Eukaryotic life spans species that diverged over a billion years ago, yet our molecular view of gene regulation remains heavily skewed toward Opisthokonta model systems, particularly yeast and humans. The degree to which the protein features governing transcriptional regulation are conserved across eukaryotes or have diversified within individual eukaryotic supergroups remains unresolved. To provide a distinct evolutionary reference point, we used a massively parallel reporter assay in plant cells to identify transcriptional effector domains and define their sequence constraints, measuring ~26,000 protein fragments tiling nearly all Arabidopsis transcription factors (1,859 proteins). Comparison with the activity of the same peptide sequences in yeast revealed that activation is governed by a mixture of deeply conserved biochemical features and lineage-dependent functional differences across widely divergent eukaryotic lineages. Likewise, plant repression domains are governed by flexible sequence grammars that extend beyond conserved repression motifs. Together, these results reveal both broadly conserved and clade-specific principles governing eukaryotic transcriptional regulation.

Protein-cofactor selectivity prediction is important for enzyme annotation, metabolic engineering, and biocatalyst discovery, but it remains challenging because experimentally supported associations are limited and unevenly distributed, and because biologically relevant differences can be subtler than those captured by general protein-ligand interaction models. Here, we present MINT (Multimodal Interaction Network with Teacher supervision), a multimodal framework for cofactor specificity prediction. MINT combines protein sequence features with cofactor sequence and graph representations and is trained with a gradual adaptation strategy that transfers useful information from a pretrained protein-ligand interaction model to the cofactor prediction task. On a fixed balanced held-out test set, MINT achieved the best overall performance among all compared methods (AUC = 0.932), and this ranking was reproduced in an independent family-stratified evaluation at the natural positive rate (pooled AUC = 0.939). Further analyses suggest that the pretrained model provides transferable structural information, while the gradual training scheme helps adapt that information to cofactor specificity. In a case study of the chemically near-identical NAD/NADP pair, MINT recovered 95.0-98.6% of true binders and resolved a single-phosphate-level distinction with direct relevance to engineering enzyme cofactor specificity. These results show that gradual transfer learning is an effective approach to cofactor specificity prediction when annotation is limited.

Microbial metagenomes encode vast catalytic diversity, but recovering enzymes of novel, yet-undescribed functionality typically requires whole-community assembly plus a means of identifying active proteins ab initio. The first step of the task is compute-intensive and misses low-abundance sequences. The second is complicated by our inability to predict novel functions. REBEAN, our DNA language model, sidesteps the latter by assigning each sequencing read a high-level Enzyme Commission (EC) class or a non-enzyme label without alignment. Here we build REFinder, a pipeline that addresses both steps by routing REBEAN-annotated reads to assemble only the putative enzymatic reads that have identified catalytic signatures. In our evaluation of 50 microbiome metagenomes, REFinder identified 1.2 to 2.1 fold more enzymes than could be recovered via homology-based annotation of the proteins from the corresponding full assemblies. Moreover, it was as much as 6.4-fold cheaper computationally than full assembly. Across all samples, REFinder identified at least three fourths and as many as 90% of the homology-accessible enzymes identified via full assembly of the complete metagenomes. Notably, a fraction of these, 22% to 45% per EC class, carried no similarity to Swiss-Prot proteins, i.e. a set of enzymes homology cannot annotate. For roughly two fifths of the over thirteen thousand such novel oxidoreductases from two saliva samples, ESMFold predicted structures aligned with TM-score≥0.7 to a characterized enzyme structure in the PDB - a substantial structural similarity without sequence homology. These results illustrate that targeted, alignment-free assembly turns even well-mapped microbiomes into a source of thousands of previously invisible but credible novel enzymes, raising our expectations for exploration of environmental microbiomes.

RNA-based therapeutics increasingly rely on nuclease-resistant xeno-nucleic acids (XNAs), but engineering polymerases capable of reading and amplifying these non-canonical substrates remains a major challenge. Natural DNA polymerases cannot process C2' sugar modifications, and the complete structural determinants governing this limitation remain poorly understood, limiting rational design strategies. In this study, we apply directed evolution methods to Thermus thermophilus DNA polymerase using a microfluidic implementation of the Compartmentalized Self-Replication (CSR). We identify an engineered variant, termed E5, capable of reverse transcribing C2'-modified RNA, a previously inaccessible activity for this enzyme. Next-generation sequencing allow us to track evolutionary trajectories across nine rounds of evolution. Through an integrated analysis combining statistical methods, protein language models, and crystallographic data, we reveal unknown epistatic networks controlling polymerase function. This large-scale experimental dataset (>196,000 thermostable enzyme variants) provides a robust basis for predicting sequence-to-function relationships, accelerating development of enzymes for aptamer discovery and synthetic biology applications.

Bacterial small-molecule messengers that function in diverse immune signaling pathways have been thought to derive strictly from biosynthesis using nucleotide and cofactor substrates. Here we report a nuclease-driven immune signaling pathway in which tRNA cleavage generates a specific small molecule that triggers cell death in response to RNA-guided transcript recognition in prokaryotes. We identify a class of type VI-B CRISPR systems in which the RNA-guided nuclease Cas13 and its accessory transmembrane protein, 2TMβ, are co-encoded in an operon and are jointly required for bacteriophage defense. Upon target RNA recognition, Cas13 activates 2TMβ by producing mono- and dinucleotide messengers bearing 2′,3′-cyclic uridine phosphate ends by tRNA cleavage. Activated 2TMβ destroys infected cells by forming a multimeric complex that disrupts cell membranes to mediate cell death. These findings demonstrate RNA-programmed tRNA cleavage for second-messenger biogenesis, revealing a new logic for prokaryotic immune signaling.

Local alignment tools struggle at querying against thousands of bacterial genomes, because the reference indexes are memory-intensive and base-level dynamic programming is slow. We present alamem (https://github.com/yunwilliamyu/alamem), an approximate local aligner that streams unprocessed reference databases and estimates average nucleotide identity (ANI) from Maximal-Exact-Match (MEM) chaining statistics. Alamem can query a bacterial genome against >85,000 prokaryotic genomes in under 30 seconds (with multithreading) using 4 GB of memory, making it over 16x faster and 21x more memory-efficient than existing local aligners using their default running modes. On top of alamem, we built alasight (https://github.com/graceoualline/alasight) for detecting signatures of horizontal gene transfer (HGT) by finding pairs of alamem hits to divergent genomes. Alasight achieves over an order of magnitude better sensitivity on data from real bovid microbiomes than existing mobile genetic element-detection software and provides depth and sparsity information supporting each putative HGT region.