ELECTROSTATICS, ENGINEERED

Charged Protein
Sequence Predictions

Designing Proteins
Nature Hasn't Built Yet

We use deep learning to predict and generate protein sequences for highly charged protein–protein interfaces, not just to fold, but to bind with precision, flexibility, and functional intent.

Our latest project focuses on making these designs even smarter and more reliable, especially in challenging environments like the bloodstream or brain. We’re improving how our system understands shape, charge, and flexibility, so that the proteins we build behave the way we need them to, not just in theory, but in life.

From gene regulators to next-gen therapeutics, we're creating protein interfaces evolution missed, but our modern life needs.

How Our Models Help the World

We aim to accelerate global research by providing an open-source, fine-tuned model that assists with structural biology, drug discovery, and synthetic biology research worldwide.

Targeted Cancer Therapies

Design custom antibodies for high-charge tumor interfaces

We are developing models that will help scientists generate novel protein sequences tailored for therapeutic binding, to advance precision immunotherapies including cancer vaccines and checkpoint inhibitors.

Next-Gen Genetic Medicine

Create nucleic acid-binding proteins for gene editing

By designing sequences with charge-aware specificity, we hope to enhance CRISPR tools and synthetic regulators for safer, more effective treatment of genetic diseases.

Eco Remediation

Engineer protein complexes to neutralise pollutants

Our model aims to help design interfaces for stable binding in extreme conditions, enabling proteins that degrade toxins, bind heavy metals, or break down plastic waste.