Our Technology

Measuring, discovering, predicting, and engineering protein-protein interactions

Our Experimental Platform

AlphaSeq

We use synthetic biology to generate high quality protein interaction data at scale.

Vastly Multiplexed

Characterize whole protein-protein interaction networks with millions of interactions

Highly Quantitative

Measure physiologically relevant interaction strengths spanning picomolar to micromolar affinities

Widely Versatile

Antibody discovery, molecular glue target discovery, affinity tuning, protein interface engineering, and more

Our Computational Platform

AlphaBind

We apply machine learning (ML) to accelerate discoveries and engineer better therapeutics.

Predict Affinities

Learn from over 300 million affinity measurements to predict binding strength from protein sequence

Capture Diversity

Explore a massive sequence space around the parental by learning the effects of stacked mutations

Iterate Rapidly

Cycle from model-proposed sequences to binding data in six weeks to quickly validate and refine models

Our PROCESS

AlphaSeq + AlphaBind Explained

AlphaSeq
BUILD
Two yeast surface display libraries are built from synthesized or template DNA. Displayed proteins may include antibodies, cell surface receptors, intracellular proteins, bacterial or viral antigens, peptides, and more. The libraries are mixed in a liquid culture, allowing A and Alpha cells to collide.
FUSE
As cells collide, proteins on the cells’ surfaces may lead them to bind, where the probability of binding depends on protein interaction strength. Each surface expressed protein is paired with multiple unique DNA barcodes. Bound A and Alpha cells fuse, combining DNA barcodes into a single fused cell. A recombination event then pairs the barcodes onto one strand of DNA.
MEASURE
Paired A and Alpha barcodes are read and counted with next generation sequencing. Stronger protein-protein interactions result in a greater frequency of observed barcode pairs. Protein-protein interaction affinities are computed using internal controls and barcode replicates.

AlphaBind
COMPILE
Data from each new AlphaSeq experiment is added to a growing database of hundreds of millions of protein-protein interaction affinity measurements, including negative data. Control interactions are added to each assay to normalize results between different experiments.
TRAIN
Interaction measurements from the database are used to train machine learning models that relate protein sequence to binding affinity. ML is particularly powerful for teasing apart nonlinear effects from combining mutations, enabling accurate predictions of sequences far from any that have been experimentally tested.
DESIGN
Once trained, ML models are used to predict diverse sequences that have desired binding properties (affinities, specificities, cross-reactivities, epitopes, etc.). Computational screens for expression, stability, solubility, and other properties are then used to enrich designs for those most likely to be developable.
Validate & REFINE
Model predicted protein sequences are synthesized and tested experimentally with AlphaSeq and orthogonal assays. A <6-week AlphaSeq + AlphaBind iteration cycle enables rapid evaluation and improvement of model performance.

ANTIBODY DISCOVERY  and OPTIMIZATION
An antibody’s therapeutic properties are driven by protein-protein interactions, making AlphaSeq + AlphaBind invaluable for finding and optimizing antibodies with a given affinity, specificity, cross-reactivity, and/or epitope. With AlphaSeq, we discover antibodies with the right binding properties, even when poorly represented in a large library. Combined with AlphaBind, we optimize antibodies rapidly for multiple binding properties and bias libraries to also improve developability properties and increase sequence diversity. We apply our antibody discovery and optimization workflow to build our therapeutic pipeline and to partner with industry leading companies and institutions.
MOLECULAR GLUE TARGET DISCOVERY
The throughput and sensitivity of AlphaSeq makes the platform well suited to uncover novel targets for molecular glues by discovering and characterizing weak interactions between E3 ubiquitin ligases (or other effector proteins) and target proteins that may be enhanced by a small molecule binding at the interface. We measure interactions between our proprietary E3 ligase library and undruggable target proteins to provide a starting point for the rational discovery of molecular glues. We have discovered and validated many pairs internally and look to partner with industry leaders to progress protein-protein binding insights into small molecule drugs.
ML MODEL Development
Our data accumulates in a rapidly growing database with hundreds of millions of quantitative protein-protein interaction measurements, including critically important negative data. AlphaSeq data is both quantitative and multi-dimensional, making it ideal for training highly predictive machine learning models for therapeutically critical properties that include affinity, specificity, cross-reactivity, and epitope. Models are used to fuel internal campaigns at A-Alpha and in partnership with industry leaders.