TIGIT/ym988/README.md

anti-TIGIT designs (YM_1068)

Overview

YM_988 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 \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. Optimization was performed on the human ortholog.

Experimental details

We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 35929 unique scFvs and 2 unique TIGIT homologs sequences.

A more extensive methods section can be found in our publication here.

Dataset schema

The dataset will contain the following columns:

• mata_description: Description of the binder designs; contains warm/cold or label-encoded/ESM information. WT indicated as "WT"
• mata_sequence: svFv sequences
• matalpha_description: TIGIT homologs
• matalpha_sequence: Sequence of the TIGIT protein
• alphaseq_affinity: Log10 Kd affinity score between the pair of sequences
• alphaseq_affinity_lower_bound: Lower bound of affinity
• alphaseq_affinity_upper_bound: Upper bound of affinity

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

Citation

Please cite