Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers reduced false positive rates in BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing machine-learning tools. We supplemented this with random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable putative BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a major addition to in-silico BGC identification.

Despite recent advances in transgenic animal models and display technologies, humanization of mouse sequences remains one of the main routes for therapeutic antibody development. Traditionally, humanization is manual, laborious, and requires expert knowledge. Although automation efforts are advancing, existing methods are either demonstrated on a small scale or are entirely proprietary. To predict the immunogenicity risk, the human-likeness of sequences can be evaluated using existing humanness scores, but these lack diversity, granularity or interpretability. Meanwhile, immune repertoire sequencing has generated rich antibody libraries such as the Observed Antibody Space (OAS) that offer augmented diversity not yet exploited for antibody engineering. Here we present BioPhi, an open-source platform featuring novel methods for humanization (Sapiens) and humanness evaluation (OASis). Sapiens is a deep learning humanization method trained on the OAS using language modeling. Based on an in silico humanization benchmark of 177 antibodies, Sapiens produced sequences at scale while achieving results comparable to that of human experts. OASis is a granular, interpretable and diverse humanness score based on 9-mer peptide search in the OAS. OASis separated human and non-human sequences with high accuracy, and correlated with clinical immunogenicity. BioPhi thus offers an antibody design interface with automated methods that capture the richness of natural antibody repertoires to produce therapeutics with desired properties and accelerate antibody discovery campaigns. The BioPhi platform is accessible at https://biophi.dichlab.org and https://github.com/Merck/BioPhi.

Long noncoding RNAs (lncRNAs), which are longer than 200 nucleotides but often unstable, contribute a substantial and diverse portion to pervasive noncoding transcriptomes. Most lncRNAs are poorly annotated and understood, although several play important roles in gene regulation and diseases. Here we systematically uncover and analyze lncRNAs in Schizosaccharomyces pombe. Based on RNA-seq data from twelve RNA-processing mutants and nine physiological conditions, we identify 5775 novel lncRNAs, nearly 4× the previously annotated lncRNAs. The expression of most lncRNAs becomes strongly induced under the genetic and physiological perturbations, most notably during late meiosis. Most lncRNAs are cryptic and suppressed by three RNA-processing pathways: the nuclear exosome, cytoplasmic exonuclease, and RNAi. Double-mutant analyses reveal substantial coordination and redundancy among these pathways. We classify lncRNAs by their dominant pathway into cryptic unstable transcripts (CUTs), Xrn1-sensitive unstable transcripts (XUTs), and Dicer-sensitive unstable transcripts (DUTs). XUTs and DUTs are enriched for antisense lncRNAs, while CUTs are often bidirectional and actively translated. The cytoplasmic exonuclease, along with RNAi, dampens the expression of thousands of lncRNAs and mRNAs that become induced during meiosis. Antisense lncRNA expression mostly negatively correlates with sense mRNA expression in the physiological, but not the genetic conditions. Intergenic and bidirectional lncRNAs emerge from nucleosome-depleted regions, upstream of positioned nucleosomes. Our results highlight both similarities and differences to lncRNA regulation in budding yeast. This broad survey of the lncRNA repertoire and characteristics in S. pombe, and the interwoven regulatory pathways that target lncRNAs, provides a rich framework for their further functional analyses.

Significance: The carboxyl-terminal domain (CTD) code encrypted within the YSPTSPS heptad repeats of RNA polymerase II is deeply rooted in eukaryal biology. This paper exploits fission yeast CTD mutants in which single coding cues (or combinations of cues) are subtracted from all consensus heptads to gauge the roles of the Tyr1, Ser2, Thr4, and Ser7 phosphoacceptors in gene expression. Transcriptome profiling revealed distinctive signatures for the CTD phosphoacceptors that were verified by cell-based activity assays. The results highlight ( i ) the role of Tyr1 and Ser2 in repressing the iron uptake regulon and ( ii ) the distinctive roles of Thr4 and Ser7 in the phosphate starvation response, whereby Thr4 facilitates and Ser7 represses the induction of Pho1 acid phosphatase.

Recent COVID-19 vaccines unleashed the potential of mRNA-based therapeutics. A common bottleneck across mRNA-based therapeutic approaches is the rapid design of mRNA sequences that are translationally efficient, long-lived and non-immunogenic. Currently, an accessible software tool to aid in the design of such high-quality mRNA is lacking. Here, we present mRNAid, an open-source platform for therapeutic mRNA optimization, design and visualization that offers a variety of optimization strategies for sequence and structural features, allowing one to customize desired properties into their mRNA sequence. We experimentally demonstrate that transcripts optimized by mRNAid have characteristics comparable with commercially available sequences. To encompass additional aspects of mRNA design, we experimentally show that incorporation of certain uridine analogs and untranslated regions can further enhance stability, boost protein output and mitigate undesired immunogenicity effects. Finally, this study provides a roadmap for rational design of therapeutic mRNA transcripts.