As someone who works with T cell antigen receptor (TCR) and peptide-major histocompatibility complex (pMHC) data, I have found several Python packages to be very useful for eliminating tedious steps in data cleaning and feature engineering stages.
Continue readingCategory Archives: Code
Building a “Second Brain” – A Functional Knowledge Stack with Obsidian
Whilst I always enjoy the acquisition of knowledge, I’ve always struggled with depositing it usefully. From pen and paper notes with a 20 colour theme which lost value with each additional colour, to OneNote or iPad GoodNotes based emulations of pen and paper, it’s been a constant quest for the optimal note taking schema. Personally there are 3 key objectives I need my note taking to achieve:
- It must be digitally compatible and accessible from any device.
- It must comfortably handle math and images.
- It must be something I look forward to – the software needs to be aesthetically clean, lightweight with none of the chunkiness of Microsoft apps, and highly customisable.
For me the solution to this was Obsidian, the perhaps more cultified sibling to Notion. Obsidian is a note taking application that uses markdown with a surprising amount flexibility, including the ability to partner it with an LLM which I’ll explore in this blog, alongside my vault organisation do or dies, and favourite customisations.
Continue readingAdvanced PyMOL Visualization for Weighted Structural Ensembles (Part 2): Efficient Weighted SASA Surfaces
In Part 1, we covered reference state handling, RMSD-based coloring, and cluster visualization for weighted structural ensembles. Now we tackle a more ambitious goal: generating solvent-accessible surface area (SASA) surfaces that reflect the weighted conformational distribution of your ensemble.
Why surfaces? Because they show the accessible conformational space—where your protein can actually be found, weighted by population. This is particularly powerful when comparing different fitting methods or showing how experimental constraints reshape the ensemble.
The challenge? A typical ensemble might have 500+ frames, each generating thousands of surface points. Naive approaches choke on the computational and memory demands. This post shares the optimizations that make weighted SASA visualization practical.
Continue readingDemocratising the Dark Arts: Writing Triton Kernels with Claude
Why would you ever want to leave the warm, fuzzy embrace of torch.nn? It works, it’s differentiable, and it rarely causes your entire Python session to segfault without a stack trace. The answer usually comes down to the “Memory Wall.” Modern deep learning is often less bound by how fast your GPU can do math (FLOPS) and more bound by how fast it can move data around (Memory Bandwidth). When you write a sequence of simple PyTorch operations, something like x = x * 2 + y the GPU often reads x from memory, multiplies it, writes it back, reads it again to add y, and writes it back again. It’s the computational equivalent of making five separate trips to the grocery store because you forgot the eggs, then the milk, then the bread. Writing a custom kernel lets you “fuse” these operations. You load the data once, perform a dozen mathematical operations on it while it sits in the ultra-fast chip registers, and write it back once. The performance gains can be massive (often 2x-10x for specific layers).But traditionally, the “cost” of accessing those gains, learning C++, understanding warp divergence, and manual memory management, was just too high for most researchers. That equation is finally changing.
Finding 250GB of Missing Storage On My Mac: A Warning For Large Dataset Users
I recently faced a puzzling issue: my 1TB MacBook Pro showed only 150GB free, but disk analyzers could only account for about 500GB of used space. After hours of troubleshooting, I discovered that Spotlight’s search index had balooned to 233GB, hundreds of times larger than normal.
The Problem
Standard disk analyzers showed that my mac had 330GB of “Inaccessible Disk Space” and 66GB of “Purgeable Disk Space” but no clear explanation for where my storage went. Removing the purgeable space was easy enough with sudo purge but none of the recommended fixes from ChatGPT like clearing Time Machine snapshots, clearing unused conda packages with pip cache purge and conda clean --all, and restarting the computer had any effect on the inaccessible disk space.
Using Node-RED as a front-end to your software
Node-RED is an, open-source, visual programming tool that lets you wire together hardware (such as sensors), APIs (such as REST/POST) and custom functions. However, its custom functions aren’t simply the JavaScript you write, they can also be containers!
This can provide an intuitive front-end to otherwise difficult software. For example, you’ve written your magnum opus, you’ve even documented it (though no-one will ever read it) and to ensure maximum compatibility for the widest possible audience, you’ve containerised it. But it’s still a command-line driven application. Using node-RED you can make this accessible to an inexperienced audience.
Out of the box, node-RED’s quite pretty, you can string together nodes to perform functions that are useful. In this case, it’s for monitoring a log file, if the log doesn’t grow, something’s gone wrong, so email me to take a look at it.
Extracting 3D Pharmacophore Points with RDKit
Pharmacophores are simplified representations of the key interactions ligands make with proteins, such as hydrogen bonds, charge interactions, and aromatic contacts. Think of them as the essential “bumps and grooves” on a key that allow it to fit its lock (the protein). These maps can be derived from ligands or protein–ligand complexes and are powerful tools for virtual screening and generative models. Here, we’ll see how to extract 3D pharmacophore points from a ligand using RDKit.
(Code adapted from Dr. Ruben Sanchez.)
Why pharmacophore “points”?
RDKit represents each pharmacophore feature (donor, acceptor, aromatic, etc.) as a point in 3D space, located at the feature center. These points capture the essential interaction motifs of a ligand without requiring the full atomic detail.
Continue readingExploring the Protein Data Bank programmatically
The Worldwide Protein Data Bank (wwPDB or just the PDB to its friends) is a key resource for structural biology, providing a single central repository of protein and nucleic acid structure data. Most researchers interact with the PDB either by downloading and parsing individual entries as mmCIF files (or as legacy PDB files), or by downloading aggregated data, such as the RCSB‘s collection in a single FASTA file of all polymer entity sequences. All too often, researchers end up laboriously writing their own file parsers to digest these files. In recent years though, more sophisticated tools have been made available that make it much easier to access only the data that you need.
Continue readingHandling OAS Scale Datasets Without The Drama
Working with Observed Antibody Space (OAS) dataset sometimes feels a bit like trying to cook dinner with the contents of the whole fridge emptied into the pan. There are countless CSVs, all of different sizes (some might not even fit onto your RAM), and you just want a clean, fast pipeline so you can get back to modelling. The trick is to stop treating the data like a giant spreadsheet you fully load into memory and start treating it like a columnar, on-disk database you stream through. That’s exactly what the 🤗 Datasets library gives you.
At the heart of 🤗 Datasets is Apache Arrow, which stores columns in a memory-mapped format (if you are curious about what that means there is a great explanation in another blog post here. In plain terms: the data mostly lives on disk, and you pull in just the slices you need. It feels interactive even when the dataset is huge. Instead of a single monolithic script that does everything (and takes forever), you layer small, composable steps—standardize a few columns, filter out junk, compute a couple of derived fields—and each step is cached automatically. Change one piece, and only that piece recomputes. Sounds great, right? But of course, the key question now is how to get OAS data into Datasets to begin with.
Continue readingA guide to fixing broken AMBER MD trajectory files and visualisations.
You’ve just finished a week-long molecular dynamics simulation. You’re excited to see what happened to your protein complex, so you load up the trajectory in VMD and… your protein looks like it’s been through a blender. Pieces are scattered across the screen, water molecules are everywhere, and half your complex seems to have teleported to the other side of the simulation box. This chaos is caused by periodic boundary conditions (PBC).
PBC
PBC is a computational trick that simulates bulk behaviour by treating your simulation box like a repeating tile. When a molecule exits one side, it immediately reappears on the opposite side. This works perfectly for physics as your protein experiences realistic bulk water behaviour.
