Integrating Diffusion and Liquid AI Models for Predicting Peptide Affinity from mRNA Display Selections.

Area of Science:

• Biochemistry and Molecular Biology
• Computational Biology and Bioinformatics
• Drug Discovery and Development

Background:

• In vitro selection and directed evolution technologies like mRNA display explore vast libraries to identify functional polypeptide ligands.
• Machine learning models, specifically Denoising Diffusion Implicit Models (DDIMs), trained on deep sequencing data can generate novel peptide sequences beyond experimental reach.
• Predicting peptide properties, such as binding free energies (ΔG°), is crucial for advancing ligand discovery but remains challenging.

Purpose of the Study:

• To apply machine learning methods for predicting binding free energies (ΔG°) of peptide ligands against the oncogenic protein Bcl-x _L.
• To evaluate the accuracy of these predictions for both experimentally derived and DDIM-generated peptide sequences.
• To establish a unified computational framework for expanding experimental ligand discovery and predicting molecular properties of peptide candidates.

Main Methods:

• Trained a Closed-form Continuous (CfC) neural network using a dataset of 15,700 peptide ligands with known sequences and binding free energies (ΔG°).
• Utilized deep sequencing data from mRNA display libraries to train DDIM models for generating novel peptide sequences.
• Applied the trained CfC model to predict binding free energies for both experimental and DDIM-generated peptides.

Main Results:

• The CfC model accurately predicted the rank order and binding free energies (ΔG°) of peptide ligands within experimental error.
• Identified five DDIM-generated peptides exhibiting single-digit picomolar binding affinities.
• Demonstrated the model's capability to predict properties for both experimental and computationally generated peptide sequences.

Conclusions:

• Combining trained DDIM and CfC models provides a powerful, unified approach for in silico ligand discovery.
• This integrated strategy significantly expands the scope of identifying high-affinity peptide candidates.
• Highlights the utility of large quantitative datasets for accurate prediction of peptide molecular properties and therapeutic potential.