Yta Labs Peaks
Our deep learning-based peptide identification solution reduces identification bias inherent in usual mass spectrometry-based identification methods, improving the quality of identifications at 5%, 1%, or lower FDR.
Thanks to the reduced bias in the biochemical properties of the peptides identified with our method, identification rates can be improved by over 300% in select cases.
Yta Labs Surface
Using pMHC-specific data-driven structural modeling, proprietary structural features, and purpose-fit deep learning algorithms, our platform aggressively selects quality targets from the initial candidate pool, leading to up to 70% reduction in validation requirements to find quality binders.
Yta Labs Capture
Our unique algorithm quickly generates high quality, low-affinity binders against many targets at once to efficiently validate target specificity and to provide starting binders ready for affinity maturation campaigns.
Lab validation
An indispensable tool to ensure robust, biologically meaningful results.