Software
Open-source tools developed at AAI Research Lab
FastMDXplora
Fully Automated SysTem for Molecular Dynamics eXploration
FastMDXplora explores a protein’s behaviour end to end from a single command. Given a structure — or just a PDB ID — it carries out molecular dynamics exploration through setup, simulation, analysis, and reporting, and hands back publication-ready results.
setup → simulation → analysis → report
| Phase | What it does |
|---|---|
| setup | Cleans up the structure and builds a simulation-ready system: fixes missing atoms, adds hydrogens, solvates, and adds ions. |
| simulation | Runs the molecular dynamics — energy minimization, equilibration, and production — with optional enhanced sampling. |
| analysis | Computes standard structural and dynamic metrics, and protein–ligand metrics where a ligand is present, with figures ready to use. |
| report | Packages everything into a slide deck, a written report, and a self-contained bundle you can share. |
Highlights
- Explore a protein’s full dynamics with one command, covering setup, simulation, analysis, and reporting
- Build a protein–ligand system from a PDB identifier alone: the ligand is identified, its chemistry retrieved, and its protonation settled in the binding site — with a refusal rather than a guess where the structure is ambiguous
- Probe protein–ligand binding automatically, with analyses for pose stability, contacts, and hydrogen bonds
- Reach beyond plain MD with built-in PLUMED enhanced sampling: metadynamics, umbrella sampling, steered MD
- Design, start, watch, and review an exploration from a browser, with a 3D viewer and live telemetry
- Scale from a quick single-protein exploration to large parallel campaigns, driven the same way from the CLI or the Python API
Install
pip install fastmdxplora
Analysis and reporting are pure pip. Setup and simulation additionally require OpenMM and PDBFixer from conda-forge — see the installation guide.
GitHub · Documentation · PyPI · MIT licence
Citation. The foundational methodology is described in:
Aina, A. and Kwan, D. FastMDAnalysis: Software for Automated Analysis of Molecular Dynamics Trajectories. Journal of Computational Chemistry 47(8), e70350 (2026). doi:10.1002/jcc.70350
A further publication describing FastMDXplora is in preparation.
Prothon
A Python package for efficient comparison of protein conformational ensembles using local order parameters.
Prothon represents an ensemble as a vector of probability distributions over local structural measures, and quantifies dissimilarity between ensembles with a Jensen–Shannon distance metric and statistical significance testing. On ubiquitin ensembles it ran up to 88 times faster than ENCORE while using 48 times fewer computing cores.
Ensemble representations
- C-beta contact number (
cbcn) and C-alpha contact number (cacn) - Virtual C-alpha–C-alpha bond angles (
caba) and torsion angles (cata) - Solvent accessible surface area (
sasa)
Also provides
- Local (per-residue) and global dissimilarity analysis with significance testing
- Dimensionality reduction — PCA, MDS, and t-SNE — with 2D scatter plots
- Ensemble matrices as CSV, heatmaps, and bar/line dissimilarity plots
- A command-line tool and a Python API, with methods to replot and customise every figure
Citation. Aina, A., Hsueh, S. C. C. and Plotkin, S. S. PROTHON: A Local Order Parameter-Based Method for Efficient Comparison of Protein Ensembles. Journal of Chemical Information and Modeling 63(11), 3453–3461 (2023). doi:10.1021/acs.jcim.3c00145
CalphaEBM
A physics-based, machine-learned protein C-alpha energy function that achieves native basin stability across diverse protein folds.
CalphaEBM decomposes the effective free energy into four interpretable terms, totalling just 13,032 trainable parameters:
| Term | Parameters | What it captures |
|---|---|---|
| LocalEnergy | 12,226 | Backbone geometry: an 8-residue sliding-window MLP over (θ, φ) angles with learned amino acid embeddings |
| SecondaryEnergy | 583 | Ramachandran basin potentials — helix, sheet, PPII, turn — with sequence-dependent mixture weights, plus helical and sheet hydrogen bond distances |
| PackingEnergy | 222 | Tertiary packing: 5-group coordination statistics with product Gaussian scoring |
| RepulsionEnergy | 1 | Excluded volume: a PDB-derived repulsive wall with differentiable interpolation |
Every term produces smooth, differentiable forces suitable for Langevin dynamics (MALA) sampling.
Trained on 2,280 high-quality monomeric protein chains (L = 40–512), the model holds native contacts (Q > 0.96) and native compactness (radius of gyration within 2% of the crystal structure) across all 16 validation proteins, which span a range of lengths and fold classes. On villin headpiece HP35 it maintained Q = 1.000 over one million MALA steps.
Developed with support from the EFA Faculty Legacy Fund award Deep Learning the Energy Landscape of a Coarse-Grained Model of Proteins.