Nitrogenase is the only enzyme capable of biological nitrogen fixation and a major target for sustainable agricultural engineering, yet its functional and structural complexity has made it difficult to identify regions that can be modified without compromising activity or assembly. Here, we use structural evolution to identify a lineage-specific N-terminal extension in NifK as a candidate engineering target within the MoFe protein interface. By generating variant libraries exceeding 9,000 members and evaluating them through deep mutational scanning, diazotrophic growth assays and in vitro characterization to map the sequence-function landscape of this interface across variable conditions. We show that NifK extension is required for nitrogenase activity while remaining broadly tolerant to mutation, revealing a sequence-function landscape that is both flexible and constrained. A subset of residues at the NifD-NifK interface are critical for maintaining complex stability. Structural analyses indicate that the extension stabilizes the MoFe complex via co-evolved electrostatic interactions, and targeted mutations can improve both enzymatic activity and thermostability. These findings identify the NifK extension as a tunable interface, providing a strategy for engineering more robust nitrogenases.

Recently, high-throughput experimental techniques have propelled improvements in deep learning-based prediction of mutation effects on protein stability. However, leading stability predictors still struggle to predict the combined effect of multiple mutations and prefer mutations that negatively impact other properties, including expressibility. To mitigate these limitations, we apply Low-Rank Adaptation (LoRA) to specialize ESM3 for stability prediction by fine-tuning on the Megascale protease susceptibility dataset, developing a novel dual-perspective inference mechanism to provide explicit mutant context information. ESM-Mutant Stability Ranker (ESM-MSR) significantly exceeds all contemporary methods tested on the prioritization of stabilizing mutations (ΔNDCG@96 >= +0.12), double mutant ranking (Δρavg >= +0.068) and direct epistasis ranking (Δρavg >= +0.164) within the Megascale test set. Further, it generalizes effectively to heterogeneous thermostability benchmarks, consistently matching or exceeding current approaches across our comprehensive suite. Finally, a single parameter σ enables tunable control of the model′s compromise between stability and more general sequence fitness, leading to state-of-the-art performance in the Human Domainome 1 benchmark (Δρavg = 0.573) at σ = 0.5, demonstrating the broad applicability of ESM-MSR as a protein engineering tool.

Signaling-based anti-bacteriophage systems such as CBASS and Thoeris synthesize infection-triggered nucleotide signals that activate antiphage effectors. However, the phage features sensed by these systems and the mechanisms phages use to evade signaling immunity remain poorly understood. Here, studying clinically relevant Pseudomonas aeruginosa phages from the Migulavirinae family, we show that closely related phages encode subtle allelic variation in side tail fiber proteins that determine sensitivity to type II Thoeris. In parallel, these same phages encode an anti-defense hotspot that contains three adjacent genes that are each sufficient to facilitate phage evasion of both CBASS and Thoeris defenses, counter-balancing the activating proteins. Comparative analysis of this anti-signaling hotspot across the broader family of related N4-like phages uncovered a new Thoeris anti-defense (Tad) protein that sponges NAD-derived molecules (e.g. gcADPR) and exhibits sequence and structural similarity to a poorly characterized nucleotide-binding region of the human ryanodine receptor. Together, these findings reveal how the balance between immune activation and antagonism shifts phage outcomes and reveals a surprising similarity between a phage molecular sponge and an important human protein.

Predicting protein-ligand complex structures is a central challenge in drug discovery. While recent co-folding models such as AlphaFold-3 achieve accurate structure prediction, they fail to generalize to underexplored binding interfaces - systematically misplacing ligands, particularly for allosteric or structurally novel targets. To address this gap, we present ACER (A daptive Co-folding via pocket E xploration and pose R anking), a training-free framework that (a) enables co-folding models to systematically explore alternative binding pockets, and (b) leverages the discovered pockets to increase pose accuracy. Our method enables the efficient discovery of non-prevalent pockets without prior expert knowledge. ACER improves pocket discovery and pose accuracy on allosteric targets and structurally novel complexes, successfully modeling binding interfaces that are under-represented or absent from the training set. Our results demonstrate how improved sampling dynamics enhance the generalizability of co-folding models without retraining.

The widespread emergence of antibiotic-resistant pathogens poses a significant global health challenge and underscores the need for novel approaches to accelerate antimicrobial discovery. Antimicrobial peptides (AMPs) have gained attention as promising candidates due to their broad-spectrum activity, including efficacy against multidrug-resistant bacterial strains. SAJO-2, an antimicrobial peptide developed by Sarojini and colleagues, features a tryptophan zipper-like motif incorporating a central d-Phe-2-Abz unit, where 2-Abz functions as a conformationally constrained β-turn-inducing peptidomimetic scaffold. Modification of SAJO-2 in prior joint work from our groups through differential fluorination enhanced its antimicrobial potency; however, it also increased susceptibility to enzymatic digestion by β-trypsin. To address this limitation, the current research focuses on improving the overall efficacy of SAJO-2 through the incorporation of D-amino acids, beta backbone modifications, and a bulky pentafluorinated amino acid residue. All modified peptides exhibit resistance to enzymatic degradation, while antimicrobial activity was retained to differing degrees across organisms.

Utilizing methane and carbon dioxide before it can enter the upper atmosphere is beneficial for mitigating climate change as well as for producing valuable chemicals. Because anaerobic methanotrophic archaea (ANME) have not yet been cultured in isolation, we previously reversed methanogenesis by cloning the genes encoding methyl-coenzyme M reductase (Mcr) derived from Black Sea ANME-1 into the methanogen Methanosarcina acetivorans. The resulting engineered archaeal strain captures, rather than produces, methane and may be used to convert methane and carbon dioxide into electricity, acetate, L-lactate, and ethanol. However, the engineered M. acetivorans strain also contains a chromosomal locus encoding its native Mcr (McrM.a.), which produces methane from substrates such as methanol, whereas the heterologously expressed ANME-1 Mcr (McrANME-1) promotes methane oxidation. Therefore, we reasoned that McrM.a. may compete with McrANME-1-mediated reversal of methanogenesis. To enhance the reversal of methanogenesis, here we implemented an antisense RNA (asRNA) silencing approach to suppress McrM.a. during growth on methane while still allowing its expression during routine growth on methanol. We found that silencing McrM.a. during McrANME-1-mediated growth on methane increased ethanol and acetate production by more than an order of magnitude. These results were corroborated by both a more than 10-fold increase in methane utilization by McrANME-1 and a greater than 1,000-fold reduction in the McrM.a. mcrBGA transcript levels under methane-grown conditions. Therefore, asRNA-mediated silencing may be used to enhance methane capture by suppressing production of the host McrM.a. for biotechnological applications.

Three different cultures of cyanobacteria: Synechocystis sp., Leptolyngbya sp., and a co-culture of both Synechocystis sp. and Synechococcus sp. were tested for their polyhydroxybutyrate (PHB) production potential under diverse conditions. High-throughput screening in a multi-well plate, using Nile blue fluorescence to estimate PHB accumulation, enabled simultaneous testing of multiple parameters without complex setups and faster PHB estimation compared to conventional gas chromatography. Fluorescence results indicated that the best conditions for PHB accumulation were nitrogen depletion under darkness at 30°C with acetate supplementation after 4 days of glycogen accumulation. Finally, these optimal conditions were validated in single-batch proof-of-concept photobioreactors (1 L working volume). The scale-up proved successful, yielding 7% PHB dry cell weight (dcw) for Synechocystis sp. and a promising 13% PHB dcw for Leptolyngbya sp. In addition, scale-up experiments in 1-L photobioreactors demonstrated that a dedicated glycogen pre-accumulation step is unnecessary, as PHB can be efficiently synthesized directly from acetate.

Indigoidine is a blue pigment biosynthesized by a single-module Non-Ribosomal Peptide Synthetase (NRPS) using L-glutamine as substrate. Despite its potential as a colorimetric reporter, no such system has been established from it to date. We used a recently characterized interdomain fusion site located between its adenylation (A) and thiolation (T) domains to develop the Indi2GO system, which provides a naked-eye detectable and quantitative optical readout of transient and covalent protein-protein-interaction (PPI) in living cells. Indi2GO enables high-throughput benchmarking and optimization of PPI tools in a standard 96-well plate reader format, without requiring exogenous substrates, specialized equipment or complex analytical workflows. We demonstrate its broad applicability with three widely used protein-protein interaction tools: SYNZIPS, inteins, and the SpyTag:SpyCatcher system. We used Indi2GO to validate novel SYNZIP pairs, which we used in NRPS engineering, highlighting its applicability for the development of novel PPI-mediating tools in the context of NRPS engineering and synthetic biology.