BIO Assignment Week 2

Assignment for Week 2
Scenario, Labnotes on the Wiki, R-functions, Databases, Sequence in Chimera (and optionally: small molecules)

Note! This assignment is currently active. All significant changes will be announced on the mailing list.

 
 

Concepts and activities (and reading, if applicable) for this assignment will be topics on next week's quiz.

 

The Scenario

Baker's yeast, Saccharomyces cerevisiae, is perhaps the most important model organism. It is a eukaryote that has been studied genetically and biochemically in great detail for many decades, and it is easily manipulated with high-throughput experimental methods. We will use information from this model organism to study the conservation of function and sequence in other fungi whose genomes have been completely sequenced; the assignments are an exercise in model-organism reasoning: the transfer of knowledge from one, well-studied organism to others.

This and the following assignments will revolve around a transcription factor that plays an important role in the regulation of the cell cycle: Mbp1 is a key component of the MBF complex (Mbp1/Swi6). This complex regulates gene expression at the crucial G1/S-phase transition of the mitotic cell cycle and has been shown to bind to the regulatory regions of more than a hundred target genes. It is therefore a DNA binding protein that acts as a control switch for a key cellular process.

One would speculate that such central control machinery would be conserved in other fungi and it will be your task in these assignments to collect evidence whether related molecular components are present in some of the newly sequenced fungal genomes. Throughout the assignments we will use freely available tools to conduct bioinformatics investigations of sequences, structures and relationships that may ultimately answer questions such as:

• Do related proteins exist in other organisms?
• What functional features can we detect in the related proteins?
• Do we have evidence that they may bind to similar sequence motifs?
• Do we believe they may function in a similar way?

Task:
Access the information page on Mbp1 at the Saccharomyces Genome Database and read the summary paragraph on the protein's function!

(If you would like to brush up on the concepts mentioned above, you could study the corresponding chapter in Lodish's Molecular Cell Biology and./or read Nobel laureate Paul Nurse's review of the key concepts of the eukaryotic cycle. It is not strictly necessary to understand the details of the yeast cell-cycle to complete the assignments, but it's obviously more satisfying to work with concepts that actually make some sense.)

For reference, this is the FASTA formatted sequence of Mbp1 from Saccharomyces cerevisiae:

>gi|6320147|ref|NP_010227.1| Mbp1p [Saccharomyces cerevisiae S288c]
MSNQIYSARYSGVDVYEFIHSTGSIMKRKKDDWVNATHILKAANFAKAKRTRILEKEVLKETHEKVQGGF
GKYQGTWVPLNIAKQLAEKFSVYDQLKPLFDFTQTDGSASPPPAPKHHHASKVDRKKAIRSASTSAIMET
KRNNKKAEENQFQSSKILGNPTAAPRKRGRPVGSTRGSRRKLGVNLQRSQSDMGFPRPAIPNSSISTTQL
PSIRSTMGPQSPTLGILEEERHDSRQQQPQQNNSAQFKEIDLEDGLSSDVEPSQQLQQVFNQNTGFVPQQ
QSSLIQTQQTESMATSVSSSPSLPTSPGDFADSNPFEERFPGGGTSPIISMIPRYPVTSRPQTSDINDKV
NKYLSKLVDYFISNEMKSNKSLPQVLLHPPPHSAPYIDAPIDPELHTAFHWACSMGNLPIAEALYEAGTS
IRSTNSQGQTPLMRSSLFHNSYTRRTFPRIFQLLHETVFDIDSQSQTVIHHIVKRKSTTPSAVYYLDVVL
SKIKDFSPQYRIELLLNTQDKNGDTALHIASKNGDVVFFNTLVKMGALTTISNKEGLTANEIMNQQYEQM
MIQNGTNQHVNSSNTDLNIHVNTNNIETKNDVNSMVIMSPVSPSDYITYPSQIATNISRNIPNVVNSMKQ
MASIYNDLHEQHDNEIKSLQKTLKSISKTKIQVSLKTLEVLKESSKDENGEAQTNDDFEILSRLQEQNTK
KLRKRLIRYKRLIKQKLEYRQTVLLNKLIEDETQATTNNTVEKDNNTLERLELAQELTMLQLQRKNKLSS
LVKKFEDNAKIHKYRRIIREGTEMNIEEVDSSLDVILQTLIANNNKNKGAEQIITISNANSHA

I have highlighted the protein's APSES domain (also known as a KilA-N domain), which is the DNA binding element of the sequence. Of course, such colouring is not part of the actual FASTA file which contains only a header and sequence letters. This is the domain we will focus on most in the following assignments.

Choosing YFO (Your Favourite Organism)

The first task is to choose a species in which to conduct your explorations.

Many fungal genomes have been sequenced and more are added each year. For the purposes of the course assignments, we need a species

• that has transcription factors containing APSES domains;
• whose genome has been completely sequenced;
• for which records exist in the RefSeq database, NCBI's unique sequence collection.

To prepare such a list of species, I have searched the NCBI's RefSeq database for proteins whose sequences are similar to the APSES domain of Mbp1 and compiled the names of organisms that contain them.

(1) Compiled a list of genome-sequenced fungi from information on the NCBI genome browser page by selecting Eukaryota / Fungi ... and downloading the entire list of species as a text document. An excerpt of the first lines of the document is shown here:

#Organism/Name            Kingdom    Group  SubGroup        Size (Mb)
Aciculosporium take       Eukaryota  Fungi  Ascomycetes     58.8364
Agaricus bisporus         Eukaryota  Fungi  Basidiomycetes  32.6144
Ajellomyces capsulatus    Eukaryota  Fungi  Ascomycetes     46.124 
Ajellomyces dermatitidis  Eukaryota  Fungi  Ascomycetes     75.4047
[...]

(2) Reformatted the document to provide an Entrez species selection command. With this string NCBI search tools can be constrained to a set of species we are interested in. One could type this list by hand, or use search/replace functions of a text editor on the original list. I used the following Perl one-liner which I give here merely for your edification.

perl -e 'while(<STDIN>){/^(.+?)\t/;print"\"$1\"[organism] OR \n"}' < genomes_overview.txt

... giving me the Entrez selection command (with over 400 species):

"Aciculosporium take"[organism] OR 
"Agaricus bisporus"[organism] OR 
"Ajellomyces capsulatus"[organism] OR 
"Ajellomyces dermatitidis"[organism] ...

(3) Performed a PSI BLAST search with the Mbp1 APSES domain sequence shown above, the search database restricted to the refseq_protein database and an the Entrez Query created as explained above. This search was iterated a few times and retrieves all sequence-similar proteins from genome sequenced fungi for which entries exist in the RefSeq database.

(4) In the header of the BLAST results page, there is a link to [Taxonomy reports] This contains a list of all hits, sorted by species. I copied the species names to a separate file - applying a bit of manual editing: removing duplicate genus entries, and the six reference species Saccharomyces cerevisiae, Aspergillus nidulans, Candida albicans, Neurospora crassa, Schizosaccharomyces pombe, and Ustilago maydis - these are not being assigned to the class.

(5) Finally, I extracted a 5 letter code from the binomial names and formatted everything as R code to be used below. Again, a Perl one-liner. It applies a regular expression to extract the first three characters of the genus name and the first two characters of the species name and combines these into a short, uppercase label.

perl -e 'while(<STDIN>){m/^((...).+?\s(..).*?)\s/;print("\t\t\"$1 (", uc($2.$3), ")\",\n");}' < BLAST_species.txt

This process with its mix of Web service, programmed reformatting and manual cleanup, is a fairly typical example of gathering and collating information across different data sources.

 

Next, I would like to assign species from this list to each student. This process should be random, but reproducible.

Here's an idea: we could use the student ID ( a unique identifier) to pick entries from the list! Indeed, the functions provided in R can easily be used to randomly but reproducibly choose an element from a list. Essentially we can write a function that creates a many-faced die, with a piece of text—a species' name— on every face. It will fall differently for each student ID, but will fall the same every time the same ID is encountered.

This makes use of the fact that "random" numbers generated by a computer algorithm aren't really random: they are "pseudorandom", generated by a deterministic algorithm. Such an algorithm takes a number—a seed— and mangles it until the result has no recognizable connection to the seed. The result is indistinguishable from a random number. However if we use the same seed, we will always get the same result. Such a random pick can be programmed with the following steps:

1. Create a list
2. Initialize a random number generator with a student ID as a seed
3. pick a random integer "i" in the range from first to last element of the list
4. return the i-th list element.

Here is R code to accomplish this:

Task:

• Read, try to understand and then execute the following R-code.

pickSpecies <- function(ID) {
	# this function randomly picks a fungal species
	# from a list. It is seeded by a student ID. Therefore
	# the pick is random, but reproducible.
	
	# first, define a list of species:
	Species <- c(
		"Agaricus bisporus (AGABI)",
		"Ajellomyces dermatitidis (AJEDE)",
		"Arthroderma otae (ARTOT)",
		"Ashbya gossypii (ASHGO)",
		"Auricularia delicata (AURDE)",
		"Baudoinia compniacensis (BAUCO)",
		"Beauveria bassiana (BEABA)",
		"Bipolaris oryzae (BIPOR)",
		"Botrytis cinerea (BOTCI)",
		"Capronia coronata (CAPCO)",
		"Chaetomium globosum (CHAGL)",
		"Cladophialophora psammophila (CLAPS)",
		"Clavispora lusitaniae (CLALU)",
		"Coccidioides immitis (COCIM)",
		"Colletotrichum fioriniae (COLFI)",
		"Coniophora puteana (CONPU)",
		"Coniosporium apollinis (CONAP)",
		"Coprinopsis cinerea (COPCI)",
		"Cryptococcus neoformans (CRYNE)",
		"Cyphellophora europaea (CYPEU)",
		"Debaryomyces hansenii (DEBHA)",
		"Dichomitus squalens (DICSQ)",
		"Endocarpon pusillum (ENDPU)",
		"Eutypa lata (EUTLA)",
		"Exophiala dermatitidis (EXODE)",
		"Fomitiporia mediterranea (FOMME)",
		"Fusarium graminearum (FUSGR)",
		"Glarea lozoyensis (GLALO)",
		"Gloeophyllum trabeum (GLOTR)",
		"Kazachstania africana (KAZAF)",
		"Kluyveromyces lactis (KLULA)",
		"Komagataella pastoris (KOMPA)",
		"Laccaria bicolor (LACBI)",
		"Lachancea thermotolerans (LACTH)",
		"Leptosphaeria maculans (LEPMA)",
		"Lodderomyces elongisporus (LODEL)",
		"Magnaporthe oryzae (MAGOR)",
		"Malassezia globosa (MALGL)",
		"Marssonina brunnea (MARBR)",
		"Melampsora larici-populina (MELLA)",
		"Metarhizium acridum (METAC)",
		"Meyerozyma guilliermondii (MEYGU)",
		"Microsporum gypseum (MICGY)",
		"Millerozyma farinosa (MILFA)",
		"Moniliophthora roreri (MONRO)",
		"Myceliophthora thermophila (MYCTH)",
		"Naumovozyma castellii (NAUCA)",
		"Nectria haematococca (NECHA)",
		"Neofusicoccum parvum (NEOPA)",
		"Neosartorya fischeri (NEOFI)",
		"Paracoccidioides sp. (PARSP)",
		"Pestalotiopsis fici (PESFI)",
		"Phaeosphaeria nodorum (PHANO)",
		"Phanerochaete carnosa (PHACA)",
		"Pneumocystis murina (PNEMU)",
		"Podospora anserina (PODAN)",
		"Postia placenta (POSPL)",
		"Pseudocercospora fijiensis (PSEFI)",
		"Pseudozyma flocculosa (PSEFL)",
		"Puccinia graminis (PUCGR)",
		"Punctularia strigosozonata (PUNST)",
		"Pyrenophora tritici-repentis (PYRTR)",
		"Scheffersomyces stipitis (SCHST)",
		"Schizophyllum commune (SCHCO)",
		"Sclerotinia sclerotiorum (SCLSC)",
		"Serpula lacrymans (SERLA)",
		"Setosphaeria turcica (SETTU)",
		"Sordaria macrospora (SORMA)",
		"Spathaspora passalidarum (SPAPA)",
		"Stereum hirsutum (STEHI)",
		"Talaromyces marneffei (TALMA)",
		"Tetrapisispora blattae (TETBL)",
		"Thielavia terrestris (THITE)",
		"Togninia minima (TOGMI)",
		"Torulaspora delbrueckii (TORDE)",
		"Trametes versicolor (TRAVE)",
		"Tremella mesenterica (TREME)",
		"Trichoderma reesei (TRIRE)",
		"Trichophyton rubrum (TRIRU)",
		"Tuber melanosporum (TUBME)",
		"Uncinocarpus reesii (UNCRE)",
		"Vanderwaltozyma polyspora (VANPO)",
		"Verticillium alfalfae (VERAL)",
		"Wallemia sebi (WALSE)",
		"Yarrowia lipolytica (YARLI)",
		"Zygosaccharomyces rouxii (ZYGRO)",
		"Zymoseptoria tritici (ZYMTR)"
		)
	l <- length(Species)    # number of elements in the list
	set.seed(ID)            # seed the random number generator
	                        # with the student ID
	i <- runif(1, 0, 1)     # pick one random number between 0 and 1
	i <- l * i              # multiply with number of elements
	i <- ceiling(i)         # round up to nearest integer
	choice <- Species[i]    # pick the i'th element from list
	return(choice)
}

• Execute the function pickSpecies() with your student ID as its parameter. Example:

 > pickSpecies(991234567)
[1] "Coccidioides immitis (COCIM)"

• Note down the species name and its five letter label on your student Wiki page. Use this species whenever this or future assignments refer to YFO.

Task:

• While you already have R open, access the R tutorial and work through the section on Simple commands. It is short, and will help you understand the code above.

 

Keeping a notebook on your Wiki

Consider it a part of your assignment to document your activities on your Wiki page.

You should write your documentation like a lab notebook—not a formal lab report, but a point-form record of your actual activities. Write such documentation as notes to your (future) self. Obviously, since much of the work will be done on the Web, an electronic notebook makes more sense than a paper notebook.

For each task:

• Write a header and give it a unique number.

This is useful so you can refer to the header number in later text. Obviously, you should "hard-code" the number and not use the Wiki's automatic section numbering scheme, since the numbers should be stable over time, not change when you add or delete a section.

• State the objective.

In one brief sentence, restate what your task is supposed to achieve.

• Document the procedure.

Note what you have done, as concisely as possible. Give enough information so that anyone could reproduce unambiguously what you have done— your future project student, or even your future self.

• Document your results.

You can distinguish different types of results -

• • Static data does not change over time and it may be sufficient to note a reference to the result. For example, there is no need to copy a genbank record into your documentation, it is sufficient to note the accession number or the GI number.
  • Variable data can change over time. For example the results of a BLAST search depend on the sequences in the database. A list of similar structures may change as new structures get solved. In principle you want to record such data, to be able to reproduce at a later time what your conclusions were based on. But be selective in what you record. For example you should not paste the entire set of results of a BLAST search into your document, but only those matches that were important for your conclusions. Indiscriminate pasting of irrelevant information will make your notes unusable.
  • Analysis results

The results of sequence analyses, alignments etc. in general get recorded in your documentation. Again: be selective. Record what is important.

• Note your conclusions.

An analysis is not complete unless you conclude something from the results. (Remember what we said about "Cargo Cult Science". If there is no conclusion possible, your activities are quite pointless.) Are two sequences likely homologues, or not? Does your protein contain a signal-sequence or does it not? Is a binding site conserved, or not? The analysis provides the data. In your conclusion you provide the interpretation of what the data means in the context of your objective. Sometimes your assignment task will ask you to elaborate on an analysis and conclusion. But this does not mean that when the assignment does not explicitly mention it, you don't need to interpret your data.

• Use discretion when uploading images

I have enabled image uploading with some reservations, we'll see how it goes. You must not:

• upload images that are irrelevant for this course;
• upload copyrighted images;
• upload any images that are larger than 500 kb. I may silently remove large images when I encounter them.

Moreover, understand that any of your uploaded images may be deleted at any time. If they are valuable to you, keep backups on your own machine.

• Prepare your images well

Don't upload uncompressed screendumps. Save images in a compressed file format on your own computer. Then use the Special:Upload link in the left-hand menu to upload images. The Wiki will only accept .jpeg or .png images.

• Use the correct image types.

In principle, images can be stored uncompressed as .tiff or .bmp, or compressed as .gif or .jpg or .png. .gif is useful for images with large, monochrome areas and sharp, high-contrast edges because the LZW compression algorithm it uses works especially well on such data; .jpg (or .jpeg) is preferred for images with shades and halftones such as the structure views you should prepare for several assignments, JPEG has excellent application support and is the most versatile general purpose image file format currently in use; .tiff (or .tif) is preferred to archive master copies of images in a lossless fashion, use LZW compression for TIFF files if your system/application supports it; The .png format is an open source alternative for lossless, compressed images. Application support is growing but still variable. .bmp is not preferred for really anything, it is bloated in its (default) uncompressed form and primarily used only because it is simple to code and ubiquitous on Windows computers.

Image dimensions and resolution

Stereo images should have equivalent points approximately 6cm apart. It depends on your monitor how many pixels this corresponds to. The dimensions of an image are stated in pixels (width x height). My notebook screen has a native display resolution of 1440 x 900 pixels/23.5 x 21 cm. Therefore a 6cm separation on my notebook corresponds to approximately 260 pixels. However on my desktop monitor, 260 pixels is 6.7 cm across. And on a high-resolution iPad display, at 227 ppi (pixels per inch), 260 pixels are just 2.9 cm across. For the assignments: adjust your stereo images so they are approximately at the right separation and are approximately 500 to 600 pixels wide. Also, scale your molecules so they fill the available window and - if you have depth cueing enabled - move them close to the front clipping plane so the molecule is are not just a dim blob, lost in murky shadows.

Considerations for print (manuscripts etc.) are slightly different: for print output you can specify the output resolution in dpi (dots per inch). A typical print resolution is about 300 dpi: 6 cm separation at 300dpi is about 700 pixels. Print images should therefore be about three times as large in width and height as screen images.

Preparation of stereo views

When assignments ask you to create molecular images, always create stereo views.

Keep your images uncluttered and expressive

Scale the molecular model to fill the available space of your image well. Orient views so they illustrate a point you are trying to make. Emphasize residues that you are writing about with a contrasting colouring scheme. Add labels, where residue identities are not otherwise obvious. Turn off side-chains for residues that are not important. The more you practice these small details, the more efficient you will become in the use of your tools.

If you have technical difficulties, post your questions to the list and/or contact me.

Keeping such a journal will be helpful, because the assignment is more or less integrated over the entire term, and later assignments will make use of earlier results. But it is also excellent practice for "real" research.

 

NCBI databases

Let us explore some of the offerings of the NCBI that can contribute to our objective of studying a particular gene in a newly sequenced organism.

Entrez

Task:
Remember to document your activities.

1. Access the NCBI website at http://www.ncbi.nlm.nih.gov/
2. In the search bar, enter mbp1 and click Search.
3. On the resulting page, look for the Protein section and click on it. What do you find?

The result page of your search in "All Databases" is the "Global Query Result Page" of the Entrez system. If you follow the "Protein" link, you get taken to the 200 or so sequences in the NCBI Protein database. But looking at that page, you see that the result is quite non-specific: searching only by gene name retrieves an Arabidopsis protein, a Saccharomyces protein (presumably one that we might be interested in), Maltose Binding Proteins - and much more. There must be a more specific way to search, and indeed there is. Time to read up on the Entrez system.

Task:

1. Navigate to the Entrez Help Page and read about the Entrez system, especially about:
   1. Boolean operators,
   2. wildcards,
   3. limits, and
   4. filters.
2. You should minimally understand:
   1. How to search by keyword;
   2. How to search by gene or protein name;
   3. How to restrict a search to a particular organism.

Don't skip this part, you don't need to know the options by heart, but you should know they exist and how to find them.

Keyword and organism searches are pretty universal, but apart from that, each NCBI database has its own set of specific fields. You can access them via the Advanced Search interface of any of the database pages.

Protein

 

Task:
Now try the search for Mbp1 in Baker's Yeast alone. Return to the Global Search page and enter:

Mbp1[protein name] AND "Saccharomyces cerevisiae"[organism]

This should find one and only one protein. Follow the link into the protein database: since this is only one record, the link takes you directly to the result—a data record in Genbank Flat File (GFF) format, not to a list of hits, as before. Explore the record and familiarize yourself with the information that is there.

All well and good - but didn't we want to find RefSeq entries, since that is expected to be the database of unique, curated sequence records? I can't tell you why the RefSeq result was not listed among the search results. But I can at least tell you how to find it:

Task:

1. In the right-hand margin of the record, you will find a section of Identical proteins ...: click on See all..."" to list them all. Among these, find the entry with an accession number like NP_123456. This is a RefSeq ID. Follow the link.
2. Explore the resulting page. You will notice that the information elements are not identical, even though these are sequence records for one and the same yeast gene product, in two similar databases, at the same data provider!
3. Note down the RefSeq ID, you will probably need it later on.

All well and good, and the Mbp1 protein is going to accompany us throughout the term—but we were actually trying to find related proteins in YFO. Let's give that a try.

Task:

1. Again in the right hand margin, find the section on Related Information and follow the link to Related Sequences. There are many. More than 21,000 actually. Definitely more than you would like to browse through to find the sequences in YFO. Let's use a filter on these results.
2. Click on the Advanced link to access the search history that brought you here. Since you have read the Entrez page, you should be able to understand clearly that you can type something like

#4 AND "Schizosaccharomyces pombe"[organism]

... or whatever your command-history number resp. YFO name suggests.

You should find a handful of genes, all of them in YFO. If you find none, or hundreds, you did something wrong. Ask on the mailing list and make sure to fix the problem.

This is one way to find related sequences: by accessing precomputed results at the NCBI. We will however explore much more principled approaches in a later assignment. Let's leave the sequence searches for the moment, and explore other information on Yeast Mbp1 that may be useful for annotating the related sequences in YFO.

PubMed

Arguably one of the most important databases in the life sciences is PubMed and this is a good time to look at PubMed in a bit more detail than you might have done previously.

Task:

1. Return back to the MBP1 RefSeq record. If you have already closed it, simply enter the RefSeq ID into the search field for a Protein database search and find it again.
2. Find the PubMed links under Related information in the right-hand margin and explore them. One will take you only to information related to the actual RefSeq record, the others find more broadly relatd information. PubMed(weighted) appears to give a pretty good overview of systems-biology type, cross-sectional and functional information. But neither of the searches finds all Mbp1 related literature.
3. Again, enter the Advanced query page and study the keywords that apply to PubMed searches. These are actually quite important and useful to remember. Make yourself familiar with the section on Search field descriptions and tags in the PubMed help document, (in particular [DP], [AU], [TI], and [TA]), how you use the History to combine searches, and the use of AND, OR, NOT and brackets. Understand how you can restrict a search to reviews only, and what the link to Related citations... is useful for.
4. Now find publications with Mbp1 in the title. In the result list, follow the links for the two Biochemistry papers by Taylor et al. (2000) and by Deleeuw et al. (2008). Download the PDFs, we will need them later.

Structure search

The search options in the PDB structure database are as sophisticated as those at the NCBI. For now, we will try a simple keyword search to get us started.

Task:

1. Visit the RCSB PDB website at http://www.pdb.org/
2. Briefly orient yourself regarding the database contents and its information offerings and services.
3. Enter Mbp1 into the search field.
4. In your journal, note down the PDB IDs for the three Saccharomyces cerevisiae Mbp1 transcription factor structures your search has retrieved.
5. Click on one of the entries and explore the information and services linked from that page.

 

Chimera

In this task we will explore the sequence interface of Chimera, use it to select specific parts of a molecule, and colour specific regions (or residues) of a molecule separately.

 

Task:

1. Open Chimera.
2. One of the three yeast Mbp1 fragment structures has the PDB ID 1BM8. Load it in Chimera (simply enter the ID into the appropriate field of the File → Fetch by ID... window).
3. Display the protein in Presets → Interactive 1 mode and familiarize yourself with its topology of helices and strands.
4. Open the sequence tool: Tools → Sequence → Sequence. You will see the sequence for each chain - here there is only one chain. By default, coloured rectangles overlay the secondary structure elements of the sequence.
5. Hover the mouse over some residues and note that the sequence number and chain is shown at the bottom of the window.
6. Click/drag one residue to select it. (Simply a click wont work, you need to drag a little bit for the selection to catch on.) Note that the residue gets a green overlay in the sequence window, as it also gets selected with a green border in the graphics window.
7. In the bottom of the sequence window, there are instructions how to select (multiple) regions. Try this: colour the protein white (Select → Select All; Actions → Color → light gray). Clear the selection. Now select all the helical regions (pale yellow boxes) by click/dragging and using the shift key. Color them red. Then select all the strands by clicking into any of the pale green boxes and color them green.
8. Finally, generate a stereo-view that shows the molecule well, in which the domain is coloured dark grey, and the APSES domain residues (as defined in the FASTA listing above, from I19 to Y93) are coloured with a colour ramp (Tools → Depiction → Rainbow)
9. Show the first and last residue's CA atom as a sphere and colour the first one blue (to mark the N-terminus) and the last one red. E.g.:
   1. Select → Atom specifier → :4@CA
   2. Actions → Ribbon → hide
   3. Actions → Atoms/bonds → show
   4. Actions → Atoms/bonds → sphere
   5. Actions → Color → cornflower blue
   6. Then click on the selection inspector (the green button with the magnifying glass at the lower right of the graphics window) and set the sphere radius to 1.0Å.
10. Save the image in your Wiki journal in JPEG format (File → Save Image and upload it to the Student Wiki).

 

Stereo vision

Task:

Continue with your stereo practice.

Practice at least ...

• two times daily,
• for 3-5 minutes each session.

• Measure your interocular distance and your fusion distance as explained here on the Student Wiki and add it to the table.

Keep up your practice throughout the course. Once again: do not go through your practice sessions mechanically. If you are not making constant progress in your practice sessions, contact me so we can help you on the right track.

Modeling small molecules (optional)

As an optional part of the assignment, here is a small tutorial for modeling and visualizing "small-molecule" structures.

Defining a molecule

A number of public repositories make small molecule information available, such as PubChem at the NCBI, the ligand collection at the PDB, the ChEBI database at the European Bioinformatics Institute, or the NCI database browser at the US National Cancer Institute. One general way to export topology information from these services is to use SMILES strings—a shorthand notation for the composition and topology of chemical compounds.

Task:

1. Access each of the databases mentioned above.
2. Enter "caffeine" as a search term.
3. Explore the contents of the result, in particular note and copy the SMILES string for the compound.

Alternatively, you can sketch your own compound. Versions of Peter Ertl's Java Molecular Editor (JME) are offered on several websites (e.g. click on Transfer to Java Editor on a NCI results page), and PubChem offers this functionality via its Sketcher tool.

Task:

1. Navigate to PubChem.
2. Follow the link to Chemical structure search (in the right hand menu).
3. Click on the 3D conformer tab and on the Launch button to launch the molecular editor in its own window.
4. Sketch the structure of caffeine. I find the editor quite intuitive but if you need help, just use the Help button in the editor.
5. Save the SMILES string of your compound.
6. Also Export your result in SMILES format as a file.

Translating SMILES to structure

Online services exist to translate SMILES to (idealized) coordinates.

Task:

1. Access the online SMILES translation service at the NCI.
2. Paste a caffeine SMILES string into the form, choose the PDB radio button, click on Translate and download your file.
3. Load the molecule in Chimera.

Chimera also has a function to translate SMILES to coordinates.

Task:

1. In Chimera:
   1. File → Close Session.
   2. Tools → Structure Editing → Build Structure.
   3. Select SMILES string, paste the string and click Apply.
2. The caffeine molecule will be generated and visualized in the graphics window.

That is all.

 

Links and resources

 

Footnotes and references

 

Ask, if things don't work for you!

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• Do consider how to ask your questions so that a meaningful answer is possible:
  • How to create a Minimal, Complete, and Verifiable example on stackoverflow and ...
  • How to make a great R reproducible example are required reading.