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TIGIT/ym693/README.md

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-# anti-TIGIT designs (YM_693)
-
-## Overview
-
-YM_693 is a dataset of anti-TIGIT designs against the TIGIT target. This dataset contains only 2 targets, but they are species homologs of human and mouse. The designs offer a few mutations to study the local interaction between  and TIGIT. This is a dataset to explore relative affinities to the parent for antibody optimization.
-
-## 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/). 
-
-## Dataset schema
-
-The dataset will contain the following columns:
-
-- `mata_description`: Candidate labels
-- `mata_sequence`: scFv sequences
-- `matalpha_description`: TIGIT proteins
-- `matalpha_sequence`: Sequence of the TIGIT homolog
-- `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:
-
-- `wt_<i>`: These are WT replicates.
-- `candidate_`: Various mutations of Pembrolizumab
-
-### Alpha-library: 
-- `TIGIT_22-137_POI-AGA2`: Human TIGIT
-- `TIGIT_Mouse`: Mouse TIGIT
-
-## Citation
-Please cite 
-

TIGIT/ym693/YM_693.csv

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TIGIT/ym988/README.md

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-# 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](https://pmc.ncbi.nlm.nih.gov/articles/PMC12296056/). 
-
-## 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 
-

TIGIT/ym988/YM_988.csv

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