1. Secondary Structure Prediction
Protein Analysis Workshop 2006
Secondary Structure Prediction
Alain Schenkel
Chris Wilton
Bioinformatics group
Institute of Biotechnology
University of helsinki

2. Overview Review of protein structure.
Introduction to structure prediction:
Different approaches.
Prediction of 1D strings of structural elements.
Server/soft review:
COILS, MPEx, …
The PredictProtein metaserver.

3. Proteins
Proteins play a crucial role in virtually all biological processes with a broad range of functions.
The activity of an enzyme or the function of a protein is governed by the three-dimensional structure.
H11_MOUSE
histocompatibility antigen
VE2_BPV1
Bovine DNA-binding domain

4. 20 amino acids - the building blocks
Clickable map at:

5. The Amino Acids - hydrophobic

6. The Amino Acids - polar

7. The Amino Acids - charged

8. Secondary Structure: a-helix
Note: alpha=4_13 ie hbonded to following forth residue (13 atoms in rings). Exist also but ver seldom: 3_10, 5_16 (pi helix)
Alpha-helix: 413
Very seldom: 310, 516 (Pi-helix)

9. Secondary Structure: a-helix
3.6 residues per turn
Axial dipole moment
Hydrogen-bonded
Protein surfaces
Typically, no Proline nor Glycine (“helix-breaker”)

10. Secondary Structure: b-sheets

11. Secondary Structure: b-sheets
Parallel or antiparallel
Alternating side-chains
Connecting loops often have polar amino acids

12. Secondary Structure: b-sheets

13. Terminology Primary structure: The sequence of amino acid residues
FTPAVHAFLDKFLAS …

14. Terminology Secondary structure:
A first level of structural organization.
Provides rigidity.
The structural form adopted by each amino-acid residue:
H: helix ( alpha )
E: extended ( beta strand )
T: turn ( often Proline )
C: coil ( random, unstructured )

15. Terminology Secondary structure elements (SSE):
Stretches of residues in H conformation are helical SSEs.
Stretches of residues in E conformation are beta-strand SSEs.
Stretches of residues in C conformation are loops or coil.
Turns (T) are isolated residues, usually Proline or Glycine.
Other notation (in 3 states): L for all but H,E.

16. Secondary Structure Elements
Example:
one helix, one beta strand, three loops
Primary: MSEGEDDFPRKRTPWCFDDEHMC
Secondary: CCHHHHHHCCCCEEEEEECCCCC

17. Terminology Tertiary structure:
The full 3D structure of a single polypeptide chain.
Secondary structure elements pack together to form a structural core.
Called a protein “fold”.

18. Terminology Quaternary structure:
How several fully folded protein chains pack together to form a fully functional protein.
Example: 1jch (ribosome inhibitor).
PDB identifier
The Protein Data Bank is the principal
repository for solved structures.

19. Example: 1jch has 4 chains
Docking. Will come back to Coiled-coils. Note how exposed to solvent, typical for coiled-coils
The elongated 2-helix structures in the center are called coiled-coils.

20. Structural classification of folds
For example (CATH):
alpha
beta
alpha+beta
alpha/beta
irregular
More on structural classification next week.

21. Biochemical classification of folds
Globular proteins:
in aqueous environment,
compact fold,
hydrophobic core and polar surfaces.
Membrane proteins:
attached to or across the cell membrane,
hydrophobic surface within membrane.
Fibrous proteins:
structural role,
repeat of regular/atypical SSE or irregular structure.

22. Globular
(2 domains)
Transmembrane
Fibrous

23. INTRODUCTION TO STRUCTURE PREDICTION

24. Why is 3D Structure Important?
A pre-requisite for understanding function
processes of molecular recognition,
eg DNA recognition by 2bop.
Catalytic mechanisms of enzymes
often require key residues to be close together in 3D space.
Structure is often preserved under evolution when sequence is not.
Drug design.

25. Structure Prediction
GPSRYIVDL… ?

26. Approaches to structure prediction
Ab initio: from physical principles only.
De novo: knowledge-based potentials from PDB.
Fold recognition: thread sequence through known structures for compatibility.
Homology modeling: use sequence alignment to infer possible template structure.
The only reliable: homology. So, needs to be lucky (ie to have an homologous prot that has been solved)
Other: various level of accuracy. Pbm: can vary a lot, no testing. So serve only as hints.
More on homology modeling next week.

27. Prediction in One-Dimension
Simplification: project 3D structure onto strings
of structural assignments. Eg:
coiled-coils
membrane helices
solvent accessibility: residue is buried or exposed
…eeebbbbeebbbbee…
secondary structure elements:
…HHHLLLEEEEEELLEEE…
If accurate: can be used to improve predictions
of 3D structures (eg, in fold recognition).

28. A Flow Chart for Structure Prediction

29. Structure Prediction
Why is structure prediction, and in particular ab initio prediction, a difficult problem?
Many degrees of freedom: atoms of all residues and solvent.
Problem increases exponentially per residue.
Remote noncovalent interactions complicate matters.
A delicate problem of stability.
Cannot exhaustively search all possible conformations.
A folding protein does not try all conformations !!
(Levinthal paradox)

30. Basic Principle of Folding (globular protein)
Pack hydrophobic side chains into the interior
of the molecule, away from solvent. So,
Hydrophobic residues predominantly within a central structural core. Tight packing (crystal-like).
Hydrophilic residues predominantly on the protein surface, exposed to solvent.
But main chain is highly polar. This forces the formation of SSEs in the core. So,
Due to oxygen and hydrogen
Core residues tend to be in SSEs.
Loops are on the outside of the protein.

31. Protein Structure and Evolution
Rate of evolution of genomic DNA sequence reflects degree of functional constraint.
Protein coding regions evolve much more slowly than non-coding regions:
need to maintain stable 3D protein structure,
need to maintain vital biological function.

32. Rates of Protein Sequence Evolution
Sequences of highly constrained structures evolve very slowly (eg: histones).
Less constrained ones evolve more quickly (eg: immunoglobulins).
In general: response to mutation is structural change, but many mutations will not (or only slightly) change the structure
=>
Structure is better conserved than sequence.

33. Evolution of SSEs and Loops
Residues in the hydrophobic core (SSEs) are constrained by the need for tight packing:
changes rarely accepted - evolution is slow.
Residues on the surface (loops) are less constrained (simply need to be hydrophilic):
aa substitution less restricted – evolution is quicker.

34. Evolution of Key Residues
Residues with key functional roles will be conserved.
Eg: active site residues involved in catalysis.
BUT: gene duplication can lead to change of function without changing structure.
Residues with key structural role also tend to be conserved. Eg:
GLY: high conformational flexibility => tight turns,…
PRO: side-chain bounds back to backbone => tight turns.
CYS: disulfide bridges.

35. Structure Prediction by Homology
Multiple sequence / structure alignments measure differences in evolutionary rates of residues, and thus
Contain more information than a single sequence for applications such as homology modeling and secondary structure prediction,
Give location of conserved regions and motifs, residues buried in the protein core or exposed to solvent, plus important secondary structures.
More on homology modeling next week.

36. Secondary Structure Prediction
Three generations:
Single residue statistical analysis:
For each amino acid type, assign its ‘propensity’ to be in a helix, sheet, or coil.
Limited accuracy: ~55-60% on average.
Eg: Chou-Fasman (1974), not used any more.

37. Secondary Structure Prediction
Segment-based statistics:
Look for correlations (within aa windows).
Many algorithms have been tried.
Most performant: Neural Networks:
Input: a number of protein sequences with their known secondary structure.
Output: a trained network that predicts secondary structure elements for given query sequences.
Accuracy < 70%.
Eg: GORII, COMBINE.
Many algorithm tried: sequence patterns, statistical inform, physico-chemical properties, graph theory, expert rules

38. Neural Networks query trained network 3 states output prediction for
this residue
prediction
query
trained network
(picture from B.Rost, 1999)

39. Secondary Structure Prediction
Using information from evolution:
Compute a sequence profile from a multiple sequence alignment.
Use profile instead of query as input to Neural Network.
6-8 % points increase in accuracy over Neural Network only.
Eg:
PHD/PROF: alignments by MaxHom (B. Rost, 1996/2000)
PSI-PRED: alignments from Psi-Blast (D.T. Jones, 1999)
Accuracy: 72% ± 11%.
# of correctly predicted 2ndary str. states
Accuracy measured as Q3=
total # of residues

40. Accuracy Illustration
Psi-Pred benchmark on set of 187 chains.
(D.T. Jones, 1999)
Your query could be here !!
In particular, accuracy can be as low as 50% for a given query
=>
Use many different methods and compare answers.

41. Other Structural Features
There are other structural features that one can try to predict:
coiled-coils,
membrane helices,
solvent accessibility,
globularity,
disulfide bridges,
confomational switches, …

42. POPULAR SERVERS FOR DEALING WITH SECONDARY STRUCTURES Coiled-coils
Transmembrane helices
Secondary structure
Metaservers

43. Prediction of coiled-coils
Coiled-coils are generally solvent exposed
multi-stranded helix structures:
two-stranded
Helix periodicity and solvent exposure impose
special pattern of heptad repeat:
Helical diagram of
2 interacting helices:
… abcdefg …
hydrophobic residues
hydrophilic residues
(From Wikipedia Leucine zipper article)

44. The COILS server at EMBnet
Compares a sequence to a database of known, parallel two-stranded coiled-coils, and derives a similarity score.
By comparing this score to the distribution of scores in globular and coiled-coil proteins, the program then calculates the probability that the sequence will adopt a coiled-coil conformation.
Options:
scoring matrices,
window size (score may vary),
weighting options.

45. COILS Limitations
The program works well for parallel two-stranded structures that are solvent-exposed but runs progressively into problems with the addition of more helices, their antiparallel orientation and their decreasing length.
The program fails entirely on buried structures.

46. COILS Demo Let us submit the sequence to the COILS server at EMBnet:
>1jch_A
VAAPVAFGFPALSTPGAGGLAVSISAGALSAAIADIMAALKGPFKFGLWGVALYGVLPSQ
IAKDDPNMMSKIVTSLPADDITESPVSSLPLDKATVNVNVRVVDDVKDERQNISVVSGVP
MSVPVVDAKPTERPGVFTASIPGAPVLNISVNNSTPAVQTLSPGVTNNTDKDVRPAFGTQ
GGNTRDAVIRFPKDSGHNAVYVSVSDVLSPDQVKQRQDEENRRQQEWDATHPVEAAERNY
ERARAELNQANEDVARNQERQAKAVQVYNSRKSELDAANKTLADAIAEIKQFNRFAHDPM
AGGHRMWQMAGLKAQRAQTDVNNKQAAFDAAAKEKSDADAALSSAMESRKKKEDKKRSAE
NNLNDEKNKPRKGFKDYGHDYHPAPKTENIKGLGDLKPGIPKTPKQNGGGKRKRWTGDKG
RKIYEWDSQHGELEGYRASDGQHLGSFDPKTGNQLKGPDPKRNIKKYL
to the COILS server at EMBnet:

47. mtidk matrix, no weights, all window lengths

48. Frame probabilities at each residue.
Columns: window size of 14, 21, 28 aa.
high probability heptads

49. Transmembrane Region Prediction
Transmembrane regions:
Usually contain residues with hydrophobic side chains (surface must be hydrophobic).
Usually ~20 residues long, can be up to 30 if not perpendicular through membrane.
Methods:
Hydropathy plots (historical, better methods now available)
Threading (TMpred, MEMSAT),
Hidden Markov Model (TMHMM),
Neural Network (PHDhtm).

50. Hydropathy Plots (Kyte-Doolittle)
compute an average hydropathy value for each position in the query sequence,
window length of 19 usually chosen for membrane-spanning region prediction.
Peaks between scales 1-2?

51. Hydropathy Plot Servers
Membrane Explorer (also as standalone MPEx),
Grease (
Let us submit the sequence
>sp|P06010|RCEM_RHOVI Reaction center protein M chain (Photosynthetic reaction center M subunit) - Rhodopseudomonas viridis. ADYQTIYTQIQARGPHITVSGEWGDNDRVGKPFYSYWLGKIGDAQIGPIYLGASGIAAFAFGSTAILIILFNMAAEVHFDPLQFFRQFFWLGLYPPKAQYGMGIPPLHDGGWWLMAGLFMTLSLGSWWIRVYSRARALGLGTHIAWNFAAAIFFVLCIGCIHPTLVGSWSEGVPFGIWPHIDWLTAFSIRYGNFYYCPWHGFSIGFAYGCGLLFAAHGATILAVARFGGDREIEQITDRGTAVERAALFWRWTIGFNATIESVHRWGWFFSLMVMVSASVGILLTGTFVDNWYLWCVKHG AAPDYPAYLPATPDPASLPGAPK to
(Membrane Explorer)

54. TM Pred Method summary:
Scans a candidate sequence for matches to a sequence scoring matrix, obtained by aligning the sequences of all transmembrane alpha-helical regions that are known from structures.
These sequences are collected in a database called TMBase.
Remark: Authors do not suggest this method for genomic sequences. Automatic methods recommended, eg, TMHMM, PHDhtm.

55. TM Pred Server Let us submit RCEM_RHOVI again
>sp|P06010|RCEM_RHOVI Reaction center protein M chain (Photosynthetic reaction center M subunit) - Rhodopseudomonas viridis. ADYQTIYTQIQARGPHITVSGEWGDNDRVGKPFYSYWLGKIGDAQIGPIYLGASGIAAFAFGSTAILIILFNMAAEVHFDPLQFFRQFFWLGLYPPKAQYGMGIPPLHDGGWWLMAGLFMTLSLGSWWIRVYSRARALGLGTHIAWNFAAAIFFVLCIGCIHPTLVGSWSEGVPFGIWPHIDWLTAFSIRYGNFYYCPWHGFSIGFAYGCGLLFAAHGATILAVARFGGDREIEQITDRGTAVERAALFWRWTIGFNATIESVHRWGWFFSLMVMVSASVGILLTGTFVDNWYLWCVKHG AAPDYPAYLPATPDPASLPGAPK
to the TMPred server at EMBnet:

59. Annotation for RCEM_RHOVI
Uniprot entry for RCEM_RHOVI:
Chain M of photosynthetic reaction center.
Integral membrane protein.
Can we see the predicted helices in the structure?
Let´s try at SCOP.

60. The Psi-Pred Server Secondary structure prediction (PSIPRED)
Transmembrane topology prediction (MEMSAT)
Fold recognition (GenTHREADER)
Let´s submit
>uniprot|P00772|ELA1_PIG Elastase-1 precursor
MLRLLVVASLVLYGHSTQDFPETNARVVGGTEAQRNSWPSQISLQYRSGSSWAHTCGGTL
IRQNWVMTAAHCVDRELTFRVVVGEHNLNQNDGTEQYVGVQKIVVHPYWNTDDVAAGYDI
ALLRLAQSVTLNSYVQLGVLPRAGTILANNSPCYITGWGLTRTNGQLAQTLQQAYLPTVD
YAICSSSSYWGSTVKNSMVCAGGDGVRSGCQGDSGGPLHCLVNGQYAVHGVTSFVSRLGC
NVTRKPTVFTRVSAYISWINNVIASN to

62. (see later for comparison with solved structure)
PSIPRED PREDICTION RESULTS
Key
Conf: Confidence (0=low, 9=high)
Pred: Predicted secondary structure (H=helix, E=strand, C=coil)
AA: Target sequence
# PSIPRED HFORMAT (PSIPRED V2.5 by David Jones)
Conf:
Pred: CHHHHHHHHHHHHHCCCCCCCCCCCCEECCEECCCCCCCCEEEEEEECCCCCEEEEEEEE
AA: MLRLLVVASLVLYGHSTQDFPETNARVVGGTEAQRNSWPSQISLQYRSGSSWAHTCGGTL
Conf:
Pred: CCCCEEEEECCCCCCCCCEEEEEEEEEEEECCCCCEEEEEEEEEEECCCCCCCCCCCCCH
AA: IRQNWVMTAAHCVDRELTFRVVVGEHNLNQNDGTEQYVGVQKIVVHPYWNTDDVAAGYDI
Conf:
Pred: HHEECCCCCCEEEEEEEECCCCCCCCCCCCEEEEEEECCCCCCCCCCCCCCEEEEEEEEE
AA: ALLRLAQSVTLNSYVQLGVLPRAGTILANNSPCYITGWGLTRTNGQLAQTLQQAYLPTVD
Conf:
Pred: CHHHHHHHCCCCCCCCCEEEECCCCCCCCCEEECCCCEEEEECCEEEEEEEEEECCCCCC
AA: YAICSSSSYWGSTVKNSMVCAGGDGVRSGCQGDSGGPLHCLVNGQYAVHGVTSFVSRLGC
Conf:
Pred: CCCCCCEEEEEHHHHHHHHHHHHHCC
AA: NVTRKPTVFTRVSAYISWINNVIASN
(see later for comparison
with solved structure)

63. Meta-Servers A server which
allows you to obtain predictions from different parallel methods under one browser window, eg:
PredictProtein:
or makes predictions based on several methods (consensus), eg:
3D-Jury:
GeneSilico:

64. The PredictProtein meta-server
Sequence motif search:
ProSite, ProDom, SEG.
One-Dim structure prediction:
secondary structure,
transmembrane helices,
solvent accessibility,
globularity,
disulfide bridge,
conformational switch.
Links to a multitude of other servers
(numerous links also from 3D-Jury).

65. Motif Search at PP SEG: finds low complexity regions.
ProSite: database of functional motifs, ie, biologically relevant short patterns.
ProDom: a comprehensive set of protein domain families automatically generated from the SWISS-PROT and TrEMBL sequence databases.
Motif: keeps reappearing: can be used to identify family of new sequence
More on domains and protein family classification next week (ADDA, Pfam etc.).
ProSite:
ProDom:

66. One-Dim predictions at PP
Use information from evolution:
Sequence database is scanned for similar sequences (Blast, Psi-Blast).
Multiple sequence alignment profiles are generated by weighted dynamic programming (MaxHom).
The PROF (improved PHD) series:
PROFsec (PHDsec): secondary structure,
PROFacc (PHDacc): solvent accessibility,
PHDhtm: transmembrane helices.

67. Meta-PP
PredictProtein allows to automatically submit a query to other servers:
Secondary structure prediction:
Psi-Pred, SAM-T02, Jpred, …
Membrane helices prediction:
TMHMM, …
Tertiary structure prediction:
Homology: Swiss-Model, 3D-Jigsaw, …
Threading: Superfamily, AGAPE, …
Inter-residue contact prediction: CMAPpro, …

68. PredictProtein Demo Let´s submit again to http://predictprotein.org/
>uniprot|P00772|ELA1_PIG Elastase-1 precursor
MLRLLVVASLVLYGHSTQDFPETNARVVGGTEAQRNSWPSQISLQYRSGSSWAHTCGGTL
IRQNWVMTAAHCVDRELTFRVVVGEHNLNQNDGTEQYVGVQKIVVHPYWNTDDVAAGYDI
ALLRLAQSVTLNSYVQLGVLPRAGTILANNSPCYITGWGLTRTNGQLAQTLQQAYLPTVD
YAICSSSSYWGSTVKNSMVCAGGDGVRSGCQGDSGGPLHCLVNGQYAVHGVTSFVSRLGC
NVTRKPTVFTRVSAYISWINNVIASN to
For a list of mirror sites:

72. Let´s explore the results here.

75. Comparison with solved structure
ELA1_PIG Elastase-1 has a solved structure: 1EST
DSSP: ??????????????????????????CBTCEECCTTTCTTEEEEEEEETTEEEEEEEEEEEETTEEEECSGGGCSCCCEE
PSIP: .HHHHHHHHHHHHH EE..EE EEEEEEE.....EEEEEEEE....EEEEE EE
PROF: ..HHHHHHHHHHH EEEE.EE EEEEEEEE......EEEEEEEE...EEEEEEEEE.....EE
DSSP: EEESCSBTTSCCSCCEEEEEEEEEECTTCCTTCGGGCCCCEEEEESSCCCCBTTBCCCCCCCTTCCCCTTCCEEEEESCB
PSIP: EEEEEEEEEE.....EEEEEEEEEEE HHHEE......EEEEEEEE EEEEEEE...
PROF EEEEEEE EEEEEEEEEEE EEEEEE EEEEEE EEEEEEEE.
DSSP: SSTTCCBCSBCEEEECCEECHHHHTSTTTTGGGSCTTEEEECCSSSSBCCTTCTTCEEEEEETTEEEEEEEEEECBTTBS
PSIP: EEEEEEEEE.HHHHHHH EEEE EEE....EEEEE..EEEEEEEEEE......
PROF: EEEEEEEEE EEEE EEEEEE...EEEEEEEE
DSSP: SBTTBCEEEEEGGGSHHHHHHHHHTC
PSIP: EEEEEHHHHHHHHHHHHH..
PROF: EEEEHHHHHHHHHHHH...
DSSP: secondary structure assignment from PDB (Kabsch-Sander, 1983)
H = alpha helix
B = residue in isolated beta-bridge
E = extended strand, participates in beta ladder
G = 3-helix (3/10 helix)
I = 5 helix (pi helix)
T = hydrogen bonded turn
S = bend

76. Conclusions Both predictions agree quite well and are quite accurate.
But: it may not be as good next time.
=>
Compare predictions from different methods to check whether there is a consensus.
Use servers that automatically combine different methods (3D-Jury, ...).

77. Benchmarks
LiveBench
CASP (critical assessment of structure prediction)
CAFASP (ca of fully automated structure prediction)

78. References Documentation: Articles: Books:
COILS:
TMPred:
MPEx:
Articles:
B. Rost: Evolution teaches neural networks. In Scientific applications of neural nets. Ed. J.W.Clark, T.Lindenau, M.L. Ristig, (1999).
D.T Jones: Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices. J.Mol.Biol. 292, (1999).
B. Rost: Prediction in 1D: Secondary Structure, Membrane Helices, and Accessibility. In Structural Bioinformatics (reference below).
Books:
P.E. Bourne, H. Weissig: Structural Bioinformatics. Wiley-Liss, 2003.
A. Tramontano: Protein Structure Prediction. Wiley-VCH, 2006.