Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Institution: University of Cambridge
Language: English
Subfield: Biomaterials
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Funders: U.S. Department of Energy, U.S. Department of Agriculture, Frances and Augustus Newman Foundation, European Commission, National Institutes of Health, Directorate for Biological Sciences, Strategic Environmental Research and Development Program, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council, Office of Naval Research, National Institute of Biomedical Imaging and Bioengineering, Army Research Office, H2020 European Research Council
Awards: G1102702650, G180023333, G2093025302, EPSRC Centre for Doctoral Training in Integrated Functional Nano (i4Nano), G3488964505, G434337789, Digital Protein Biophysics of Aggregation, Models to Predict Protein Biomaterial Performance, G5679282920, G7452299184, G7707145192, G824756401, G8876996369, G8989574591, G9000193136
OpenAlex ID: W4404389587