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laxy.py: Wrapper around JAX for basic neural networks, see https://github.com/sokrypton/laxy.
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sw_functions.py: Differentiable JAX implementations of smooth Smith Waterman and Needleman Wunsch. Features affine gap and temperature parameters.
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network_functions.py: SMURF pipeline including the BasicAlign and TrainMRF modules.
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ssw_examples.ipynb: Tutorial on how to use our smooth Smith Waterman implementation.
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run_smurf.py: Code that executes SMURF and MLM-GREMLIN. Selected hyperparameters described in the comments. Outputs a single file containing the contact prediction AUCs for the families.
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run_smurf_w_contacts_aln.py: Code that executes SMURF and MLM-GREMLIN. Selected hyperparameters described in the comments. Outputs a one file per family that contains the predicted contacts, contact prediction AUC, and learned alignment (for SMURF only).
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ablation_test.py: Code that executes ablations described in [citation coming soon].
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nw_speedtest.ipynb: Runtime comparison of our vectorized code to a naive implementation and to the "deepBLAST" implementation given in [Morton et al. 2020].
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af_msa_backprop.ipynb: Backprop through AlphaFold to "learn" an MSA from a collection of unaligned sequences that maximizes the confidence metric (and hopefully returns a more accurate structure).
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data_description.txt: Description of data used in [citation coming soon].
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make_SMURF_figures.ipynb: Code to generate figures in [citation coming soon].