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| The sampled stripped trajectories and intermediate data, including the trained neural network weights, are available here: covampnet_data.tar.gz (17 GB). Below is a description of the data structure. |
covampnet_data/
├── trajectories/ # MD simulations
│ ├── ZS-ab2/ # simulated system
│ │ ├──e1s1_0/ # episode 1, simulation 1
│ │ │ └── output.filtered.xtc # compressed MD simulation
│ │ ├──e1s2_0/ # episode 1, simulation 2
│ │ │ └── output.filtered.xtc
│ │ ├── ...
│ │ └── filtered.pdb # topology file
│ ├── ZS-ab3/
│ └── ZS-ab4/
├── models/ # trained models
│ ├── ZS-ab2/
│ ├── ZS-ab3/
│ └── ZS-ab4/
├── model_outputs/ # Markov state probabilities for each frame, Koopman matrices,
│ │ # Chapman-Kolmogorov tests, implied timescales, eq. distribution
│ └── ...
├── trained_model_histories/ # train/val losses of final models
│ └── ...
├── frames_for_gradient_evaluation/ # frame ids for reproduction of gradient analysis
│ └── ...
├── training_splits/ # training splits for reproduction of results
│ └── ...
└── training_seeds/ # training seeds for reproduction of results
└── ...
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| The code and example data are available on GitHub: https://github.com/KoubaPetr/CoVAMPnet |
| Computational study of the effect of drug candidates on intrinsically disordered biomolecules is challenging due to their vast and complex conformational space. Here, we developed a comparative Markov state analysis (CoVAMPnet) framework to quantify changes in the conformational distribution and dynamics of a disordered biomolecule in the presence and absence of small organic drug candidate molecules. First, molecular dynamics trajectories are generated using enhanced sampling, in the presence and absence of small molecule drug candidates, and ensembles of soft Markov state models (MSMs) are learned for each system using unsupervised machinelearning. Second, these ensembles of learned MSMs are aligned across different systems based on a solution to an optimal transport problem. Third, the directional importance of inter-residue distances for the assignment to different conformational states is assessed by a discriminative analysis of aggregated neural network gradients. This final step provides interpretability and biophysical context to the learned MSMs. We applied this novel computational framework to assess the effects of ongoing phase 3 therapeutics tramiprosate (TMP) and its metabolite 3-sulfopropanoic acid (SPA) on the disordered Aβ42 peptide involved in Alzheimer’s disease. Based on adaptive sampling molecular dynamics and CoVAMPnet analysis, we observed that both TMP and SPA preserved more structured conformations of Aβ42 by interacting nonspecifically with charged residues. SPA impacted Aβ42 more than TMP, protecting α-helices and suppressing the formation of aggregation-prone β-strands. Experimental biophysical analyses showed only mild effects of TMP/SPA on Aβ42 and activity enhancement by the endogenous metabolization of TMP into SPA. Our data suggest that TMP/SPA may also target other biomolecules than Aβ peptides. The CoVAMPnet method is broadly applicable to study the effects of drug candidates on the conformational behavior of intrinsically disordered biomolecules. |
Acknowledgements |