Task 3 (MSUD)

From Bioinformatikpedia

Secondary structure

Lab journal


Approach for predicting secondary structure with ReProf

For P10775, ReProf was run with the protein sequence fasta file and position specific scoring matrices (PSSM) derived from big_80 and SwissProt as input. The following tables show the comparison of the prediction results to the secondary structure assignment of DSSP. The f-measure is the harmonic mean of recall and precision, it gives a good indication for the quality of a classificator.

Comparison of ReProf prediction (fasta input) to DSSP assignment
secondary structure element recall precision f-measure
H 0.719 0.585 0.645
E 0.211 0.500 0.296
L 0.616 0.654 0.635

Comparison of ReProf prediction (big_80 PSSM input) to DSSP assignment
secondary structure element recall precision f-measure
H 0.944 0.889 0.916
E 0.649 0.685 0.667
L 0.826 0.866 0.846

Comparison of ReProf prediction (SwissProt PSSM input) to DSSP assignment
secondary structure element recall precision f-measure
H 0.923 0.914 0.919
E 0.807 0.523 0.634
L 0.719 0.859 0.782

Predictions using a PSSM instead of a simple sequence have a considerably better quality. All methods predict helices better than loops and these better than beta sheets. The results of the run with the big_80 PSMM are better for E and L and only slightly worse for H than those using the SwissProt PSMM.

The percentages of correctly identified secondary structure (H, E or L) for the three methods are 61 %, 86 % and 82 %. So for the remaining sequences, the method with the best performance (usage of PSSM derived from big_80 as input for ReProf) was used.

Comparison of ReProf to PsiPred and DSSP

The following tables show the percentages of agreement for secondary structure between ReProf and PsiPred or DSSP.


secondary structure element ReProf vs. PsiPred ReProf vs. DSSP
H 0.804 0.812
E 0.400 0.585
L 0.876 0.782
all 0.849 0.816


secondary structure element ReProf vs. PsiPred ReProf vs. DSSP
H 0.798 0.889
E 0.691 0.649
L 0.779 0.828
all 0.849 0.855


secondary structure element ReProf vs. PsiPred ReProf vs. DSSP
H 0.794 0.816
E 0.487 0.615
L 0.830 0.807
all 0.827 0.807


secondary structure element ReProf vs. PsiPred ReProf vs. DSSP
H 0.897 0.923
E 0.694 0.643
L 0.636 0.545
all 0.802 0.802

Altogether, ReProf agrees in 80-85% of the predictions with PsiPred and DSSP. In most cases the agreement for H and L is higher than for E.

Information from UniProt and PDB

A summary of interesting features for the proteins taken from UniProt and PDB:

P12694, 2BFD:

  • name: 2-oxoisovalerate dehydrogenase subunit alpha, mitochondrial
  • EC:
  • gene: BCKDHA
  • organism: Homo sapiens (Human)
  • sequence length: 445 AA
  • subunit structure: heterotetramer of alpha and beta chains
  • subcellular location: mitochondrion matrix
  • secondary structure: 42% helical, 10% beta sheet
  • 3D similarity: pyruvate dehydrogenase E1
  • ligands: chloride ion, glycerol, potassium ion, manganese (II) ion, (4S)-2-methyl-2,4-pentanediol, thiamin diphosphate

P10775, 2BNH:

  • name: ribonuclease inhibitor
  • gene: RNH1
  • organism: Sus scrofa (Pig)
  • sequence length: 456 AA
  • subcellular location. cytoplasm
  • sequence similarities: contains 15 LRR (leucine-rich) repeats
  • secondary structure: alternating helix and strand, 42% helical, 12% beta sheet

Q08209, 1AUI:

  • name: serine/threonine-protein phosphatase 2B catalytic subunit alpha isoform
  • EC:
  • gene: PPP3CA
  • organism: Homo sapiens (Human)
  • sequence length: 521 AA
  • subunit structure: heterodimer of alpha and beta chain (human calcineurin heterodimer)
  • subcellular location: nucleus
  • secondary structure: 27% helical, 11% beta sheet
  • ligands: calcium ion, Fe (III) ion, zinc ion

Q9X0E6, 1KR4:

  • name: divalent-cation tolerance protein CutA
  • gene: cutA
  • organism: Thermotoga maritima (strain ATCC 43589 / MSB8 / DSM 3109 / JCM 10099)
  • sequence length: 101 AA
  • subunit structure: homotrimer
  • subcellular location: cytoplasm
  • secondary structure: great fraction of strands, 29% helical, 35% beta sheet


In the first step, the ReProf results for P10775 were evaluated against the DSSP assignment. Here, DSSP was viewed as a standard of truth, because it assigns secondary structure based on the measured 3D structure by examining angles and H bonds between atoms.

The prediction of secondary structure is much better if a PSSM is used instead of the sequence. The reason is that a PSSM describes the requirements for each position better than the amino acid sequence, because it uses evolutionary information. So it identifies for each position alternatives for the residues in the primary sequence, that do not alter the overall structure of the protein. The difference between the usage of big_80 or SwissProt for generating the PSSM is not that obvious, but we decided to take big_80 for the remaining proteins because it showed a slightly better performance in our test with the example protein P10775.

For all proteins these ReProf results were compared to PsiPred and DSSP, to see how much the methods agree in the secondary structure assignment. The methods agree for most residues. The highest discrepancies can be observed for the prediction of beta strands, which shows that these are not as easy to predict as alpha helices or loops. A reason for this could be that beta strands are less frequent than alpha helices in most proteins, as can be seen in the informations taken from UniProt and PBD.

Other method for prediction of secondary structure are GOR, Jpred and PHDsec. Another secondary structure assignment tool is STRIDE.

Disordered protein

Lab journal



IUPred performs prediction of intrinsic disordered regions of proteins by the observation of pairwise energy content from proteins with known structures. The results of IUPred fall into 3 categories: long (disordered regions), short (disordered regions) and globular (regions of protein where pairwise interactions between residues are more potential).

For the prediction of disordered regions (prediction mode long and short), position-specific score is assigned to each residue of the query sequence. The score indicates the tendency that the corresponding residue belongs to a disordered region. Following are the profiles of disorder tendency generated by web-tool of IUPred:




Because DisProt dose not contain all uniprot sequences, we have searched for homologous sequences in DisProt for the protein sequences.



Structural stability of Q9X0E6. Red parts have symmetry-related crystal contacts (within 5 Å). Thickness of backbone represents variation of B-values.
  • Generally the profiles of long and short disorders are similar because they are overall highly correlated (except for protein Q9X0E6).
  • At both ends of the protein short disorder scores are much higher. This reflects the fact that residues at ends of proteins have higher spatial flexibility and are structurally more unstable.
  • High short disorder scores of Q9X0E6 can be explained by its structure.
    • As is shown in its X-ray structure, at the both ends of the thick representation of backbone indicates high B-values which mean the conformation of residues is either very flexible or even undefined.
    • While red parts of the protein show symmetry-related crystal contacts (within 5 Å), the remaining parts have barely symmetry-related crystal contacts.
    • The overall lower long disorder score may be explained by the fact that the protein still fold into a definite 3D structure, despite of local flexibility.
  • Q9X0E6 also falls into different category in comparison to the other 3 proteins.
    • As is described in InterPro its function is not clear but should have a role in signal transduction.
    • The other 3 proteins are either subunits or inhibitor of enzymes.


  • In comparison to IUPred, MetaDisorder seems to be more sensitive to input data. It finds out more short structural regions that locate in disordered regions.
    • For sequence Q08209, the prediction result of MetaDisorder shows almost the same disordered regions which are annotated in DisProt.
  • Generally IUPred and MetaDisorder share very similar results.
    • For sequence Q9X0E6, the predicted results are most dissimilar. The annotation of homologous sequence in DisProt can not show significant information for comparison to prediction methods because the E-value of local alignment is too high.

Comparison between Prediction and Annotation

The prediction results of Human Sulfotransferase 1A3/1A4(P50224) are dissimilar to the annotations in DisProt. It seems structural features like B-factors and symmetry-related crystal contacts give important clue to intrinsically disordered regions. As is shown in PDB structure of P50224, large range of amino-acids have high B-factors.

X-ray crystallographic structure of P50224 (Source: PDBe)

Transmembrane helices

Lab journal



Except BCKDHA all other proteins were predicted to have transmembrane helices. Although there is a weak signal for transmembrane region, the protein P35462 is predicted to have 6 transmembrane helices. The protein P47863 is predicted to have 6 transmembrane helices. And protein Q9YDF8 is predicted to have 7 transmembrane helices.


BCKDHA is predicted to have 1 transmembrane helix. All the other proteins are predicted to have 6 transmembrane helices.

Annotations in OPM and PDBTM

  • BCKDHA: no annotation was found in OPM and PDBTM.
  • P35462(3PBL):
  • P47863(2D57): The PDB structure is a homo tetramer. Protein P47863 is one of the 4 identical chains.
  • Q9YDF8(2KYH):


  • Comparison between OPM and PDBTM:
    • PDBTM seems to have more narrow transmembrane region than the OPM.
    • For same structure, PDBTM tends to assign less transmembrane helices in comparison to OPM.
  • As we already know, BCKDHA is a intra-mitochondrial protein. The prediction result of MEMSAT-SVM is wrong.
  • Generally prediction results of PolyPhobius and MEMSAT-SVM are similar to annotations in OPM and PDBTM.

Signal peptides

Lab journal


SignalP predictions

The following diagrams show the C- (cleavage site), S- (signal peptide) and Y- (combined cleavage site) scores for the three proteins according to SignalP version 4.1. The combined cleavage site score combines the cleavage score with the slope of the signal peptide score to optimize the recognition of cleavage sites.

P02768 signalp.png

P47863 signalp.png

P11279 signalp.png

For P02768 and P11279 signal peptides are predicted, with the clevage site between position 18 and 19 for P02768 and between 28 and 29 for P11279. For P47863, no signal peptide is predicted.

SignalP version 3.0 came to the same result for P02768 and P11279. However for P47863 it predicts a signal peptide with cleavage site between positions 54 and 55 (neural network) or 56 and 57 (hidden markov model), although only with the probability 0.723 compared to near 1 for the other two proteins.

Known signal peptides

On the Signal Peptide Website there are entries for P02768 and P11279 but not for P47863:

Accession Number Entry Name Protein Name Organism Length Status Signal Sequence
P02768 ALBU_HUMAN Serum albumin Homo sapiens 18 confirmed MKWVTFISLLFLFSSAYS
P11279 LAMP1_HUMAN Lysosome-associated membrane glycoprotein 1 Homo sapiens 28 confirmed MAAPGSARRPLLLLLLLLLLGLMHCASA


The predictions of the newest version of SignalP agree with the confirmed signal peptides. The older version predicted a signal peptide in P47863, where no one is. According to UniProt, P47863 has transmembrane helices, so these might be mistaken for a signal peptide by the old version because they resemble each other.

Other methods for signal peptide prediction are Phobius, PrediSi and Signal-3L.

GO terms

Lab journal



GOid Aspect Confidence GO term
GO:0003824 F 97% catalytic activity
GO:0016491 F 96% oxidoreductase activity
GO:0016624 F 95% oxidoreductase 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% pyruvate dehydrogenase activity
GO:0003826 F 77% alpha-ketoacid dehydrogenase activity
GO:0047101 F 75% 2-oxoisovalerate dehydrogenase acylating 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

Most terms concerning the catalytic activity (GO:0003824, GO:0016491, GO:0016624, GO:0003863, GO:0003826, GO:0047101) are consistent with the knowledge about the enzyme activity of the 2-oxoisovalerate dehydrogenase (see biochemical description of MSUD). Also the terms about metal binding (GO:0030955, GO:0046872) correspond to the characterization in PDB.


############## ProtFun 2.2 predictions ##############


# Functional category                  Prob     Odds
  Amino_acid_biosynthesis              0.187    8.520
  Biosynthesis_of_cofactors            0.246    3.413
  Cell_envelope                        0.035    0.581
  Cellular_processes                   0.041    0.560
  Central_intermediary_metabolism   => 0.321    5.096
  Energy_metabolism                    0.208    2.310
  Fatty_acid_metabolism                0.023    1.738
  Purines_and_pyrimidines              0.257    1.059
  Regulatory_functions                 0.031    0.194
  Replication_and_transcription        0.170    0.636
  Translation                          0.047    1.078
  Transport_and_binding                0.029    0.071

# Enzyme/nonenzyme                     Prob     Odds
  Enzyme                            => 0.769    2.683
  Nonenzyme                            0.231    0.324

# Enzyme class                         Prob     Odds
  Oxidoreductase (EC 1.-.-.-)          0.178    0.857
  Transferase    (EC 2.-.-.-)          0.238    0.690
  Hydrolase      (EC 3.-.-.-)          0.190    0.601
  Lyase          (EC 4.-.-.-)          0.076    1.614
  Isomerase      (EC 5.-.-.-)          0.010    0.321
  Ligase         (EC 6.-.-.-)       => 0.085    1.673

# Gene Ontology category               Prob     Odds
  Signal_transducer                    0.098    0.458
  Receptor                             0.006    0.038
  Hormone                              0.001    0.206
  Structural_protein                   0.005    0.170
  Transporter                          0.025    0.226
  Ion_channel                          0.009    0.163
  Voltage-gated_ion_channel            0.004    0.170
  Cation_channel                       0.010    0.215
  Transcription                        0.060    0.470
  Transcription_regulation             0.053    0.427
  Stress_response                      0.010    0.110
  Immune_response                      0.012    0.136
  Growth_factor                        0.009    0.609
  Metal_ion_transport                  0.012    0.025


The protein is predicted to have a function in the central intermediary metabolism, be an enzyme and belong to the enzyme class EC 6 (ligase). The last aspect is not correct, since it is a oxidoreductase (compare Information from UniProt and PDB).


Pfam sequence search gives one significant Pfam-A match:

  • E1_dh (dehydrogenase E1 component, PF00676)
    • use thiamine pyrophosphate as cofactor
    • includes pyruvate dehydrogenase, 2-oxoglutarate dehydrogenase and 2-oxoisovalerate dehydrogenase
    • members of multienzyme complex
    • interactions: E1_dh, Transketolase_C (transketolase, C-terminal domain), Transket_pyr (transketolase, pyrimidine binding domain)
    • 9023 sequences
    • belongs to clan THDP-binding (CL0254): thiamin diphosphate-binding superfamily


The GOPET results give a good overview about the catalytic and binding activities of our protein, of which most are consistent with the current knowledge. Whether the enzyme can really accept the other substrates pyruvate, 2-dehydropantoate and acetoin, as the prediction suggests, is not clear. It is possible that these activities were only predicted because of the high sequence identity to pyruvate dehydrogenase and other dehydrogenases. The fact that the confidences for these predictions are not as high as for some of the others, argues for this interpretation.

That ProtFun predicted the false enzyme class for our protein shows that this prediction is not always easy. In support of the method it has to be stated, that the probability and odds values for the different enzyme classes are close to each other. Also these values for the "right" enzyme class (EC 1) are some of the higher ones.

Pfam helps to find related proteins that are clustered into families that have common domains. Families are grouped together in clans, so one can also find out more about the distant relationship between proteins. Pfam found a family with different dehydrogenases from diverse organisms that are homolog to our protein. They have in common to be the first part of a large enzyme complex. So the concept of oxidative decarboxylation has adapted to different substrates and different organisms during evolution, but still uses the same basic principle.

Other methods for GO term prediction are GOPred, Blast2GO and GOCat.

From protein sequence alone, additional features can be predicted:

  • solvent accesibility
  • posttranslational modifications
  • localization
  • metal binding sites
  • active sites
  • disulfide bridges
  • SNP effects