Target specific peptide design using latent space approximate trajectory collector

TL;DR

This paper introduces LSATC, a novel machine learning approach for peptide design targeting specific proteins, overcoming data scarcity by sampling in a latent space to generate peptides with improved binding properties.

Contribution

The paper presents LSATC, a new end-to-end peptide design architecture that uses latent space trajectory sampling to target specific proteins, demonstrating superior performance over existing models.

Findings

LSATC samples peptides with 36% lower binding scores.

LSATC produces peptides with 284% less hydrophobicity.

All experimentally validated peptides showed at least 20% improved binding.

Abstract

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.