Biology subjects

Corley, N.

Publications and source records attributed to Corley, N..

5 recordsLinked to original sources

bioRxiv2026

Programmable amide bond formation using de novo ligases

Amide bond formation is of central importance in the chemical industry, underpinning the synthesis of small bioactive molecules, macrocycles, peptides and their conjugates. Conventional methods of amide synthesis rely on stoichiometric coupling agents and costly protecting group manipulations. While biocatalysis can alleviate some of these challenges, enzymes typically operate across a narrow range of substrates and their adaptation to new coupling partners requires extensive engineering. Here we show that de novo amide ligases featuring a cysteine catalytic triad can be programmed computationally to operate across diverse substrate classes with high selectivity. We first designed complementary peptide ligases to selectively functionalise either the N-terminus or a single lysine residue of a polypeptide sequence containing 36 competing lysines. Crystal structures of both ligases show excellent agreement with the designs, with structural modelling suggesting that amidation outcompetes hydrolysis because the catalytically competent geometry forms only upon acceptor binding. We then extended our approach to the synthesis of small bioactive molecules by designing stereoselective ligases that mediate a key amide bonding forming step in the synthesis of the preventative HIV therapeutic lenacapavir. These results establish de novo enzyme design as a programmable platform for achieving selective amide bond formations across a wide range of molecular targets.

biochemistry↗

bioRxiv2026

Design of readers for pathological PTM proteoforms in Alzheimer's disease

Alzheimer's disease (AD) is characterized by heterogeneous Tau phosphorylation and aberrant amyloid-beta (Abeta) processing, yet molecular tools that distinguish closely related molecular states of tau and Abeta with predefined specificity remain difficult to develop. Here, we designed de novo protein readers targeting three disease-associated phospho-Tau regions and N-terminal pyroglutamate-modified Abeta (N3pG-Abeta). Screening 192 designs per target identified 51 modification-selective hits among 768 candidates (6.6%). Representative readers exhibited nanomolar to subnanomolar affinities for modified peptides, discriminated authentic modifications from unmodified or substitution-based controls, and preferentially recognized modified full-length targets. We further programmed local modification specificity by interface redesign, generating pT231 readers conditional on the phosphorylation state of neighboring S235 and S237. A 2.0-angstrom crystal structure of an N3pG-Abeta reader closely matched the computational design model (interface C-alpha RMSD = 0.52 angstrom), and redesign of pT231 readers tuned apparent binding affinities from greater than 1 uM to below 1 nM. In a human 3D neuronal culture with both pathological Abeta and tau, selective readers targeting N3pG-Abeta, Tau pT231, and Tau pT217 recognized disease-associated signals, exhibiting colocalization with related antibodies and much higher signal in the AD line than the control. Tau pT231 readers further quantitatively distinguish AD patients and control human brain lysates in dot-blot assays. Together, these results establish genetically encoded readers with programmable specificity for defined Tau and Abeta modification states, enabling molecular-state-resolved detection in human disease models and biological samples, and lay a foundation for controlled pathological molecular perturbation.

bioengineering↗

bioRxiv2025

De novo Design of All-atom Biomolecular Interactions with RFdiffusion3

Deep learning has accelerated protein design, but most existing methods are restricted to generating protein backbone coordinates and often neglect interactions with other biomolecules. We present RFdiffusion3 (RFD3), a diffusion model that generates protein structures in the context of ligands, nucleic acids and other non-protein constellations of atoms. Because all polymer atoms are modeled explicitly, conditioning the model on complex sets of atom-level constraints for enzyme design and other challenges is both simpler and more effective than previous approaches. RFD3 achieves improved performance compared to prior approaches on a range of in silico benchmarks with one tenth the computational cost. Finally, we demonstrate the broad applicability of RFD3 by designing and experimentally characterizing DNA binding proteins and cysteine hydrolases. The ability to rapidly generate protein structures guided by complex sets of atom-level constraints in the context of arbitrary non-protein atoms should further expand the range of functions attainable through protein design.

biochemistry↗

bioRxiv2025

Accelerating Biomolecular Modeling with AtomWorks andRF3

Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilitate the development of new models, we present AtomWorks: a broadly applicable data framework for developing state-of-the-art biomolecular foundation models spanning diverse tasks, including structure prediction, generative protein design, and fixed backbone sequence design. We use AtomWorks to train RosettaFold-3 (RF3), a structure prediction network capable of predicting arbitrary biomolecular complexes with an improved treatment of chirality that narrows the performance gap between closed-source AlphaFold3 (AF3) and existing open-source implementations. We expect that AtomWorks will accelerate the next generation of open-source biomolecular machine learning models and that RF3 will be broadly useful as a structure prediction tool. To this end, we release the AtomWorks framework (https://github.com/RosettaCommons/atomworks), together with curated training data, code and model weights for RF3 (https://github.com/RosettaCommons/modelforge) under a permissive BSD license.

biochemistry↗

bioRxiv2024

ProtNote: a multimodal method for protein-function annotation

Understanding the protein sequence-function relationship is essential for advancing protein biology and engineering. However, fewer than 1% of known protein sequences have human-verified functions. While deep learning methods have demonstrated promise for protein function prediction, current models are limited to predicting only those functions on which they were trained. Here, we introduce ProtNote, a multimodal deep learning model that leverages free-form text to enable both supervised and zero-shot protein function prediction. ProtNote not only maintains near state-of-the-art performance for annotations in its train set, but also generalizes to unseen and novel functions in zero-shot test settings. We envision that ProtNote will enhance protein function discovery by enabling scientists to use free text inputs, without restriction to predefined labels - a necessary capability for navigating the dynamic landscape of protein biology.

bioengineering↗