Initial Release
Initial Release of pyODEM
Basic features of pyODEM are implemented and tested. The goal is to optimize a coarse-grained protein model using either real or synthetic histogram data, i.e. FRET (Forster Resonance Energy Transfer).
Basic Features
- Load protein models per ajkluber's model_builder package.
- Compute coarse-grained protein potential energy.
- Compute a quality factor and its logarithm based on histogram data, i.e. distance FRET data.
- Determine discrete state probabilities due to changing the coarse-grained protein model's parameters.
- Select optimal coarse-grained protein model parameters based upon quality factor using a non-linear optimizer.
Dependencies
- model_builder (https://github.com/ajkluber/model_builder)
- mdtraj
- numpy
- scipy
- multiprocessing
See publication: Chen, J., Chen, J., Pinamonti, G. & Clementi, C. Learning Effective Molecular Models from Experimental Observables. J. Chem. Theory Comput. 14, 3849–3858 (2018).