Biology subjects

Durrant, M. G.

Publications and source records attributed to Durrant, M. G..

13 recordsLinked to original sources

bioRxiv2026

Prokaryotic homologs of the major vault protein form engineerable vault nanocapsules

Vaults are the largest ribonucleoprotein particles in eukaryotic cells, built from 78 copies of the major vault protein (MVP), yet their function remains unknown four decades after their discovery. MVP homologs occur in prokaryotes and bacteriophages, but whether they form vaults is unknown. Here, we show that prokaryotic and bacteriophage MVP homologs assemble into vault particles. We surveyed approximately 15 billion proteins, found MVP homologs in 29 bacterial and 4 archaeal phyla, and used size-exclusion chromatography to rapidly screen and isolate vault particles of eight homologs expressed in Escherichia coli. Cryo-electron microscopy structures of the phage homolog L05 at high resolution reveal a shell of 96 MVP chains enclosing a volume ~25% larger than human vaults, among the largest protein shells of defined size outside virus capsids. Prokaryotic MVPs typically carry seven wall repeats and eukaryotic MVPs nine, and adding wall repeats to L05 yields larger vaults. Prokaryotic vaults expand the known diversity of vault architecture and offer a tractable system for studying vault assembly and a size-tunable scaffold for cargo encapsulation.

molecular biology↗

bioRxiv2026

Systematic mapping of the prokaryotic noncoding sequence space with a genomic language model

Prokaryotic genomes encode a vast repertoire of noncoding elements such as ribozymes, riboswitches, and CRISPR RNAs. The sequence-level diversity of these intergenic elements precludes alignment-based clustering that would enable annotation and discovery of novel noncoding RNA families. Here, we show that a genomic language model fine-tuned on prokaryotic intergenic sequences learns representations of noncoding elements from DNA sequence. We used these embeddings to organize 376 million intergenic regions from 1.3 terabase pairs of prokaryotic genomes and metagenomes into 5.14 million clusters. This approach nominated 20,149 clusters of putative structured RNAs with a candidate protein partner. Periodicity in token entropies revealed internal architecture, recovering CRISPR, TIGR and VIPR arrays together with their cognate effector proteins from sequence alone. Targeted clustering of bridge recombinase-adjacent sequences identified bridge RNA variants with longer guide recognition sequences than any characterized ortholog. Finally, we directed teams of AI agents to mine the database under researcher-defined criteria, uncovering divergent riboswitches, ribozymes, and other structured RNAs with biological hypotheses for their function. We release a publicly available database that transforms the dark matter of prokaryotic genomes into an explorable resource for human-agent molecular discovery.

genomics↗

bioRxiv2026

Autonomous AI agents discover reverse transcriptases with tandem repeat arrays

Metagenomic databases hold vast quantities of unexplored genetic material that can seed tools for biotechnology and medicine, but discovery is limited by our ability to examine sequence data at scale. Here, we show that large language models (LLMs) in an autonomous harness can alleviate this bottleneck and surface novel molecular systems. We deployed Claude Code instances to survey reverse transcriptase (RT) loci across 1.9 billion protein clusters. Among the top candidates were array-associated RTs (ART), a novel family of jumbo-phage RTs coupled with an array of ~200-nucleotide units and a dedicated partner gene. Session transcripts show that Claude identified the ART family by directly examining its DNA sequences and recognizing the atypical repeats, a behavior attributable to specific Mythos 5 internal signals that respond to repeated DNA. ART arrays are highly expressed and appear as discrete units during Staphylococcus phage infection, suggesting an RT system directed by a repertoire of distinct RNAs. These findings indicate that LLMs can autonomously detect anomalies and drive analyses to initiate biological discoveries.

bioinformatics↗

bioRxiv2025

Megabase-scale human genome rearrangement with programmable bridge recombinases

Bridge recombinases are a class of naturally occurring RNA-guided DNA recombinases. We previously demonstrated they can programmably insert, excise, and invert DNA in vitro and in bacteria. Here, we report the discovery and engineering of IS622, a simple two-component system capable of universal DNA rearrangements of the human genome. We define strategies for the optimal application of bridge systems, leveraging mechanistic insights to improve their targeting specificity. Through rational engineering of the IS622 bridge RNA and deep mutational scanning of its recombinase, we achieve up to 20% insertion efficiency into the human genome and genome-wide specificity as high as 82%. We further demonstrate intra-chromosomal inversion and excision, mobilizing up to 0.93 megabases of DNA. Finally, we provide proof-of-concept for excision of a gene regulatory region or expanded repeats relevant for the treatment of genetic diseases.

bioengineering↗

bioRxiv2025

Genome modeling and design across all domains of life with Evo 2

All of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a deep understanding of the immense complexity encoded by genomes. We introduce Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life. We train Evo 2 with 7B and 40B parameters to have an unprecedented 1 million token context window with single-nucleotide resolution. Evo 2 learns from DNA sequence alone to accurately predict the functional impacts of genetic variation--from noncoding pathogenic mutations to clinically significant BRCA1 variants--without task-specific finetuning. Applying mechanistic interpretability analyses, we reveal that Evo 2 autonomously learns a breadth of biological features, including exon-intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. Beyond its predictive capabilities, Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Guiding Evo 2 via inference-time search enables controllable generation of epigenomic structure, for which we demonstrate the first inference-time scaling results in biology. We make Evo 2 fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.

genomics↗

bioRxiv2024

Site-specific DNA insertion into the human genome with engineered recombinases

Technologies for precisely inserting large DNA sequences into the genome are critical for diverse research and therapeutic applications. Large serine recombinases (LSRs) can mediate direct, site-specific genomic integration of multi-kilobase DNA sequences without a pre-installed landing pad, but current approaches suffer from low insertion rates and high off-target activity. Here, we present a comprehensive engineering roadmap for the joint optimization of DNA recombination efficiency and specificity. We combined directed evolution, structural analysis, and computational models to rapidly identify additive mutational combinations. We further enhanced performance through donor DNA optimization and dCas9 fusions, enabling simultaneous target and donor recruitment. Top engineered LSR variants achieved up to 53% integration efficiency and 97% genome-wide specificity at an endogenous human locus, and effectively integrated large DNA cargoes (up to 12 kb tested) for stable expression in challenging cell types, including non-dividing cells, human embryonic stem cells, and primary human T cells. This blueprint for rational engineering of DNA recombinases enables precise genome engineering without the generation of double-stranded breaks.

bioengineering↗

bioRxiv2024

Sequence modeling and design from molecular to genome scale with Evo

The genome is a sequence that completely encodes the DNA, RNA, and proteins that orchestrate the function of a whole organism. Advances in machine learning combined with massive datasets of whole genomes could enable a biological foundation model that accelerates the mechanistic understanding and generative design of complex molecular interactions. We report Evo, a genomic foundation model that enables prediction and generation tasks from the molecular to genome scale. Using an architecture based on advances in deep signal processing, we scale Evo to 7 billion parameters with a context length of 131 kilobases (kb) at single-nucleotide, byte resolution. Trained on 2.7M prokaryotic and phage genomes, Evo can generalize across the three fundamental modalities of the central dogma of molecular biology to perform zero-shot function prediction that is competitive with, or outperforms, leading domain-specific language models. Evo also excels at multi-element generation tasks, which we demonstrate by generating synthetic CRISPR-Cas molecular complexes and entire transposable systems for the first time. Using information learned over whole genomes, Evo can also predict gene essentiality at nucleotide resolution and can generate coding-rich sequences up to 650 kb in length, orders of magnitude longer than previous methods. Advances in multi-modal and multiscale learning with Evo provides a promising path toward improving our understanding and control of biology across multiple levels of complexity.

synthetic biology↗

bioRxiv2024

Bridge RNAs direct modular and programmable recombination of target and donor DNA

Genomic rearrangements, encompassing mutational changes in the genome such as insertions, deletions, or inversions, are essential for genetic diversity. These rearrangements are typically orchestrated by enzymes involved in fundamental DNA repair processes such as homologous recombination or in the transposition of foreign genetic material by viruses and mobile genetic elements (MGEs). We report that IS110 insertion sequences, a family of minimal and autonomous MGEs, express a structured non-coding RNA that binds specifically to their encoded recombinase. This bridge RNA contains two internal loops encoding nucleotide stretches that base-pair with the target DNA and donor DNA, which is the IS110 element itself. We demonstrate that the target-binding and donor-binding loops can be independently reprogrammed to direct sequence-specific recombination between two DNA molecules. This modularity enables DNA insertion into genomic target sites as well as programmable DNA excision and inversion. The IS110 bridge system expands the diversity of nucleic acid-guided systems beyond CRISPR and RNA interference, offering a unified mechanism for the three fundamental DNA rearrangements required for genome design.

genetics↗

bioRxiv2023

Transcriptomics and chromatin accessibility in multiple African population samples

Mapping the functional human genome and impact of genetic variants is often limited to European-descendent population samples. To aid in overcoming this limitation, we measured gene expression using RNA sequencing in lymphoblastoid cell lines (LCLs) from 599 individuals from six African populations to identify novel transcripts including those not represented in the hg38 reference genome. We used whole genomes from the 1000 Genomes Project and 164 Maasai individuals to identify 8,881 expression and 6,949 splicing quantitative trait loci (eQTLs/sQTLs), and 2,611 structural variants associated with gene expression (SV-eQTLs). We further profiled chromatin accessibility using ATAC-Seq in a subset of 100 representative individuals, to identity chromatin accessibility quantitative trait loci (caQTLs) and allele-specific chromatin accessibility, and provide predictions for the functional effect of 78.9 million variants on chromatin accessibility. Using this map of eQTLs and caQTLs we fine-mapped GWAS signals for a range of complex diseases. Combined, this work expands global functional genomic data to identify novel transcripts, functional elements and variants, understand population genetic history of molecular quantitative trait loci, and further resolve the genetic basis of multiple human traits and disease.

genomics↗

bioRxiv2021

Large-scale discovery of recombinases for integrating DNA into the human genome

Recent microbial genome sequencing efforts have revealed a vast reservoir of mobile genetic elements containing integrases that could be useful genome engineering tools. Large serine recombinases (LSRs), such as Bxb1 and PhiC31, are bacteriophage-encoded integrases that can facilitate the insertion of phage DNA into bacterial genomes. However, only a few LSRs have been previously characterized and they have limited efficiency in human cells. Here, we developed a systematic computational discovery workflow that identifies thousands of new LSRs and their cognate DNA attachment sites by. We validate this approach via experimental characterization of LSRs in human cells, leading to three classes of LSRs distinguished from one another by their efficiency and specificity. We identify landing pad LSRs that efficiently integrate into synthetically installed attachment sites orthogonal to the human genome, human genome-targeting LSRs with computationally predictable pseudosites, and multi-targeting LSRs that can unidirectionally integrate cargos at with similar efficiency and superior specificity to commonly used transposases. LSRs from each category were functionally characterized in human cells, overall achieving up to 7-fold higher plasmid recombination than Bxb1 and genome insertion efficiencies of 40-70% with cargo sizes over 7 kb. Overall, we establish a paradigm for large-scale discovery of microbial recombinases and reconstruction of their target sites directly from microbial sequencing data. This strategy provides a rich resource of over 60 experimentally characterized LSRs that can function in human cells and thousands of additional candidates for large-payload genome editing without exposed DNA double-stranded breaks.

bioengineering↗

bioRxiv2021

Chromatin accessibility changes induced by the microbial metabolite butyrate reveal possible mechanisms of anti-cancer effects

Butyrate is a four-carbon fatty acid produced in large quantities by bacteria found in the human gut. It is the major source of colonic epithelial cell energy, can bind to and agonize short-chain fatty acid G-protein coupled receptors and functions as a histone deacetylase (HDAC) inhibitor. Anti-cancer effects of butyrate are attributed to a global increase in histone acetylation in colon cancer cells; however, the role that corresponding chromatin remodeling plays in this effect is not fully understood. We used longitudinal paired ATAC-seq and RNA-seq on HCT-116 colon cancer cells to determine how butyrate-related chromatin changes functionally associate with cancer. We detected distinct temporal changes in chromatin accessibility in response to butyrate with less accessible regions enriched in transcription factor binding motifs and distal enhancers. These regions significantly overlapped with regions maintained by the SWI/SNF chromatin remodeler, and were further enriched amongst chromatin regions that are associated with ARID1A/B synthetic lethality. Finally, we found that butyrate-induced chromatin regions were enriched for both colorectal cancer GWAS loci and somatic mutations in cancer. These results demonstrate the convergence of both somatic mutations and GWAS risk variants for colon cancer within butyrate-responsive chromatin regions, providing a molecular map of the mechanisms by which this microbial metabolite might confer anti-cancer properties. HighlightsO_LIChromatin accessibility changes longitudinally upon butyrate exposure in colon cancer cells. C_LIO_LIChromatin regions that close in response to butyrate are enriched among distal enhancers. C_LIO_LIThere is strong overlap between butyrate-induced peaks and peaks associated with SWI/SNF synthetic lethality. C_LIO_LIButyrate-induced peaks are enriched for colorectal cancer GWAS loci and somatic variation in colorectal cancer. C_LI

genomics↗

bioRxiv2021

An integrated approach to identify environmental modulators of genetic risk factors for complex traits

Complex traits and diseases can be influenced by both genetics and environment. However, given the large number of environmental stimuli and power challenges for gene-by-environment testing, it remains a critical challenge to identify and prioritize specific disease-relevant environmental exposures. We propose a novel framework for leveraging signals from transcriptional responses to environmental perturbations to identify disease-relevant perturbations that can modulate genetic risk for complex traits and inform the functions of genetic variants associated with complex traits. We perturbed human skeletal muscle, fat, and liver relevant cell lines with 21 perturbations affecting insulin resistance, glucose homeostasis, and metabolic regulation in humans and identified thousands of environmentally responsive genes. By combining these data with GWAS from 31 distinct polygenic traits, we show that heritability of multiple traits is enriched in regions surrounding genes responsive to specific perturbations and, further, that environmentally responsive genes are enriched for associations with specific diseases and phenotypes from the GWAS catalogue. Overall, we demonstrate the advantages of large-scale characterization of transcriptional changes in diversely stimulated and pathologically relevant cells to identify disease-relevant perturbations.

genomics↗

bioRxiv2020

Automated prediction and annotation of small proteins in microbial genomes

Recent work performed by Sberro et al. (2019) revealed a vast unexplored space of small proteins existing within the human microbiome. At present, these small open reading frames (smORFs) are unannotated in existing reference genomes and standard genome annotation tools are not able to accurately predict them. In this study, we introduce an annotation tool named SmORFinder that predicts small proteins based on those identified by Sberro et al. This tool combines profile Hidden Markov models (pHMMs) of each small protein family and deep learning models that may better generalize to smORF families not seen in the training set. We find that combining predictions of both pHMM and deep learning models leads to more precise smORF predictions and that these predicted smORFs are enriched for Ribo-Seq or MetaRibo-Seq translation signals. Feature importance analysis reveals that the deep learning models learned to identify Shine-Dalgarno sequences, deprioritize the wobble position in each codon, and group codons in a way that strongly corresponds to the codon synonyms found in the codon table. We perform a core genome analysis of 26 bacterial species and identify many core smORFs of unknown function. We pre-compute small protein annotations for thousands of RefSeq isolate genomes and HMP metagenomes, and we make these data available through a web portal along with other useful tools for small protein annotation and analysis. The systematic identification and annotation of those important small proteins will help researchers to expand our understanding of this exciting field of biology.

bioinformatics↗