About
OpenProtein.AI is an AI-driven protein design and optimization platform built by pioneers in protein language modeling. At its core is PoET-2, a next-generation foundation model that learns from billions of years of evolutionary sequence data to capture the structural, functional, and evolutionary properties of proteins across the full protein universe. The platform is organized around three core workflows: Learn, Generate, and Review. In the Learn phase, researchers train sequence-to-function prediction models on their own mutagenesis data, visualize variant library relationships, predict variant effects, and map mutagenesis hotspots. In the Generate phase, users design optimized libraries using custom and multi-objective criteria, map insertion and deletion sites, and create substitution, combinatorial, or bespoke sequence libraries. In the Review phase, teams compare library designs by success probability and cost-effectiveness, and visualize predicted protein structures. OpenProtein.AI supports any protein type — from antibodies and enzymes to capsid proteins — and any target property, including activity, expressibility, and thermostability. The platform scales seamlessly from small 96-well plate experiments to high-throughput pipelines generating hundreds of thousands of data points. By providing GPU infrastructure, OpenProtein.AI eliminates IT overhead so research teams can focus on science. Backed by peer-reviewed publications in Nature Communications, NeurIPS, and Cell Systems, and with partnerships including Boehringer Ingelheim, the platform is purpose-built for biotech and pharma researchers seeking to accelerate protein engineering and reduce experimental cycles.
Key Features
- PoET-2 Foundation Model: Next-generation protein language model trained on evolutionary sequence data to predict variant effects, generate novel proteins, and capture functional and structural properties.
- Optimized Variant Library Design: Create substitution, combinatorial, and bespoke sequence libraries using custom single- or multi-objective design criteria, including insertion and deletion site mapping.
- Sequence-to-Function Prediction: Train machine learning models on your own mutagenesis data to predict protein properties like activity, expressibility, and thermostability across novel variants.
- Library Review & Comparison: Compare library designs by success probability and cost-effectiveness, visualize predicted protein structures, and quantify expected experimental outcomes before running experiments.
- Seamless Workflow Integration: Integrates with existing mutagenesis workflows and supports open-source models (AlphaFold2, ESM2, Clustal Omega) alongside proprietary models, with no GPU infrastructure required.
Use Cases
- Antibody optimization for pharmaceutical drug discovery, generating diverse sub-nanomolar affinity variant libraries in fewer experimental rounds.
- Enzyme engineering for industrial biotechnology applications, optimizing activity, thermostability, or substrate specificity using ML-guided variant design.
- Capsid protein engineering for gene therapy, designing functional variants with improved delivery efficiency or reduced immunogenicity.
- Predicting variant effects and mapping mutagenesis hotspots across a protein sequence to prioritize high-value experimental candidates.
- Multi-round protein engineering campaigns where predictive models are continuously refined with new experimental data to converge on optimal sequences faster.
Pros
- Cutting-Edge Foundation Models: PoET-2 is developed by pioneers in protein language modeling, validated in peer-reviewed publications at NeurIPS, Nature Communications, and Cell Systems.
- End-to-End Protein Engineering Workflow: Covers the full cycle from predictive model training and library generation to structural visualization and success probability review in a single platform.
- Scales to Any Project Size: Supports both small 96-well plate experiments and high-throughput pipelines with hundreds of thousands of data points, making it accessible across research scales.
- No Infrastructure Required: Managed GPU cloud infrastructure eliminates the need for expensive in-house compute resources, reducing IT overhead for research teams.
Cons
- Highly Specialized Use Case: The platform is designed specifically for protein engineering researchers in biotech and pharma, making it inaccessible or irrelevant outside life sciences.
- Early Access / Limited Availability: Access is gated via an early access request process, which may delay onboarding for new teams.
- Opaque Pricing: No public pricing information is available; costs are likely enterprise-negotiated, which can be a barrier for academic or smaller research groups.
Frequently Asked Questions
What is PoET-2?
PoET-2 is OpenProtein.AI's next-generation protein language foundation model. It is trained on vast evolutionary protein sequence databases to learn the statistical and functional patterns of proteins, enabling high-precision variant effect prediction and de novo protein generation.
What types of proteins can OpenProtein.AI handle?
The platform supports any protein type, including antibodies, enzymes, capsid proteins, and more. It can optimize for any target property such as binding affinity, thermostability, expressibility, or enzymatic activity.
Do I need to provide my own computational infrastructure?
No. OpenProtein.AI is a fully managed cloud platform that provides the GPU infrastructure. Researchers can run AI-accelerated protein design without managing any hardware or IT setup.
Can I use my own experimental data with the platform?
Yes. The platform allows you to fine-tune predictive models using your own mutagenesis data, enabling it to learn your specific sequence-to-function relationships and generate variants tailored to your objectives.
How does OpenProtein.AI integrate with existing workflows?
The platform is designed to complement existing mutagenesis and protein engineering workflows. It also integrates with widely-used open-source tools like AlphaFold2, ESM2, and Clustal Omega for structure prediction and sequence analysis.
