Secondary Structure Prediction
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Transcript of Secondary Structure Prediction
Secondary Structure Prediction
Protein Analysis Workshop 2006
Bioinformatics groupInstitute of BiotechnologyUniversity of helsinki
Alain Schenkel
Chris Wilton
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.
ProteinsProteins
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_MOUSEhistocompatibility antigen
VE2_BPV1Bovine DNA-binding domain
20 amino acids - the building blocks20 amino acids - the building blocks
Clickable map at: http://www.russell.embl-heidelberg.de/aas/
The Amino Acids - hydrophobicThe Amino Acids - hydrophobic
The Amino Acids - polarThe Amino Acids - polar
The Amino Acids - chargedThe Amino Acids - charged
Secondary StructureSecondary Structure: -helix-helix
Very seldom: 310, 516 (Pi-helix)
Alpha-helix: 413
3.6 residues per turn
Axial dipole moment
Hydrogen-bonded
Protein surfaces Typically, no Proline nor
Glycine (“helix-breaker”)
Secondary StructureSecondary Structure: -helix-helix
Secondary StructureSecondary Structure: -sheets-sheets
Secondary StructureSecondary Structure: -sheets-sheets
Parallel or antiparallel
Alternating side-chains
Connecting loops often have polar amino acids
Secondary StructureSecondary Structure: -sheets-sheets
Terminology
Primary structure: The sequence of amino acid residues
FTPAVHAFLDKFLAS …
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 )
TerminologyTerminology
• 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.
TerminologyTerminology
Secondary structure elements (SSE):
Example:one helix, one beta strand, three loops
Primary: MSEGEDDFPRKRTPWCFDDEHMC
Secondary: CCHHHHHHCCCCEEEEEECCCCC
Secondary Structure ElementsSecondary Structure Elements
• The full 3D structure of a single polypeptide chain.
• Secondary structure elements pack together to form a structural core.
• Called a protein “fold”.
TerminologyTerminology
Tertiary structure:
• How several fully folded protein chains pack together to form a fully functional protein.
• Example: 1jch (ribosome inhibitor).
TerminologyTerminology
Quaternary structure:
PDB identifierThe Protein Data Bank is the principal repository for solved structures.
Example: 1jch has 4 chains
The elongated 2-helix structures in the center are called coiled-coils.
Structural classification of folds
For example (CATH): alpha beta alpha+beta alpha/beta irregular
More on structural classification next week.
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.
Biochemical classification of foldsBiochemical classification of folds
Fibrous
Globular(2 domains)
Transmembrane
INTRODUCTION TO
STRUCTURE PREDICTION
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.
Why is 3D Structure Important?Why is 3D Structure Important?
Structure PredictionStructure Prediction
GPSRYIVDL… ?
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.
More on homology modeling next week.
Prediction in One-Dimension
Simplification: project 3D structure onto stringsof 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 predictionsof 3D structures (eg, in fold recognition).
http://speedy.embl-heidelberg.de/gtsp/flowchart2.html
A Flow Chart for Structure PredictionA Flow Chart for Structure Prediction
Structure PredictionStructure Prediction
• 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)
Why is structure prediction, and in particular ab initio prediction, a difficult problem?
Hydrophobic residues predominantly within a central structural core. Tight packing (crystal-like).
Hydrophilic residues predominantly on the protein surface, exposed to solvent.
Basic Principle of Folding Basic Principle of Folding (globular protein)(globular protein)
Pack hydrophobic side chains into the interiorof the molecule, away from solvent. So,
Core residues tend to be in SSEs. Loops are on the outside of the protein.
But main chain is highly polar. This forces the formation of SSEs in the core. So,
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.
Protein Structure and EvolutionProtein Structure and 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.
Rates of Protein Sequence EvolutionRates of Protein Sequence Evolution
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.
Evolution of SSEs and LoopsEvolution of SSEs and Loops
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.
Evolution of Key ResiduesEvolution of Key Residues
Multiple sequence / structure alignments measure differences in evolutionary rates of residues, and thus
Structure Prediction by HomologyStructure Prediction by Homology
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.
Secondary Structure Prediction
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.
Three generations:
Segment-based statistics:• Look for correlations (within 11-21 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.
Secondary Structure PredictionSecondary Structure Prediction
Neural Networks
(picture from B.Rost, 1999)
trained networkquery
3 states outputprediction for
this residue
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%.
Secondary Structure PredictionSecondary Structure Prediction
Accuracy measured as Q3=# of correctly predicted 2ndary str. states
total # of residues
Accuracy Illustration
In particular, accuracy can be as low as 50% for a given query =>Use many different methods and compare answers.
Psi-Pred benchmark on set of 187 chains.(D.T. Jones, 1999)
Your query could be here !!
Other Structural Features
coiled-coils, membrane helices, solvent accessibility, globularity, disulfide bridges, confomational switches, …
There are other structural features that one can try to predict:
POPULAR SERVERS
FOR DEALING WITH
SECONDARY STRUCTURES
• Coiled-coils• Transmembrane helices• Secondary structure • Metaservers
Prediction of coiled-coilsPrediction of coiled-coils
Coiled-coils are generally solvent exposed multi-stranded helix structures:
Helix periodicity and solvent exposure imposespecial pattern of heptad repeat:
… abcdefg … hydrophobic residues hydrophilic residues
two-stranded
(From Wikipedia Leucine zipper article)
Helical diagram of2 interacting helices:
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.
The COILS server at EMBnetThe COILS server at EMBnet
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.
COILS LimitationsCOILS Limitations
COILS DemoCOILS Demo
Let us submit the sequence
to the COILS server at EMBnet:
http://www.ch.embnet.org/software/COILS_form.html
>1jch_AVAAPVAFGFPALSTPGAGGLAVSISAGALSAAIADIMAALKGPFKFGLWGVALYGVLPSQIAKDDPNMMSKIVTSLPADDITESPVSSLPLDKATVNVNVRVVDDVKDERQNISVVSGVPMSVPVVDAKPTERPGVFTASIPGAPVLNISVNNSTPAVQTLSPGVTNNTDKDVRPAFGTQGGNTRDAVIRFPKDSGHNAVYVSVSDVLSPDQVKQRQDEENRRQQEWDATHPVEAAERNYERARAELNQANEDVARNQERQAKAVQVYNSRKSELDAANKTLADAIAEIKQFNRFAHDPMAGGHRMWQMAGLKAQRAQTDVNNKQAAFDAAAKEKSDADAALSSAMESRKKKEDKKRSAENNLNDEKNKPRKGFKDYGHDYHPAPKTENIKGLGDLKPGIPKTPKQNGGGKRKRWTGDKGRKIYEWDSQHGELEGYRASDGQHLGSFDPKTGNQLKGPDPKRNIKKYL
mtidk matrix, no weights, all window lengths
• Frame probabilities at each residue.
• Columns: window size of 14, 21, 28 aa.
high probability heptads
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).
Transmembrane Region PredictionTransmembrane Region Prediction
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?
>sp|P06010|RCEM_RHOVI Reaction center protein M chain (Photosynthetic reaction center M subunit) - Rhodopseudomonas viridis. ADYQTIYTQIQARGPHITVSGEWGDNDRVGKPFYSYWLGKIGDAQIGPIYLGASGIAAFAFGSTAILIILFNMAAEVHFDPLQFFRQFFWLGLYPPKAQYGMGIPPLHDGGWWLMAGLFMTLSLGSWWIRVYSRARALGLGTHIAWNFAAAIFFVLCIGCIHPTLVGSWSEGVPFGIWPHIDWLTAFSIRYGNFYYCPWHGFSIGFAYGCGLLFAAHGATILAVARFGGDREIEQITDRGTAVERAALFWRWTIGFNATIESVHRWGWFFSLMVMVSASVGILLTGTFVDNWYLWCVKHG AAPDYPAYLPATPDPASLPGAPK
Hydropathy Plot ServersHydropathy Plot Servers
Let us submit the sequence
to
Membrane Explorer (also as standalone MPEx), Grease (http://fasta.bioch.virginia.edu/fasta/grease.htm)
http://blanco.biomol.uci.edu/mpex/ (Membrane Explorer)
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.
TM PredTM Pred
Method summary:
Remark: Authors do not suggest this method for genomic sequences. Automatic methods recommended, eg, TMHMM, PHDhtm.
TM Pred ServerTM Pred Server
>sp|P06010|RCEM_RHOVI Reaction center protein M chain (Photosynthetic reaction center M subunit) - Rhodopseudomonas viridis. ADYQTIYTQIQARGPHITVSGEWGDNDRVGKPFYSYWLGKIGDAQIGPIYLGASGIAAFAFGSTAILIILFNMAAEVHFDPLQFFRQFFWLGLYPPKAQYGMGIPPLHDGGWWLMAGLFMTLSLGSWWIRVYSRARALGLGTHIAWNFAAAIFFVLCIGCIHPTLVGSWSEGVPFGIWPHIDWLTAFSIRYGNFYYCPWHGFSIGFAYGCGLLFAAHGATILAVARFGGDREIEQITDRGTAVERAALFWRWTIGFNATIESVHRWGWFFSLMVMVSASVGILLTGTFVDNWYLWCVKHG AAPDYPAYLPATPDPASLPGAPK
Let us submit RCEM_RHOVI again
to the TMPred server at EMBnet:
http://www.ch.embnet.org/software/TMPRED_form.html
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.
The Psi-Pred Server
Let´s submit
to http://bioinf.cs.ucl.ac.uk/psipred/
>uniprot|P00772|ELA1_PIG Elastase-1 precursor MLRLLVVASLVLYGHSTQDFPETNARVVGGTEAQRNSWPSQISLQYRSGSSWAHTCGGTLIRQNWVMTAAHCVDRELTFRVVVGEHNLNQNDGTEQYVGVQKIVVHPYWNTDDVAAGYDIALLRLAQSVTLNSYVQLGVLPRAGTILANNSPCYITGWGLTRTNGQLAQTLQQAYLPTVDYAICSSSSYWGSTVKNSMVCAGGDGVRSGCQGDSGGPLHCLVNGQYAVHGVTSFVSRLGCNVTRKPTVFTRVSAYISWINNVIASN
• Secondary structure prediction (PSIPRED)
• Transmembrane topology prediction (MEMSAT)
• Fold recognition (GenTHREADER)
(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: 978999999997404555676678816988988788877499999934884158982897Pred: CHHHHHHHHHHHHHCCCCCCCCCCCCEECCEECCCCCCCCEEEEEEECCCCCEEEEEEEE AA: MLRLLVVASLVLYGHSTQDFPETNARVVGGTEAQRNSWPSQISLQYRSGSSWAHTCGGTL 10 20 30 40 50 60
Conf: 138734320122478742368754345663179827995679998026888865344411Pred: CCCCEEEEECCCCCCCCCEEEEEEEEEEEECCCCCEEEEEEEEEEECCCCCCCCCCCCCH AA: IRQNWVMTAAHCVDRELTFRVVVGEHNLNQNDGTEQYVGVQKIVVHPYWNTDDVAAGYDI 70 80 90 100 110 120
Conf: 010005863201367530113433210010268995234110254467622168863110Pred: HHEECCCCCCEEEEEEEECCCCCCCCCCCCEEEEEEECCCCCCCCCCCCCCEEEEEEEEE AA: ALLRLAQSVTLNSYVQLGVLPRAGTILANNSPCYITGWGLTRTNGQLAQTLQQAYLPTVD 130 140 150 160 170 180
Conf: 024554202566567752773344343221110467438998993899999972376889Pred: CHHHHHHHCCCCCCCCCEEEECCCCCCCCCEEECCCCEEEEECCEEEEEEEEEECCCCCC AA: YAICSSSSYWGSTVKNSMVCAGGDGVRSGCQGDSGGPLHCLVNGQYAVHGVTSFVSRLGC 190 200 210 220 230 240
Conf: 88988779999687678899886049Pred: CCCCCCEEEEEHHHHHHHHHHHHHCC AA: NVTRKPTVFTRVSAYISWINNVIASN
250 260
allows you to obtain predictions from different parallel methods under one browser window, eg:• PredictProtein: http://predictprotein.org
or makes predictions based on several methods (consensus), eg:• 3D-Jury: http://bioinfo.pl/meta• GeneSilico: http://www.genesilico.pl/meta
Meta-ServersMeta-Servers
A server which
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).
The PredictProtein meta-server
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 Search at PPMotif Search at PP
More on domains and protein family classification next week (ADDA, Pfam etc.).
ProSite: http://au.expasy.org/prosite/
ProDom: http://protein.toulouse.inra.fr/prodom/current/html/home.php
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.
One-Dim predictions at PP
Meta-PP
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, …
PredictProtein allows to automatically submit a query to other servers:
PredictProtein Demo
Let´s submit again
to http://predictprotein.org/
>uniprot|P00772|ELA1_PIG Elastase-1 precursor MLRLLVVASLVLYGHSTQDFPETNARVVGGTEAQRNSWPSQISLQYRSGSSWAHTCGGTLIRQNWVMTAAHCVDRELTFRVVVGEHNLNQNDGTEQYVGVQKIVVHPYWNTDDVAAGYDIALLRLAQSVTLNSYVQLGVLPRAGTILANNSPCYITGWGLTRTNGQLAQTLQQAYLPTVDYAICSSSSYWGSTVKNSMVCAGGDGVRSGCQGDSGGPLHCLVNGQYAVHGVTSFVSRLGCNVTRKPTVFTRVSAYISWINNVIASN
For a list of mirror sites: http://predictprotein.org/newwebsite/doc/mirrors.html
Let´s explore the results here.
Comparison with solved structure
DSSP: ??????????????????????????CBTCEECCTTTCTTEEEEEEEETTEEEEEEEEEEEETTEEEECSGGGCSCCCEEPSIP: .HHHHHHHHHHHHH............EE..EE........EEEEEEE.....EEEEEEEE....EEEEE.........EEPROF: ..HHHHHHHHHHH............EEEE.EE.......EEEEEEEE......EEEEEEEE...EEEEEEEEE.....EE
DSSP: EEESCSBTTSCCSCCEEEEEEEEEECTTCCTTCGGGCCCCEEEEESSCCCCBTTBCCCCCCCTTCCCCTTCCEEEEESCBPSIP: EEEEEEEEEE.....EEEEEEEEEEE.............HHHEE......EEEEEEEE............EEEEEEE...PROF EEEEEEE........EEEEEEEEEEE.............EEEEEE........EEEEEE............EEEEEEEE.
DSSP: SSTTCCBCSBCEEEECCEECHHHHTSTTTTGGGSCTTEEEECCSSSSBCCTTCTTCEEEEEETTEEEEEEEEEECBTTBSPSIP: ...........EEEEEEEEE.HHHHHHH.........EEEE.........EEE....EEEEE..EEEEEEEEEE......PROF: ..........EEEEEEEEE..................EEEE...............EEEEEE...EEEEEEEE.......
DSSP: SBTTBCEEEEEGGGSHHHHHHHHHTCPSIP: ......EEEEEHHHHHHHHHHHHH..PROF: .......EEEEHHHHHHHHHHHH...
ELA1_PIG Elastase-1 has a solved structure: 1EST
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
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, ...).
Benchmarks
LiveBench http://bioinfo.pl/meta/livebench.pl
CASP (critical assessment of structure prediction) http://predictioncenter.gc.ucdavis.edu/
CAFASP (ca of fully automated structure prediction) http://www.cs.bgu.ac.il/~dfisher/CAFASP5/index.html
Documentation:• COILS: http://www.ch.embnet.org/software/coils/COILS_doc.html • TMPred: http://www.ch.embnet.org/software/tmbase/TMBASE_doc.html • MPEx: http://blanco.biomol.uci.edu/mpex/MPEXdoc.html
Articles: B. Rost: Evolution teaches neural networks. In Scientific applications of neural nets. Ed.
J.W.Clark, T.Lindenau, M.L. Ristig, 207-223 (1999).
D.T Jones: Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices. J.Mol.Biol. 292, 195-202 (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.
References