Track: Biology: datasets and/or experimental results
Nature Biotechnology: Yes
Keywords: de novo peptide design, dual-target peptide, high bio-activity, few-shot learning, active learning
Abstract: Despite the urgent need for high bio-activity peptides in novel biomedical therapies, the de novo design of such peptides, especially those with dual targets, remains an unsolved challenge. Here, we introduce ORIDTP, a few-shot active learning pipeline that integrates in silico peptide generation with in vitro experimental feedback for de novo design of both single-target and dual-target peptides with high bio-activity. ORIDTP involves single-target or dual-target oriented peptide de novo generation, binding affinity maturation, and iterative reinforcement of bio-activity based on wet-laboratory feedback. Using ORIDTP, we successfully designed high bio-activity peptides targeting GLP-1R after four iterative rounds, achieving $\rm EC_{50}$ (half maximal effective concentration) values ranging from 35.1 pM to 8.1 pM, which outperform the natural peptide with the highest known bio-activity of 40.8 pM. Furthermore, ORIDTP successfully designed de novo dual-target peptides for activating GLP-1R ($\rm EC_{50}$ values ranging from 53.4 pM to 8.2 pM) and GCGR ($\rm EC_{50}$ values ranging from 0.82 nM to 0.21 nM) after four iterative rounds. The best dual-target peptide outperformed two natural peptides with the highest known bio-activity for their respective target proteins (8.2 pM versus 40.8 pM for GLP-1R, and 0.24 nM versus 1.4 nM for GCGR). ORIDTP represents a significant advancement in the rapid and effective design of dual-target peptides for therapeutic applications.
Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.
Presenter: ~Tianxu_Lv1
Format: Maybe: the presenting author will attend in person, contingent on other factors that still need to be determined (e.g., visa, funding).
Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.
Submission Number: 2
Loading