Recently I was dealing with a set of compounds with known target activities from the ChEMBL database, and I wanted to find out which of them also had PDB crystal structures in complex with that target.
Referencing this manually is very easy for cases where we are interested in 2-3 compounds, but for any larger number, using the ChEMBL and PDB web services greatly reduces the number of clicks.
Continue readingCommand-Line Interfaces (CLIs) are one of the best ways of providing your programs with useful parameters to customize their execution. If you are not familiar with CLI, in this blog post we will introduce them. Let’s say that you have a program that reads a file, computes something, and then, writes the results into another file. The simplest way of providing those arguments would be:
$ python mycode.py my/inputFile my/outputFile
### mycode.py ###
def doSomething(inputFilename):
with open(inputFilename) as f:
return len(f.readlines())
if __name__ == "__main__":
#Notice that the order of the arguments is important
inputFilename = sys.argv[1]
outputFilename = sys.argv[2]
with open(outputFilename, "w") as f:
f.write( doSomething(inputFilename))
Continue reading The combination of Python and the cheminformatics toolkit RDKit has opened up so many ways to explore chemistry on a computer. Jupyter — named for the three languages, Julia, Python, and R — ties interactivity and visualization together, creating wonderful environments (Notebooks and JupyterLab) to carry out, share and reproduce research, including:
“data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.”
—https://jupyter.org
At this year’s annual RDKit UGM (User Group Meeting), Cédric Bouysset shared a tutorial explaining how to create a grid of molecules that you can interact with, using his “mols2grid“:
Now that PDBbind 2020 has been released, I want to draw some attention to an issue with using the SDF files that are supplied in the PDBbind refined set 2020.
Normally, SDF files save the chirality information of compounds in the atom block of the file which is shown belowas a snipped of the full sdf file for the ligand of PDB entry 4qsv. The column that defines chirality is marked in red.
As you can see, all columns shown here are 0. The SDF files supplied by PDBbind for some reason do NOT encode chirality information explicitly. This will be a problem when using RDKit to read the molecule and transform it into a smiles string. By using the following commands to read the ligand for 4qsv from PDBBind 2020 and write a SMILES string, we get:
There are more python libraries than you can shake a stick at, but here are a handful that don’t get much love and may save you some brain power, compute time or both.
Fire is a library which turns your normal python functions into command-line utilities without requiring more than a couple of additional lines of copy-and-paste code. Being able to immediately access your functions from the command line is amazingly helpful when you’re making quick and dirty utilities and saves needing to reach for the nuclear approach of using getopt.
Continue readingIt’s not as simple as you’d think.
If you want to skip the small talk, the code is at the bottom. Sampling 2D rotations uniformly is simple: rotate by an angle from the uniform distribution 
In Fast Random Rotation Matrices (James Avro, 1992), a method for uniform random 3D rotation matrices is outlined, the main steps being:
Continue readingHave you ever tried to use someone else’s code and spent a whole day trying to install it? Have you ever decided not to use a tool because installing it was a massive pain? Both of those have happened to me and, to be honest, it is a massive shame. The authors may spend large amounts of time developing these tools and in the end, no one uses them because they can’t get them to work. So I have decided to try and make all code I develop as easy and painless as possible to install and use.
Continue readingThe ability to successfully apply previously acquired knowledge to novel and unfamiliar situations is one of the main hallmarks of successful learning and general intelligence. This capability to effectively generalise is amongst the most desirable properties a prediction model (or a mind, for that matter) can have.
In supervised machine learning, the standard way to evaluate the generalisation power of a prediction model for a given task is to randomly split the whole available data set 
Since in this scenario the model has never seen any of the examples in 
Detecting intermolecular interactions is often one of the first steps when assessing the binding mode of a ligand. This usually involves the human researcher opening up a molecular viewer and checking the orientations of the ligand and protein functional groups, sometimes aided by the viewer’s own interaction detecting functionality. For looking at single digit numbers of structures, this approach works fairly well, especially as more experienced researchers can spot cases where the automated interaction detection has failed. When analysing tens or hundreds of binding sites, however, an automated way of detecting and recording interaction information for downstream processing is needed. When I had to do this recently, I used an open-source Python module called ODDT (Open Drug Discovery Toolkit, its full documentation can be found here).
My use case was fairly standard: starting with a list of holo protein structures as pdb files and their corresponding ligands in .sdf format, I wanted to detect any hydrogen bonds between a ligand and its native protein crystal structure. Specifically, I needed the number and name of the the interacting residue, its chain ID, and the name of the protein atom involved in the interaction. A general example on how to do this can be found in the ODDT documentation. Below, I show how I have used the code on PDB structure 1a9u.
Continue readingConsider a sensor which tells you whether it is cloudy or clear, but is wrong with some probability. Now, the weather *is* cloudy or clear, we could go and see which it was, so there is a “true” state, but we only have noisy observations on which to attempt to infer it.
We might model this process (with the assumption of sufficiently precious weather), and attempt to make inferences about the true state of the weather over time, the rate of change of the weather and how noisy our sensor is by using a Hidden Markov Model.
The Hidden Markov Model describes a hidden Markov Chain which at each step emits an observation with a probability that depends on the current state. In general both the hidden state and the observations may be discrete or continuous.
But for simplicity’s sake let’s consider the case where both the hidden and observed spaces are discrete. Then, the Hidden Markov Model is parameterised by two matrices:
Continue reading