Atlas Oracle Suite
OracleA set of fast protein property predictors for triaging sequence quality, function, localization, and developability.
- property prediction
- interpretability
Atlas Oracle Suite
The Atlas Oracle Suite gives researchers fast, sequence-based estimates for protein properties that often matter before an experiment is run.
What It Does
The suite includes readouts for solubility, expression, localization, taxonomy, enzyme function, homodimerization, naturalness, pH preference, turnover, and thermal behavior.
Each readout is meant to add context. A promising binder that looks poorly soluble, for example, may need redesign before synthesis.
Why It Matters
Protein workflows rarely depend on one property. A candidate can bind its target and still fail because it does not express, aggregates, localizes incorrectly, or carries the wrong functional signature.
The Oracle Suite gives teams a fast first pass across those questions so design and validation decisions can be made with more context.
Intended Use
Use the Oracle Suite to screen designed or natural proteins before synthesis, compare variants across multiple developability signals, add property context to Atlas results, and prioritize candidates for wet-lab validation.
Limitations
Oracle predictions are sequence-based estimates. They do not replace assays, expression tests, localization experiments, kinetic measurements, or stability measurements. Cellular environment, construct design, tags, purification conditions, and assay format can all change the observed result.
Try Atlas Oracle Suite
Run predictions with this model through the Synthyra platform.
Related Models
ESMC
Foundation ModelEvolutionaryScale Biohub's ESM Cambrian model family, exposed through FastPLMs as ESM++ checkpoints.
Solubility
OraclePrioritizes protein sequences that are more likely to remain soluble.
Subcellular Localization
OraclePredicts likely cellular localization signals from protein sequence.
Temperature Stability
OracleEstimates whether a protein sequence is likely to tolerate higher-temperature conditions.
Related News
August 21, 2025
Protify: Model Choice As An Experiment
Across 32 protein tasks, 13 different models won at least once and none of the wins were statistically significant. Picking a protein language model is an experiment, not a preference.
July 30, 2024
Annotation Vocabulary: Teaching Protein Models the Language of Function
Replace free-text protein descriptions with a vocabulary of ontology terms, and a model trained for three dollars in compute produces better functional embeddings than models a thousand times its cost.