2024-11-17-zhao24a.md

abstract	title	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
The computational prediction and design of peptide binders targeting specific epitopes within disordered protein regions is crucial in biological and biomedical research, yet it remains challenging due to their highly dynamic nature and the scarcity of experimentally solved binding data. To address this problem, we built an unprecedentedly large-scale library of peptide pairs within stable secondary structures (beta sheets), leveraging newly available AlphaFold predicted structures. We then developed a machine learning method based on the Transformer architecture for the design of specific linear binders, in analogy to a language translation task. Our method, TransformerBeta, accurately predicts specific beta strand interactions and samples sequences with beta-sheet-like molecular properties, while capturing interpretable physico-chemical interaction patterns. As such, it can propose specific candidate binders targeting disordered regions for experimental validation to inform protein design.	Computational design of target-specific linear peptide binders with TransformerBeta	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	zhao24a	0	Computational design of target-specific linear peptide binders with TransformerBeta	1	27	1-27	1	false	Zhao, Haowen and Aprile, Francesco and Bravi, Barbara	given family Haowen Zhao given family Francesco Aprile given family Barbara Bravi	2024-11-17		Proceedings of the 19th Machine Learning in Computational Biology meeting	261	inproceedings	date-parts 2024 11 17	https://raw.githubusercontent.com/mlresearch/v261/main/assets/zhao24a/zhao24a.pdf