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 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.
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.
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 pretrainingPembro144_label_encoded_warm: Label encoded sequences with pretrainingPembro144_esm_cold: ESM featurized sequences with no pretrainingPembro144_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.