RMH

Obtaining sufficient quantities of purified proteins in their intended molecular states is essential for biological research and protein engineering. Advances in computational protein design have expanded the functional capabilities of de novo proteins and accelerated candidate generation. However, reliable methods for ranking expected monomeric yield of candidate de novo designs, including molecular binders, fluorescent proteins and enzymes, have yet to be established. Standardized measurements of monomeric yield across diverse sequences remain scarce, limiting our understanding of its determinants and the development of accurate predictors for candidate selection. Here we developed a scalable pooled assay combining peptide barcodes, size-exclusion chromatography and mass spectrometry to quantify monomeric yield for over 39,000 natural protein domain variants, de novo proteins and nanobodies under uniform and standardized E. coli production conditions. We used this dataset to (1) quantify the relationship between folding stability and monomeric yield, (2) benchmark inverse-folding methods, (3) evaluate the utility of structure-prediction confidence metrics in selecting high-yield design candidates, and (4) identify sequence and structural patterns in nanobodies associated with monomeric yield and monomer-to-dimer yield ratios. We further developed ESMC-MonoYield, a protein language model fine-tuned to predict monomeric yield from amino acid sequence alone, and prospectively demonstrated enrichment of high-yield candidates among BindCraft-designed binders. Our approach enables standardized, large-scale measurement of monomeric yield across diverse proteins, and establishes monomeric yield as a sequence-encoded property that can be learned, predicted and ultimately designed.

Hyperuricemia is associated with renal injury and disturbances in gut microbial ecology. Intestinal urate clearance by engineered probiotics is limited by antibiotic-free functional maintenance and the oxygen requirement of urate oxidase (UOX). We reprogrammed the native cryptic plasmids of E. coli Nissle 1917 (EcN) to construct M12U, a marker-free dual-plasmid strain. pMUT2 carried an HucR-regulated urate-responsive UOX module, while pMUT1 encoded a low-oxygen-responsive UOX module and Tat-targeted Vitreoscilla haemoglobin (tat-VHB). M12U degraded urate in vitro and retained activity under low-oxygen conditions. In hyperuricemic mice (n = 5 per group), oral M12U reduced serum urate, creatinine, blood urea nitrogen, inflammatory cytokines and renal histopathological injury. 16S rRNA profiling showed increased microbial richness and a partial shift in community composition toward healthy controls. Shotgun metagenomics revealed enrichment of Lactobacillus johnsonii and Bacteroides acidifaciens, together with increased metabolic potential related to the pentose phosphate pathway and glutathione metabolism. Faecal metabolomics showed reduced uric acid abundance and increased thiamine monophosphate and argininosuccinic acid. These results support further evaluation of dual-native-plasmid architectures for gut-directed urate clearance.

Protein language models (pLMs) learn rich sequence semantics from vast volume of protein sequences, yet the information encoded in their high-dimensional embeddings remains largely a black box, posing an interpretability challenge. Here we show that semantic features learned by pLMs can be decoded to reveal biologically meaningful signals using coevolutionary analysis without downstream supervised-training. Building on this insight, we developed MaskNGly, a zero-shot framework that identifies protein N‑glycosylation sites directly from PLM-embeddings while achieving performance comparable to state-of-the-art supervised predictors. MaskNGly is computationally efficient and intrinsically transferable, enabling omics-scale characterization of protein glycosylation landscape. Applied across proteomes, it reveals that canonical N-X-S/T motifs gain higher glycosylation propensities with increasing organismal complexity and that glycosylation preferentially occurs at structured, solvent‑exposed regions. Mutations altering high-scoring motifs are more frequently disease-associated. The framework also captures signatures of glycosylation‑mediated protein allostery. These results establish an interpretable and scalable route to study glycobiology from pLM-embeddings.

Searching large databases of predicted protein structures can reveal remote homologs, but reliable discovery requires accurate significance estimates. We show that native E-values reported by protein structure search tools such as Foldseek and Reseek can be unreliable, leading to false discoveries or missed homologs. To address this problem, we introduce Struct-Caliper, which provides reliable query-specific p-values for protein structure search. The method uses representation-aware query randomization to generate background queries and compares observed alignment scores with the highest scores from randomized searches of the same database. In simulations, SG p-values generally kept reported false positive at or below the chosen cutoff under the evaluated randomization procedures. In searches of 15,177 ASTRAL40 structures, SG p-values increased Foldseek homolog recovery by 18.2% with slightly fewer false discoveries compared with native E-values at a cutoff of 0.01. For Reseek, false discoveries even decreased by 99.5%. Across 73 queries searched against more than two million AlphaFoldDB structures, SG p-values increased the mean recovery of DALI-supported matches by 23.5% at the same cutoff. These results show that Struct-Caliper can improve the reliability of structure searches and help prioritize discoveries for further biological analysis.

Agrobacteria can deliver transfer DNA (T-DNA) into plant cells, causing tumorous growth. This process, agrobacteria-mediated transformation (AMT), underlies plant genetic engineering and has been regarded as an evolutionary innovation specific to these pathogens. By screening 63,759 alphaproteobacterial genomes for T-DNA border sequences, we identify mini-T-DNAs in diverse rhizobial genera. Each typically encodes a single cargo, a RolB/RolC family protein or a distant rhizobial homologue. We show that native rhizobia-mediated transformation (RMT) systems deliver these mini-T-DNAs into the root nodules of common bean, pea, and chickpea, where the cargo genes are expressed, with no signal in adjacent root tissue. RMT systems share VirG-dependent activation of the core virulence regulon but respond to distinct upstream signals. Our genome screen identifies many additional candidate T-DNA transfer systems, and phylogenetic analysis places their genes into two of the many type IV secretion system (T4SS) clades. RMT systems occur in both clades, together with other systems unrelated to T-DNA transfer, and typically have an ancestral-like T4SS configuration. In contrast, AMT systems occupy a single subclade, consistent with later acquisition of their T4SS module. AMT and RMT thus represent two specialized outcomes of an older, more widespread capability for bacteria-to-plant DNA transfer.

Circadian clocks allow organisms to anticipate predictable environmental changes across the day-night cycle. Here we show that the bacterium E. coli exhibits intrinsic timekeeping that anticipates daily temperature cycles. E. coli biofilms entrained to daily temperature shifts, producing 24-hour rhythms in curli plaque formation that persisted under constant temperature. Mechanistically, genetic analyses localized the autonomous timing machinery to the Rcs phosphorelay, which exhibits structural and conceptual parallels with other circadian systems. The BluFR sensory pathway, which acts upstream of the Rcs phosphorelay, linked temperature and blue-light cycles to rhythmic curli production. Flagellar promoter activity and curli production peaked at opposite times of day, suggesting daily coordination of adhesion and motility programs. These findings identify an intrinsic timing system in E. coli that expands our understanding of biological timekeeping.

Long-read amplicon sequencing profiles communities at high resolution, but taxonomic accuracy depends on the reference, classifier and the parameters used. Here, we present HiFiTaxa, a reproducible, single command Nextflow pipeline for PacBio HiFi 16S rRNA gene and fungal ITS amplicons. It builds versioned references from current GTDB and UNITE releases and supports Bayesian lowest common ancestor (BLCA) and naive Bayes (NB) classifiers and the read level profilers Emu (16S rRNA gene) and EMITS (ITS). We benchmarked them on mock communities and on holdouts with 10% of GTDB or UNITE databases sequences withheld. On the 16S rRNA gene mock of 20 species, BLCA detected all 20 species and reported two false positives, NB detected 17 and reported none, and Emu detected all 20 and reported eight. In the GTDB holdout, for taxa represented in the reference, BLCA reached 80.0% species level accuracy against 48.1% for the exact matching species step of NB. However, NB produced fewer incorrect assignments for taxa absent from the reference. On fungal ITS, BLCA had slightly higher holdout accuracy at species level(54.7%) against 52.2% for NB. NB was more conservative for novel taxa, and EMITS detected the most mock species. HiFiTaxa therefore offers a single reproducible workflow that uses current GTDB and UNITE releases and enables users to choose between different classifier algorithms depending on their needs.

The methanotroph Methylococcus capsulatus encodes the carbon dioxide (CO2)-fixing enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO), which, along with CO2 and methane (CH4), are required for growth of this bacterium. This unique, dual one-carbon metabolism is present in phylogenetically diverse methanotrophs and is likely widespread in nature, but the connection between organic and inorganic carbon metabolism in these methanotrophs is unclear. Here we used a systems approach to investigate the requisite role of CO2 in M. capsulatus metabolism and physiology. We show that atmospheric CO2 levels are sufficient for M. capsulatus cultivation, and that CO2 assimilation is linked to cellular redox balance. In the absence of CO2, M. capsulatus elicits a global transcriptional response that includes induction of RubisCO- and other inorganic carbon assimilation-encoding genes with a concomitant decrease in ribosome biogenesis and nitrogen assimilation genes typical of a bacterial stringent response. This CO2 starvation response balances biosynthesis with carbon availability. Genetic and biochemical evidence show that a LysR-type transcriptional regulator (CbbR) activates RubisCO and other CO2 assimilation gene transcription via direct promoter binding in response to elevated intracellular RubisCO substrate, ribulose-1,5-bisphosphate, in CO2-starved cells. These results offer insights into the essentiality of CO2 for M. capsulatus metabolism and establish a function for CbbR-family regulators in methanotrophs beyond their canonical association with CO2 fixation in autotrophic bacteria.

Accurate and reproducible metagenomic analysis is essential for effective infectious disease surveillance. Targeted diagnostics such as culturing, antigen assays, and multiplex PCR panels identify only predefined organisms and can fail to identify atypical or novel infections. Shotgun metagenomics offers hypothesis-free detection, but adoption is limited by workflow complexity, dependence on mutable reference databases, and high computational cost. To combat these challenges, we introduce MicrobeRT, an integrated suite of genomic language models fine-tuned to classify metagenomic sequencing reads across three tasks: (i) species level taxonomic identification, (ii) antibiotic resistance gene detection, and (iii) pathogenicity assessment. The framework builds on three pre-trained genomic language models - Nucleotide Transformer (Dalla-Torre et al 2025), DNABERT (Ji et al 2021, Zhou et al 2024), and HyenaDNA (Nguyen et al 2024) - leveraging their large-scale genomic pre-training, adaptability to downstream tasks, and, in the case of HyenaDNA, ability to capture long-range sequence dependencies. The models are fine-tuned on large, curated datasets spanning microbial, viral, fungal, and human genomes, including more than 500,000 genomes and 250,000 antibiotic resistance gene sequences. To evaluate the performance of our fine-tuned genomic language models across classification tasks, we developed a benchmarking framework that compares MicrobeRT against state-of-the-art (SOTA) bioinformatics tools. Across both simulated and empirical datasets, the models achieve strong performance while reducing computational overhead. MicrobeRT is released as open-source software, enabling further fine-tuning and reproducible integration of alternative models and classifiers. We also demonstrate seamless, drop-in integration by incorporating our fine-tuned taxonomic classification models as an optional add-on within the open-source TaxTriage pipeline (Merritt et al 2026). MicrobeRT demonstrates the feasibility of three clinically relevant multi-label analyses, offering a practical path to scalable, reproducible metagenomic diagnostics and enabling broader deployment of AI/ML-enhanced infectious disease testing.

AlphaFold 3 (AF3) models RNA and protein-RNA complexes, but when its predictions and confidence metrics can be trusted is unclear. We evaluated AF3 on more than 1,300 protein-RNA complexes and about 270 RNA-only systems, and compared post-cutoff RNAs with pre-cutoff candidates selected by sequence and fold similarity. On structurally novel RNAs, AF3 largely preserves the protein fold, while RNA-fold and interface accuracy drop sharply with little change in predicted RNA confidence score. Input ablations show that protein alignments and templates from RNA-bound proteins govern complex assembly, whereas RNA alignments matter most for single-chain and long RNAs. The most frequent failed interface retains protein-side contacts but places an often-misfolded RNA in the wrong pose. RNA interface confidence only partly distinguishes this error from correct placements. Against CLIP-seq, SELEX and RNAcompete motifs, AF3 usually places the protein against the experimentally observed motif-bearing RNA segment, even where the interface fails, but often not in the native pose. In protein-RNA complexes released in 2026, a score combining AF3 confidence with RNA familiarity and template use ranked RNA chains with accurate folds and correctly placed interfaces better than AF3's ranking score.

Despite growing demand for biocompatible materials that regulate ice crystallization in cryopreservation, antifreeze peptide discovery remains constrained by limited candidate sequence diversity and reliance on known protein scaffolds. Here, we establish an AI-guided discovery framework integrating multimodal antifreeze protein prediction, generative sequence expansion and local sequence-pattern analysis. AFPFusionLM combines protein sequence and structure-aware representations, achieving an AUROC of 0.937 and an AUPRC of 0.957 on an independent reference set. Antifreeze-domain-adapted protein sequence generation and multimodel consensus screening of UniProt, glacier metagenomes and generated sequences yielded 87,305 candidate protein records. Large-scale sequence analysis revealed recurring periodic sequence architectures across multiple candidate protein clusters, enabling the identification of short-peptide families with shared repeat features and their natural variants. Experimental evaluation revealed ice recrystallization inhibition activity in multiple peptides from repeat-associated families, with inhibitory effects comparable to or greater than those of reference peptides reported in the literature. Cell-based experiments using a representative peptide-containing formulation also supported its cryoprotective potential under reduced-DMSO conditions. These findings show that large-scale sequence exploration can reveal function-associated peptide sequence architectures, providing a research framework for the systematic discovery and subsequent optimization of antifreeze peptides.

The adenosine triphosphate (ATP)-regulated GroEL/ES chaperonin of E. coli promotes protein folding by transiently encapsulating unfolded substrate protein in a nano-cage formed by the cylindrical GroEL and the lid-shaped GroES. How this confinement modulates the folding energy landscape, allowing accelerated protein folding unimpaired by aggregation, remains incompletely understood. Prompted by recent observations suggesting that the chaperonin cavity may favor unfolding, here we reinvestigated the effect of encapsulation on protein thermodynamic stability. To allow thermal unfolding of an encapsulated substrate protein, we generated a destabilized variant of maltose-binding protein (MBP) by introducing four mutations, quadruple mutant-MBP (QM-MBP), lowering the melting temperature (Tm) from 63.6 °C to 44.3 °C. GroEL/ES accelerated the slow spontaneous folding of QM-MBP ~20-fold. The protein reached a native-like structure while encapsulated inside GroEL:ES, as demonstrated using hydrogen/deuterium exchange and single molecule fluorescence resonance energy transfer analyses in presence and absence of the ligand maltose. Importantly, confinement in the thermally stable chaperonin shifted the cooperative unfolding transition of QM-MBP to a Tm of 52.4 °C, corresponding to a stabilization by 4.6 kcal mol−1. In-cage thermal unfolding was reversible, indicating that the environment of the chaperonin cavity is conducive to folding. These findings suggest that a thermodynamically stabilizing effect of the GroEL:ES cage contributes to promoting protein folding.