High-throughput screening of protein domains enables the systematic discovery of protein sequences that encode specific cellular functions. Fluorescence-activated cell sorting-based assays have long been the standard readout for such screens but remain time- and resource-intensive, imposing practical limits on library size and coverage. Here we describe a scalable magnetic separation-based workflow that provides an alternative to FACS for screening large protein libraries in mammalian cells. We engineered a modular synthetic surface marker, consisting of a fusion between the fragment crystallizable (Fc) region of human immunoglobulin G and the transmembrane domain of platelet-derived growth factor receptor-β, that allows cells to be magnetically separated on the basis of surface reporter expression using Protein G-coated magnetic beads. The procedure covers pooled library cloning, lentiviral delivery, magnetic separation and sequencing-based quantification, enabling reproducible screening of more than 100,000 protein domain variants. The approach is suitable for the identification of functional protein domains capable of transcriptional and post-transcriptional RNA regulation and may lead to the selection of improved transmembrane domains for efficient protein surface display. The entire workflow, from library design to data analysis, can be completed in 4–6 weeks and requires skills in cell culture, molecular cloning and computational techniques. This scalable and accessible Protocol enables researchers to systematically measure protein domain functions across biological contexts, thus accelerating both biological discovery and protein engineering. This Protocol presents a scalable magnetic separation-based method for screening large protein domain libraries in mammalian cells, providing a simple and quantitative alternative to fluorescence-activated cell sorting for functional protein discovery.

The high capsular diversity restricting the host range of many Klebsiella phages has driven the evolution of branched receptor-binding protein (RBP) systems as a strategy for host range expansion. These dual RBP systems offer a unique opportunity for modular engineering. However, most previous approaches lacked a standardized and systematic framework to exploit an engineering platform that enables efficient and modular reprogramming of Klebsiella podophages with dual RBP systems. The workflow integrates (I) the VersaTile technique for rapid assembly of chimeric RBP gene clusters, (II) Gibson assembly for in vitro genome construction, and (III) electroporation-based rebooting. By retaining one native RBP, the system ensures that a suitable host is always available for rebooting, reducing technical failure, and allowing a clear distinction between biological incompatibility and assembly issues. Using this approach, we systematically swapped full-length RBPs and receptor-binding domains (RBDs) at both positions of the dual RBP system in podophages K11 and KP32, confirming the modularity and interchangeability of structural and enzymatic domains. Advanced designs, including cross-swapping and position swapping, were successfully implemented, while attempts to graft phylogenetically distant RBPs revealed structural constraints that inform future design strategies. This work introduces a standardized, scalable, and plug-and-play framework for phage engineering that leverages the modularity of dual RBP systems. By ensuring rebooting through an unmodified RBP, the platform provides a robust foundation for systematic host range reprogramming and functional studies of RBP architecture, paving the way for rational design of therapeutic phages.

Emergent microbial community function drives host memory. Conceptual framework illustrating how cognitive enhancement in the honey bee arises from coordinated interactions within gut microbial communities rather than from individual microbial taxa acting independently. (A) A reductionist model in which single microbial taxa are considered primary drivers of cognition. Monocolonization with individual bacterial members fails to reproduce the improvements in odor discrimination learning and short-term memory observed in bees harboring a complete gut microbiota, indicating that isolated taxa are insufficient to fully support cognitive enhancement. (B) Community-level model highlighting how interactions among multiple core bacterial taxa collectively shape host cognition. Cooperative and interdependent microbial interactions generate emergent properties that cannot be inferred from individual species alone, consistent with a systems-level “microbial hive mind” framework. (C) Multidimensional view of the microbiota–gut–brain axis in the honey bee. Distributed microbial metabolism within the gut community produces neuroactive metabolites and signaling molecules that influence neural and immune pathways linked to cognitive function. These integrated microbial activities ultimately affect brain regions involved in learning and memory, including the mushroom bodies, leading to enhanced odor discrimination and short-term memory performance. Together, the model emphasizes that community composition determines metabolic output, which in turn shapes cognitive performance through emergent microbiota–brain interactions.

Fatty acid esters are building blocks in next-generation biofuels, eco-friendly detergents, crop-enhancing adjuvants, and high-value cosmetic emollients. However, their industrial production relies on two unsustainable approaches: petrochemical-derived chemical catalysis that poses sustainability challenges and botanical extraction processes that exacerbates land-use conflicts. Here we develop a de novo microbial biosynthesis platform using the non-conventional oleaginous yeast Rhodotorula toruloides as a chassis, and achieve high-level biosynthesis of structurally diversified esters without the addition of alcohol or lipid precursors. By screening specific pathway enzymes and conducting modular pathway engineering, we successfully reprogram the native lipogenic metabolism of R. toruloides for de novo synthesis of fatty acid ethyl esters (FAEEs) at 579 mg/L, fatty acid short-branched chain esters (FASBEs) at 169 mg/L, and wax esters (WEs) at 1.30 g/L in shake-flask fermentation. As a case study, we optimize the synthesis of WEs in a 5 L fermenter, and achieve a production of 13.04 g/L. These engineered strains potentially offer an efficient, economical and environmentally friendly platform for the industrial production of fatty acid esters and oleochemicals. Fatty acid esters are biofuels, eco-friendly detergents, and cosmetic emollients, however, their industrial production is unsustainable. Here the authors develop a de novo biosynthesis platform using the yeast Rhodotorula toruloides and achieve high-level production of structurally diverse esters.

Prime editing has not been established in filamentous fungi, which are major ecological contributors and industrial hosts with vast biosynthetic capacity. Here we develop fPE7max, a prime editing platform optimized for fungi, which supports different edit types, including base substitutions and defined small insertions or deletions, with an average editing efficiency approaching 90%, across diverse genomic loci and species. fPE7max further enables larger insertions of up to 1 kb and deletions of up to 10 kb. We perturb upstream open reading frames in the pleiotropic regulator gene, laeA, to modulate metabolic output across multiple fungal species. Metabolomic profiling reveals activation of previously lowly biosynthetic pathways, leading to the identification of 18 metabolites, including 8, to our knowledge, previously unreported structures, 3 of which with cytotoxic activity. These results establish fPE7max as an efficient platform for genome engineering in filamentous fungi and show upstream open reading frame editing as a strategy for modulating endogenous regulatory networks and accessing the fungal chemical repertoire. fPE7max efficiently installs base substitutions, insertions and deletions to modulate filamentous fungal metabolism.

Protein-protein interactions (PPI) are central to biological processes. Designing small molecules that modulate dysregulated PPIs holds strong promise for targeting undruggable proteins. However, existing structure-based drug design approaches focus on well-defined small-molecule binding pockets and struggle to generalize to large, shallow, and chemically complex PPI interfaces. Here, we introduce Pep2Mol, a diffusion-based generative model for 3D molecule design that targets orthosteric PPI sites by explicitly incorporating binding peptides or proteins as structural guidance, moving beyond conventional pocket-conditioned generation. To enable model development and benchmarking, we curate a large-scale, high-quality dataset of 10,956 experimentally resolved protein complex structure pairs, each pairing an orthosteric competitive ligand with a protein binder at overlapping receptor interfaces. Pep2Mol integrates two SE(3)-equivariant graph neural networks that encode protein-ligand and protein-peptide interactions respectively, and fuses these representations via attention-based conditioning to jointly guide the diffusion trajectory. Extensive evaluations demonstrate that Pep2Mol generates chemically valid ligands with state-of-the-art binding affinities, providing a strong foundation for small-molecule inhibitor design against challenging PPI interfaces.

Metalloproteins are essential to many cellular processes. They use metal ions as cofactors to catalyze reactions, stabilize protein structures, and mediate electron transfer. Identifying their metal-binding sites remains difficult because of the complexity of protein environments and the promiscuous binding of metal ions, and existing computational methods are limited by accuracy and data scarcity. Here we introduce PRIME, a hybrid deep learning framework that combines evolutionary and structural signals to predict metal-binding sites accurately and efficiently. PRIME employs protein language models and pre-trained structure models to extract information from protein sequences and structures, together with a probe generation algorithm that bridges sequence- and structure-based predictions by scanning candidate sites. PRIME outperforms existing methods across diverse metal ions, from abundant zinc and calcium to challenging potassium and sodium. Ablation analysis shows that pretrained structure models improve accuracy. Case studies on AlphaFold2 models further demonstrate PRIME’s potential for high-throughput metalloproteomics. The Authors present PRIME, a deep learning tool that fuses protein sequence and structure to pinpoint metal-binding sites across diverse metal ions, from abundant zinc to rare potassium, offering improvements in speed and accuracy over existing methods.

Despite the groundbreaking advances in deep learning-enabled methods for biomolecular modeling, predicting accurate three-dimensional (3D) structures of RNA remains challenging owing to the highly flexible nature of RNA molecules combined with the limited availability of evolutionary sequences or structural homology. Here we introduce RNAbpFlow, a sequence- and base pair-conditioned SE(3)-equivariant flow-matching model for generating RNA 3D structural ensembles. Leveraging a nucleobase center representation, RNAbpFlow enables end-to-end generation of all-atom RNA structures without the explicit or implicit use of evolutionary information or homologous structural templates. Experimental results show that base-pairing conditioning leads to broadly generalizable performance improvements over current approaches for RNA topology sampling and predictive modeling in large-scale benchmarking. RNAbpFlow generates all-atom RNA conformational ensembles for single-chain RNA monomers without the explicit or implicit use of evolutionary information or homologous structural templates.