut.pdf
Prediction type: short disorder
1 | ||||||||||||||||||||||
M | ||||||||||||||||||||||
0.5623 | ||||||||||||||||||||||
33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 |
L | A | R | s | H | P | P | R | Q | Q | Q | Q | F | S | S | L | D | D | K | P | Q | F | P |
0.5846 | 0.6756 | 0.7605 | 0.7688 | 0.7688 | 0.7688 | 0.6756 | 0.6827 | 0.7275 | 0.7232 | 0.7501 | 0.8311 | 0.7869 | 0.8158 | 0.8200 | 0.7817 | 0.7458 | 0.6789 | 0.6827 | 0.6035 | 0.5173 | 0.5253 | 0.5008 |
92 | 93 | |||||||||||||||||||||
S | E | |||||||||||||||||||||
0.5711 | 0.5473 | |||||||||||||||||||||
393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 401 | 402 | 403 | 404 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | ||||
A | F | E | Q | A | E | R | K | P | K | P | N | P | N | L | L | F | S | D | ||||
0.5514 | 0.5900 | 0.5992 | 0.6174 | 0.6293 | 0.5941 | 0.5667 | 0.5084 | 0.6124 | 0.5549 | 0.5008 | 0.5667 | 0.5802 | 0.5296 | 0.5802 | 0.5623 | 0.5846 | 0.5253 | |||||
415 | ||||||||||||||||||||||
E | ||||||||||||||||||||||
0.5008 | ||||||||||||||||||||||
420 | 421 | |||||||||||||||||||||
L | R | |||||||||||||||||||||
0.5296 | 0.5253 | |||||||||||||||||||||
423 | 424 | 425 | ||||||||||||||||||||
Q | Q | E | ||||||||||||||||||||
0.5126 | 0.5711 | 0.5008 | ||||||||||||||||||||
427 | 428 | |||||||||||||||||||||
L | A | |||||||||||||||||||||
0.5374 | 0.5126 | |||||||||||||||||||||
433 | ||||||||||||||||||||||
T | ||||||||||||||||||||||
0.5084 | ||||||||||||||||||||||
438 | 439 | 440 | 441 | 442 | 443 | 444 | ||||||||||||||||
Y | P | L | D | H | F | D | K | |||||||||||||||
0.5374 | 0.6035 | 0.6442 | 0.6827 | 0.7951 | 0.8158 | 0.8556 | 0.9257 |
When using the short disorder-tool of IUPred it predicts several disordered regions. They are located at the positions 1, 33-55, 92-93, 393-411, 415, 420-421, 423-425, 427-428, 433 and 438-444.
Detailed sequence with disordered region probability: File:ShortSeqOut.pdf
Prediction type: structured regions
With the option "structured regions" there was no prediction of disordered regions.
Only the command "Unkown globular domains: 1-445" appeared.
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3. Prediction of transmembrane alpha-helices and signal peptides
General
Transmembrane Topology
Prediction tools: TMHMM, OCTOPUS and SPOCTOPUS
Signal Peptides
Signal peptides are N-terminal sequence motifs directing proteins to their cellular destination, like secretory pathway, mitochondria and chloroplast.
One example for a signal peptide is the secretory signal peptide (SP), which is an N-terminal peptide that is typically 15-30 amino acids long. There are three regions of a signal peptide: an N-terminal region (n-region) which is often built up by positively charged residues, a hydrophobic region (h-region) in the middle of at least six residues and a C-terminal region (c-region) of polar uncharged residues. In Eukaryotes the SP targets proteins across the endoplasmic reticulum, in prokaryotes across the plasma membrane. The SP is cleaved when the protein crosses the membrane.
Furthermore there exists chloroplast transit peptides (cTP) which are also N-terminal and are cleaved when the protein enters the choloplast. The most conserved site in cTPs is an Alanine directly after the N-terminal methionine...
Prediction tools: SignalP, TargetP
Combined transmembrane and signal peptide prediction As the hydrophobic regions of a transmembrane helix and a signal peptide are highly similar, this leads to cross reaction between these two types of prediction. <ref>http://www.ebi.ac.uk/Tools/phobius/help.html</ref>
Prediction tools: Phobius and Polyphobius
In the following section different tools for predicting transmembrane helices and signal peptides are tested. As the BCKDHA protein isn't a transmembrane protein, additional proteins were used for the transmembrane and signal peptide analysis:
name | organism | location | transmembrane protein | function | reference |
---|---|---|---|---|---|
A4_HUMAN | Human | Cell membrane | yes | Protease Inhibitor | P05067 |
BACR_HALSA | Halobacterium salinarium | Cell membrane | yes | ion transport | P02945 |
INSL5_HUMAN | Human | extracellular region | no | hormone | Q9Y5Q6 |
LAMP1_HUMAN | Human | Cell membrane, Lysosome membrane, Endosome membrane | yes | Presents carbohydrate ligands to selectins | P11279 |
RET4_HUMAN | Human | extracellular space | no | Transport | P02753 |
TMHMM
- TMHMM was developed by Sonnhammer, Heijne and Krogh in 1998 <ref> E.L. Sonnhammer, Heijne and A. Krogh, A hidden Markov model for predicting transmembrane helices in protein sequences, Proc Int Conf Intell Syst Mol Biol.(1998)</ref>
- TMHMM predicts transmembrane helices in proteins.
- TMHMM is a membrane topology prediction method based on a hidden Markov model.
Execution Before we could execute TMHMM we had to change all occurrences of "/usr/local/bin/" to "/usr/bin" in the following files: tmhmm, tmhmm.ORIG and tmhmmformat.pl
To execute the program we used these commands:
- tmhmm all.fa > task_33/tmhmm_out.txt
Phobius and Polyphobius
- Phobius was developed by Käll et al <ref>Käll et al., "A Combined Transmembrane Topology and Signal Peptide Prediction Method", Journal of Mol. Biology,338(5):1027-1036, 2004 </ref>
- combined prediction of transmembrane regions and signal peptids
- Required input information: only sequence in FASTA-Format (20 amino acids and B, Z, X are recognized)
- As transmembrane topology and signal peptides are likely to be conserved during evolution, Polyphobius was established <ref>Käll et al., "An HMM posterior decoder for sequence feature prediction that includes homology information", Bioinformatics, 21 (Suppl 1):i251-i257, 2005</ref>, which includes information from homologous sequences to the query.
- Required input: 2 Options: Query Sequence in FASTA-Format, which is then blasted agains uniprot_trembl or upload of an alignment in FASTA-Format which provides information about homologs.
A4_HUMAN | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Phobius | Polyphobius | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
BACR_HALSA | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Phobius | Polyphobius | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
INSL5_HUMAN | |||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Phobius | Polyphobius | ||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
LAMP1_HUMAN | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Phobius | Polyphobius | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
RET4_HUMAN | |||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Phobius | Polyphobius | ||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
For the BCKDHA-protein Phobius predicted a signal peptide with about 90% probability at the beginning of the sequence. The predicted signal peptide is 34 amino acids long. This matches the information given on Uniprot, which says, that BCKDHA contains a 45bp long signal peptide for the transfer into the mitochondrion. The rest of the amino acid is a non cytoplasmic protein sequence. No part of the protein is predicted to be transmembrane spanning. This is also true, as BCKDHA is a protein located in the mitochondrion matrix according to Uniprot.
BCKDHA | |||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Phobius | Polyphobius | ||||||||||||||||||||||||||||||||||
|
|
Considering the information given on Uniprot, Polyphobius performed worse than Phobius on the BCKDHA-protein sequence. It predicted no signal sequence at the beginning of the protein sequence. There is a low probability for the amino acids between position 1-45 to be a signal sequence, but all in all the whole sequenc is predicted to be a non cytoplasmic protein.
OCTOPUS and SPOCTOPUS
- OCTOPUS was developed by Viklund and Elofsson in 2008 <ref>Håkan Viklund and Arne Elofsson, "Improving topology prediction by two-track ANN-based preference scores and an extended topological grammar", Bioinformatics (2008)</ref>
- OCTOPUS (obtainer of correct topologies for uncharacterized sequences) uses a combination of hidden Markov models and artificial neural networks.
- It creates a sequence profile by doing a BLAST search to obtain homologous sequences. The profile is used as input for a neural network that predicts the probability for each residue to be located in a transmembrane(M), interface (I), close loop (L), or globular loop (G) environment as well as the preference to be inside (i) or outside (o) of the membrane. A hidden Markov model is used to calculate the most likely Protein Topology.
- Required input: Protein Sequence in FASTA-Format
- SPOCTOPUS (Viklund et al., 2008<ref>Viklund et al., "A combined predictor of signal peptides and membrane protein topology", Bioinformatics (2008)</ref>) is an extension of OCTOPUS which also predicts signal peptides. A neural network is used to predict a signal peptide preference score. The signal peptide's location is determined by a hidden Markov model. The output contains the information retrieved by OCTOPUS as well as the probabilty if a residue is predicted to be N-terminal of a signal peptide (n) or in a signal peptide (S).
- Required input information: Protein sequence in FASTA-Format
A4_HUMAN | |
OCTOPUS | |
SPOCTOPUS | |
BACR_HALSA | |
OCTOPUS | |
SPOCTOPUS | |
INSL5_HUMAN | |
OCTOPUS | |
SPOCTOPUS | |
LAMP1_HUMAN | |
OCTOPUS | |
SPOCTOPUS | |
RET4_HUMAN | |
OCTOPUS | |
SPOCTOPUS | |
BCKDHA | |
OCTOPUS | |
SPOCTOPUS |
SignalP
- SignalP was established by Nielsen et al. in 1997<ref>Nielsen et al., "Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites", Protein Engineering, 10:1-6, 1997</ref>
- SignalP is neural network based. It identifies signal peptides and cleavage sites.
Execution
To run the command line SignalP tool, the path in the SignalP file had to be adapted to /apps/signalp-3.0
Following commands were used to execute SignalP:
- signalp -t euk P05067.fasta > signalp_out_P05067.txt
- signalp -t gram- P02945.fasta > signalp_out_P02945.txt
- signalp -t euk Q9Y5Q6.fasta > signalp_out_Q9Y5Q6.txt
- signalp -t euk P11279.fasta > signalp_out_P11279.txt
- signalp -t euk P02753.fasta > signalp_out_P02753.txt
- signalp -t euk P12694.fasta > signalp_out_P12694.txt
Results
TargetP
- TargetP was developed by Emanuelsson et al. in 2002 <ref> Emanuelsson et al., "Predicting subcellular localization of proteins based on their N-terminal amino acid sequence", J. Mol. Biol., 200: 1005-1016, 2002</ref>
- TargetP predicts the subcellular location of eukaryotic proteins. additionally: cleavage site predictions
- This method is neural network based. The prediction is based on the N-terminal presequences: chloroplast transit peptide(cTP), mitochondiral targeting peptide (mTP) or secretory pathway signal peptide (SP)
- Required input information: Sequence(s) in FASTA format, organism group
The TargetP prediction results can be seen in the following table:
The ODBA_HUMAN (BCKDHA) is predicted to be located in the mitochondrion, which is true according to Uniprot.
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4. Prediction of GO terms
GOPET
- GOPET (Gene Ontology Term Prediction and Evaluation Tool) was described by Vinayagam et al.<ref> Arunachalam Vinayagam, Coral Del Val, Falk Schubert, Roland Eils, Karl-Heinz Glatting, Sándor Suhai, Rainer König, "GOPET: A tool for automated predictions of Gene Ontology terms", BMC Bioinformatics (2006), Volume: 7, Issue: 161, Publisher: BioMed Central, Pages: 161</ref>
- GOPET is a complete automated to for assigning molecular function terms to a given sequence.
- Required input information: cDNA or protein sequence
- Gene Ontology is used for annotation terms, GO-mapped protein databases for performing homology searches and Support Vector Machines for the prediction and the assignment of confidence values.
- The prediction is organism independent.
The GOPET results for the given proteins are shown below.
BCKDHA
GOid | Aspect | Confidence | GOTerm |
---|---|---|---|
GO:0003824 | F | 97% | catalytic activity |
Go:0016491 | F | 96% | oxidoreductase activity |
GO:0016624 | F | 95% | oxidoredusctase activity acting on the aldehyde or oxo group of donors disulfide as acceptor |
GO:0003863 | F | 90% | 3-methyl-2-oxobutanoate dehydrogenase 2-methylpropanoyl-transferring activity |
GO:0004739 | F | 89% | pyruvate dehydrogenase acetyl-transferring activity |
GO:0004738 | F | 78% | pyruvat dehydrogenase activity |
GO:0003826 | F | 77% | alpha-ketoacid dehydrogenase activity |
GO:0047101 | F | 75% | 2-oxoisovalerate dehydrogenase acylting activity |
GO:0008677 | F | 65% | 2-dehydropantoate 2-reductase activity |
GO:0019152 | F | 63% | acetoin dehydrogenase activity |
GO:0030955 | F | 63% | potassium ion binding |
GO:0016616 | F | 62% | oxidoreductase activity acting on the CH-OH group of donors NAD or NADP as acceptor |
GO:0046872 | F | 62% | metal ion binding |
A4_HUMAN
GOid | Aspect | Confidence | GOTerm |
---|---|---|---|
GO:0004866 | F | 87% | endopeptidase inhibitor activity |
GO:0004867 | F | 86% | serine-type endopeptidase inhibitor activity |
GO:0030568 | F | 83% | plasmin inhibitor activity |
GO:0030304 | F | 83% | trypsin inhibitor activity |
GO:0030414 | F | 82% | peptidase inhibitor activity |
GO:0005488 | F | 79% | binding |
GO:0005515 | F | 74% | protein binding |
GO:0046872 | F | 73% | metal ion binding |
GO:0003677 | F | 71% | DNA binding |
GO:0008201 | F | 70% | heparin binding |
GO:0008270 | F | 69% | zinc ion binding |
GO:0005507 | F | 69% | copper ion binding |
GO:0005506 | F | 67% | iron ion binding |
BACR_HALSA
GOid | Aspect | Confidence | GOterm |
---|---|---|---|
GO:0005216 | F | 77% | ion channel activiy |
GO:0008020 | F | 75% | G-protein coupled photoreceptor activity |
GO:0015078 | F | 60% | hydrogen ion transmembrane transporter activity |
INSL5_HUMAN
GOid | Aspect | Confidence | GOterm |
---|---|---|---|
GO:0005179 | F | 80% | hormone activity |
LAMP1_HUMAN
GOid | Aspect | Confidence | GOterm |
---|---|---|---|
GO:0004812 | F | 60% | aminoacyl-tRNA ligase activity |
GO:0005524 | F | 60% | ATP binding |
RET4_HUMAN
GOid | Aspect | Confidence | GOterm |
---|---|---|---|
GO:0005488 | F | 90% | binding |
GO:0005501 | F | 81% | retinoid binding |
GO:0008289 | F | 80% | lipid binding |
GO:0019841 | F | 78% | retinol binding |
GO:0005215 | F | 78% | transporter activity |
GO:0016918 | F | 78% | retinal binding |
GO:0005319 | F | 69% | lipid transporter activity |
GO:0008035 | F | 60% | high-density lipoprotein particle binding |
Pfam
- Pfam was established by Finn et al. in 2008. It is described in <ref>Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, Hotz HR, Ceric G, Forslund K, Eddy SR, Sonnhammer EL, Bateman A (2008). "The Pfam protein families database.". Nucleic Acids Res 36 (Database issue): D281–8</ref>
Query | Cellular Component | Molecular function | Biological Process | |
---|---|---|---|---|
BCKDHA | GO:0016624 (oxidoreductase activity, acting on the aldehyde or oxo group of donors, disulfide as acceptor) | GO:0008152 (metabolic process) | ||
A4_HUMAN | GO:0016021 (integral to membrane) | GO:0005488 (binding) | ||
BACR_HALSA | GO:0016020 (membrane) | GO:0005216 (ion channel activity) | GO: 0006811 (ion transport) | |
INSL5_HUMAN | GO:0005576 (extracellular region) | GO:0005179 (hormone activity) | ||
LAMP1_HUMAN | GO:0016020 (membrane) | |||
RET4_HUMAN | GO:0005488 (binding) |
ProtFun 2.2
- ProtFun is described in : Jensen et al.<ref>Prediction of human protein function from post-translational modifications and localization features.
L. Juhl Jensen, R. Gupta, N. Blom, D. Devos, J. Tamames, C. Kesmir, H. Nielsen, H. H. Stærfeldt, K. Rapacki, C. Workman, C. A. F. Andersen, S. Knudsen, A. Krogh, A. Valencia and S. Brunak. J. Mol. Biol., 319:1257-1265, 2002</ref>
- ProtFun is an ab initio prediction server of protein function from sequence. Various servers are queried and the provided information is integrated into the final prediciton.
References
<references />
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