A-Alpha Bio utilizes its AlphaSeq and AlphaBind platforms to generate high-resolution protein-protein interaction data at scale, enabling the discovery and engineering of effective protein therapeutics. The company addresses the challenge of limited high-quality interaction data, which hampers the understanding and treatment of diseases such as cancer and neurological disorders.
Funding
$45.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
USFounders
Product
Problem
The discovery and development of effective protein therapeutics are hindered by a lack of comprehensive, high-quality protein-protein interaction (PPI) data. This limitation impedes the understanding of disease mechanisms and the identification of promising drug candidates for diseases like cancer and neurological disorders.
Solution
A-Alpha Bio addresses this challenge with its AlphaSeq and AlphaBind platforms, which generate high-resolution PPI data at scale. AlphaSeq leverages synthetic biology to quantitatively characterize millions of PPIs, creating a large repository of interaction data. AlphaBind uses this data to train machine learning models that predict binding affinity from protein sequence, enabling the design and optimization of protein therapeutics. This combination of high-throughput data generation and machine learning accelerates the discovery of rare antibodies, identification of degradable neo-substrates, and engineering of protein interfaces.
Target Audience
A-Alpha Bio's primary customers are pharmaceutical and biotechnology companies, as well as research institutions, involved in the discovery and development of protein therapeutics and biologics.
Features
- AlphaSeq platform for quantitative and multiplexed characterization of protein-protein interactions.
- Large repository of high-quality protein interaction data generated through synthetic biology.
- AlphaBind platform utilizing machine learning models to predict binding from protein sequence.
- Computational design capabilities for protein therapeutics with experimental validation and refinement.
- Identification of weakly binding protein pairs that can be strengthened by small molecule glues.