Biology subjects

Petrenas, R.

Publications and source records attributed to Petrenas, R..

6 recordsLinked to original sources

bioRxiv2026

Expanding all-α-helical protein space through rational computational design

De novo protein design is advancing rapidly1,2. This is being driven by AI to generate protein backbones, sequences, and structural models3-7. As a result, de novo designed proteins are becoming larger and more complex8-10, and increasingly explore new protein structures11,12. By contrast, natural proteins have evolved structural and functional complexity by modular combination of recurring protein domains13. Approximately 25% of these natural domains are mostly -helical structures14. Here we show how these can be expanded using rational computational design. Following the domain classification scheme CATH15, we build complex all- de novo proteins hierarchically using sequence-to-structure relationships for helix-helix interactions, systematic rules to connect helices, computational tools to design loops, and in silico evaluation. The pipeline starts with a target architecture of free-standing helices. These are connected into a topology by considering local arrangements of helical bundles using understood sequence-to-structure relationships for helix packing. Single-chain sequences are completed using template- and AI-based methods. Finally, AlphaFold models are assessed to give small numbers of designs for experimental validation. We test 31 designs for 14 different architectures and 25 topologies. 75% of these express as stable, monomeric, water-soluble proteins; and >30% yield X-ray crystal structures matching the designs to atomic accuracy and with new-to-nature structures. Finally, several of the scaffolds are functionalised through one-shot designs to deliver ion, small-molecule and protein binders.

synthetic biology↗

bioRxiv2025

Biomolecular condensation using de novo designed globular proteins

De novo protein design is advancing rapidly, but many targets remain inaccessible to current AI-based tools. Here we describe de novo designed globular domains that drive biomolecular condensation. Starting from a water-soluble, monomeric protein, we make variants with the same amino-acid composition but different surface-charge distributions: one with large patches of surface charge, and another with a more-homogeneous charge distribution. The individual domains form stable and discrete structures in solution, with the large-patch variant exhibiting more-attractive interprotein interactions. Next, two copies of each variant are joined with disordered linkers to generate dumbbell-like proteins. When expressed in eukaryotic cells, the large-patch variant forms intracellular puncta, whereas that with small patches does not. The assemblies are dynamic, liquid condensates in vitro and in cells. The structured domains facilitate functionalisation: we introduce fluorophore-binding sites to visualise fluorescent condensates directly in cells without a GFP reporter; and we manipulate the condensates using motor proteins.

synthetic biology↗

bioRxiv2025

Rapid Assessment of Size, Shape, and Chemical Complementarity of Ligands for Computational Protein Design

Driven by deep-learning approaches, computational protein design is advancing rapidly, and it is now possible to generate many de novo protein structures quickly and robustly. This sets new frontiers for the field, including designing proteins that bind small molecules tightly and specifically, and understanding the non-covalent interactions that underpin such designs to make binding predictable and tunable. Here we address these challenges 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-organized binding sites confirmed by X-ray crystallography, which bind the targets selectively with micromolar affinities or better. To illustrate the scope and applications of this approach, we incorporate distinct 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 visualize sub-cellular structures.

biophysics↗

bioRxiv2024

Confinement and Catalysis Within De Novo Designed Peptide Barrels

De novo protein design has advanced such that many peptide assemblies and protein structures can be generated predictably and quickly. The drive now is to bring functions to these structures, for example, small-molecule binding and catalysis. The formidable challenge of binding and orienting multiple small molecules to direct chemistry is particularly important for paving the way to new functionalities. To address this, here we describe the design, characterization, and application of small-molecule:peptide ternary complexes in aqueous solution. This uses -helical barrel (HB) peptide assemblies, which comprise 5 or more -helices arranged around central channels. These channels are solvent accessible, and their internal dimensions and chemistries can be altered predictably. Thus, HBs are analogous to molecular flasks made in supramolecular, polymer, and materials chemistry. Using Forster resonance energy transfer as a readout, we demonstrate that specific HBs can accept two different organic dyes, 1,6-diphenyl-1,3,5-hexatriene and Nile Red in close proximity. In addition, two anthracene molecules can be accommodated within an HB to promote photocatalytic anthracene-dimer formation. However, not all ternary complexes are productive, either in energy transfer or photocatalysis, illustrating the control that can be exerted by judicious choice and design of the HB.

synthetic biology↗

bioRxiv2023

Rationally seeded computational protein design

Computational protein design is advancing rapidly. Here we describe efficient routes to two families of -helical-barrel proteins with central channels that bind small molecules. The designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides. Adjacent helices are connected using computational loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels computationally. Throughout these pipelines, residues that define open states of the barrels are maintained. This minimises sequence sampling and accelerates routes to successful designs. For each of 6 targets, just 2 - 6 synthetic genes are made for expression in E. coli. On average, 80% express to give soluble monomeric proteins that are characterized fully, including high-resolution structures for most targets that match the seed structures and design models with high accuracy.

synthetic biology↗

bioRxiv2023

CC + : A Searchable Database of Validated Coiled coils in PDB Structures and AlphaFold2 Models

-Helical coiled coils are common tertiary and quaternary elements of protein structure. In coiled coils, two or more helices wrapped around each other to form bundles. This apparently simple structural motif can generate many architectures and topologies. Understanding the variety of and limits on coiled-coil assemblies and their sequence-to-structure relationships impacts on protein structure, design, and engineering. Coiled coil-forming sequences can be predicted from heptad repeats of hydrophobic and polar residues, hpphppp, although this is not always reliable. Alternatively, coiled-coil structures can be identified using the program SOCKET, which finds knobs-into-holes (KIH) packing between side chains of neighboring helices. SOCKET also classifies coiled-coil architecture and topology, thus allowing sequence-to-structure relationships to be garnered. In 2009, we used SOCKET to create a relational database of coiled-coil structures, CC+, from the RCSB Protein Data Bank (PDB). Here we report an update of CC+ following the recent explosion of structural data and the success of AlphaFold2 in predicting protein structures from genome sequences. With the most-stringent SOCKET parameters, CC+ contains {approx}12,000 coiled-coil assemblies from experimentally determined structures, and {approx}120,000 potential coiled-coil structures within single-chain models predicted by AlphaFold2 across 48 proteomes. CC+ allows these and other less-stringently defined coiled coils to be searched at various levels of structure, sequence, and side-chain interactions. The identified coiled coils can be viewed directly from CC+ using the Socket2 application, and their associated data can be downloaded for further analyses. CC+ is available freely at http://coiledcoils.chm.bris.ac.uk/CCPlus/Home.html. It will be regularly updated automatically. FOR THE BROADER AUDIENCEProtein assemblies and protein-protein interactions are key to all biological processes. -Helical coiled coils are one of the most common modes of directing and stabilising these interfaces. Here, we report an updated CC+ database of structurally validated coiled coils from experimental protein structures and AlphaFold2 models. CC+ contains many thousands of coiled-coil structures and models, associated parameters, and sequences. It enables the compilation of rich datasets for advancing protein structure, design, and engineering research.

bioinformatics↗