HECBioSim: The UK High End Computing Consortium for Biomolecular Simulation.
Lead Research Organisation:
University of Oxford
Department Name: Biochemistry
Abstract
Biomolecular simulations enable us to predict the behaviour of biological systems given the structures (or models) of the components. Combined with the structural biology and new advancements in machine learning methods, molecular simulations provide the scope for unprecedented insights into biomolecular systems.
The level of detail afforded by these methods, along with their ability to rationalise and augment experimental data and their predictive power in generating new hypotheses are already enabling them to make significant contributions in a wide variety of areas that are crucial for healthcare, the environment, quality of life and consequently, to the economy. The UK biomolecular simulation community has a strong international reputation, with world-leading research in in drug design and development, biocatalysis, bionano-technology, chemical biology and medicine. This has recently been demonstrated by the enormous impact our work has made on research into the SARS-CoV2 virus, in helping identify target sites for vaccine and antiviral development and has lead to our members winning and being shortlisted for international prizes, establishing new collaborations with industrial partners and being key participants in large international consortia.
Furthermore, in addition to our work on Covid-19, we have delivered outstanding research with impact in bionanotechology, drug design, AMR as well as in developing novel models (e.g. fluctuating finite elements models), methodologies (e.g. conversion between fine-grained and coarse-grained resolutions) and enabling technologies (e.g.tools for efficient submission of calculations) for application across the full spectrum of biophysical and biochemical sciences. We are now at a time when we can take giant leaps in terms of the scope of our work. Having access to the largest, most modern computing facilities in the UK is essential for this. Renewal of the Consortium will enable us to continue allocating time ARCHER2 and Tier 2 resources for our cutting-edge biomolecular simulations.
We will place a special emphasis on reaching out to experimentalists (indeed we already have a strong reputation for doing this) and scientists working in industry in order to foster interactions between computational and experimental scientists, and academia and industry to encourage integrated multidisciplinary studies of key problems.
Biomolecular simulation is an integral part of drug design and development. The pharmaceutical industry needs well-trained scientists in this area, as well as the development of new methods (e.g. for prediction of drug binding affinities, ligand selectivity and metabolism). Members of the consortium have a strong track record of collaboration with industry to deliver trained scientists and new methodologies. For example, PhD students trained by consortium members have recently taken up positions in UCB, Unilever, Oxford Nanopore Technologies and Exscientia. Many of these academic-industry collaborations have been strengthened and in some cases, been established on the basis of work done through HECBioSim allocations.
The Consortium will continue to welcome new members from across the whole community including ECRs. Indeed we have an excellent track record of ECRs from within our community going on to independent academic posts. We will continue to develop computational tools and training for both experts and non-experts using biomolecular simulation on HEC resources. We propose to develop new tools that will enable more efficient simulations by harnessing tools from the machine learning field an augmenting them with our own codes to make best use of the UK HEC landscape.
In summary, HECBioSim will expand our portfolio of collaborative endeavours with experimental scientists and those working in industry to harness expertise from all domains to inform our own work and thus to maintain the UK as a world-leader in biomolecular simulation.
The level of detail afforded by these methods, along with their ability to rationalise and augment experimental data and their predictive power in generating new hypotheses are already enabling them to make significant contributions in a wide variety of areas that are crucial for healthcare, the environment, quality of life and consequently, to the economy. The UK biomolecular simulation community has a strong international reputation, with world-leading research in in drug design and development, biocatalysis, bionano-technology, chemical biology and medicine. This has recently been demonstrated by the enormous impact our work has made on research into the SARS-CoV2 virus, in helping identify target sites for vaccine and antiviral development and has lead to our members winning and being shortlisted for international prizes, establishing new collaborations with industrial partners and being key participants in large international consortia.
Furthermore, in addition to our work on Covid-19, we have delivered outstanding research with impact in bionanotechology, drug design, AMR as well as in developing novel models (e.g. fluctuating finite elements models), methodologies (e.g. conversion between fine-grained and coarse-grained resolutions) and enabling technologies (e.g.tools for efficient submission of calculations) for application across the full spectrum of biophysical and biochemical sciences. We are now at a time when we can take giant leaps in terms of the scope of our work. Having access to the largest, most modern computing facilities in the UK is essential for this. Renewal of the Consortium will enable us to continue allocating time ARCHER2 and Tier 2 resources for our cutting-edge biomolecular simulations.
We will place a special emphasis on reaching out to experimentalists (indeed we already have a strong reputation for doing this) and scientists working in industry in order to foster interactions between computational and experimental scientists, and academia and industry to encourage integrated multidisciplinary studies of key problems.
Biomolecular simulation is an integral part of drug design and development. The pharmaceutical industry needs well-trained scientists in this area, as well as the development of new methods (e.g. for prediction of drug binding affinities, ligand selectivity and metabolism). Members of the consortium have a strong track record of collaboration with industry to deliver trained scientists and new methodologies. For example, PhD students trained by consortium members have recently taken up positions in UCB, Unilever, Oxford Nanopore Technologies and Exscientia. Many of these academic-industry collaborations have been strengthened and in some cases, been established on the basis of work done through HECBioSim allocations.
The Consortium will continue to welcome new members from across the whole community including ECRs. Indeed we have an excellent track record of ECRs from within our community going on to independent academic posts. We will continue to develop computational tools and training for both experts and non-experts using biomolecular simulation on HEC resources. We propose to develop new tools that will enable more efficient simulations by harnessing tools from the machine learning field an augmenting them with our own codes to make best use of the UK HEC landscape.
In summary, HECBioSim will expand our portfolio of collaborative endeavours with experimental scientists and those working in industry to harness expertise from all domains to inform our own work and thus to maintain the UK as a world-leader in biomolecular simulation.
People |
ORCID iD |
| Syma Khalid (Principal Investigator) |
Publications
Akter F
(2023)
Binding pocket dynamics along the recovery stroke of human ß-cardiac myosin
in PLOS Computational Biology
Benn G
(2024)
OmpA controls order in the outer membrane and shares the mechanical load
in Proceedings of the National Academy of Sciences
Blazquez S
(2023)
Location and Concentration of Aromatic-Rich Segments Dictates the Percolating Inter-Molecular Network and Viscoelastic Properties of Ageing Condensates
in Advanced Science
Brandner A
(2024)
Faster but Not Sweeter: A Model of Escherichia coli Re-level Lipopolysaccharide for Martini 3 and a Martini 2 Version with Accelerated Kinetics
in Journal of Chemical Theory and Computation
Brandner AF
(2025)
Systematic Approach to Parametrization of Disaccharides for the Martini 3 Coarse-Grained Force Field.
in Journal of chemical information and modeling
Burman M
(2025)
Atomic Description of the Reciprocal Action between Supercoils and Melting Bubbles on Linear DNA.
in Physical review letters
Cho C
(2025)
Diacylation of Peptides Enables the Construction of Functional Vesicles for Drug-Carrying Liposomes
in Angewandte Chemie International Edition
Clark F
(2025)
Robust Automated Truncation Point Selection for Molecular Simulations.
in Journal of chemical theory and computation
Clark R
(2024)
Titratable residues that drive RND efflux: Insights from molecular simulations.
in QRB discovery
| Description | We have developed pipelines to predict how well designed proteins will embed into membranes. These proteins are used for applications in bionanotechnology such as for DNA sequencing and potentially for protein sequencing - for this purpose they must be embedded into membranes. We have developed a computational protocol to improve the membrane embedding of such proteins. The protocols are still being tested experimentally |
| Exploitation Route | They cam be used by others to design novel proteins for bionanotechnology |
| Sectors | Manufacturing including Industrial Biotechology |
| Title | Code for parameterisation of coarse-grained models of disaccharides and a set of parameters for glucose and mannose based disaccharides |
| Description | Here, we present a parameter set for glucose- and mannose-based disaccharides for Martini 3. The generation of the CG parameters from atomistic trajectories is automated as fully as possible, and where not possible, we provide details of the protocol used for manual intervention. All code is provided for parameter generation. A set of parameters for disaccharides already parameterised is also provided |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2025 |
| Provided To Others? | Yes |
| Impact | This code will significantly reduce the time taken to generate coarse-grained models of disaccharides - it can be extended by the user to generate longer glycan models too. Furthermore it is all done in a systematic way and thus the models are compatible with the existing, popular, martini 3 models of other molecules. |
| URL | https://pubs.acs.org/doi/10.1021/acs.jcim.4c01874?goto=supporting-info |
| Title | Conformational state labels for Molecular Dynamics trajectory of ADK double mutant (V135G, V142G) |
| Description | The table contains the values for the projection of each MD frame into the (PC1, PC2) essential space. Density values of the associated region of the space are reported. Basin and state labels are assigned alongside an occurence table for the frame in each of the subsets derived for the study. The file is in CSV format with header columns included. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2025 |
| Provided To Others? | Yes |
| URL | https://brunel.figshare.com/articles/dataset/Conformational_state_labels_for_Molecular_Dynamics_traj... |
| Title | Molecular Dynamics trajectory for 4AKE protein - chain A in XTC format |
| Description | Molecular Dynamics trajectory in GRO format. The file contains 1000 frames recorded every 1 ns. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://brunel.figshare.com/articles/dataset/Molecular_Dynamics_trajectory_for_4AKE_protein_-_chain_... |
| Title | Molecular Dynamics trajectory for 4AKE protein - chain A in XTC format |
| Description | Molecular Dynamics trajectory in GRO format. The file contains 1000 frames recorded every 1 ns. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://brunel.figshare.com/articles/dataset/Molecular_Dynamics_trajectory_for_4AKE_protein_-_chain_... |
| Title | Molecular Dynamics trajectory for 4AKE protein - chain A in XTC format - full data |
| Description | Molecular Dynamics trajectory in GRO format. The file contains 1000 frames recorded every 10 ps. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://brunel.figshare.com/articles/dataset/Molecular_Dynamics_trajectory_for_4AKE_protein_-_chain_... |
| Title | Molecular Dynamics trajectory for 4AKE protein - chain A in XTC format - full data |
| Description | Molecular Dynamics trajectory in GRO format. The file contains 1000 frames recorded every 10 ps. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://brunel.figshare.com/articles/dataset/Molecular_Dynamics_trajectory_for_4AKE_protein_-_chain_... |
| Title | Molecular Dynamics trajectory for ADK double mutant (V135G, V142G) - chain A in XTC format |
| Description | Molecular Dynamics trajectory in XTC format. Original wild-type structure from PDBID 4AKE. Mutations were inserted using PyRosetta. Simulation was run using GROMACS 2022.4 with the AMBER ff99SB*-ILDN force field. The file contains 50000 frames recorded every 20 ps. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2025 |
| Provided To Others? | Yes |
| URL | https://brunel.figshare.com/articles/dataset/Molecular_Dynamics_trajectory_for_ADK_double_mutant_V13... |
| Title | Raw data for "OmpA controls order in the outer membrane and shares the mechanical load" |
| Description | This repository contains raw data, repeats and analysis, including codes, used in the paper "OmpA controls order in the outer membrane and shares the mechanical load" at DOI 10.1073/pnas.2416426121. All content, with exceptions below, was generated by Georgina Benn, Princeton University. The content in the folder "Supplementary Figure 7" was generated by Dheeraj Prakaash, University of Oxford. The content in the folder "AFMrawDataWithAnalysis\AFMdata\jpkFiles\LPP" was generated by Carolina Borrelli and Vincent A Fideli, University College London. All content was generated for the paper mentioned above at DOI 10.1073/pnas.2416426121 and the details of how the data was generated are explained in the paper. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| URL | https://datacommons.princeton.edu/discovery/doi/10.34770/ymvr-mg79 |
| Title | Simulations For: Biophysical basis of filamentous phage tactoid-mediated antibiotic tolerance in P. aeruginosa |
| Description | Coordinate, simulation input and simulation output files for atomistic molecular dynamics simulations in Biophysical basis of filamentous phage tactoid-mediated antibiotic tolerance in P. aeruginosa. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.10175089 |
| Title | Simulations For: Biophysical basis of filamentous phage tactoid-mediated antibiotic tolerance in P. aeruginosa |
| Description | Coordinate, simulation input and simulation output files for atomistic molecular dynamics simulations in Biophysical basis of filamentous phage tactoid-mediated antibiotic tolerance in P. aeruginosa. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.10175088 |
| Title | Structure for 4AKE protein - chain A in GRO format |
| Description | Protein structure in GRO format |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://brunel.figshare.com/articles/dataset/Structure_for_4AKE_protein_-_chain_A_in_GRO_format/2353... |
| Title | Structure for 4AKE protein - chain A in GRO format |
| Description | Protein structure in GRO format |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://brunel.figshare.com/articles/dataset/Structure_for_4AKE_protein_-_chain_A_in_GRO_format/2353... |
| Title | Structure for 4AKE protein - chain A in PDB format |
| Description | Protein structure in PDB format |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://brunel.figshare.com/articles/dataset/Structure_for_4AKE_protein_-_chain_A_in_PDB_format/2353... |
| Title | Structure for 4AKE protein - chain A in PDB format |
| Description | Protein structure in PDB format |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://brunel.figshare.com/articles/dataset/Structure_for_4AKE_protein_-_chain_A_in_PDB_format/2353... |
| Title | Structure for ADK double mutant (V135G, V142G) - chain A in GRO format |
| Description | Protein structure in GRO format. Original wild-type structure from PDBID 4AKE. Mutations were inserted using PyRosetta. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2025 |
| Provided To Others? | Yes |
| URL | https://brunel.figshare.com/articles/dataset/Structure_for_ADK_double_mutant_V135G_V142G_-_chain_A_i... |
| Description | Collaboration with Arvind Ramanathan |
| Organisation | Argonne National Laboratory |
| Country | United States |
| Sector | Public |
| PI Contribution | We provided details of MD codes and also the essential biophysics and biochemistry that must be incorporated into the simulations. As well as details of how we would like to use ML to augment the MD simulations. |
| Collaborator Contribution | They provided training for my postdoc on how to use their software and we are working together at the moment to integrate their software into our workflows. |
| Impact | N/A |
| Start Year | 2024 |