# PP489 Optimization Variants Iter1 x TIGIT (YM_0988)

## Overview

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.

## Experimental details

We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 26726 unique scFvs and 2 unique target sequences.

A more extensive methods section can be found in our publication [here](https://pmc.ncbi.nlm.nih.gov/articles/PMC12296056/).

## Misc dataset details

We define the following binders:

### A-library (scFvs)

There are several terms you can filter by:

- `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

### Alpha-library

- `TIGIT_22-137_POI-AGA2`: Human TIGIT
- `TIGIT_Mouse`: Mouse TIGIT