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The endogenous inhibitor of ATP synthase in mitochondria, called IF₁, conserves cellular energy when the proton-motive force collapses by inhibiting ATP hydrolysis. Around neutrality, the 84-amino-acid bovine IF₁ is thought to self-assemble into active dimers and, under alkaline conditions, into inactive tetramers and higher oligomers. Dimerization is mediated by formation of an antiparallel α-helical coiled-coil involving residues 44–84. The inhibitory region of each monomer from residues 1–46 is largely α-helical in crystals, but disordered in solution. The formation of the inhibited enzyme complex requires the hydrolysis of two ATP molecules, and in the complex the disordered region from residues 8–13 is extended and is followed by an α-helix from residues 14–18 and a longer α-helix from residue 21, which continues unbroken into the coiled-coil region. From residues 21–46, the long α-helix binds to other α-helices in the C-terminal region of predominantly one of the β-subunits in the most closed of the three catalytic interfaces. The definition of the factors that influence the self-association of IF₁ is a key to understanding the regulation of its inhibitory properties. Therefore, we investigated the influence of pH and salt-types on the self-association of bovine IF₁ and the folding of its unfolded region. We identified the equilibrium between dimers and tetramers as a potential central factor in the in vivo modulation of the inhibitory activity and suggest that the intrinsically disordered region makes its inhibitory potency exquisitely sensitive and responsive to physiological changes that influence the capability of mitochondria to make ATP.

We designed a protein biosensor that uses thermodynamic coupling for sensitive and rapid detection of neutralizing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in serum. The biosensor is a switchable, caged luciferase–receptor-binding domain (RBD) construct that detects serum-antibody interference with the binding of virus RBD to angiotensin-converting enzyme 2 (ACE-2) as a proxy for neutralization. Our coupling approach does not require target modification and can better distinguish sample-to-sample differences in analyte binding affinity and abundance than traditional competition-based assays. SARS-CoV-2 neutralizing antibodies are readily detected with a biosensor.

The design of modular protein logic for regulating protein function at the posttranscriptional level is a challenge for synthetic biology. Here, we describe the design of two-input AND, OR, NAND, NOR, XNOR, and NOT gates built from de novo–designed proteins. These gates regulate the association of arbitrary protein units ranging from split enzymes to transcriptional machinery in vitro, in yeast and in primary human T cells, where they control the expression of the TIM3 gene related to T cell exhaustion. Designed binding interaction cooperativity, confirmed by native mass spectrometry, makes the gates largely insensitive to stoichiometric imbalances in the inputs, and the modularity of the approach enables ready extension to three-input OR, AND, and disjunctive normal form gates. The modularity and cooperativity of the control elements, coupled with the ability to de novo design an essentially unlimited number of protein components, should enable the design of sophisticated posttranslational control logic over a wide range of biological functions.

Transcription Activator-Like (TAL) effectors are DNA-binding proteins secreted by phytopathogenic bacteria that interfere with native cellular functions by binding to plant DNA promoters. The key element of their architecture is a domain of tandem-repeats with almost identical sequences. Most of the polymorphism is located at two consecutive amino acids termed Repeat Variable Diresidue (RVD). The discovery of a direct link between the RVD composition and the targeted nucleotide allowed the design of TAL-derived DNA-binding tools with programmable specificities that revolutionized the field of genome engineering. Despite structural data, the molecular origins of this specificity as well as the recognition mechanism have remained unclear. Molecular simulations of the recent crystal structures suggest that most of the protein-DNA binding energy originates from non-specific interactions between the DNA backbone and non-variable residues, while RVDs contributions are negligible. Based on dynamical and energetic considerations we postulate that, while the first RVD residue promotes helix breaks--allowing folding of TAL as a DNA-wrapping super-helix--the second provides specificity through a negative discrimination of matches. Furthermore, we propose a simple pharmacophore-like model for the rationalization of RVD-DNA interactions and the interpretation of experimental findings concerning shared affinities and binding efficiencies. The explanatory paradigm presented herein provides a better comprehension of this elegant architecture and we hope will allow for improved designs of TAL-derived biotechnological tools.

In pseudocyclic proteins, such as TIM barrels, β barrels, and some helical transmembrane channels, a single subunit is repeated in a cyclic pattern, giving rise to a central cavity that can serve as a pocket for ligand binding or enzymatic activity. Inspired by these proteins, we devised a deep-learning-based approach to broadly exploring the space of closed repeat proteins starting from only a specification of the repeat number and length. Biophysical data for 38 structurally diverse pseudocyclic designs produced in Escherichia coli are consistent with the design models, and the three crystal structures we were able to obtain are very close to the designed structures. Docking studies suggest the diversity of folds and central pockets provide effective starting points for designing small-molecule binders and enzymes. Here, the authors constructed a deep-learning approach to design closed repeat proteins with central binding pockets—a step towards designing proteins to specifically bind small molecules.

Pseudosymmetric hetero-oligomers with three or more unique subunits with overall structural (but not sequence) symmetry play key roles in biology, and systematic approaches for generating such proteins de novo would provide new routes to controlling cell signaling and designing complex protein materials. However, the de novo design of protein hetero-oligomers with three or more distinct chains with nearly identical structures is a challenging unsolved problem because it requires the accurate design of multiple protein-protein interfaces simultaneously. Here, we describe a divide-and-conquer approach that breaks the multiple-interface design challenge into a set of more tractable symmetric single-interface redesign tasks, followed by structural recombination of the validated homo-oligomers into pseudosymmetric hetero-oligomers. Starting from de novo designed circular homo-oligomers composed of 9 or 24 tandemly repeated units, we redesigned the inter-subunit interfaces to generate 19 new homo-oligomers and structurally recombined them to make 24 new hetero-oligomers, including ABC heterotrimers, A2B2 heterotetramers, and A3B3 and A2B2C2 heterohexamers which assemble with high structural specificity. The symmetric homo-oligomers and pseudosymmetric hetero-oligomers generated for each system have identical or nearly identical backbones, and hence are ideal building blocks for generating and functionalizing larger symmetric and pseudosymmetric assemblies. Hetero-oligomeric proteins offer many advantages for bioengineering efforts but are difficult to make from scratch. Here, authors present a simple method for creating pseudosymmetric hetero-oligomers from input symmetrical proteins.

There has been considerable recent progress in designing new proteins using deep-learning methods 1 – 9 . Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models 10 , 11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence–structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications. Fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks yields a generative model for protein design that achieves outstanding performance on a wide range of protein structure and function design challenges.

Intrinsically disordered proteins (IDPs) are characterized by a lack of defined structure. Instead, they populate ensembles of rapidly interconverting conformations with marginal structural stabilities. Changes in solution conditions such as temperature and crowding agents consequently affect IDPs more than their folded counterparts. Here we reveal that the residual structure content of IDPs is modulated both by ionic strength and by the type of ions present in solution. We show that these ion-specific structural changes result in binding affinity shifts of up to sixfold, which happen through alteration of both association and dissociation rates. These effects follow the Hofmeister series, but unlike the well-established effects on the stability of folded proteins, they already occur at low, hypotonic concentrations of salt. We attribute this sensitivity to the marginal stability of IDPs, which could have physiological implications given the role of IDPs in signaling, the asymmetric ion profiles of different cellular compartments, and the role of ions in biology.

Adenosine triphosphate (ATP), the fuel of biology, is produced by a molecular machine with a rotary action. Inside the mitochondria of eukaryotic cells, rotation is driven by a proton-motive force across the inner membranes of the organelle generated by oxidation of sugars and fats in food. Under anoxic conditions, the rotary machine hydrolyzes ATP and reverses rotation. This wastage is prevented by an intrinsically disordered region of the inhibitor protein, IF 1 , which inserts itself in the machine and stops reverse rotation. The inhibitory activity of IF 1 is regulated by self-association, which is influenced by pH and ion-types, providing a potential molecular mechanism for the modulation of ATPase activity by cellular physiology via this solution-responsive, self-associating protein. The endogenous inhibitor of ATP synthase in mitochondria, called IF 1 , conserves cellular energy when the proton-motive force collapses by inhibiting ATP hydrolysis. Around neutrality, the 84-amino-acid bovine IF 1 is thought to self-assemble into active dimers and, under alkaline conditions, into inactive tetramers and higher oligomers. Dimerization is mediated by formation of an antiparallel α-helical coiled-coil involving residues 44–84. The inhibitory region of each monomer from residues 1–46 is largely α-helical in crystals, but disordered in solution. The formation of the inhibited enzyme complex requires the hydrolysis of two ATP molecules, and in the complex the disordered region from residues 8–13 is extended and is followed by an α-helix from residues 14–18 and a longer α-helix from residue 21, which continues unbroken into the coiled-coil region. From residues 21–46, the long α-helix binds to other α-helices in the C-terminal region of predominantly one of the β-subunits in the most closed of the three catalytic interfaces. The definition of the factors that influence the self-association of IF 1 is a key to understanding the regulation of its inhibitory properties. Therefore, we investigated the influence of pH and salt-types on the self-association of bovine IF 1 and the folding of its unfolded region. We identified the equilibrium between dimers and tetramers as a potential central factor in the in vivo modulation of the inhibitory activity and suggest that the intrinsically disordered region makes its inhibitory potency exquisitely sensitive and responsive to physiological changes that influence the capability of mitochondria to make ATP.

Library screening and selection methods can determine the binding activities of individual members of large protein libraries given a physical link between protein and nucleotide sequence, which enables identification of functional molecules by DNA sequencing. However, the solution properties of individual protein molecules cannot be probed using such approaches because they are completely altered by DNA attachment. Mass spectrometry enables parallel evaluation of protein properties amenable to physical fractionation such as solubility and oligomeric state, but current approaches are limited to libraries of 1,000 or fewer proteins. Here, we improved mass spectrometry barcoding by co-synthesizing proteins with barcodes optimized to be highly multiplexable and minimally perturbative, scaling to libraries of >5,000 proteins. We use these barcodes together with mass spectrometry to assay the solution behavior of libraries of -designed monomeric scaffolds, oligomers, binding proteins and nanocages, rapidly identifying design failure modes and successes.