Recently, I’ve been using a Convolutional Neural Network (CNN), and other methods, to predict the binding affinity of antibodies from their sequence. However, nine months ago, I applied a CNN to a far more important task – distinguishing images of butter from margarine. Please check out the GitHub link below to learn moo-re.
https://github.com/lewis-chinery/AI_cant_believe_its_not_butter
Ever wondered whether opiglet keep their ketchup in the fridge or cupboard. Perhaps you’ve wanted to know how to create nice figure to display lots of information simultaniously. Publication quality figures are easy within R with the ggplot package. We may also learn some good visualisation.
Continue readingIf you are running lots of little jobs in SLURM and want to make use of free nodes that suddenly become available, it is helpful to have a way of rapidly shipping your environments that does not rely on installing conda or rebuilding the environment from scratch every time. This is useful with complex rebuilds where exported .yml files do not always work as expected, even when specifying exact versions and source locations.
In these situations a tool such a conda-pack becomes incredibly useful. Once you have perfected the house of cards that is your conda environment, you can use conda-pack to save that exact state as a tar.gz file.
conda-pack -n my_precious_env -o my_precious_env.tar.gz
This can provide you with a backup to be used when you accidentally delete conda from your system, or if you irreparable corrupt the environment and cannot roll back to the point in time when everything worked. These tar.gz files can also be copied to distant locations by the use of rsync or scp, unpacked, sourced and used without installing conda…
Continue readingFinding the parts in common between two molecules appears to be a straightforward, but actually is a maze of layers. The task, maximum common substructure (MCS) searching, in RDKit is done by Chem.rdFMCS.FindMCS, which is highly customisable with lots of presets. What if one wanted to control in minute detail if a given atom X and is a match for atom Y? There is a way and this is how.
The Observed Antibody Space (OAS) [1,2] is an amazing resource for investigating observed antibodies or as a resource for training antibody specific models, however; its size (over 2.4 billion unpaired and 1.5 million paired antibody sequences as of June 2023) can make it painful to work with. Additionally, OAS is extremely information rich, having nearly 100 columns for each antibody heavy or light chain, further complicating how to handle the data.
From spending a lot of time working with OAS, I wanted to share a few tricks and insights, which I hope will reduce the pain and increase the joy of working with OAS!
Continue readingIn this post I will cover how to calculate sequence identity and Tanimoto similarity between any pairs of complexes in PDBbind 2020. I used RDKit in python for Tanimoto similarity and the MMseqs2 software for sequence identity calculations.
A few weeks back I wanted to cluster the protein-ligand complexes in PDBbind 2020, but to achieve this I first needed to precompute the sequence identity between all pairs sequences in PDBbind, and Tanimoto similarity between all pairs of ligands. PDBbind 2020 includes 19.443 complexes but there are much fewer distinct ligands and proteins than that. However, I kept things simple and calculated the similarities for all 19.443*19.443 pairs. Calculating the Tanimoto similarity is relatively easy thanks to the BulkTanimotoSimilarity function in RDKit. The following code should do the trick:
from rdkit.Chem import AllChem, MolFromMol2File
from rdkit.DataStructs import BulkTanimotoSimilarity
import numpy as np
import os
fps = []
for pdb in pdbs:
mol = MolFromMol2File(os.path.join('data', pdb, f'{pdb}_ligand.mol2'))
fps.append(AllChem.GetMorganFingerprint(mol, 3))
sims = []
for i in range(len(fps)):
sims.append(BulkTanimotoSimilarity(fps[i],fps))
arr = np.array(sims)
np.savez_compressed('data/tanimoto_similarity.npz', arr)
Sequence identity calculations in python with Biopandas turned out to be too slow for this amount of data so I used the ultra fast MMseqs2. The first step to running MMseqs2 is to create a .fasta file of all the sequences, which I call QUERY.fasta. This is what the first few lines look like:
Continue readingDetecting atom clashes in protein structures can be useful in a number of scenarios. For example if you are just about to start some molecular dynamics simulation, or if you want to check that a structure generated by a deep learning model is reasonable. It is quite straightforward to code, but I get the feeling that these sort of functions have been written from scratch hundreds of times. So to save you the effort, here is my implementation!!!
Continue readingIf you’ve ever found yourself typing out the same long commands over and over again, or if you’ve ever wished you could teleport directly to your favourite directories, then this post is for you.
Before we jump into some useful examples, let’s go over what bash functions and aliases are, and how to set them up.
A bash function is like a mini script stored in your .bashrc or .bash_profile file. It can accept arguments, execute a series of commands, and even return a value.
The PyMOL Python API is a useful resource for most people doing research in OPIG, whether focussed on antibodies, small molecule drug design or protein folding. However, the documentation is poorly structured and difficult to interpret without first having understood the structure of the module. In particular, the differences between use of the PyMOL command line and the API can be unclear, leading to a much longer debugging process for code than you’d like.
While I’m reluctant to continue the recent theme of ChatGPT-related posts, this is a use for ChatGPT that would have been incredibly useful to me when I was first getting to grips with the PyMOL API.
Continue readingPresentations are a great opportunity to explain your work to a new audience and receive valuable feedback. A vital aspect of a presentation is keeping the audience’s attention which is generally quite tricky I have found (from experience).
One thing that I have noticed other presenters using, which has helped maintain my focus, is an indication of the progression of the presentation. Including in your slides information that there are only a few slides remaining, encourages the listeners to keep their focus for a little longer.
Instead I will show you how to do it using Apps Script, Google’s cloud platform that allows you to write JavaScript code which can work with its online products such as Docs or Slides.
Continue reading