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


Parallel or antiparallel

Alternating side-chains

Connecting loops often have polar amino acids


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.


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”.


Tertiary structure:

• How several fully folded protein chains pack together to form a fully functional protein.

• Example: 1jch (ribosome inhibitor).


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

Documents

Secondary Structure Prediction