Date published: May 26, 2026
Summary:
BilboMD is a web-based software pipeline that combines X-ray scattering data with macromolecular structure prediction and molecular dynamics simulations to accurately model the three-dimensional structures of biological molecules in solution.
Applications:
- Structure-based drug design
- Therapeutic antibody development
- Biologics formulation and stability
- Target protein characterization
Advantages/Benefits:
- Dynamic conformational flexibility modeling
- Accurate solution-state representation
- Seamless SAXS structure prediction and MD simulation integration
- Accessible web-based pipeline
The Challenge
Current methods like X-ray crystallography, CryoEM, NMR, and predictive algorithms yield only static representations of biological macromolecules. These traditional approaches fail to capture the full range of dynamic solution-state behaviors.
Technology Description
Scientists at Berkeley Lab have developed a web-based modeling pipeline called BilboMD that characterizes the conformational ensembles of biological macromolecules in solution. The system integrates Small Angle X-ray Scattering (SAXS) data with Molecular Dynamics simulations. Built for Docker environments, it can run on secure local hardware. Future developments may enable HPC/supercomputer compatibility.
Unlike traditional structural biology methods like X-ray crystallography, CryoEM, or AlphaFold that only yield static models, BilboMD uniquely models the dynamic flexibility of macromolecules in their natural solution state. By matching theoretical profiles to experimental data, it identifies the most accurate structural ensembles. The pipeline successfully generates and evaluates thousands of physically plausible macromolecular conformations.
Development Stage: V. 1
Investigators:
Status: Copyrighted
Opportunities: Available for licensing and/or collaborative research
For More Information:
Classen, S. et al. (27 April 2026) BilboMD: a web-accessible SAXS and AlphaFold-guided modeling pipeline. Nucleic Acids Research. https://doi.org/10.1093/nar/gkag377
