The Bioinformatics of Protein Modification

Transcription

1 The Bioinformatics of Protein Modification (Part 2) Vorlesung 4610 Universität Basel Dr. Michael Rebhan, Friedrich Miescher Institute, Basel, January

2 1. Introduction: what role does bioinformatics play? 2. Mining information related to protein modifications - known modifications - finding proteins with particular modifications 3. Predicting modification sites in proteins: - general concepts - filtering and interpretation - generic tools - modification-specific tools and issues - building your own motif Part 2 4. Related topics: - protein function - mutation effects 5. Online Materials: Exercises, Links

3 Predicting modification sites: Building Your Own Motif: 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search

4 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search Collect all relevant sequences: Your own + Public - ExPASy: SWISSPROT - Specialized datasets? online materials (PubMed, Google) Eisenhaber et al (2004) Proteomics 4, Prediction of sequence signals for lipid post-translational modifications: Insights from case studies Keep in mind: - how reliable is the data? (direct evidence?) - importance of the sequence environment around the main motif (see part 1) can reduce false positive rate

5 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search Collect all relevant sequences: Your own + Public - ExPASy: SWISSPROT Example: C-linked (man) in the feature descriptions (= C-mannosylation) only those with direct exper. evidence! (is the dataset large & diverse enough?)

6 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search Collect all relevant sequences: Your own + Public - ExPASy: SWISSPROT Example: C-linked (man) in the feature descriptions Features look OK query is OK (no preditions etc.) Now get more info, incl. sequence environment

7 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search Collect all relevant sequences: Your own + Public - ExPASy: SWISSPROT Example: C-linked (man) in the feature descriptions Back to the query form: Retrieve entry instead of feature, and display key fields in output.

8 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search Collect all relevant sequences: Your own + Public - ExPASy: SWISSPROT Example: C-linked (man) in the feature descriptions Why 11? We had 49 features before? (each entry (=protein) can carry a number of features (=modifications)) Click on the entry link (if you d like to include this protein)

9 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search Collect all relevant sequences: Your own + Public 1. Find the features you d like to include in the data set ( training set ) 2. Click on its position to get the sequence context 3. Build the alignment in FASTA format (by copy & paste, if it s a small set) 4. Import into alignment viewers (like Jalview,

10 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search Analysis of the alignment / data set: - any corrections needed, esp. gaps? - is it large/diverse enough? - sorting, try different color views: In Jalview: By conservation: - which positions show clear constraints? motif boundaries Other constraints: - conserved? ( BLAST ) - secondary structure, accessibility? (Quick2D, SABLE) see part 1 Color: Zappo

11 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search Which kind of model to use? - regular expressions (PROSITE patterns) - profiles, like PSI-BLAST - support vector machines (SVMs) Regular expressions: [WDMLYSFHQ]-[TGSAYF]-[QSGCTNEPA]-W- [TGSAI]-[SCGPTVEDQ]-[CW]-[SGEDRANTF] or: W-X-X-[CW] (in S-rich env.) could be useful, but doesn t impose a lot of constraints (and no scoring ) If you d like to use it anyway, you can scan protein databases with this motif at ScanProsite (ExPASy)

12 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search Which kind of model to use? - regular expressions (PROSITE patterns) - profiles, like PSI-BLAST - support vector machines (SVMs) ScanProsite: enter pattern, options

13 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search Which kind of model to use? - regular expressions (PROSITE patterns) - profiles, like PSI-BLAST - support vector machines (SVMs) ScanProsite results: More: online materials

14 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search Which kind of model to use? - regular expressions (PROSITE patterns) - profiles, like PSI-BLAST! - support vector machines (SVMs) Search with the alignment using PSI-BLAST, e.g. at the Bioinformatics Toolkit (MPI Tuebingen) PSSM profile (see part 1)

15 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search First against SWISSPROT to check which proteins get the highest scores e value: 1000, ungapped alignment Also: ScanSite (MIT)! (enhanced regular expressions and PSSM search) Validation / filtering: - Quick2D: secondary structure, disorder - conservation (?)

16 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search Which kind of model to use? - regular expressions (PROSITE patterns) - profiles, like PSI-BLAST - support vector machines (SVMs) SVMs: training data function (classification / regression) For classification, SVMs operate by finding a hypersurface in the space of possible inputs. This hypersurface will attempt to split the positive examples from the negative examples. The split will be chosen to have the largest distance from the hypersurface to the nearest of the positive and negative examples. AutoMotif server (using SVMs) Need: - reformat sequences (with a simple replace, e.g. in WordPad) - register at the AutoMotif site (immediate) - submit reformatted alignment & search

17 Predicting modification sites: Building your own motif My dataset is very small and not very diverse anything I can do? Collecting & aligning orthologs: 1. Check SWISSPROT for by similarity features, and, if that s not enough, use myhits (SIB) to collect orthologs with considerable variation (lots of flanking sequence, use 90% identity clustering, against SWISSPROT [and Ensembl], E values 1e-6 and 0.01 select clear hits, then next cycle, then align trustworthy hits) 2. Trim the alignment in Jalview (e.g. in myhits), sort by pairwise id. Demo with MARRSVLYFILLNALINKGQACFCDHYAWTQWTSCSKTCNSGTQSRHRQIVVDKYYQENF

18 Predicting modification sites: Building your own motif Which residues are conserved? Do all these orthologs still carry the same modification? experiments! Search: PSI-BLAST at MPI (as before) (this example: 2 C-mannosyl. sites next to each other)

19 Predicting modification sites: Building your own motif If there are no substrates at all anything I can do? Your have a kinase, by chance? PREDIKIN: potential substrates for different kinds of kinases, based on sequence and type ideas for experiments Brinkworth et al. (2003) PNAS 100:74

20 Predicting modification sites: Building your own motif 1. Building the data set 2. Alignment 3. Analysis of the alignment 4. Motif building & search Which kind of model to use? - regular expressions (PROSITE patterns) - profiles, like PSI-BLAST - support vector machines (SVMs) Need advice? Ask a protein sequence analysis expert

21 SUMMARY Building your own motif Building your own motif is not as hard as you may think The main issue: building a good and informative alignment! Motif building & search: Regular expressions: ScanProsite PSSMs: PSI-BLAST at MPI SVMs: AutoMotif

22 Overview 1. Introduction: what role does bioinformatics play? 2. Mining information related to protein modifications - known modifications - finding proteins with particular modifications 3. Predicting modification sites in proteins: - general concepts - filtering and interpretation - generic tools - modification-specific tools and issues - building your own motif 4. Related topics: - protein function prediction - mutation effects 5. Online Materials: Exercises, Links

23 Protein Function Prediction: Predicting modifications in the context of function prediction Also: - Protein isoforms and the prediction of modifications - Interpretation of potential motifications, e.g. phospho-sites

24 Protein function prediction: Prediction modifications in the context of function prediction MARRSVLYFI LLNALINKGQ ACFCDHYAWT QWTSCSKTCN SGTQSRHRQI VVDKYYQENF CEQICSKQET RECNWQRCPI NCLLGDFGPW SDCDPCIEKQ SKVRSVLRPS QFGGQPCTEP What can be (reliably) predicted from the sequence alone? Domain architecture (and signal peptides): potential molecular interactions proteins with similar domain architecture Tertiary or secondary structure, disorder & accessibility Small motifs: targeting, modifications, transmembrane regions, coiled coils Genomic context & phylogenetic occurrence: hints on functional interactions New predictions are coming out all the time

25 Protein function prediction: our sequence, alternative transcripts How good/complete is the protein sequence we want to check? - is the sequence itself reliable? - is it as complete as we think? - alternative transcripts? Quick check: BLAT at UCSC In this example (translated ORF): - some exons are missing! (alternatively spliced) - alternative TSS exists pick a better sequence! (maybe run the predictions on both & compare)

26 Protein function prediction: Predicting modifications in the context of function prediction Domain architecture, signal peptide & low complexity regions: PFAM, Interpro molecular interactions (if you re lucky), e.g. RNA-binding proteins with similar domain architecture (or composition): PFAM, SMART Signal peptide Low complexity

27 Protein function prediction: Prediction modifications in the context of function prediction MARRSVLYFI LLNALINKGQ ACFCDHYAWT QWTSCSKTCN SGTQSRHRQI VVDKYYQENF CEQICSKQET RECNWQRCPI NCLLGDFGPW SDCDPCIEKQ SKVRSVLRPS QFGGQPCTEP

28 Protein function prediction: Prediction modifications in the context of function prediction MARRSVLYFI LLNALINKGQ ACFCDHYAWT QWTSCSKTCN SGTQSRHRQI VVDKYYQENF CEQICSKQET RECNWQRCPI NCLLGDFGPW SDCDPCIEKQ SKVRSVLRPS QFGGQPCTEP Small motifs: targeting, modifications, transmembrane regions Modifications part 1 Targeting: TargetP (part of ProtFun, see part 1) Disorder, secondary structure, coiled coils etc: Quick2D (at MPI) Quick2D output Transmembrane regions: TMHMM, also: Quick2D, SABLE

29 Protein function prediction: Prediction modifications in the context of function prediction Transmembrane Regions: TMHMM (at CBS), in ProtFun

30 Protein function prediction: Prediction modifications in the context of function prediction Genomic context & phylogenetic occurrence: STRING at EMBL: Which interactions are supported by different methods?

31 Protein function prediction: Protein isoforms and the prediction of modifications BLAT at UCSC alternative transcripts protein isoforms Also: check SWISSPROT! Do they show differences in their potential modification sites? (How could that affect function?) e.g. SWISSPROT:TAU_HUMAN (pos )

32 Protein function prediction: Interpretation of potential motifications Predicted phosphorylation sites protein-protein interactions? ScanSite at MIT (see part 1)

33 SUMMARY Prediction of modification sites in the context of protein function prediction Prediction of protein modifications is often/best done in the context of protein function prediction (comprehensive protein annotation) Many kinds of signals can be found in such sequences, and often they can provide interesting hypotheses Any isoform-specific things? (modifications?) Functional consequences of the modification? (e.g. phospho-sites) Synergy between analyses! (e.g. structure modification sites evolution) Reviews: - F. Eisenhaber (2005) Eurekah Bioscience Collection (at NCBI Books) and the online recipe at - J. Bienkowska (2005) Expert Rev. Proteomics 2:129 - B. Rost (2003) Cell.Mol.Life Sci. 60:2637

34 Mutation Effects: Will a mutation / polymorphism (e.g. SNP) weaken/destroy the potential modification site, or even create a new one? Example: NetPhosK analysis of p53_human cancer variants (pos. 151) some modification sites disappear, others appear! wt Blom et al. (2004) Proteomics 4:1633

35 Overview 1. Introduction: what role does bioinformatics play? 2. Mining information related to protein modifications - known modifications - finding proteins with particular modifications 3. Predicting modification sites in proteins: - general concepts - filtering and interpretation - generic tools - modification-specific tools and issues - building your own motif 4. Related topics: - protein function - mutation effects - analysis of mass spectrometry data 5. Online Materials: Exercises, Links

36 Online Materials: Exercises, Links 1. Protein Function & Structure 2. Modifications: Generic Tools 3. Modification-specific Tools 4. Building Your Own Motif 5. Recommended Materials 6. Exercises