OpenProtein.AI offers a machine learning platform that enables biologists to enhance protein engineering processes, resulting in faster, more cost-effective, and reliable product development. By providing accessible ML tools, the platform addresses the limitations of traditional methods in protein design and optimization.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional protein engineering methods are often slow, expensive, and unreliable, hindering the efficient development of optimized proteins for various applications. These methods struggle to effectively navigate the vast sequence space and predict the functional impact of mutations, leading to inefficient library designs and extensive experimental validation.
Solution
OpenProtein.AI offers a machine learning platform that accelerates and enhances protein engineering by providing accessible AI tools for biologists. The platform leverages protein language models, including their proprietary PoET-2 model, to design optimized variant libraries, predict variant effects, and streamline data analysis. By integrating sequence and structural information, OpenProtein.AI enables users to achieve desired protein properties with fewer experimental iterations and reduced costs. The platform facilitates the training of sequence-to-function prediction models using user's own mutagenesis data, visualization of variant library data, and comparison of library designs based on predicted outcomes. OpenProtein.AI aims to democratize AI-driven protein design, making it accessible to both small and large research teams working with diverse protein types and properties.
Target Audience
The primary target audience includes protein engineers, synthetic biologists, and researchers in biotechnology and pharmaceutical companies who seek to accelerate protein design and optimization using machine learning.
Features
- AI-driven design of optimized sequence libraries using custom, multi-objective criteria
- Sequence-to-function prediction models trained on user-provided mutagenesis data
- Visualization tools for variant library data and property relationship analysis
- Prediction of variant effects and identification of mutagenesis hotspots
- Generation of sequence embeddings and inferences from open-source and proprietary foundation models
- Support for designing substitution libraries, combinatorial variant libraries, and bespoke sequence libraries
- Library design capabilities using state-of-the-art evolutionary models
- Tools for exploring expected library outcomes and comparing library designs by success probability and cost-effectiveness
- Visualization of predicted protein structures, including multimers, using the AlphaFold2 web interface
- Python client and APIs for integration into existing workflows