Demo Instance Lab Dataset
This repo contains three example datasets that would be available to the user for downstream model training. The derived data is calculated from log probabilities of each library candidate in an assay sample. The different data modalities are:
- Expression
- Binding
- Specificity
Expression scores are log enrichments of the candidate translated protein frequency relative to the initial DNA pool candidate frequency. The binding score is defined as the ratio of the relative enrichment of a candidate over the enrichment of the weakest candidate in the dataset represented on a log scale. The specificity score is defined in terms of binding scores of off-target and on-target antigens, and is the ratio of the targeting binding score divided by the average to the binding scores of all off-target interactions.
Common Metadata
There are columns that are common to each of the three datasets, which facilitate simple associations if one wants to compute features over multiple different scores. The columns are:
candidate_library_id: This ID refers to the specific sample that contains all the translated proteins expressed together. In binding and specificity contexts, this ID represents pool of binder proteins that is mixed with different antigens to generate binding and specificity scores.candidate_name: The user-provided name of the candidate sequence.candidate_sequence: The amino acid sequence of the candidate binder.candidate_id: A cryptographic hash of the candidate sequence.candidate_type: An enum field representing the type of candidate protein. In this demo, it's eitherminiproteinorvhh.target_pdb_id: The PDB ID of the target protein that conditioned the model to generate the candidate sequence.
Expression Dataset
The unique identifiers for each row in this dataset is ["candidate_library_id", "candidate_id"]. So, the number of rows are equal to the number of candidate proteins in each distinct pool. The scoring columns are:
expression_score: The log enrichment of the expression of the translated candidate relative to the double stranded pool.expression_score_std: The standard error of the expression score estimate calculated from each of the replicates measured.
Binding Dataset
The unique identifiers for each row in this dataset is ["candidate_library_id", "candidate_id", "antigen_pdb_id"]. So, the number of rows are equal to the number of candidate protein + antigen combinations in each distinct library. The columns specific to the binding dataset are:
antigen_pdb_id: The PDB ID of the antigen protein used in the binding assay.targeting: Boolean value that specifies whether the antigen used in the binding assay matches the target for the candidate protein.binding_score_50nM: The mean binding score from the assay where the candidate library was tested with 50 nM of the antigen protein.binding_score_500nM: The mean binding score from the assay where the candidate library was tested with 500 nM of the antigen protein.binding_score_50nM_std: The standard error of the mean 50 nM binding score estimate calculated from each of the replicates measured.binding_score_500nM_std: The standard error of the mean 500 nM binding score estimate calculated from each of the replicates measured.
Specificity Dataset
The unique identifiers for each row in this dataset is ["candidate_library_id", "candidate_id"]. So, the number of rows are equal to the number of candidate proteins in each distinct pool. This columns specific to the specificity dataset are:
log_specificity_score_50nM: The mean specificity score from the assay where the candidate library was tested with 50 nM of the antigen protein.log_specificity_score_500nM: The mean specificity score from the assay where the candidate library was tested with 500 nM of the antigen protein.log_specificity_score_50nM_std: The standard error of the mean 50 nM specificity score estimate calculated from each of the replicates measured.log_specificity_score_500nM_std: The standard error of the mean 500 nM specificity score estimate calculated from each of the replicates measured.