| # PP489 Optimization Variants Iter1 x TIGIT (YM_0988) |
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| ## Overview |
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| YM_0988 includes ABC001 against 2 TIGIT homologs. 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. Optimization was performed on the human ortholog. |
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| ## Experimental details |
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| We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 26726 unique scFvs and 2 unique target sequences. |
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| A more extensive methods section can be found in our publication [here](https://pmc.ncbi.nlm.nih.gov/articles/PMC12296056/). |
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| ## Misc dataset details |
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| We define the following binders: |
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| ### A-library (scFvs) |
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| There are several terms you can filter by: |
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| - `ABC001_WT_<i>`: These are WT replicates. |
| - `ABC001_label_encoded_cold`: Label encoded sequences with no pretraining |
| - `ABC001_label_encoded_warm`: Label encoded sequences with pretraining |
| - `ABC001_esm_cold`: ESM featurized sequences with no pretraining |
| - `ABC001_esm_warm`: ESM featurized sequences with pretraining |
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| ### Alpha-library |
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| - `TIGIT_22-137_POI-AGA2`: Human TIGIT |
| - `TIGIT_Mouse`: Mouse TIGIT |
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