Co-Founder & Co-CEO
Profile summary: MBA in Finance at WHU – Otto Beisheim School of Management
Orbion
7/10Traction
Orbion provides an AI platform for comprehensive protein characterization, rational engineering, and automated experimental protocol generation. The platform integrates structure and omics data to predict PTMs, binding sites, and functional domains, accelerating discovery for diverse proteins and complexes. Researchers utilize Orbion to design optimized mutants and generate literature-validated bench protocols, significantly reducing wet-lab iteration time and cost.
- Artificial Intelligence
- Data & Analytics
- Biotechnology
Updated 6 months ago
Funding
Raised to date
$103KRaised 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.
Funding rounds are not available yet.
Founders
Product
Problem
Protein research is often hindered by challenges in purification, protein instability, and difficulties in obtaining high-resolution structural data for complex macromolecules. These obstacles lead to significant time and resource expenditure, limiting the exploration of challenging protein targets.
Solution
Orbion provides an AI-driven predictive platform designed to accelerate protein research by identifying stabilizing mutations and optimizing experimental conditions. The platform analyzes protein sequences and available data to predict protein-protein interactions, suggest modifications like truncations or mutations to enhance stability, and recommend optimal conditions for structure determination techniques such as Cryo-EM and X-ray crystallography. By offering these insights, Orbion aims to reduce research time and costs, enabling scientists to work with more challenging proteins and achieve better structural resolution.
Target Audience
The platform targets researchers in structural biology, biochemistry, and molecular biology, including those in academic institutions and pharmaceutical companies, who are involved in protein design, purification, and structure determination.
Features
- AI-powered prediction of stabilizing mutations and truncations for protein engineering.
- Simulation and prediction of protein-protein interactions to guide purification strategies.
- Assessment of protein stability and identification of instability hotspots.
- Recommendations for optimal experimental conditions for Cryo-EM and X-ray crystallography.
- Analysis of post-translational modifications (PTMs) and functional sites.
- Support for a wide range of protein types, including complex and membrane-bound structures.
- Machine learning models trained on extensive protein data for scientifically grounded predictions.