Skip to main content

On models

So, after chatting some stuff about the PDBReader in CDK versus the Biojava PDBReader on CDK-devel (devel archive) I realised that I don't actually know how these different frameworks model biopolymers.

This is my attempt to understand them:
it shows a partial hierarchy for each framework, without the detail of which are classes, and which are interfaces. Notably, biojava has an interface and an implementing class for each of the things shown.

One thing I should point out, is that "Strand" in CDK should really (really) be called "Chain" as in Biojava and Jmol (and probably every other known framework). A kind of spelling mistake, I think. I also don't understand what the PhosporousMonomer and PhosporousPolymer are in Jmol.

Comments

Andreas said…
Hi,

just to quickly add, BioJava can also represent multiple models within PDB files.

There are different iterators and utiliy classes that allow to access
arrays of atoms or iterate over all amino acids, do calculations etc. It can deal with ATOM vs SEQRES issues.

Since all objects in the datamodel are JavaBeans, it is also possible to serialize a structure back to a database using Hibernate.

For more docu on what you can do with the BioJava structure modules
see here:
http://biojava.org/wiki/BioJava:CookBook#Protein_Structure

Andreas
gilleain said…
Ahh, yes. When I looked into the data models of various java molecular viewers, I noticed some that could handle multiple models. That is good.

The ATOM<->SEQRES stuff is also helpful.

I'm not so fussed about the hibernate aspect. I suppose it could be good, but I don't think that should be the driver behind a design.

I guess that there's more than one way to skin a protein...

Popular posts from this blog

How many isomers of C4H11N are there?

One of the most popular queries that lands people at this blog is about the isomers of C4H11N - which I suspect may be some kind of organic chemistry question on student homework. In any case, this post will describe how to find all members of a small space like this by hand rather than using software.

Firstly, lets connect all the hydrogens to the heavy atoms (C and N, in this case). For example:


Now eleven hydrogens can be distributed among these five heavy atoms in various ways. In fact this is the problem of partitioning a number into a list of other numbers which I've talked about before. These partitions and (possible) fragment lists are shown here:


One thing to notice is that all partitions have to have 5 parts - even if one of those parts is 0. That's not strictly a partition anymore, but never mind. The other important point is that some of the partitions lead to multiple fragment lists - [3, 3, 2, 2, 1] could have a CH+NH2 or an NH+CH2.

The final step is to connect u…

Havel-Hakimi Algorithm for Generating Graphs from Degree Sequences

A degree sequence is an ordered list of degrees for the vertices of a graph. For example, here are some graphs and their degree sequences:



Clearly, each graph has only one degree sequence, but the reverse is not true - one degree sequence can correspond to many graphs. Finally, an ordered sequence of numbers (d1 >= d2 >= ... >= dn > 0) may not be the degree sequence of a graph - in other words, it is not graphical.

The Havel-Hakimi (HH) theorem gives us a way to test a degree sequence to see if it is graphical or not. As a side-effect, a graph is produced that realises the sequence. Note that it only produces one graph, not all of them. It proceeds by attaching the first vertex of highest degree to the next set of high-degree vertices. If there are none left to attach to, it has either used up all the sequence to produce a graph, or the sequence was not graphical.



The image above shows the HH algorithm at work on the sequence [3, 3, 2, 2, 1, 1]. Unfortunately, this produce…

Generating Trees

Tree generation is a well known (and solved!) problem in computer science. On the other hand, it's pretty important for various problems - in my case, making tree-like fusanes. I'll describe here the slightly tortuous route I took to make trees.

Firstly, there is a famous theorem due to Cayley that the number of (labelled) trees on n vertices is nn - 2 which can be proved by using Prüfer sequences. That's all very well, you might well say - but what does all this mean?

Well, it's not all that important, since there is a fundamental problem with this approach : the difference between a labelled tree and an unlabelled tree. There are many more labeled trees than unlabeled :


There is only one unlabeled tree on 3 vertices, but 3 labeled ones
this is easy to check using the two OEIS sequences for this : A000272 (labeled) and A000055 (unlabeled). For n ranging from 3 to 8 we have [3, 16, 125, 1296, 16807, 262144] labeled trees and [1, 2, 3, 6, 11, 23] unlabeled ones. Only 23 …