Update data/YM_0990/README.md

data/YM_0990/README.md

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 ## Overview
 
-YM_0990 are Trastuzumab designs against HER2. We explored several model hypothesis: (i) Does pre-training aid predicitivity and (ii) does the featurization of the input sequences matter. To test pretraining, we refer to `mata_descriptions` with the term \textbf{warm} to include pretraining, and \textbf{cold} to start from a randomly initialized seed. For featurization, we explored \textbf{label-encoded} sequences with a one-hot-encoder of amino acid identities, versus an \textbf{ESM}-featurized embedding to represent each sequence in the PPI.
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+YM_0990 are Trastuzumab designs against HER2. We explored several model hypothesis: (i) Does pre-training aid predicitivity and (ii) does the featurization of the input sequences matter. To test pretraining, we refer to `mata_descriptions` with the term **warm** to include pretraining, and **cold** to start from a randomly initialized seed. For featurization, we explored **label-encoded** sequences with a one-hot-encoder of amino acid identities, versus an **ESM-featurized** embedding to represent each sequence in the PPI.
 ## Experimental details
 
 We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 20828 unique VHHs and 1 unique sequences. In this experiment, we enable designs to span a window that encompasses the frameworks and CDR regions.