Learning the rules of peptide self-assembly through data mining with large language models

TL;DR

This study combines literature mining and machine learning to systematically analyze peptide self-assembly, creating a database and models that improve understanding and prediction of assembly behavior for various applications.

Contribution

It introduces a curated peptide assembly database and fine-tunes GPT models for literature mining, advancing systematic understanding of peptide self-assembly rules.

Findings

ML models achieve >80% accuracy in phase classification

Fine-tuned GPT outperforms pre-trained models in literature extraction

Workflow accelerates discovery of self-assembling peptides

Abstract

Peptides are ubiquitous and important biologically derived molecules, that have been found to self-assemble to form a wide array of structures. Extensive research has explored the impacts of both internal chemical composition and external environmental stimuli on the self-assembly behaviour of these systems. However, there is yet to be a systematic study that gathers this rich literature data and collectively examines these experimental factors to provide a global picture of the fundamental rules that govern protein self-assembly behavior. In this work, we curate a peptide assembly database through a combination of manual processing by human experts and literature mining facilitated by a large language model. As a result, we collect more than 1,000 experimental data entries with information about peptide sequence, experimental conditions and corresponding self-assembly phases. Utilizing the collected data, ML models are trained and evaluated, demonstrating excellent accuracy (>80\%) and efficiency in peptide assembly phase classification. Moreover, we fine-tune our GPT model for peptide literature mining with the developed dataset, which exhibits markedly superior performance in extracting information from academic publications relative to the pre-trained model. We find that this workflow can substantially improve efficiency when exploring potential self-assembling peptide candidates, through guiding experimental work, while also deepening our understanding of the mechanisms governing peptide self-assembly. In doing so, novel structures can be accessed for a range of applications including sensing, catalysis and biomaterials.