2023/02/02 by Tong Lin, Sijie Chen, Lin, Tong +9
Biochemistry, Genetics and Molecular Biology · Immunology and Microbiology · #Antimicrobial Peptides and Activities #Artificial Intelligence (cs.AI) #Chemical Synthesis and Analysis #Computational Engineering #FOS: Computer and information sciences #Finance #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2302.01435
openalex publication_date 2023/02/02 · openalex created_date 2023/02/08 · openalex updated_date 2026/07/28
Despite the prevalence and many successes of deep learning applications in de novo molecular design, the problem of peptide generation targeting specific proteins remains unsolved. A main barrier for this is the scarcity of the high-quality training data. To tackle the issue, we propose a novel machine learning based peptide design architecture, called Latent Space Approximate Trajectory Collector (LSATC). It consists of a series of samplers on an optimization trajectory on a highly non-convex energy landscape that approximates the distributions of peptides with desired properties in a latent space. The process involves little human intervention and can be implemented in an end-to-end manner. We demonstrate the model by the design of peptide extensions targeting Beta-catenin, a key nuclear effector protein involved in canonical Wnt signalling. When compared with a random sampler, LSATC can sample peptides with 36% lower binding scores in a 16 times smaller interquartile range (IQR) and 284% less hydrophobicity with a 1.4 times smaller IQR. LSATC also largely outperforms other common generative models. Finally, we utilized a clustering algorithm to select 4 peptides from the 100 LSATC designed peptides for experimental validation. The result confirms that all the four peptides extended by LSATC show improved Beta-catenin binding by at least 20.0%, and two of the peptides show a 3 fold increase in binding affinity as compared to the base peptide.