Find out information about a new protein

To make the project interesting, I selected a protein for which some information can be found, but which is not already a well-known protein.

VKVMQKDVLAQLMEHLETGQYKKREKTLAYMTKIIEQGIHEYYKNFDNAT
ARKMALDYFKRINDDKGMIYMVVVDKNGVVLFDPVNPKTVGQSGLDAQSV
DGVYYVRGYLEAAKKGGGYTYYKMPKYDGGVPEKKFAYSHYDEVSQMVIA
TTSYYTDINTENKAIKEGVNKVFDENTTKLFLWILTATIALVVLTLIY

What you turn in should be a stand-alone paper that a biologist or bioinformatician can read without having any knowledge of this class or of the protein. Be sure to provide proper citations for all papers and web sites that you get information from. You should cite a paper for each tool you use (they generally tell you what to cite). A bare URL is not an adequate citation for a web site—you need to provide enough information that someone can find it with google if it has moved without being changed. A typical number of citations in the past has been around 20.

This paper really should look like a report on the protein, not like a homework exercise. I have given some suggestions below to guide your research, but these are not questions to answer sequentially, nor are they necessarily the most productive directions for your search.

Don't just print out the results of web searches, but interpret the results to see what (if anything) they say about the target protein.

Find the sequence

[NCBI]

Data=/projects/compbio/programs/blast2/data

blastall -p blastp -d /projects/compbio/data/nrp/nr -i search.seq

Use the blastp program to do protein-protein search of the nr (non-redundant protein) database. Get the name(s) and organism of the sequence.

Literature search

Do google searches using the protein name and its accession number or database identifier(s) to try to find web pages about the protein.

Use PUBMED and other databases at Entrez http://www3.ncbi.nlm.nih.gov/Entrez/ to find papers that talk about the protein.

Use BIOSIS from the library website http://library.ucsc.edu/ to see if there are articles there. (BIOSIS is better at plant biology and non-pathogenic microbiology, for example, than PUBMED is.)

Find out what else you can get from the protein sequence

Make a FASTA file of probable homologs (using blastp or psi-blast) for use in subsequent questions.

You might also want to check for transmembrane helices and signal peptides. The servers at http://www.cbs.dtu.dk/services/ are among the best for these tasks.

Finding other motifs (active sites, metal-binding sites, disulfide bonds, DNA-binding sites, ATP-binding sites, ...) is often useful. The Pfam website at http://pfam.wustl.edu/ is one useful site for looking for known motifs. Prosite http://www.expasy.org/prosite/ can also be useful, though you have to be aware for the high probability of false positives.

Multiple alignments

This is a progressive method of multiple alignment. It will do all-pairs scoring on a sequence set, then build a guide tree with the sequences on the leaves. Sequences with a high similarity score are assigned to nodes with a common parent on this tree. The alignment is built from the bottom of the tree by merging sibling sequences into pairwise alignments, and then progressively merging the most similar pairwise alignments into multiple alignments.

Now you are going to try it out. Go to the CLUSTALW web server. Enter your email address and paste your set of proteins into the window. You probably do not want to put in 1000s of sequences, as CLUSTALW gets slower with the square of the number of sequences. Use all the defaults except (possibly) for one: you may want to change "Output Order" to "input". This will order the sequences in your multiple alignment in the same way as they are ordered in your FASTA file. Create a CLUSTALW multiple alignment and print out your results. You might want to turn on color using the master form, as the color information helps see residue conservation more quickly.

• Include the multiple alignment in your paper. Compare it to the alignments found by BLAST. Where they differ, which one looks more reasonable to you?
• How many positions in this alignment are completely conserved (identical amino acids)?
• How many have a conservative substitution (similar amino acids)? Note: the notion of "similar amino acids" is not a well-defined one. One common working approximation is define to amino acids as similar if the BLOSUM62 matrix has a positive score for that pair. There are patterns of similarity that are not well captured by this definition, though.
• What does this tell you about these proteins? (If anything)

Bonus points: other multiple aligners

Another good multiple alignment program is MUSCLE (see http://www.drive5.com/muscle/). Use MUSCLE to align your sequences—how does it differ from CLUSTAL and T-COFFEE. (Note: all three are global aligners, so may suffer from similar alignment errors—of the three MUSCLE does the best in alignment tests and is usually the fastest.)

TCoffee is another method for progressive multiple alignment. It was designed to improve on the alignment quality of CLUSTALW. It tends to be quite slow when given many sequences, so pick a subset of no more than about 12 sequences to give it. You can use some of the tree information you got from clustalW to help you choose which to align.

Go to the TCoffee web server. Once again paste your small set of sequences into the window. T-coffee is much slower than ClustalW, and time grows with the cube of the number of sequences. When your results are returned, click on the link to the "clustalw(aln)". This will take you to a page where the alignment is formatted in the same way as the first CLUSTALW alignment generated above. Print out this page. How does this multiple alignments differ from the one you created with CLUSTALW? Is it better or worse? Can you tell?

Look also at the html scores page, where TCoffee colors the alignment according to its view of how well the residues align. (Note: you may have to edit the HTML page or play with your browser settings to force the page to use fixed-width fonts.)

ClustalW trees

ClustalW creates a guide tree using neighbor-joining and displays it with the multiple alignment. Take a moment to study this phylogenetic tree. Which proteins look most worth examining for inferring structure or function? Consider not only the similarity of the proteins, but the likelihood of finding experimental data.

ClustalW uses a fairly simple neighbor-joining method to generate phylogenetic trees. There are other techniques that are considered better (though slower). You could try some of them, though I don't have any handy web pointers for you, since phylogenetic software is not something I've looked at much. We do have some installed on the SoE machines in the /projects/compbio/programs/phylip/ directory (not the latest release though), but not on the campus-wide machines. The Phylip package is provided as web service by a few places (google for "phylip"), including http://bioweb.pasteur.fr/seqanal/phylogeny/phylip-uk.html (Warning: the Phylip programs won't tolerate long ID lines in the FASTA file—you have to filter the a2m files to make them acceptable to Phylip.)

SAM-T06

Use just the initial sequence as a seed for the SAM-T06 search page http://www.soe.ucsc.edu/research/compbio/SAM_T06/T06-query.html. You should expect the search to take several hours—run it overnight and look at it after you've had some sleep!

Compare the resulting multiple alignment and sequence logo with the ones you got using blast, clustalw, and t-coffee. What are some of the main differences between this multiple alignment and the others you have looked at? What positions contain highly conserved residues? Does the sequence logo suggest any conservation to look for that you did not expect from having just looked at the multiple alignments?

Take a look at the sequence logos for secondary-structure prediction also. Do the proteins have strongly or weakly predicted secondary structure? Are the conserved residues in areas of strongly predicted secondary structure?

Does the SAM-T06 software make a strong prediction for tertiary structure? What E-value does it give to the best template?

You might also want to give the sequence to a metaserver (such as http://bioinfo.pl/meta), though this may take a few days to gather results from all the servers. You can also access some servers directly (psipred, hmmstr, ...).

Viewing with Rasmol

Pick one of the strongly predicted PDB structures from the protein-structure prediction step, and look at it with RASMOL. If you are on a School of Engineering machine, you can download the protein with

/programs/compbio/bin/pdb-get 1foo

rasmol `pdb-get 1foo`

On the campus-wide Solaris machines, rasmol has been installed, but you will have to download your PDB file yourself from the PDB website: http://www.rcsb.org/pdb/

If you need to download Rasmol for your home computer, there are several sources, including http://www.bernstein-plus-sons.com/software/rasmol/.

Rasmol is a command-based viewer, and you will have to use "help" a lot while learning to use it. The download site listed above also has pointers to the web-based Rasmol manual.

Look at the protein in various ways (as cartoons, as ball-and-stick models, as a backbone trace, ...). With the protein in cartoon view, use "Select hetero and not HOH" to select ligands (if there are any), and view them in space-filling mode.

Where are there insertions or deletions in the target relative to the template you chose? Are these in sensible places?

Note: there are many other protein viewers on the web (DeepView=Swiss-pdbviewer, molmol, chime, protein explorer, molscript, jmol, firstview, cn3d, pymol, kinemage, ...). If you wish, you may substitute some other viewer for rasmol. Make sure you pick one that will allow you to see ligands and to highlight the residues near ligands.

Microarray data

Note: there is a strong possibility that this protein is not closely related to proteins from any of the model organisms---or that it is related to lots of proteins which don't all share the same function. Discuss the difficulties as well as the successes!

What can you conclude?

Things learned after assignment

• Remind students that reference list should contain all and only those papers cited in the main body.
• \cite{a,b,c} for multiple citations in LaTeX.
• Italicize species names and use unbreakable space between genus initial and species.
• pH is higher (less acid) at the epithelium than in the center of the stomach.
• Explain meaning of "hypothetical protein" in a database---just talks about the sequence having come from a translation of a gene model. Doesn't say anything about how "real" the protein is. There may since have been extensive experimental work without the database annotation having been updated.
• TMHMM gets inside/outside wrong frequently---may be even more than half the time? Phobius http://phobius.sbc.su.se/ seems to get the inside/outside prediction better, but believes that TM helices near the beginning of the sequence are all signal peptides.
• Precision is needed in describing actions. Not just "blast", but which version of blast on what database?
• Misannotation in databases is common. This sequence had a homolog that was misannotated as a histidine kinase when it was actually a methyl-accepting chemotaxis receptor (probably based on faulty transference through homology of the HAMP domains).

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