Provolut is an AI-powered protein design platform that helps biotech and pharma companies engineer proteins with desired properties using significantly fewer lab experiments. The web-based tool analyzes user-uploaded datasets to predict protein evolutions, delivering quality candidates at a fraction of the cost of traditional directed evolution or deep mutational scanning. It offers flexible deployment options including on-site installation or API integration.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional protein engineering methods such as directed evolution and deep mutational scanning require extensive laboratory experiments to identify protein variants with desired properties. This trial-and-error approach is time-consuming, costly, and often fails to explore the full sequence space, limiting the ability of biotech and pharma companies to develop optimized proteins for therapeutic, industrial, or research applications.
Solution
Provolut provides an AI-based protein design web application that helps biotech innovators predict protein evolutions more efficiently. Users upload a spreadsheet with their starting dataset, and the algorithm analyzes patterns to deliver quality protein candidates with significantly fewer experiments—up to 100 times fewer compared to standard screening methods. The platform can handle targets for multiple properties simultaneously, distilling complex data patterns into actionable findings. Provolut offers flexible deployment options including on-site installation or API connection, while ensuring data and intellectual property remain secure throughout the design process.
Target Audience
Primary customers are biotech and pharmaceutical companies, as well as research institutions, seeking to accelerate protein engineering projects with AI-driven design capabilities.
Features
- AI-based algorithm that distills data patterns into actionable protein design insights
- Handles targets for multiple protein properties simultaneously in a single workflow
- Delivers superior outcomes with up to 100 times fewer experiments compared to standard screening methods
- Web-based application requiring only spreadsheet uploads of starting datasets
- Flexible deployment via on-site installation or API integration
- Data and IP security measures to protect proprietary research