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@@ -66,3 +66,12 @@ YM_985.csv filter=lfs diff=lfs merge=lfs -text
 YM_988.csv filter=lfs diff=lfs merge=lfs -text
 YM_989.csv filter=lfs diff=lfs merge=lfs -text
 YM_990.csv filter=lfs diff=lfs merge=lfs -text
+archive/COVID/ym005/YM_005.csv filter=lfs diff=lfs merge=lfs -text
+archive/COVID/ym1068/YM_1068.csv filter=lfs diff=lfs merge=lfs -text
+archive/COVID/ym549/YM_549.csv filter=lfs diff=lfs merge=lfs -text
+archive/HER2/ym989/YM_989.csv filter=lfs diff=lfs merge=lfs -text
+archive/HER2/ym990/YM_990.csv filter=lfs diff=lfs merge=lfs -text
+archive/PD1/ym852/YM_852.csv filter=lfs diff=lfs merge=lfs -text
+archive/PD1/ym985/YM_985.csv filter=lfs diff=lfs merge=lfs -text
+archive/TIGIT/ym693/YM_693.csv filter=lfs diff=lfs merge=lfs -text
+archive/TIGIT/ym988/YM_988.csv filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+scratch/
+.DS_Store

README.md

@@ -4,7 +4,44 @@ tags:
 - biology
 pretty_name: A-Alpha Bio open source data
 size_categories:
-- 10K<n<100K
+- 10K<n<10M
+configs:
+  - config_name: YM_0005
+    data_files:
+      - split: all
+        path: "data/YM_0005/data.parquet"
+  - config_name: YM_0549
+    data_files:
+      - split: all
+        path: "data/YM_0549/data.parquet"
+  - config_name: YM_0693
+    data_files:
+      - split: all
+        path: "data/YM_0693/data.parquet"
+  - config_name: YM_0852
+    data_files:
+      - split: all
+        path: "data/YM_0852/data.parquet"
+  - config_name: YM_0985
+    data_files:
+      - split: all
+        path: "data/YM_0985/data.parquet"
+  - config_name: YM_0988
+    data_files:
+      - split: all
+        path: "data/YM_0988/data.parquet"
+  - config_name: YM_0989
+    data_files:
+      - split: all
+        path: "data/YM_0989/data.parquet"
+  - config_name: YM_0990
+    data_files:
+      - split: all
+        path: "data/YM_0990/data.parquet"
+  - config_name: YM_1068
+    data_files:
+      - split: all
+        path: "data/YM_1068/data.parquet"
 ---
 # A-Alpha Datasets
 
@@ -16,24 +53,31 @@ To help bridge the gap between experimental affinity measurements and computatio
 
 Each dataset captures the results of a yeast mating experiment between two protein libraries—one of binders and one of targets. Detailed experimental context and metadata are provided in the accompanying data cards.
 
-
 ## Dataset schema
 
-Every dataset will contain the following columns:
-
-- `mata_description`: Description of a-library proteins; usually VHHs/scFvs
-- `mata_sequence`: sequence from the A-library
-- `matalpha_description`: Description of the alpha-library proteins; usually some antigens
-- `matalpha_sequence`: sequence from the Alpha-library
-- `alphaseq_affinity`: Log10 Kd affinity score between the pair of sequences. Lower is better
-- `alphaseq_affinity_lower_bound`: Lower bound of affinity expected
-- `alphaseq_affinity_upper_bound`: Upper bound of affinity expected
+| Column | Description |
+|:--------|:-------------|
+| **mata_description** | Unique description of each MATa library element. Also contains negative control strains (ANeg1, ANeg2, ANeg3). Often supplemented with assay-specific information such as nomenclature or replicate labeling. |
+| **matalpha_description** | Unique description of each MATα library element. Also contains negative control strains (AlphaNeg1, AlphaNeg2, AlphaNeg3). Often supplemented with assay-specific information such as nomenclature or replicate labeling. |
+| **mata_sequence** | Amino acid sequence corresponding to the MATa library element. The sequence excludes linkers, epitope tags, and the cell wall anchor Aga2, and is empty for negative controls. |
+| **matalpha_sequence** | Amino acid sequence corresponding to the MATα library element. The sequence excludes linkers, epitope tags, and the cell wall anchor Aga2, and is empty for negative controls. |
+| **alphaseq_affinity** | Pairwise interaction affinity, reported as log₁₀(estimated Kd in nM). Values map as –1 = 0.1 nM, 0 = 1 nM, 1 = 10 nM, 2 = 100 nM, etc. Missing values indicate no observed cell fusions between the protein pair (see Younger et al., 2017). |
+| **affinity_upper_bound** | Upper (stronger) bound of the 95 % confidence interval for `alphaseq_affinity`, computed with the Wilson score interval. Provided even when no point estimate is available. |
+| **affinity_lower_bound** | Lower (weaker) bound of the 95 % confidence interval for `alphaseq_affinity`, computed with the Wilson score interval. |
+| **normalized_affinity** | Z-score comparing each interaction’s affinity to the distribution of all interactions involving the same MATa or MATα protein. Computed by successive normalization of the affinity matrix (Olshen & Rajaratnam 2010). More negative values indicate higher specificity (e.g., –2 ≈ 2 SD below the mean). Strongly negative values are not expected when many proteins bind broadly. |
+| **above_background** | Boolean flag indicating whether the interaction affinity is significantly stronger than the assay background (q < 0.05, Benjamini–Hochberg corrected t-test). |
+| **sufficient_replicate_observations** | Boolean flag indicating whether the interaction was observed in > 50 % of replicate samples. A value of False suggests a potentially spurious or unreliable interaction. |
 
 ## FAQ
 
 Please see below for some clarifying details:
 
+### Where can I find experimental details for each dataset?
+
+Each dataset has a corresponding README.md in its subfolder summarizing the experiment's goals, library composition, and citation info. See `./data/YM_0005/README.md` for an example.
+
 ### What kind of sequences are in the library?
+
 While not a strict rule, the A-libraries typically contain designed sequences, while the Alpha-libraries contain corresponding targets of interest. Historically, we’ve used VHHs or scFvs in the A-library and antigen targets in the Alpha-library. Each dataset will have a card that details specific information of the individual assay run. When building or training models, note that PPIs can generally be treated as symmetric. However, members within the same library may share sequence, functional, or structural similarities. Also, some models are sensitive to input order — so ensure that (A, Alpha) pairs are treated consistently between training and testing.
 
 ### Why are there duplicate PPIs in the dataset?
@@ -41,17 +85,22 @@ While not a strict rule, the A-libraries typically contain designed sequences, w
 Some datasets include technical replicates, often for the wild-type (“WT”) or parent sequence in mutation studies. Replicates help capture the experimental and biological variation in measured affinities. This can be useful for analyses that assess the statistical significance of observed affinity difference, such as identifying how much a vaiant changes binding strength relative to a parent protein.
 
 ### What is considered a strong or good binder?
-Affinity measurements are reported in log-10 Kd. Lower values indicate stronger binding. In practice, we often compare relative affinities - for example, assessing differences in binding strength as a target interface is mutated, or comparing variant binders to their parent.
+
+Affinity measurements are reported in log10 Kd nM (a value of 0 indicates 1 nM, 3 is 1 uM, 5 is 100 uM). Lower values indicate stronger binding. In practice, we often compare relative affinities - for example, assessing differences in binding strength as a target interface is mutated, or comparing variant binders to their parent.
 
 ### The dataset has NaN values in the affinity, why?
-Not all PPIs form detectable interactions; weak or non-binding interactions may yield NaN values. For these cases, it may be more useful to look at the lower or upper bound affinities to help interpret the range of possible affinity within the assay. 
+
+Not all PPIs form detectable interactions; weak or non-binding interactions may result in no paired barcode reads, yielding NaN values. For these cases, it may be more useful to look at the lower or upper bound affinities to help interpret the range of possible affinity within the assay.
 
 ### How should I cite this dataset?
+
 Please cite:
-_A-Alpha Bio (2025). Open Protein–Protein Interaction Affinity Datasets. https://huggingface.co/aalphabio
+A-Alpha Bio (2025). Open Protein–Protein Interaction Affinity Datasets. https://huggingface.co/aalphabio
 
 ### Can I use this dataset for model training or benchmarking?
+
 Yes — the dataset is released fully open source, and is suitable for both academic and commercial use.
 
 ### Who can I contact with questions or feedback?
-Feel free to email maintainers [Natasha Murakowska](mailto:nmurakowska@aalphabio.com) or [David Noble](mailto:dnoble@aalphabio.com). We will host a discussions tab for open discourse as well.
+
+Feel free to email maintainers [Natasha Murakowska](mailto:nmurakowska@aalphabio.com) or [David Noble](mailto:dnoble@aalphabio.com). We will host a discussions tab for open discourse as well.

archive/COVID/ym005/README.md

@@ -0,0 +1,43 @@
+# Covid mutagenesis dataset (YM_005)
+
+## Overview
+
+YM_005 is a Covid receptor-binding domain (RBD) single-site mutagenesis (SSM) library against a panel of 33 ScFvs.
+
+## Experimental details
+
+We studied the effects of a panel of ScFvs against COVID RBD. In this dataset, our panel of ScFvs are tested in 2 orientations: LH for light-heavy chains and HL heavy-light chains. We explore the local landscape of the RBD in tandem with Covid for epitope mapping by mutating each position. We include a control of ACE2, as the protein of most therapeutic antibodies disrupt.
+
+This dataset includes 62 unique scFvs and 2431 unique RBD sequences.
+
+A more extensive methods section can be found in our publication [here](https://academic.oup.com/abt/article/5/2/130/6584706#372391532).
+
+## Dataset schema
+
+The dataset will contain the following columns:
+
+- `mata_description`: Description of the scfvs; HL/LH indicate orientation of light/heavy or heavy/light
+- `mata_sequence`: Scfv sequences
+- `matalpha_description`: Description of the RBD mutation
+- `matalpha_sequence`: Sequence of the covid binding 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)
+- `ACE2_Full`:
+- `CR3022_scFv_LH_Mod`:
+- `MERS_VHH55`:
+- `SARS_VHH72`:
+- `m396_scFv_LH_Mod`:
+
+### Alpha-library: 
+- Any `matalpha_description` that contains the term "WT" can be assumed as a replicate of the WT target. Other sequences will be indicated by their original residue, position of mutation, and mutated residue (i.e. S23A is Ser -> Ala mutation in position 23). 
+
+## Citation
+Please cite 
+

archive/COVID/ym005/YM_005.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6729ce3cd038b35a4c8849a91234b2b05f01d51cadda0defc91c86d5e0f7f08
+size 80151625

archive/COVID/ym1068/README.md

@@ -0,0 +1,50 @@
+# Covid antibody optimization designs (YM_1068)
+
+## Overview
+
+YM_1068 are VHH72 designs against SARS CoV-2 RBD. 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.
+
+## Experimental details
+
+We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 36862 unique VHHs and 8 unique RBD 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`: VHH sequences
+- `matalpha_description`: SARS-Cov 2 RBD proteins
+- `matalpha_sequence`: Sequence of the covid binding 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:
+
+- `VHH_WT_<i>`: These are WT replicates.
+- `VHH_label_encoded_cold`: Label encoded sequences with no pretraining
+- `VHH_label_encoded_warm`: Label encoded sequences with pretraining
+- `VHH_esm_cold`: ESM featurized sequences with no pretraining
+- `VHH_esm_warm`: ESM featurized sequences with pretraining
+
+### Alpha-library: 
+- `SARS-CoV2_RBD_(6LZG)`
+- `WIV1`
+- `LYRa11`
+- `MERS_RBD`
+- `OMICRON_BA2_TStarr_Seq&Trunc`
+- `SARS-COV-2_GAMMA_RBD_LLNL_Trunc`
+- `SARS-CoV-2_Delta_RBD_L452R_T478K`
+- `SARS-CoV1_RBD_(6LZG_Mimic)`
+
+## Citation
+Please cite 
+

archive/COVID/ym1068/YM_1068.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:80c4c446476014a95feaa080c791017a12298655b85de32d85edae107548afa8
+size 128842385

archive/COVID/ym549/README.md

@@ -0,0 +1,45 @@
+# VHH72 COVID designs (YM-549)
+
+## Overview
+
+YM_549 are VHH72 designs against SARS CoV-2 RBD. We wanted to perturb the local landscape of VHH72, so we performed mutagenesis to observe the sensitivity of VHH-72 to different RBD targets. This dataset is great to study the local sensitivity of mutations relative to parent "WT" VHH72. 
+
+## Experimental details
+
+This dataset includes 29765 unique VHHs and 8 unique RBD sequences. The alpha-library is the same as YM_1068.
+
+## Dataset schema
+
+The dataset will contain the following columns:
+
+- `mata_description`: Candidate labels
+- `mata_sequence`: VHH sequences
+- `matalpha_description`: SARS-Cov 2 RBD proteins
+- `matalpha_sequence`: Sequence of the covid binding 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:
+
+- `wt_<i>`: These are WT replicates.
+- `candidate_`: Various mutations of VHH-72
+
+### Alpha-library: 
+- `SARS-CoV2_RBD_(6LZG)`
+- `WIV1`
+- `LYRa11`
+- `MERS_RBD`
+- `OMICRON_BA2_TStarr_Seq&Trunc`
+- `SARS-COV-2_GAMMA_RBD_LLNL_Trunc`
+- `SARS-CoV-2_Delta_RBD_L452R_T478K`
+- `SARS-CoV1_RBD_(6LZG_Mimic)`
+
+## Citation
+Please cite 
+

archive/COVID/ym549/YM_549.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:549e9e645e6a089dc2b2ef889fcd3b317e68ecf8d8e399ccbe328801f157c767
+size 99893870

archive/HER2/ym989/README.md

@@ -0,0 +1,43 @@
+# Trastuzumab CDR designs (YM_989)
+
+## Overview
+
+YM_989 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.
+
+## Experimental details
+
+We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 20828 unique VHHs and 1 unique sequences. All designs are limited to the CDRs of the proteins of interest
+
+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`: scFv sequences
+- `matalpha_description`: HER2 protein
+- `matalpha_sequence`: HER2 sequence
+- `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:
+
+- `TrastuzumabCDR_WT_<i>`: These are WT replicates.
+- `TrastuzumabCDR_label_encoded_cold`: Label encoded sequences with no pretraining
+- `TrastuzumabCDR_label_encoded_warm`: Label encoded sequences with pretraining
+- `TrastuzumabCDR_esm_cold`: ESM featurized sequences with no pretraining
+- `TrastuzumabCDR_esm_warm`: ESM featurized sequences with pretraining
+
+### Alpha-library: 
+There is only 1 target of interest.
+
+## Citation
+Please cite 
+

archive/HER2/ym989/YM_989.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76c0b0c751707ba5c6376323940d7736e0c1d5ededa63727b78472e0d53c3461
+size 11312796

archive/HER2/ym990/README.md

@@ -0,0 +1,43 @@
+# Trastuzumab CDR + framework designs (YM_989)
+
+## Overview
+
+YM_989 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.
+
+## 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.
+
+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`: scFv sequences
+- `matalpha_description`: HER2 protein
+- `matalpha_sequence`: HER2 sequence
+- `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:
+
+- `TrastuzumabCDR_WT_<i>`: These are WT replicates.
+- `TrastuzumabCDR_label_encoded_cold`: Label encoded sequences with no pretraining
+- `TrastuzumabCDR_label_encoded_warm`: Label encoded sequences with pretraining
+- `TrastuzumabCDR_esm_cold`: ESM featurized sequences with no pretraining
+- `TrastuzumabCDR_esm_warm`: ESM featurized sequences with pretraining
+
+### Alpha-library: 
+There is only 1 target of interest.
+
+## Citation
+Please cite 
+

archive/HER2/ym990/YM_990.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1211f21ee40759bcc9a049c22a15c0d57152e66b6ebfb22a200fd38ecf52e25a
+size 17966062

archive/PD1/ym852/README.md

@@ -0,0 +1,43 @@
+# {DATASET}
+
+## Overview
+
+YM_852 are Pembrolizumab mutations against its native target; we introduce mutations that include deletions, insertions, double and single mutations to quantify the sensitivity of the local landscape in engaging the native target.
+
+## Experimental details
+
+This dataset includes 29883 unique scFvs and 1 unique native target sequence. 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`: VHH sequences
+- `matalpha_description`: SARS-Cov 2 RBD proteins
+- `matalpha_sequence`: Sequence of the covid binding 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:
+
+- `WT_<i>`: These are WT replicates.
+- `del_<i>`: A deletion from the WT
+- `ins_<i>`: An insertion from WT
+- `pair_<i>`: A double mutation (2 residues mutated from WT)
+- `single_<i>`: A single mutation 
+
+To get the mutations of interest relative to the parent, we recommend an alignment to the WT sequence.
+
+### Alpha-library: 
+There is only 1 sequence, which is the native target.
+
+## Citation
+Please cite 
+

archive/PD1/ym852/YM_852.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34b186fe5d14dc6284e109c919e27d20ef3245e6e7bdf990f99eb559dce6a590
+size 13669983

archive/PD1/ym985/README.md

@@ -0,0 +1,43 @@
+# Pembrolizumab Designs (YM_985)
+
+## Overview
+
+YM_985 includes Alphabind designs against PD-1. 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.
+
+## Experimental details
+
+We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 34890 unique VHHs and 1 unique RBD 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`: scFv sequences
+- `matalpha_description`: The target protein of interest
+- `matalpha_sequence`: Target sequence (only 1)
+- `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:
+
+- `Pembro144_WT_<i>`: These are WT replicates.
+- `Pembro144_label_encoded_cold`: Label encoded sequences with no pretraining
+- `Pembro144_label_encoded_warm`: Label encoded sequences with pretraining
+- `Pembro144_esm_cold`: ESM featurized sequences with no pretraining
+- `Pembro144_esm_warm`: ESM featurized sequences with pretraining
+
+### Alpha-library: 
+There is only 1 target
+
+## Citation
+Please cite 
+

archive/PD1/ym985/YM_985.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5f846ba0fac92a4b0b6c35cbcee521f7623c9479b44d6c0d47c481092678d52
+size 17767857

archive/TIGIT/ym693/README.md

@@ -0,0 +1,41 @@
+# 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 
+

archive/TIGIT/ym693/YM_693.csv

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archive/TIGIT/ym988/README.md

@@ -0,0 +1,44 @@
+# 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 
+

archive/TIGIT/ym988/YM_988.csv

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data/YM_0005/README.md

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+# Anti-CoV Ab Panel x CoV2-RBD Mutagenesis (YM_0005)
+
+## Overview
+
+YM_0005 is a Covid receptor-binding domain (RBD) single-site mutagenesis (SSM) library against a panel of 33 ScFvs.
+
+## Experimental details
+
+We studied the effects of a panel of ScFvs against COVID RBD. In this dataset, our panel of ScFvs are tested in 2 orientations: LH for light-heavy chains and HL heavy-light chains. We explore the local landscape of the RBD in tandem with Covid for epitope mapping by mutating each position. We include a control of ACE2, as the protein of most therapeutic antibodies disrupt.
+
+This dataset includes 62 unique scFvs and 2431 unique RBD sequences.
+
+A more extensive methods section can be found in our publication [here](https://academic.oup.com/abt/article/5/2/130/6584706#372391532).
+
+## Misc dataset details
+
+We define the following binders:
+
+### A-library (scFvs)
+
+- Binder descriptor. HL / LH denotes orientation of the scFv as expressed on the yeast surace.
+
+### Alpha-library
+
+- Any `matalpha_description` that contains the term "WT" can be assumed as a replicate of the WT target. Other sequences will be indicated by their original residue, position of mutation, and mutated residue (i.e. S23A is Ser -> Ala mutation in position 23).

data/YM_0005/data.parquet

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data/YM_0549/README.md

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+# VHH72 Optimization Variants Iter0 x CoV2-RBD (YM_0549)
+
+## Overview
+
+YM_0549 consists of VHH72 designs against SARS CoV-2 RBD. We wanted to perturb the local landscape of VHH72, so we performed mutagenesis to observe the sensitivity of VHH-72 to different RBD targets. This dataset is great to study the local sensitivity of mutations relative to parent "WT" VHH72.
+
+## Experimental details
+
+This dataset includes 29765 unique VHHs and 8 unique RBD sequences. The alpha-library is the same as YM_1068.
+
+## 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 VHH-72
+
+### Alpha-library
+
+- `SARS-CoV2_RBD_(6LZG)`
+- `WIV1`
+- `LYRa11`
+- `MERS_RBD`
+- `OMICRON_BA2_TStarr_Seq&Trunc`
+- `SARS-COV-2_GAMMA_RBD_LLNL_Trunc`
+- `SARS-CoV-2_Delta_RBD_L452R_T478K`
+- `SARS-CoV1_RBD_(6LZG_Mimic)`

data/YM_0549/data.parquet

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data/YM_0693/README.md

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+# PP489 Optimization Variants Iter0 x TIGIT (YM_693)
+
+## Overview
+
+YM_693 is a dataset of anti-TIGIT designs against TIGIT. 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/).
+
+## 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

data/YM_0693/data.parquet

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+size 3124450

data/YM_0852/README.md

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+# Pembrolizumab-scFv Optimiziation Variants Iter0 x PD-1 (YM_0852)
+
+## Overview
+
+YM_852 are Pembrolizumab mutations against its native target; we introduce mutations that include deletions, insertions, double and single mutations to quantify the sensitivity of the local landscape in engaging the native target.
+
+## Experimental details
+
+This dataset includes 29883 unique scFvs and 1 unique native target sequence. 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:
+
+- `WT_<i>`: These are WT replicates.
+- `del_<i>`: A deletion from the WT
+- `ins_<i>`: An insertion from WT
+- `pair_<i>`: A double mutation (2 residues mutated from WT)
+- `single_<i>`: A single mutation
+
+To get the mutations of interest relative to the parent, we recommend an alignment to the WT sequence.
+
+### Alpha-library
+
+There is only 1 sequence, which is the native target.

data/YM_0852/data.parquet

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data/YM_0985/README.md

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+# Pembrolizumab-scFv Optimiziation Variants Iter1 x PD-1 (YM_0985)
+
+## Overview
+
+YM_0985 includes Alphabind designs against PD-1. 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.
+
+## Experimental details
+
+We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 34890 unique VHHs and 1 unique RBD 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:
+
+- `Pembro144_WT_<i>`: These are WT replicates.
+- `Pembro144_label_encoded_cold`: Label encoded sequences with no pretraining
+- `Pembro144_label_encoded_warm`: Label encoded sequences with pretraining
+- `Pembro144_esm_cold`: ESM featurized sequences with no pretraining
+- `Pembro144_esm_warm`: ESM featurized sequences with pretraining
+
+To get the mutations of interest relative to the parent, we recommend an alignment to the WT sequence.
+
+### Alpha-library
+
+There is only 1 sequence, which is the native target.

data/YM_0985/data.parquet

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data/YM_0988/README.md

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+# PP489 Optimization Variants Iter0 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 \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 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

data/YM_0988/data.parquet

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data/YM_0989/README.md

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+# Trastuzumab scFv CDR3 Optimization Variants Iter0 x HER-2 (YM_0989)
+
+## Overview
+
+YM_0989 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.
+
+## Experimental details
+
+We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 20828 unique VHHs and 1 unique sequences. All designs are limited to the CDRs of the proteins of interest
+
+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:
+
+- `TrastuzumabCDR_WT_<i>`: These are WT replicates.
+- `TrastuzumabCDR_label_encoded_cold`: Label encoded sequences with no pretraining
+- `TrastuzumabCDR_label_encoded_warm`: Label encoded sequences with pretraining
+- `TrastuzumabCDR_esm_cold`: ESM featurized sequences with no pretraining
+- `TrastuzumabCDR_esm_warm`: ESM featurized sequences with pretraining
+
+### Alpha-library
+
+There is only 1 target of interest.

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data/YM_0990/README.md

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+# Trastuzumab scFv CDR+FW Optimization Variants Iter0 x HER-2 (YM_0990)
+
+## 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.
+
+## 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.
+
+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:
+
+- `TrastuzumabCDR_WT_<i>`: These are WT replicates.
+- `TrastuzumabCDR_label_encoded_cold`: Label encoded sequences with no pretraining
+- `TrastuzumabCDR_label_encoded_warm`: Label encoded sequences with pretraining
+- `TrastuzumabCDR_esm_cold`: ESM featurized sequences with no pretraining
+- `TrastuzumabCDR_esm_warm`: ESM featurized sequences with pretraining
+
+### Alpha-library
+
+There is only 1 target of interest.

data/YM_0990/data.parquet

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data/YM_1068/README.md

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+# VHH72 Optimization Variants Iter1 x CoV2-RBD (YM_1068)
+
+## Overview
+
+YM_1068 consists of VHH72 variants measured against SARS CoV-2 RBD. 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.
+
+## Experimental details
+
+We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 36862 unique VHHs and 8 unique RBD 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 (VHHs)
+
+There are several terms you can filter by:
+
+- `VHH_WT_<i>`: These are WT replicates.
+- `VHH_label_encoded_cold`: Label encoded sequences with no pretraining
+- `VHH_label_encoded_warm`: Label encoded sequences with pretraining
+- `VHH_esm_cold`: ESM featurized sequences with no pretraining
+- `VHH_esm_warm`: ESM featurized sequences with pretraining
+
+### Alpha-library
+
+- `SARS-CoV2_RBD_(6LZG)`
+- `WIV1`
+- `LYRa11`
+- `MERS_RBD`
+- `OMICRON_BA2_TStarr_Seq&Trunc`
+- `SARS-COV-2_GAMMA_RBD_LLNL_Trunc`
+- `SARS-CoV-2_Delta_RBD_L452R_T478K`
+- `SARS-CoV1_RBD_(6LZG_Mimic)`

data/YM_1068/data.parquet

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