Peptide binders for all
Meet PQ Librarian: AI-driven Library Scoring
Today, we’re thrilled to introduce a game-changer in the world of peptide discovery — PQ Librarian. This tool is the result of our experience working for years at the intersection of AI and display screening research. ProteinQure invites you to join the revolution and make data-driven decisions in drug discovery a reality.
Want to try it for yourself? Visit: https://librarian.proteinqure.com/
How to find the right peptide library for your target: A User’s Guide
Ready to dive into the world of accelerated peptide drug discovery? PQ Librarian is now at your fingertips with a sleek online interface, ready to make early-stage drug discovery a whole lot more efficient,
- All it takes is a human gene name, UniProt ID, or full name of a protein, and you’re set. We’ve pre-computed the viability of commonly available libraries for the entire human proteome!
- The result? Color-coded scores for various peptide libraries against your chosen target. We output color-coded scores for multiple peptide libraries against this target. Green for high hit potential, yellow for caution, and red for a low chance of success — simple, right?
Let’s test it out with “KRAS”. Predictions range from the optimistic green for our proprietary libraries to the cautious yellow for fully randomized linear and cyclic peptide libraries. Specifically, we define a Green target as an approximately 80% chance to find at least one hit with an affinity better than 1 µM. Note for some targets you will see Red as well.
It is important to note that the ProteinQure score is the maximum of multiple peptide libraries each with a unique scaffold.
Why PQ Librarian Matters: Our Motivation
Peptides are the unsung heroes of the therapeutic modalities, yet many library screening projects hit roadblocks. Display screening projects like phage display or mRNA display fail for unknown reasons, go unreported, and data is not fed back to improve the probability of success of peptide screening in the future. Enter PQ Librarian, combining Large Language Models, predictive neural networks, and training on a huge database of peptide-receptor interactions. We’ve also validated it on proprietary data from 20+ targets!
Not all hits are created equal! Our computational-designed libraries are not your average players. They are optimized for affinity, specificity, solubility, charge, and other properties that set them aside from fully randomized libraries. That being said, a simple library can be a useful starting point. That is why we are making PQ Librarian available to all — to help everyone get started with peptides.
Become part of a community that simplifies the complex, and let’s redefine the future of peptide library screening together — welcome to PQ Librarian!
Solving the Puzzle: Your Peptide Quest Begins! (Contact us at bd@proteinqure.com)
Peptide drug development is like a puzzle — every project is a unique challenge waiting to be conquered. But here’s the fun part: there’s no one-size-fits-all solution. Since we founded ProteinQure, our team has been on a wild ride, tackling the trickiest puzzles in peptide drug design. Some problems require more bespoke solutions. Reach out for any of the following:
- Tailored Libraries: We specialize in designing biased combinatorial libraries for specific targets and applications, such as membrane proteins or drug delivery (like siRNA).
- Enhanced Scoring Solutions: Our capabilities extend to scoring bespoke peptide libraries including non-canonical amino acid-containing libraries (like mRNA display) with novel scaffolds.
- Custom Target Scoring: We can score libraries against any antigen sequence, including proteins with mutations or alternative splicing, as well as higher precision library scoring against specific epitopes of targets (e.g. the extracellular domain of a target protein).
- Target Identification: We can use AI-driven methods to suggest possible targets across the entire human proteome if a specific cell-binding peptide sequence is known.
FAQ: PQ Librarian in Practice
Q) What are some technical assumptions being made in your model?
We’re looking forward to sharing behind-the-scenes details on how PQ Librarian was created, but for now, here are some important points.
- Targets are evaluated as monomers with no post-translational modifications
- We do not evaluate the binding of all peptides in a combinatorial library (often larger than 10⁹). We subsample probabilistically within a library to predict binding scores.
- Peptide binding prediction is predicted in the absence of any linker or phage coat protein. In some circumstances, we expect this would result in overestimation of the binding score to some targets.
- PQ Librarian is not making any predictions about solubility, specificity, or other developability properties (we have other tools for that!)
Q) My target was found in your database, but all libraries had a “Red” score. Is there any hope for hit identification?
Yes. Contact us and we can share custom solutions for challenging targets, along with feasibility evaluations for those solutions (subject to Confidential Disclosure Agreements, of course). See “Solving the Puzzle”.
Q) My target was not found in your database, can you provide a score?
Yes. Contact us and we would be happy to score any protein antigen sequence. However, we cannot score oligomeric targets where binding may be expected to occur at the interface of two proteins. See “Solving the Puzzle”.
Q) My target was found in your database, but all libraries had a “Green” score. Does this mean that I should screen all libraries?
Yes, if you have the resources. Starting with low-cost, easily accessible libraries can be a good starting point. However, there is always a potential to produce hits that are non-specific, insoluble, or include other developability liabilities. Given the choice between multiple libraries, you should seek a library with the potential to generate drug-like properties beyond binding. Contact us to screen ProteinQure’s proprietary libraries to maximize downstream success in hit validation and lead optimization.
