Biology subjects

Glasscock, C.

Publications and source records attributed to Glasscock, C..

5 recordsLinked to original sources

bioRxiv2026

Design of synthetic activators for programmable gene expression

Transcription factors control gene expression by recruiting transcriptional coactivators through intrinsically disordered transcriptional activation domains (TADs), whose weak and promiscuous interactions make it difficult to establish quantitative relationships between coactivator recruitment and transcriptional output, and to precisely modulate gene expression. To overcome this limitation, we set out to design coactivator-recruiting proteins using AlphaFold3-predicted TAD-coactivator interactions as a guide. We developed a high-throughput functional screening platform in mammalian cells and identified over 300 well folded nanomolar affinity binders to the transcription coactivators Mediator (multiple subunits), BRD4, NCOA, and p300; targeted interface mutations enabled systematic tuning of their binding affinities. Using a panel of p300 binders spanning nanomolar to micromolar affinities, we found an ultrasensitive relationship between coactivator-binding affinity and transcriptional output, with a sharp transcriptional activation threshold at ~500 nM. Incorporating p300 binder designs into CRISPRa yielded compact synthetic effectors that drove stronger activation than VPR across multiple genomic loci with minimal detectable cytotoxicity. Combining p300- and Mediator-specific binders further enhanced transcription, revealing functional synergy between these coactivators. Together, our work establishes a quantitative framework linking coactivator-binding affinity to transcriptional output and provides compact synthetic effectors for programmable gene activation.

synthetic biology↗

bioRxiv2025

RNA sequence design and protein-DNA specificity prediction with NA-MPNN

RNA sequence design and protein-DNA binding specificity prediction can both be framed as nucleic acid inverse-folding problems: finding the most likely nucleic acid sequences given a fixed three-dimensional structure of a nucleic acid or nucleic acid-protein complex. While task-specific tools have been developed, no unified deep learning model for nucleic acid inverse folding has been described; a single model would have larger and more diverse datasets available for training and a considerably greater range of applicability. Here we introduce Nucleic Acid MPNN (NA-MPNN), a message-passing neural network that treats proteins, DNA, and RNA within a unified biopolymer graph representation. NA-MPNN outperforms previous methods on RNA sequence design and fixed-dock protein-DNA specificity prediction, and should be broadly useful for de novo RNA structure design and prediction of DNA-binding specificity.

biochemistry↗

bioRxiv2025

De novo design of RNA and nucleoprotein complexes

Nucleic acids fold into sequence-dependent tertiary structures and carry out diverse biological functions, much like proteins. However, while considerable advances have been made in the de novo design of protein structure and function, the same has not yet been achieved for RNA tertiary structures of similar intricacy. Here, we describe a generative diffusion framework, RFDpoly, for generalized de novo biopolymer (RNA, DNA and protein) design, and use it to create diverse and designable RNA structures. We design RNA structures with novel folds and experimentally validate them using a combination of chemical footprinting (SHAPE-seq) and electron microscopy. We further use this approach to design protein-nucleic acid assemblies; the crystal structure of one such design is nearly identical to the design model. This work demonstrates that the principles of structure-based de novo protein design can be extended to nucleic acids, opening the door to creating a wide range of new RNA structures and protein-nucleic acid complexes.

biochemistry↗

bioRxiv2023

Atomic context-conditioned protein sequence design using LigandMPNN

Protein sequence design in the context of small molecules, nucleotides, and metals is critical to enzyme and small molecule binder and sensor design, but current state-of-the-art deep learning-based sequence design methods are unable to model non-protein atoms and molecules. Here, we describe a deep learning-based protein sequence design method called LigandMPNN that explicitly models all non-protein components of biomolecular systems. LigandMPNN significantly outperforms Rosetta and ProteinMPNN on native backbone sequence recovery for residues interacting with small molecules (63.3% vs. 50.4% & 50.5%), nucleotides (50.5% vs. 35.2% & 34.0%), and metals (77.5% vs. 36.0% & 40.6%). LigandMPNN generates not only sequences but also sidechain conformations to allow detailed evaluation of binding interactions. Experimental characterization demonstrates that LigandMPNN can generate small molecule and DNA-binding proteins with high affinity and specificity. One-sentence summaryWe present a deep learning-based protein sequence design method that allows explicit modeling of small molecule, nucleotide, metal, and other atomic contexts.

biochemistry↗

bioRxiv2017

Improving Designer Glycan Production in Escherichia coli through Model-Guided Metabolic Engineering

Asparagine-linked (N-linked) glycosylation is the most common protein modification in eukaryotes, affecting over two-thirds of the proteome. Glycosylation is also critical to the pharmacokinetic activity and immunogenicity of many therapeutic proteins currently produced in complex eukaryotic hosts. The discovery of a protein glycosylation pathway in the pathogen Campylobacter jejuni and its subsequent transfer into laboratory strains of Escherichia coli has spurred great interest in glycoprotein production in prokaryotes. However, prokaryotic glycoprotein production has several drawbacks, including insufficient availability of non-native glycan precursors. To address this limitation, we used a constraint-based model of E. coli metabolism in combination with heuristic optimization to design gene knockout strains that overproduced glycan precursors. First, we incorporated reactions associated with C. jejuni glycan assembly into a genome-scale model of E. coli metabolism. We then identified gene knockout strains that coupled optimal growth to glycan synthesis. Simulations suggested that these growth-coupled glycan overproducing strains had metabolic imbalances that rerouted flux toward glycan precursor synthesis. We then validated the model-identified knockout strains experimentally by measuring glycan expression using a flow cytometric-based assay involving fluorescent labeling of cell surface-displayed glycans. Overall, this study demonstrates the promising role that metabolic modeling can play in optimizing the performance of a next-generation microbial glycosylation platform.

systems biology↗