Bacterial enhancer-binding proteins (bEBPs) are specialised transcriptional activators. bEBPs are hexameric AAA⁺ ATPases and use ATPase activities to remodel RNA polymerase (RNAP) complexes that contain the major variant sigma factor, σ⁵⁴ to convert the initial closed complex to the transcription competent open complex. Earlier crystal structures of AAA⁺ domains alone have led to proposals of how nucleotide-bound states are sensed and propagated to substrate interactions. Recently, the structure of the AAA⁺ domain of a bEBP bound to RNAP-σ⁵⁴-promoter DNA was revealed. Together with structures of the closed complex, an intermediate state where DNA is partially loaded into the RNAP cleft and the open promoter complex, a mechanistic understanding of how bEBPs use ATP to activate transcription can now be proposed. This review summarises current structural models and the emerging understanding of how this special class of AAA⁺ proteins utilises ATPase activities to allow σ⁵⁴-dependent transcription initiation.

Chlamydia pneumoniae is a Gram-negative bacterium responsible for a number of human respiratory diseases and linked to some chronic inflammatory diseases. The major outer membrane protein (MOMP) of Chlamydia is a conserved immunologically dominant protein located in the outer membrane, which, together with its surface exposure and abundance, has led to MOMP being the main focus for vaccine and antimicrobial studies in recent decades. MOMP has a major role in the chlamydial outer membrane complex through the formation of intermolecular disulphide bonds, although the exact interactions formed are currently unknown. Here, it is proposed that due to the large number of cysteines available for disulphide bonding, interactions occur between cysteine-rich pockets as opposed to individual residues. Such pockets were identified using a MOMP homology model with a supporting low-resolution (~4 Å) crystal structure. The localisation of MOMP in the E. coli membrane was assessed using direct stochastic optical reconstruction microscopy (dSTORM), which showed a decrease in membrane clustering with cysteine-rich regions containing two mutations. These results indicate that disulphide bond formation was not disrupted by single mutants located in the cysteine-dense regions and was instead compensated by neighbouring cysteines within the pocket in support of this cysteine-rich pocket hypothesis.

Directed evolution and machine-learning-assisted evolution have transformed biocatalysis by enabling rapid optimisation of catalytic performance within compatible protein scaffolds. In parallel, advances in deep-learning-driven de novo protein design have expanded accessible protein architectural space, enabling the generation of functional catalysts for increasingly challenging transformations. We herein report a synergistic design-evolution strategy to encode new-to-nature regio- and enantioselective C–H amination reactions using engineered de novo haemproteins to synthesise chiral piperidines, a privileged pharmacophore in drug discovery. By integrating motif scaffolding, computational re-design, and directed evolution, we demonstrate de novo enzyme design can enable challenging, high-energy-barrier enzyme chemistry with programmable regio- and enantioselectivity, high catalytic efficiency, excellent enzyme stability, and scaffold diversity.

Creating highly active enzymes for arbitrary reactions is a transformative goal for the molecular sciences ¹ . Traditional protein engineering ^2,3 is constrained by a reliance on existing natural starting points, which are often challenging to discover. De novo design offers the potential to overcome this by creating enzymes from first principles ⁴ , but has yet to achieve practically relevant catalytic properties. Here we present AlphaProtein Novo (AP Novo), a machine-learning pipeline that demonstrates, for the first time, that de novo enzyme design can outperform natural sequence mining in addressing challenging chemistry. We used AP Novo to design new-to-nature nitrene transferases to synthesise the pharmacophore piperidine with unprecedented product selectivity, and to create enzymes that degrade the environmental toxin DEHP under conditions that denature natural enzymes. We also obtained state-of-the-art catalytic efficiencies on two well-studied model reactions, and through iterative design and analysis found that key enablers of success were mechanism-inspired metrics based on AlphaFold 3 ⁵ predictions and sequence-ensemble-based scoring of designed backbones. Our results show that de novo design has become a powerful complement to natural enzyme diversity for discovering catalysts for a range of applications.