Rapid assessment of chemical complementarity of ligands for protein design
Abstract
Driven by deep-learning methods, computational protein design can now rapidly generate de novo structures, opening new frontiers for creating proteins that bind small molecules tightly and specifically, although achieving predictable and tunable binding remains challenging. Here we address this with a rapid physics-based computational method to generate isosteric and chemically complementary binding pockets for small-molecule targets in de novo designed proteins. We test this experimentally by constructing and characterizing binding proteins for several synthetic and natural chromophores. By evaluating only single-digit numbers of designs, the pipeline delivers stable proteins with pre-organised binding sites in apo structures confirmed by X-ray crystallography, which bind the targets with micromolar affinities or better. To illustrate the scope and applications of this approach, we incorporate selective and coupled chromophore-binding sites in a two-domain de novo protein enabling controlled energy transfer between the two sites, and we develop a small de novo binding protein that can be used in live mammalian cells to visualise sub-cellular structures.
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BibTeX
@article{petrenas2026rapid,
title = {{Rapid assessment of chemical complementarity of ligands for protein design}},
author = {Rokas Petrenas and Katarzyna Ożga and Joel J. Chubb and Andrey V. Romanyuk and Dominic Alibhai and Jennifer J. McManus and Graham J. Leggett and Nigel S. Scrutton and Thomas A. A. Oliver and Derek N. Woolfson},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78764-7},
url = {https://www.nature.com/articles/s41467-026-78764-7},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Rapid assessment of chemical complementarity of ligands for protein design
AU - Rokas Petrenas
AU - Katarzyna Ożga
AU - Joel J. Chubb
AU - Andrey V. Romanyuk
AU - Dominic Alibhai
AU - Jennifer J. McManus
AU - Graham J. Leggett
AU - Nigel S. Scrutton
AU - Thomas A. A. Oliver
AU - Derek N. Woolfson
PY - 2026
DA - 2026/10/19
JO - Nature Communications
DO - 10.1038/s41467-026-78764-7
UR - https://www.nature.com/articles/s41467-026-78764-7
AB - Driven by deep-learning methods, computational protein design can now rapidly generate de novo structures, opening new frontiers for creating proteins that bind small molecules tightly and specifically, although achieving predictable and tunable binding remains challenging. Here we address this with a rapid physics-based computational method to generate isosteric and chemically complementary binding pockets for small-molecule targets in de novo designed proteins. We test this experimentally by constructing and characterizing binding proteins for several synthetic and natural chromophores. By evaluating only single-digit numbers of designs, the pipeline delivers stable proteins with pre-organised binding sites in apo structures confirmed by X-ray crystallography, which bind the targets with micromolar affinities or better. To illustrate the scope and applications of this approach, we incorporate selective and coupled chromophore-binding sites in a two-domain de novo protein enabling controlled energy transfer between the two sites, and we develop a small de novo binding protein that can be used in live mammalian cells to visualise sub-cellular structures.
SN - 2041-1723
ER -