A high-throughput methodology for intelligently designing antibodies or proteins that can be used in a variety of therapeutic settings. These settings range from the development of Chimeric Antigen Receptors for T cells (CAR-T cells) to the engineering of highly specific antibodies or proteins for diagnostic and/or therapeutic purposes.
Currently, existing antibody design methods use randomization schemes to create new peptide sequences, and to test their affinity towards targets. However, these methods are time-consuming and often expensive. Using deep learning models that can integrate experimental data to create antibody sequences would greatly streamline the development of effective antibodies.
To train machines to identify antibody sequences with high affinity for the target molecule, an iterative process is used to improve antibody designs. First, millions of computationally designed antibody sequences are developed using large-scale commercial oligonucleotide synthesis. They are then tested with phage or yeast display assays. The resulting data is fed into various machine learning models, using high-performance graphic processing units, to generate new antibody sequences for testing. Antibody properties that are selected in the machine learning models include improved affinity to the target protein, as well as the absence of cross-reactivity to other proteins. This iterative framework is able to identify highly effective antibodies with a minimal number of experiments. This approach not only streamlines the antibody designing process, but also provides scientists with the ability to predict and improve the affinity antibody with a known sequence to any target of interest.
High throughput testing
millions of new antibody sequences
Expansion of the
antibody sequence space by a factor of ten to one hundred compared to current
Ability to predict and
engineer sequences for CAR-T cells and other therapeutic applications
Faster, more effective,
lower cost method to engineer antibodies suitable for in vivo therapeutic