Secondary Structure Prediction

Secondary Structure Prediction

Protein Analysis Workshop 2010

Bioinformatics groupInstitute of BiotechnologyUniversity of helsinki

Earlier version: Hung Ta

Current: Petri Törönen

Overview

Hierarchy 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.

Protein structure leads to protein function.

Hierachy of Protein Structure Hierachy of Protein Structure

Primary Structure: a Primary Structure: a Linear Arrangement Linear Arrangement of Amino Acidsof Amino Acids

An amino acid has several structural components: a central carbon atom (C), an amino group (NH2), a carboxyl group (COOH), a hydrogen atom (H), a side chain (R). There are 20 amino acids

The peptide bond is formed as the cacboxyl group of an aa bind to the amino group of the adjacent aa.

The primary structure of a protein is simply the linear arrangement, or sequence, of the amino acid residues that compose it

Secondary Structure: Secondary Structure: Core Elements of Core Elements of Protein ArchitectureProtein Architecture

resulted from the folding of localized parts of a

polypeptide chain.

α-helix

β-sheet

Coils, turns,

} major internal supportive elements, 60 percent of the polypeptide chain

αα-Helix-Helix

Hydrogen-bonded

3.6 residues per turn

Axial dipole moment

Side chains point outward

Average length is 10 amino acids

(3 turns).

Typically, rich of Analine,

Glutamine, Leucine, Methione;

and poor of Proline, Glycine,

Tyrosine and Serine.

ββ-Sheet-Sheet

parallel anti-parallel

Formed due to hydrogen bonds

between β-strands which are short

polypeptide segments (5-8

residues).

Adjacent β-strands run in the

same directions -> parallel sheet.

Adjacent β-strands run in the

oposite directions -> anti-parallel

sheet.Ribbon diagram

Turns, loops, coils…Turns, loops, coils…

A turn, composed of 3-4 residues, forms

sharp bends that redirect the polypeptide

backbone back toward the interior.

A loop is similar with turns but can form

longer bends

Turns and loops help large proteins fold into

compact structures.

A random coil is a class of conformations

that indicate an absence of regular

secondary structure. Turn

Tertiary Structure: Overall Folding of Tertiary Structure: Overall Folding of Polypeptide Chain.Polypeptide Chain.

stabilized by hydrophobic interactions between the nonpolar side chains,

hydrogen bonds between polar side chains, and peptide bonds

Quaternary Structure: Arrangement of Quaternary Structure: Arrangement of Multiple Folded Protein Molecules.Multiple Folded Protein Molecules.

HemoglobinDNA polymerase

Structure PredictionStructure Prediction

GPSRYIVDL… ?

High importance in medicine (for example,

in drug design) and biotechnology (for

example, in the design of novel enzymes)

Structure PredictionStructure Prediction

Why: experimental methods, X-ray crystallography or NMR

spectroscopy, are very time-consuming and relatively expensive.

Structure is more conserved than sequence.

This can be useful when analyzing unknown sequence.

Challenges:

Extremely large number of possible structures.

the physical basis of protein structural stability is not fully

understood.

•Two options for structure prediction:•Ab initio (Start from scratch)•Homology modelling

•If homology is found => use latter•If no homology => secondary structure can still be estimated

Secondary Structure PredictionSecondary Structure Prediction

Primary: MSEGEDDFPRKRTPWCFDDEHMC

Secondary: CCHHHHHHCCCCEEEEEECCCCC

Why: the first level of structural organization.

The tasks:

• H: α-helix

• E: β- strand

• T: turn

• C: coil

aa

?


Single residue statistical analysis (Chou-Fasman -1974): For each amino acid type, assign its ‘propensity’ to be in a helix, β-

sheet, or coil.

Based on 15 proteins of known conformation, 2473 total amino

acids.

Limited accuracy: ~55-60% on average.

Eg: Chou-Fasman (1974), not used any more


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%.

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

Correct answer:http://www.rcsb.org/pdb/explore/explore.do?structureId=1JCH

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)

The hydropathy index of an amino acid is a number

representing the hydrophobic or hydrophilic properties of

its side-chain

compute an average hydropathy value for each position

in the query sequence,

window length of 19 usually chosen for membrane-

spanning region prediction.

>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_www2/fasta_www.cgi?rm=misc1)

Remove the FASTA header, if seq reading is not working.

Hydropathy PlotHydropathy Plot

The larger the number is, the more hydrophobic the amino acid

Correct answer (http://pir.uniprot.org/uniprot/P06010)

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

allows you to obtain many informations based on your sequence including structure predictions, motif or domain search… The predictions are based on several methods.

PredictProtein: http://predictprotein.org

Meta-ServersMeta-Servers

A server which

For sequence analysis, structure and function prediction. When you submit

any protein sequence PredictProtein retrieves similar sequences in the

database and predicts aspects of protein structure and function

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.

PROFsec (PHDsec): secondary structure,

PROFacc (PHDacc): solvent accessibility,

PHDhtm: transmembrane helices.

Sequence database is scanned for similar sequences (Blast, Psi-Blast).

Multiple sequence alignment profiles are generated by weighted dynamic

programming (MaxHom).

The PredictProtein meta-server

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

Results

Low-complexity regions

Marked by ’X’

Secondary structure prediction results

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