Please note that this is intended to be a code and data repository for a specific set of systems (i.e. protein kinases) For a view of the full pipeline excluding multiple walker learning and static bias, please use the repository for AF2RAVE
[Arxiv link will be added soon.] This protocol is essentially a methodology that combines two schools of thought: structure prediction, and enhanced sampling to preserve thermodynamics. In particular, we aim to make the process of sampling kinase activation loop conformational diversity smoother and faster, and demonstrate this idea on DDR1 wild type and 3 mutants.
Movies, bias files, and trajectories can be found in this Drive
In folder mdscripts
openmm_utils.py- functions required to run this instance of our methodologybasicmd.py- to run basic unbiased MDequilprotocol.py- to run equilibration protocol as in the paperdistancemetad.py- to run a metadynamics simulation using OP computed with SPIB with distances as input CVskinaseCVs.py- to extract the CVs we use
To run the code, biases and initial structures need to be downloaded from the drive linked above and run with the code in distancemetad.py
Use the following command:
distancemetad.py -source -name -metad _ -bias 1000000000
Please cite the following reference if using this protocol with or without the provided code:
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"AlphaFold2-RAVE: From sequence to Boltzmann ensemble" Bodhi P. Vani, Akashnathan Aranganathan, Dedi Wang, Pratyush Tiwary J. Chem. Theory Comput. 2023; doi: https://doi.org/10.1021/acs.jctc.3c00290
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"State predictive information bottleneck", Dedi Wang and Pratyush Tiwary, J. Chem. Phys. 154, 134111 (2021) https://doi.org/10.1063/5.0038198
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