Lecture 02

Update Warning! This page has not been revised yet for the 2007 Fall term. Some of the slides may be reused, but please consider the page as a whole out of date as long as this warning appears here.

 

 

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The Sequence Abstraction

What you should take home from this part of the course

• Know the one-letter code and key properties of all 20 proteinogenic aminoacids;
• Understand the benefits and limitations of the sequence abstraction;
• Recognize common sequence identifiers, utilize them confidently;
• Know about the contents of key sequence databases;
• Be able to retrieve sequence data;
• Know about and confidently use the fields in GenBank and GenPept records.

 

Links summary

• IUPAC (Wikipedia)
• adenine, cytosine, guanine, thymine (Wikipedia)
• SMILES (Wikipedia)
• Rob Russel's Amino Acid Pages
• GO (Gene Ontology)
• Open Biology Ontologies
• FASTA format
• Genbank Overview
• A GenBank record example
• A GenPept record example
• A UniProt record example

• Entrez tutorial (pdf)

Exercises

• Find a protein that contains a selenocysteine residue (e.g. human glutathione peroxidase). Check the Genbank record to see how this residue is represented in the sequence and in the record. Find and compare the corresponding SwissProt record.
• Find a secreted protein such as E. coli beta-lactamase. Look into the Genbank record whether you can identify the signal-peptide that is post-translationally removed. Find the corresponding SwissProt entry and look for the annotation.
• Human mitochondrial proteins are translated according to a different genetic code from human nuclear proteins. Looking at the CDS of mitochondrial Cytochrome B (NC_001807), how would you know?

Lecture Slides

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Deconstruct this statement: first, we need to consider what it means to compute. Then we need to consider the best representations.

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In order to make biology computable, we have to clarify the concepts we are working with. This is useful even beyond the requirements of bioinformatics. It is an exercise in clarifying the conceptual foundations of biology itself. In many instances, definitions in current, common use are deficient, either because our current state of knowledge has gone beyond the original ideas we were trying to subsume with a term (e.g. gene, or pathway), or because an inconsistent formal and colloquial meaning of terms lead to ambiguities (e.g. function), or because the technical meaning of terms is poorly understood and generally misused (e.g. homology).

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Think about the following question: this image representis a particular biomolecule. What is the best abstraction ?

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Asking about the best is an ill-posed question if the purpose is not specified. The best for what? There are many possible abstractions, each serving different purposes. Each of the abstractions above can serve as a representation of the biomolecule, each one emphasizes a different perspective and can serve a different purpose.

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Working with abstractions implies we are no longer manipulating the biological entity, but it's representation. This distinction becomes crucial, when we start computing with representations to infer facts about the original entities! Inferences must be related back to biology! Common problems include that the abstraction may not be rich enough to capture the property we are investigating (e.g. one-letter sequence codes cannot represent amino acid modifications or sequence numbers), or the abstraction may be ambiguous (e.g. one protein may have more than one homologue in a related organism, thus the relationship between gene IDs is ambiguous) or the abstraction may not be unique (e.g. one protein may have more than one function, the same protein name may refer to unrelated proteins in different species).

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Lecture 02, Slide 007

Slide 008

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Nucleic acids can form heterocopolymers as DNA or RNA, thus their structural formula can be described (to a close approximation) simply by listing the nucleotide bases in a defined order. A one-letter code has been defined as a shorthand notation for this. By convention, DNA or RNA sequence is written in the direction from the bond with the 5' carbon of the ribose (or deoxyribose), to the bond with the 3'-carbon. Since the 5'-carbon carries a free 5'-phosphate at the terminus of the polynucleotide and the 3'-carbon carries a free OH, this direction happens to be the same direction that nucleic acid polymers are replicated by the DNA-polymerase: a phosphate of a single nucleotide is attached to the free -OH of the polynucleotide. This direction is also the direction of transcription of the RNA polymerase (for the same reason) and (incidentally) the direction of translation by the ribosome. Due to base-pair complementarity, only one strand of a double stranded sequence needs to be recorded since the sequence of the complementary strand is implied: it is simply the reverse complement. The one letter abbreviations are defined by the International Union of Pure and Applied Chemistry (IUPAC) as follows:

A = adenine
C = cytosine
G = guanine
T = thymine
R = G A (purine)
Y = T C (pyrimidine)
K = G T (keto)
M = A C (amino)
S = G C (strong bonds)
W = A T (weak bonds)
B = G T C (not A)
D = G A T (not C)
H = A C T (not G)
V = G C A (not T)
N = A G C T (any)

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Proteins are amino acid heterocopolymers, thus their structural formula can be described (to a close approximation) simply by listing their constituent amino acid residues in a defined order. The one-letter code is a shorthand notation for this. By convention, protein sequence is written from aminoterminus (N-) to carboxyterminus (C-). This happens to be the same direction in which the protein is synthesized on the ribosome.

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An amino acid is a molecule. A number of abstractions are in common use for this molecule: its chemical formula simply describes the elemental composition, a so called SMILES string (see also here) captures bonding topology as well, its information is equivalent to a chemical graph. A set of records of 3D coordinates can describe the three-dimensional conformation, this can in turn be displayed in a number of different image options - like a simple line drawing or a set of spheres, color coded by element, with relative sizes corresponding to the elements' Van der Waals radii.

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... see IUPAC

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A Venn diagram of biophysical amino acid properties. Of course, the precise role of a particular amino acid depends on its context in a folded protein.

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The physicochemical properties of amino acids determines their role in e.g. a folded protein structure. For example, consider the amino acid distribution in a typical enzyme, such as cathepsin K (1ATK).

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Hydrophobic amino acids - the group FAMILYVW - are found predominantly in the core of a protein, small amino acids such as GASC are often found in turns, charged amino acid sidechains - (+):KRH and (-)DE - are almost exclusively found on the surface; the energetic requirements for desolvation of the sidechain makes their incorporation into the core unfavourable.

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Cysteine can take on a number of different roles, depending on its context. Here, cysteine forms part of the active site, it is the nucleophile in the catalytic triad C-H-N; cathepsin is thuss an example of a cysteine-protease. This particular cysteine is absolutely conserved in related proteins.

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In secreted proteins only, cysteine often forms structural disulfide bridges in which two thiol groups oxidize to a covalent disulfide bond. These cysteines usually are highly conserved.

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Cysteine can also be found in a very general role, simply as a somewhat polar, small residue. This general role is infrequent in secreted proteins since it can interfere with the formation of the correct disulfide topology and generally makes the protein sensitive to oxidation. Such cysteines are poorly conserved in related proteins.

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Sequence is the most important abstraction in biology; you need to know your amino acids in order to relate a sequence back to the biopolymer. Required knowledge is: the structural formula, the one- and three- letter codes and key properties (such as charge, relative size, polarity) for all 20 proteinogenic amino acids.

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In Shakespeare's classic tragedy of romantic love and family allegiance, Juliet encapsulates the play's central struggle in this phrase by claiming that Romeo's family name is an artificial and meaningless convention. Just like in the world of the sequence abstraction, this is only partially true: the problems are not just based in the fact that Romeo is called a Montague, but that he is in fact a member of that family. Even if a Thing does not change if it's abstract label changes, such labels rarely exist in isolation: other Things might be referred to by the same label and changing one changes the composition of the entire set. (Or, to remain with our example, as soon as Romeo renounces his name and thus his family, the family would be no longer the same.) Even worse - and this is something we encounter every day in bioinformatics - if identifiers are not stable over time, cross-references to that identifier fail. If you decide you'll call a rose a skunk, people would become very confused.

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Some ambiguity can only be resolved if the context of the representation is specified.

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Often synonym constrained controlled vocabularies are presented as option lists on Web forms. If the CV is too long for this approach to be practical, defining the correct form becomes a challenge. In well engineered databases a lot of effort is spent on this task; typically a large dictionary of synonym mappings is employed in some form.

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NB. The situation that a unique property of an entity can be concisely described is the ideal case: in that case the identifier captures the most fundamental aspect of the molecule. For calcium, the element does not just have the atomic number 20, it is the element with 20 protons. Similarly oxytocin does not just have the sequence CYIQNCPLG, it is the peptide with that sequence. However, these are favourable exceptions and more commonly unique, abstract labels - such as identifiers - have to be defined.

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Read more about the Gene Ontology project and the Open Biology Ontologies.

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Read more about the FASTA format.

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Just like in human language, rigourous syntactical rules enforce that you can't use bad grammar and get away with it.

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XML formatted files are human readable - in principle. The abundance of tags can make this challenging in practice. The loss of readability is a trade-off for the gain in rigour.

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A "Database" is much more than just the data it contains.

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At the core of the database is the data and of course the data should be the primary focus of attention of the database providers. Issues are the correctness of the data, mechanisms to validate and maintain records, frequency of updates, consistency etc.

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Data is stored in some storage system.

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Data is found through some query system.

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Storage resources, query and maintenance functions, and meta-information services are all united into a common interface that is presented to the user.

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UniProt is arguably the more comprehensive resource, however it is not integrated with the GenBank world, although that would be reasonably straightforward to do. For example, neither does the NCBI record for the Swi4 protein contain a reference to the UniProt accession number, nor does the UniProt record for the same protein contain an NCBI accession number or a GI. National database politics are not always in the best interest of the worldwide scientific community.

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SwissProt records - a subset of the UniProt Knowledge Base - are the highest standard, manually curated, non-redundant protein records available. Unfortunately the growth of the sequence databases far exceeds any human curation capabilities!

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