When running molecular dynamics (MD) simulations, we are usually interested in measuring an ensemble average of some metric (e.g., RMSD, RMSF, radius of gyration, …) and use this to draw conclusions about the investigated system. While calculating the average value of a metric is straightforward (we can simply measure the metric in each frame and average it) calculating a statistical uncertainty is a little more tricky and often forgotten. The main challange when trying to calculate an uncertainty of MD oveservables is that individual frames of the simulation are not samped independently but they are time correlated (i.e., frame N depends on frame N-1). In this blog post, I will breifly introduce block averaging, a statistical technique to estimate uncertainty in correlated data.
Continue readingCategory Archives: Molecular Dynamics
Memory Efficient Clustering of Large Protein Trajectory Ensembles
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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.
Continue readingBig 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.
Continue readingMaking Peace with Molecular Entropy
I first stumbled upon OPIG blogs through a post on ligand-binding thermodynamics, which refreshed my understanding of some thermodynamics concepts from undergrad, bringing me face-to-face with the concept that made most molecular physics students break out in cold sweats: Entropy. Entropy is that perplexing measure of disorder and randomness in a system. In the context of molecular dynamics simulations (MD), it calculates the conformational freedom and disorder within protein molecules which becomes particularly relevant when calculating binding free energies.
In MD, MM/GBSA and MM/PBSA are fancy terms for trying to predict how strongly molecules stick together and are the go-to methods for binding free energy calculations. MM/PBSA uses the Poisson–Boltzmann (PB) equation to account for solvent polarisation and ionic effects accurately but at a high computational cost. While MM/GBSA approximates PB, using the Generalised Born (GB) model, offering faster calculations suitable for large systems, though with reduced accuracy. Consider MM/PBSA as the careful accountant who considers every detail but takes forever, while MM/GBSA is its faster, slightly less accurate coworker who gets the job done when you’re in a hurry.
Like many before me, I made the classic error of ignoring entropy, assuming that entropy changes that were similar across systems being compared would have their terms cancel out and could be neglected. This would simplify calculations and ease computational constraints (in other words it was too complicated, and I had deadlines breathing down my neck). This worked fine… until it didn’t. The wake-up call came during a project studying metal-isocitrate complexes in IDH1. For context, IDH1 is a homodimer with a flexible ‘hinge’ region that becomes unstable without its corresponding subunit, giving rise to very high fluctuations. By ignoring entropy in this unstable system, I managed to generate binding free energy results that violated several laws of thermodynamics and would make Clausius roll in his grave.
Continue readingMDAnalysis: Work with dynamics trajectories of proteins
For a long time crystallographers and subsequently the authors of AlphaFold2 had you believe that proteins are a static group of atoms written to a .pdb file. Turns out this was a HOAX. If you don’t want to miss out on the latest trend of working with dynamic structural ensembles of proteins this blog post is exactly right for you. MDAnalysis is a python package which as the name says was designed to analyse molecular dyanmics simulation and lets you work with trajectories of protein structures easily.
Continue readingIncorporating conformer ensembles for better molecular representation learning
The spatial or 3D structure of a molecule is particularly relevant to modeling its activity in QSAR. The 3D structural information affects molecular properties and chemical reactivities and thus it is important to incorporate them in deep learning models built for molecules. A key aspect of the spatial structure of molecules is the flexible distribution of their constituent atoms known as conformation. Given the temperature of a molecular system, the probability of each of its possible conformation is defined by its formation energy and this follows a Boltzmann distribution [McQuarrie and Simon, 1997]. The Boltzmann distribution tells us the probability of a certain confirmation given its potential energy. The different conformations of a molecule could result in different properties and activity. Therefore, it is imperative to consider multiple conformers in molecular deep learning to ensure that the notion of conformational flexibility is embedded in the model developed. The model should also be able to capture the Boltzmann distribution of the potential energy related to the conformers.
Continue readingConference Summary: MGMS Adaptive Immune Receptors Meeting 2024
On 5th April 2024, over 60 researchers braved the train strikes and gusty weather to gather at Lady Margaret Hall in Oxford and engage in a day full of scientific talks, posters and discussions on the topic of adaptive immune receptor (AIR) analysis!
The stuff MDAnalysis didn’t implement: CPU Parallel HOLE conductance analysis
Some time ago, I needed to find a way to computationally estimate conductance values for every protein frame from several molecular dynamics (MD) trajectories.
In a previous post, I wrote about how to clean the resulting instant conductance timeseries from outliers. But, I never described how I generated these timeseries.
In this post, I will show how you can parallelise the computation of instant conductance given an MD trajectory. I will touch on the difficulties of this process. And why I had to implement a custom tool for it given that MDAnalysis seems to already have implemented a routine of this sort. Finally, I will provide two Python scripts that you can easily adapt to run your parallel calculations – for which I’ll provide some important notes you don’t wanna skip.
Demystifying the thermodynamics of ligand binding
Chemoinformatics uses a curious jumble of terms from thermodynamics, wet-lab techniques and statistical terminology, which is at its most jarring, it could be argued, in machine learning. In some datasets one often sees pIC50, pEC50, pKi and pKD, in discussion sections a medchemist may talk casually of entropy, whereas in the world of molecular mechanics everything is internal energy. Herein I hope to address some common misconceptions and unify these concepts.
Continue readingBe a computational chemist and you must be a jack of all trades
Being a jack of all trades brings to mind someone who has extensive multidisciplinary expertise and is equipped with many tools in their toolbox to solve different problems. A jack of all trades is a great succinct description for computational chemists in drug discovery.
Recently I had a great conversation with Dr. Arjun Narayanan, a Senior Research Scientist at Vertex Pharmaceuticals and a jack of all trades as a computational chemist. In this blog post, I’ll describe what he does as a computational chemist, the problems he solves, and the new tools he’s looking forward to adding to his toolbox.