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An Introduction to Bioinformatics. Protein Structure Prediction. Aims. Understand the use of algorithms Recognize different approaches Understand the limitations. Objectives. Predict occurrence of aspects of structure To select appropriate tools. Introduction.
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An Introduction to Bioinformatics Protein Structure Prediction
Aims • Understand the use of algorithms • Recognize different approaches • Understand the limitations Objectives • Predict occurrence of aspects of structure • To select appropriate tools
Introduction • Structure has several levels • 1 primary • 2 secondary • 3 tertiary • 4 quaternary
1 primary • Amino acid sequence NH2-MRLSWYDPDFQARLTRSNSKCQGQLEV YLKDGWHMVC SQSWGRSSKQWEDPSQASKVCQRLNCGVPLSLGPFLVTYTP QSSIICYGQLGSFSNCSHSRNDMCHSLGLTCLE-COOH
2 secondary • Localized organisation -helices and -sheets
3 tertiary Three-dimensional organisation
4 quaternary Multi protein assembly
The problem….. • The best way is by X-ray crystallography or NMR etc… • Structure databases only hold about 10,000 + structures • Therefore devise programs to deduce structural solutions • Complex!
Secondary Structure prediction • Signal peptides • Intracellular targeting • Trans-membrane -helices • -helices and -sheets • Super-secondary structure (motifs)
Signal peptides • Short N-terminal amino acid sequences • Direct to membrane • Cleaved after translocation • SignalP • Nobel Prize 1999 Günter Blobel
SignalP predicts signal peptide cleavage sites Only first 50-70 Using neural networks
Is the sequence a signal peptide? # Measure Position Value Cutoff Conclusion max. C 25 0.910 0.37 YES max. Y 25 0.861 0.34 YES max. S 12 0.960 0.88 YES mean S 1-24 0.892 0.48 YES # Most likely cleavage site between pos. 24 and 25: SRA-LE
Intracellular targeting • TargetP • Predict subcellular location of eukaryotic protein • Presequences • Chloroplasts • Mitochondria • signal peptide
Transmembrane Domains • Lots of programs • TMHMM • -helices • hydrophobic • helix topology • R or K +ve charge cytoplasmic side • Hidden Markov Modelling
Paste as FASTA file e.g Serotonin Receptor
-helices and -sheets • GOR algorithim • Assigns each residue to one conformational state of -helix, extended chain, reverse turn or coil • 64.4% accurate • Many other sites • most use multiple alignments
-helices and -sheets 10 20 30 40 50 60 70 | | | | | | | MKFSWRTALLWSLPLLVVGFFFWQGSFGGADANLGSNTANTRMTYGRFLEYVDAGRITSVDLYENGRTAI cccceeeeeecccceeeeeeeeccccccccccccccccccchhhhcceeeeccccceeeeeeccccceee VQVSDPEVDRTLRSRVDLPTNAPELIARLRDSNIRLDSHPVRNNGMVWGFVGNLIFPVLLIASLFFLFRR eeccccccchhhhccccccccchhhhhhhhhccccccccceecccceeeeecccccchhhhhhhhheeec SSNMPGGPGQAMNFGKSKARFQMDAKTGVMFDDVAGIDEAKEELQEVVTFLKQPERFTAVGAKIPKGVLL cccccccccchhhhcchhhhhhhhccceeeecchhhhhhhhhhhhhhhhhhcccchhhhhcccccceeee VGPPGTGKTLLAKAIAGEAGVPFFSISGSEFVEMFVGVGASRVRDLFKKAKENAPCLIFIDEIDAVGRQR ecccccchhhhhhhhhcccccceeecccccceeeeeecccchhhhhhhhhcccccceeeecchhhhcccc GAGIGGGNDEREQTLNQLLTEMDGFEGNTGIIIIAATNRPDVLDSALMRPGRFDRQVMVDAPDYSGRKEI ccccccccchhhhhhhhhhhhhcccccccceeeeeeccccchhhhhhccccccceeeeecccccccchhh LEVHARNKKLAPEVSIDSIARRTPGFSGADLANLLNEAAILTARRRKSAITLLEIDDAVDRVVAGMEGTP hhhhhhhhccccccchhhhccccccccchhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhheeecccccc LVDSKSKRLIAYHEVGHAIVGTLLKDHDPVQKVTLIPRGQAQGLTWFTPNEEQGLTTKAQLMARIAGAMG cccccccchhhhhcccceeeeeecccccccceeeecccccccceeccccccccchhhhhhhhhhhhhhhh GRAAEEEVFGDDEVTTGAGGDLQQVTEMARQMVTRFGMSNLGPISLESSGGEVFLGGGLMNRSEYSEEVA hhhhhhhcccccceeeccccchhhhhhhhhhhhhhhccccccccccccccceeeecccccccccchhhhh TRIDAQVRQLAEQGHQMARKIVQEQREVVDRLVDLLIEKETIDGEEFRQIVAEYAEVPVKEQLIPQL hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhcccccchhhhhhhhhhcccccccccccc
Super-secondary Structure • Secondary structure elements combined into specific geometric arrangements known as motifs Beta corner
Super-secondary Structure Several programs/websites for specific domains e.g. • PAIRCOIL and MULTICOIL - detect coiled-coiled regions • regions separating domains • TRESPASSER - detects Leucine Zippers • Leu-X6-Leu-X6-Leu-X6-Leu protein interaction domain • NPS@nalysis Helix-Turn-Helix • Protein interaction/DNA binding
Integrated stucture prediction • One stop shop! • Predict Protein at EBI • secondary structure • solvent accessibility globular regions • transmembrane helices coiled-coil regions • a multiple sequence alignment ProSite sequence motifs • low-complexity retions • ProDom domain assignments
Tertiary Structure Prediction • Homology modelling • Fold recognition • Threading • Model building
Protein sequence (primary structure) Homologue of known structure Fold prediction, ab initio methods etc. Comparative modelling Database searching for homologues 3D-structure No homologue of known structure
Homology Modelling • Method of choice following BLAST search • SWISSModel is agood WWWInterface URL: http://www.expasy.ch/swissmod/SWISS-MODEL.html
Homology Modelling • Requires at least one sequence of known 3D-structure with significant similarity to the target sequence. • Compare the target sequence with database - FastA and BLAST. • Sequences with a FastA score 10.0 standard deviations above the mean of the random scores or a P(N) lower than 10-5 (BLAST) considered for the model building • Restrict to those which share at least 30% residue identity
Homology Modelling • Framework construction • compare atom positions - Cs • Build non-conserved loops • Complete backbone - add other atoms • Add side chains • Refine
Insulin like gene from C.elegans Red = Insulin Blue = ILGF1
What if I have no homologue? Ab initio methods - Threading • Sequence of unknown structure • Thread through a through a sequence of known structure • Move query sequence through residue by resudue and compare computationally • include thermodynamic criteria, solvent accessibility, secondary structure information • Computing intensive
