View source for Task 3 - Sequence-based predictions

From Bioinformatikpedia

← Task 3 - Sequence-based predictions

You do not have permission to edit this page, for the following reason:

---

You can view and copy the source of this page.

'''''May 7, 2013: STILL UNDER CONSTRUCTION! You can start with secondary structure, but I am still updating some of the databases and task for other prediction features.''''' In contrast to the vast amount of known protein sequences, information about structure and function is available for only very few proteins. Sequence-based predictions of protein features aim to decrease this gap. Many sequence-based prediction methods use evolutionary information. Sequence alignments are therefore often a prerequisite for the predictions. == Theoretical background talk == The talk will give an introduction to sequence-based protein predictions. In particular: * secondary structure * disorder * transmembrane helices * signal peptides * GO terms == Where to run the jobs == * You can log in to the student computer pool from outside: <code>i12k-biolab??.informatik.tu-muenchen.de</code>, where <code>??</code> goes from 01 to 10. * Work in the student computer pool. * You can also install the programs on your own computer. == Protein sequence databases == Up-to-date /mnt/project/pracstrucfunc13/data/swissprot/uniprot_sprot == Secondary structure == Use ReProf to predict secondary structure for your protein. Apply ReProf to your protein and also to these proteins (UniProt AC): * P10775 * Q9X0E6 * Q08209 ReProf is installed on the student computers. Usage: <code>reprof</code> (<code>man reprof</code>). Use as input a fasta sequence as well as a position specific matrix (PSSM). The PSSM can be genereated with PSI-BLAST or HHblits. ReProf uses the PSI-BLAST PSSM format, i.e. HHblits output will have to be transformed. Compare the ReProf results to [http://bioinf.cs.ucl.ac.uk/psipred/ PsiPred] and [http://mrs.cmbi.ru.nl/hsspsoap/ DSSP_server] ([http://swift.cmbi.ru.nl/gv/dssp/ DSSP]). Find out more about the example proteins (and yours) using [http://www.uniprot.org UniProt] and the [http://www.pdb.org PDB]. == Disorder == Use IUPred to predict disorder for your protein. Apply IUPred to the example proteins given above, too. IUPred is installed on the student computers. Usage: <code>iupred</code>. Try the different types: "long", "short" and "glob". You can find more information here: <code>/opt/iupred/</code> (README and example). Compare the results to the information in the [http://www.disprot.org/ DisProt] database. ''Note:'' DisProt does not cover the complete UniProt list of sequences. If your protein is not in DisProt, you can use "Search by sequence" in DisProt to look for similar proteins. Look for at least one other (interesting!) disordered protein, use for example DisProt. == Transmembrane helices == Use PolyPhobius to predict transmembrane helices for your protein and for the following proteins (UniProt IDs given): * P35462 * Q9YDF8 * P47863 PolyPhobius is installed in <code>/mnt/project/pracstrucfunc13/polyphobius/</code>. Usage: <code>jphobius -h</code>. In contrast to its precursor Phobius, PolyPhobius uses homology information for the prediction. You input a multiple sequence alignment (MSA) in Kalign format instead of a fasta sequence. You can use any MSA, for example one generated during task 2. PolyPhobius provides some scripts, too: * a perl script <code>blastget</code> (<code>/mnt/project/pracstrucfunc12/polyphobius/blastget</code>) to run BLAST. Usage: <code>blastget -h</code>. Use only the -db and -ix parameters. Input is the fasta sequence of the above given proteins. Use SwissProt (<code>/mnt/project/rost_db/data/swissprot/uniprot_sprot</code>) as database and <code>/mnt/project/pracstrucfunc12/data/index_pp/uniprot_sprot.idx</code> as index. * Kalign (<code>/mnt/opt/T-Coffee/bin/kalign</code>) to generate the MSA. * (<code>/mnt/project/pracstrucfunc12/polyphobius/jphobius</code>) with MSA as input. Do not forget the -poly parameter. Compare the results to the membrane assignment of the structures for these proteins in [http://opm.phar.umich.edu/ OPM] and/or [http://pdbtm.enzim.hu/ PDBTM]. == Signal peptides == Use SignalP to predict signal peptides for the following proteins: * P02768 * P47863 * P11279 You can look for more example proteins with different signal peptides or targeting signals. You can run <code>signalp</code> in the student computer pool. This is version 3.0 right now. However, Tim will update to version 4.0 soon. Please note which version you are using. You can also use the [http://www.cbs.dtu.dk/services/SignalP/ SignalP] server, version 4.0. For one of the example proteins I got a different prediction from SignalP 3.0 and 4.0. You can use the [http://www.signalpeptide.de/index.php Signal Peptide Website] to look up the proteins. Check also for predicted transmembrane helices using PolyPhobius. == GO terms == Use [http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar GOPET] and [http://www.cbs.dtu.dk/services/ProtFun/ ProtFun2.0] to predit GO terms for your protein. What can you learn about its function form sequence alone? Use [http://pfam.sanger.ac.uk/ Pfam] -> SEQUENCE SEARCH to find out more about the Pfam family of your protein. == Questions to answer == * What features are predicted? * Discuss the results for your protein and the example proteins. Using the predictions, what could you learn about your protein and the example proteins? Compare to the available knowledge in UniProt, PDB, DisProt, OPM, Pfam... * Look for other methods to get an idea how many different are available. You should be able to name several more methods in the discussion. (You can also try out more methods.) * What else can/is be predicted from protein sequence alone?

Return to Task 3 - Sequence-based predictions.