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Nicotiana benthamiana is increasingly used for transient gene expression to produce antibodies, vaccines, and other pharmaceutical proteins but transient gene expression is low in fully developed, 6–8‐week old plants. This low gene expression is thought to be caused by the perception of the cold shock protein (CSP) of Agrobacterium tumefaciens . The CSP receptor is contested because both Nb CSPR and Nb CORE have been claimed to perceive CSP. Here, we demonstrate that CSP perception is abolished in 6‐week‐old plants silenced for Nb CORE but not Nb CSPR. Importantly, older Nb CORE‐silenced plants support a highly increased level of GFP fluorescence and protein upon agroinfiltration. The drastic increase in transient protein production in Nb CORE‐depleted plants offers new opportunities for molecular farming, where older plants with larger biomass can now be used for efficient protein expression.

Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree. A comprehensive microbial resource reconciles genomic and 16S rRNA data in a single tree.

As structure prediction methods are generating millions of publicly available protein structures, searching these databases is becoming a bottleneck. Foldseek aligns the structure of a query protein against a database by describing tertiary amino acid interactions within proteins as sequences over a structural alphabet. Foldseek decreases computation times by four to five orders of magnitude with 86%, 88% and 133% of the sensitivities of Dali, TM-align and CE, respectively. Foldseek speeds up protein structural search by four to five orders of magnitude.

ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 40−60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com . ColabFold is a free and accessible platform for protein folding that provides accelerated prediction of protein structures and complexes using AlphaFold2 or RoseTTAFold.

Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs. We introduce SignalP 6.0, a machine learning model that detects all five SP types and is applicable to metagenomic data. A new version of SignalP predicts all types of signal peptides.

Routine haplotype-resolved genome assembly from single samples remains an unresolved problem. Here we describe an algorithm that combines PacBio HiFi reads and Hi-C chromatin interaction data to produce a haplotype-resolved assembly without the sequencing of parents. Applied to human and other vertebrate samples, our algorithm consistently outperforms existing single-sample assembly pipelines and generates assemblies of similar quality to the best pedigree-based assemblies. Haplotype-resolved genome assemblies are generated by combining HiFi reads with Hi-C long-range interactions.

Computational methods that model how gene expression of a cell is influenced by interacting cells are lacking. We present NicheNet ( https://github.com/saeyslab/nichenetr ), a method that predicts ligand–target links between interacting cells by combining their expression data with prior knowledge on signaling and gene regulatory networks. We applied NicheNet to tumor and immune cell microenvironment data and demonstrate that NicheNet can infer active ligands and their gene regulatory effects on interacting cells. NicheNet uses expression data, in combination with a previous model built on known signaling and gene regulatory networks, to predict ligand–target links in cell-to-cell communications.

We are at the beginning of a genomic revolution in which all known species are planned to be sequenced. Accessing such data for comparative analyses is crucial in this new age of data-driven biology. Here, we introduce an improved version of DIAMOND that greatly exceeds previous search performances and harnesses supercomputing to perform tree-of-life scale protein alignments in hours, while matching the sensitivity of the gold standard BLASTP.An updated version of DIAMOND uses improved algorithmic procedures and a customized high-performance computing framework to make seemingly prohibitive large-scale protein sequence alignments feasible.

Here we developed an adenine transversion base editor, AYBE, for A-to-C and A-to-T transversion editing in mammalian cells by fusing an adenine base editor (ABE) with hypoxanthine excision protein N-methylpurine DNA glycosylase (MPG). We also engineered AYBE variants enabling targeted editing at genomic loci with higher transversion editing activity (up to 72% for A-to-C or A-to-T editing). A base editor is engineered for A-to-T and A-to-C transversion editing.