Category Archives: Protein Structure

Can we make Boltz predict allosteric binding?

Orthosteric vs Allosteric binding (Nano Banana generated)

(While this post is meant to shed light on the problem of making AI structure prediction models like Boltz become better for allosteric binding, it is also an open call for collaborating on this problem.)

I recently took part in a Boltz hackathon organised by the MIT Jameel Clinic. I worked on improving Boltz 2 predictions for allosteric binders. The validation dataset provided was from a recent paper, Co-folding, the future of docking – prediction of allosteric and orthosteric ligands, which benchmarks some of the recent state-of-the-art AI structure prediction models on a curated set of allosteric and orthosteric binders. Generally, all AI structure prediction models are trained mostly on orthosteric binding cases, which means that their performance on allosteric binding is significantly worse.

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Controlling the Diffusion Denoising Process: A Molecular Show

This blog post is supporting my poster at Young Modellers Forum and makes things way easier to see and understand. Underneath each GIF, is the explanation of what you should look for as things denoise throughout the diffusion trajectory. Click the GIFs for higher quality viewing!

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Is attention all you need for protein folding?

Researchers from Apple have released SimpleFold, a protein structure prediction model which uses exclusively standard Transformer layers. The results seem to show that SimpleFold is a little less accurate than methods such as AlphaFold2, but much faster and easier to integrate into standard LLM-like workflows. SimpleFold also shows very good scaling performance, in line with other Transformer models like ESM2. So what is powering this seemingly simple development?

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Exploring 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.

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Accelerating AlphaFold 3 for high-throughput structure prediction

Introduction

Recently, I have been conducting a project in which I need to predict the structures of a dataset comprising a few thousand protein sequences using AlphaFold 3. Taking a naive approach, it was taking an hour or two per entry to get a predicted structure. With a few thousand structures, it seemed that it would take months to be able to run…

In this blog post, I will go through some tips I found to help accelerate the structure predictions and make all of the predictions I needed in under a week. In general, following the tips in the AlphaFold 3 performance documentation is a useful starting place. Most of the tips I provide are related to accelerating the MSA generation portion of the predictions because this was the biggest bottleneck in my case.

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A more robust way to split data for protein-ligand tasks?

As I was recently reading through the paper on the PLINDER dataset while preparing for my next project, one of the aspects of the dataset that caught my attention was how the dataset splits were done to ensure minimal leakage for various protein-ligand tasks that PLINDER could be used for. They had task-specific splits as the notion of data leakage differed from task to task. For instance, in rigid body docking, having a similar protein in the train and test may not be considered leakage if the binding pocket location, conformation, or pocket interactions with a ligand are significantly different. On the other hand, in the case of co-folding, having similar proteins in the train and test sets would be considered data leakage, as predicted protein structures play a significant role in accuracy scoring. The effort that went into creating task-specific splits resonates strongly with OPIG’s view on ensuring minimal data leakage for validating the generalisability of protein-ligand models. However, it may become tedious to create task-specific dataset splits for every protein-ligand task when dealing with a large suite of such tasks. This had me thinking of potential avenues to streamline the dataset split process across the tasks, and one way to do this is by using protein-ligand interaction fingerprints or PLIFs.

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Memory Efficient Clustering of Large Protein Trajectory Ensembles

Molecular dynamics simulations have grown increasingly ambitious, with researchers routinely generating trajectories containing hundreds of thousands or even millions of frames. While this wealth of data offers unprecedented insights into protein dynamics, it also presents a formidable computational challenge: how do you extract meaningful conformational clusters from datasets that can easily exceed available system memory?

Traditional approaches to trajectory clustering often stumble when faced with large ensembles. Loading all pairwise distances into memory simultaneously can quickly consume tens or hundreds of gigabytes of RAM, while conventional PCA implementations require the entire dataset to fit in memory before decomposition can begin. For many researchers, this means either downsampling their precious simulation data or investing in expensive high-memory computing resources.

The solution lies in recognizing that we don’t actually need to hold all our data in memory simultaneously. By leveraging incremental algorithms and smart memory management, we can perform sophisticated dimensionality reduction and clustering on arbitrarily large trajectory datasets using modest computational resources. Let’s explore how three key strategies—incremental PCA, mini-batch clustering, and intelligent memory management—can transform your approach to analyzing large protein ensembles.

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AI generated linkers™: a tutorial

In molecular biology cutting and tweaking a protein construct is an often under-appreciated essential operation. Some protein have unwanted extra bits. Some protein may require a partner to be in the correct state, which would be ideally expressed as a fusion protein. Some protein need parts replacing. Some proteins disfavour a desired state. Half a decade ago, toolkits exists to attempt to tackle these problems, and now with the advent of de novo protein generation new, powerful, precise and way less painful methods are here. Therefore, herein I will discuss how to generate de novo inserts and more with RFdiffusion and other tools in order to quickly launch a project into the right orbit.
Furthermore, even when new methods will have come out, these design principles will still apply —so ignore the name of the de novo tool used.

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Making Pretty Pictures in PyMOL v2

Throughout my PhD I’ve needed nice PyMOL visualizations, but struggled to quickly and easily make the pictures I wanted. I’ve used Claire Marks‘ blopig post, Making Pretty Pictures in PyMOL, many times and wanted to expand it with what I’ve learned to make satisfying visualizations quickly!

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