Author Archives: Alexi Hussain

Advanced PyMOL Visualization for Weighted Structural Ensembles (Part 1): Ensemble Comparison

When working with structural ensembles from molecular dynamics, AlphaFold2 subsampling, or ensemble reweighting against experimental data, you quickly run into visualization problems. Many of these problems standard PyMOL tutorials don’t address: what do you do when there’s no single reference structure?

In this two-part series, I’ll share the PyMOL techniques I’ve developed for visualizing weighted ensembles where multiple conformational states coexist. Part 1 covers reference state handling, RMSD-based coloring, and cluster visualization. Part 2 will tackle efficient SASA surface generation for large ensembles. To the best of my knowledge, this is the most advanced PyMOL guide EVER.

The code snippets here are extracted from full scripts attached at the end of this post. All examples use two systems: TeaA (a membrane transporter with distinct open/closed states) and MoPrP (mouse Prion Protein with partially unfolded forms).

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Advanced 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.

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ISMB/ECCB conference feedback 

The ISMB/ECCB conference took place in Liverpool this year. So, a couple of OPIGlets took the train up north to attend this biyearly joint conference. Here we will give some general feedback on the conference and highlight some interesting talks/posters. 

General feedback 

ISMB/ECCB is a 4.5 day conference starting on the Sunday evening and running until Thursday evening. The conference is attended by around 2500 people, mostly from academic groups around the world. With more than 20 different tracks, it is a broad conference with lots of tracks happening at the same time. As always, it is thus recommended to have a look at the schedule beforehand to not get too overwhelmed. Each day there is one keynote, two poster sessions, and three blocks of talks. These talks are often given by PIs, but also PostDocs and PhD students get the opportunity to present. There are also some smaller slots for highlighting posters which are presented that day. 

This year there was a very interesting line-up of Distinguished Keynote speakers. The conference was kicked off by John Jumper talking about AlphaFold2, with a focus on how the team went about the various problems during the process of going from the initial AlphaFold model to AlphaFold2. On Monday Prof. Amos Bairoch talked about biocuration and importance and challenges of public databases. He discussed the FAIR principles for Findable, Accessible, Interoperable, and Reusable for data management [1]. The next Keynote was by Prof. James Zou about computational biology in the age of AI agents (later more). On Wednesday we had our own Prof. Charlotte Deane (woo!) talking about structure-based drug discovery with a focus on the importance of baselines and benchmarking. The conference was ended by a short interview with Prof. David Baker, followed by a talk from Prof. Fabian Theis on decoding cellular systems. He discussed Cellflow [2], an AI tool that predicts how perturbations like drugs effect the cellular phenotype. 

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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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LOADING: an art and science collaborative project

For the past few months, OPIGlets Gemma, Charlie and Alexi have been engaged in a collaboration between scientists from Oxford and artists connected to Central St Martins art college in London. This culminated in February with the publication of a zine detailing our work, and a final symposium where we presented our projects to the wider community.

This collaboration was led by organisers Barney Hill and Nina Gonzalez-Park and comprised a series of workshops in various locations across Oxford and London, where the focus was to discuss commonalities between contemporary artistic and scientific research and the concept of transdisciplinary work. Additionally, scientists and artists were paired up to explore shared interests, with the goal of creating a final piece to exhibit.

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Big Compute doesnt want you to know this! Maximising GPU Usage with CUDA MPS

Accelerating Simulations with CUDA MPS: An OpenMM Implementation Guide

Introduction

High-performance molecular dynamics simulations often require running concurrent simulations on GPUs. However, traditional GPU resource allocation can lead to inefficient utilization when running multiple processes, with users often resorting to using multiple GPUs to achieve this. While parrallelsing across nodes can improve time to solution, many processes require coordination and hence communication which quickly becomes a bottleneck. This is exacerbated with more powerful hardware as internal node communication for a single simulation on a single GPU can also become a bottleneck. This problem has been addressed for CPU parrallelism with multiprocessing and multithreading but previously this was challenging to do this efficiently on GPUs.

NVIDIA’s Multi-Process Service (MPS) offers a solution by enabling efficient and easy sharing of GPU resources among multiple processes with just a few commands. In this blog post, we’ll explore how to implement CUDA MPS with Python multiprocessing and OpenMM to accelerate molecular dynamics simulations.

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Aider and Cheap, Free, and Local LLMs

Aider and the Future of Coding: Open-Source, Affordable, and Local LLMs

The landscape of AI coding is rapidly evolving, with tools like Cursor gaining popularity for multi-file editing and copilot for AI-assisted autocomplete. However, these solutions are both closed-source and require a subscription.

This blog post will explore Aider, an open-source AI coding tool that offers flexibility, cost-effectiveness, and impressive performance, especially when paired with affordable, free, and local LLMs like DeepSeek, Google Gemini, and Ollama.

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An Open-Source CUDA for AMD GPUs – ZLUDA

Lots of work has been put into making AMD designed GPUs to work nicely with GPU accelerated frameworks like PyTorch. Despite this, getting performant code on non-NVIDIA graphics cards can be challenging for both users and developers. Even in the case where the developer has appropriately optimised for each platform there are often gaps in performance where, at the driver-level, instructions to the GPU may not be optimised fully. This is because software developed using CUDA can benefit from optimisations like operation-fusing without having to specify in many cases.

This may not be much of a concern for most researchers as we simply use what is available to us. Most of the time this is usually NVIDIA GPUs and there is hardly a choice to it. NVIDIA is aware of this and prices their products accordingly. Part of the problem is that system designers just dont have an incentive to build AMD platfroms other than for highly specialised machines.

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PHinally PHunctionalising my PHigures with PHATE feat. Plotly Express.

After being recommended by a friend, I really wanted to try plotly express but I never had the inclination to read more documentation when matplotlib gives me enough grief. While experimenting with ChatGPT I finally decided to functionalise my figure making scripts. With these scripts I manage to produce figures that made people question what I had actually been doing with my time – but I promise this will be worth your time.

I have been using with dimensionality reducition techniques recently and I came across this paper by Moon et al. PHATE is a technique that represents high dimensional (ie biological) data in a way that aims to preserve connections over preserving distance and I knew I wanted to try this as soon as I saw it. Why should you care? PHATE in 3D is faster that t-SNE in 2D. It would almost be rude to not try it out.

PHATE

In my opinion PHATE (or potential of heat diffusion for affinity-based transition embedding) does have a lot going on but that the choices at each stage feel quite sensisble. It might not come as a surprise this was primarily designed to make visual inspection of data easier on the eyes.

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