Biology subjects

Baker, D.

Publications and source records attributed to Baker, D..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Topological reprogramming transforms an integral membrane oligosaccharyltransferase into a water-soluble glycosylation catalyst

Glycosyltransferases (GTs) catalyze the formation of new glycosidic bonds and thus are vital for synthesizing natures vast repertoire of glycans and glycoconjugates and for engineering glycan-related medicines and materials. However, obtaining detailed structural and functional insights for the >750,000 known GTs is limited by difficulties associated with their efficient recombinant expression. Members of the GT-C fold, in particular, pose the most significant expression challenges due to the integration and folding requirements of their multiple membrane-spanning regions. Here, we address this challenge by engineering water-soluble variants of an archetypal GT-C fold enzyme, namely the oligosaccharyltransferase PglB from Campylobacter jejuni (CjPglB), which possesses 13 hydrophobic transmembrane helices. To render CjPglB water-soluble, we leveraged two advanced protein engineering methods: one that is universal called SIMPLEx (solubilization of IMPs with high levels of expression) and the other that is custom tailored called WRAPs (water-soluble RFdiffused amphipathic proteins). Each approach was able to transform CjPglB into a water-soluble enzyme that could be readily expressed in the cytoplasm of Escherichia coli cells at yields in the 3-6 mg/L range. Importantly, solubilization was achieved without the need for detergents and with retention of catalytic function. Collectively, our findings demonstrate that both SIMPLEx and WRAPs are promising platforms for advancing the molecular characterization of even the most structurally complex GTs, while also enabling broader GT-mediated glycosylation capabilities within synthetic glycobiology applications.

synthetic biology↗

bioRxiv2026

Templating and confining calcium phosphate mineralization within designed protein assemblies

Bone formation involves the deposition of ordered hydroxyapatite (HAp) on collagen fibrils, but the underlying molecular mechanisms remain largely unresolved, limiting the design of protein-apatite hybrid materials. Here we show that computationally designed de novo proteins can template and confine HAp mineralization. We design C3-symmetric oligomers with inner surfaces chemically complementary to the HAp {010} facet, and assemble these through additional interfaces into D3 oligomers, one-dimensional nanotubes, and two-dimensional arrays. Electron microscopy revealed templated mineralization and confinement at each hierarchical level, with mineral shape guided by the underlying protein architecture. Conversion of an initially formed amorphous phase to HAp is driven by the designed protein-mineral interface. Our results establish a framework for programmable protein-guided mineralization, providing a foundation for next-generation hybrid materials.

biochemistry↗

bioRxiv2025

Standardizing RNA-seq Analysis of Fungal Pathogens Using BRC-Analytics and Agentic AI: A Candidozyma auris Case Study

Candidozyma auris has emerged as a critical global health threat due to multidrug resistance and healthcare-associated transmission. While RNA-seq has become the primary tool for studying C. auris pathogenesis, inconsistent use of reference genomes and bioinformatics tools complicate cross-study comparisons. Here we demonstrate how BRC-Analytics, a platform for pathogen genomics, combined with an agentic AI assistant, enables reproducible RNA-seq analysis. By re-analyzing data from two publications we achieved near-perfect correlation with published results despite annotation version differences. We addressed provenance challenges associated with using AI agents with Galaxy by forcing them to invoke Galaxys native tools rather than manipulating data directly. For custom analyses outside Galaxys toolset, we provide standalone JupyterLite notebooks that reproduce our analysis without AI involvement. This framework--combining AI-assisted automation with rigorous provenance tracking--establishes a template for standardized, reproducible fungal pathogen genomics. To the best of our knowledge, this is the first example of integration between public data repositories, reproducible analysis workflows, and agentic AI tools. Our subsequent efforts will focus on improving the seamlessness of this integration.

genomics↗

bioRxiv2025

Template-based RNA structure prediction advanced through a blind code competition

Automatically predicting RNA 3D structure from sequence remains an unsolved challenge in biology and biotechnology. Here, we describe a Kaggle code competition engaging over 1700 teams and 43 previously unreleased structures to tackle this challenge. The top three submitted algorithms achieved scores within statistical error of the winners of the recent CASP16 competition. Unexpectedly, the top Kaggle strategy involved a pipeline for discovering 3D templates, without the use of deep learning. We integrated this template-modeling pipeline and other Kaggle strategies to develop a single model RNAPro that retrospectively outperformed individual Kaggle models on the same test set. These results suggest a growing importance of template-based modeling in RNA structure prediction.

biophysics↗

bioRxiv2025

Using experimental results of protein design to guide biomolecular energy-function development

Computational models of macromolecules have many applications in biochemistry, but physical inaccuracies limit their utility. One class of models uses energy functions rooted in classical mechanics. The standard datasets used to train these models are limited in diversity, pointing to a need for new training data. Here, we sought to explore a new paradigm for training an energy function, where the Rosetta energy function was used to design de novo proteins. Experimental results on these designs were then used to identify failure modes of design, which were subsequently used as a "guiding principle" to retrain the energy function. Specifically, we examined a diverse set of de novo protein designs experimentally tested for their ability to stably fold, identifying unstable designs that were predicted to be stable by the Rosetta energy function. Using deep mutational scanning, we identified single amino-acid mutations that rescued the stability of these designs, providing insight into common failure modes of the energy function. We identified one key failure mode, involving steric clashing in protein cores. We identified similar overpacking when using Rosetta to refine high-resolution protein crystal structures, quantified the degree of overpacking, and refit a small set of energy-function parameters to better recapitulate native-like packing. Following fitting, we largely eliminated the failure mode in the refinement task, while retaining performance on other benchmarks, resulting in an updated version of the Rosetta energy function. This work shows how learning from protein designs can guide energy-function development. Author summaryComputational models of macromolecules have many applications, such as predicting structures, predicting mutational effects, or designing new proteins. One type of model uses an energy function to explicitly model the physical forces at play. These models are trained using experimental data. However, available training data are limited in number and diversity, prompting a need for new sources of training data. In this paper, we explore a new paradigm for training an energy function, which involves using the energy function to design de novo proteins and then learning from which designs succeed and which fail when experimentally tested in the lab. We used experimental data to learn common failure modes in design, identified examples of a failure mode in a high-quality benchmark, and then used this benchmark to retrain the energy function. Using this strategy, we identified and largely resolved a bias in the Rosetta energy function that involved energetically unfavorable steric clashes in protein cores. Overall, this work helps establish a framework for how learning from design can be used to guide the development of macromolecular models.

biochemistry↗

bioRxiv2025

TagF coordinates spike-loading as an intermediate checkpoint in Type VI Secretion System assembly

Diderm bacteria use contact-dependent Type VI secretion systems (T6SS) to gain a competitive advantage within bacterial communities and during infection. Whereas the structural core assembly of T6SS is well characterized, the role of diverse accessory proteins during assembly remains under investigation. One well-conserved accessory protein is TagF, a post-translational inhibitor of T6SS dynamics, which was previously characterized in the context of a kinase-phosphatase signaling relay. Here, we identify a subset of T6SS clusters in which TagF occurs in a distinct regulatory context and without known binding partners. Investigating the role of TagF in the constitutively active Acinetobacter baylyi T6SS, we show that though TagF can suppress assembly dynamics, it primarily acts as an assembly coordinator. Using structured illumination microscopy, we show that TagF coordinates the transition from baseplate assembly to sheath elongation by preventing premature and nonproductive sheath assembly in absence of the VgrG spike. Direct interactions with the conserved T6SS tube protein Hcp suggested that TagF blocks sheath elongation by binding and disrupting Hcp hexamers and thus preventing tube formation. Finally, we show that TagF activity depends on crosstalk with TagZ, a previously uncharacterized membrane-associated accessory protein that recruits TagF to the cell periphery, and demonstrate that this interaction can be specifically disrupted by expression of an artificial TagF-binding protein. Together, our findings establish a novel role of TagF as a checkpoint protein that controls for spike-insertion into the baseplate to ensure effective T6SS assembly.

microbiology↗

bioRxiv2025

Computational design of cysteine proteases

Despite advances in de novo enzyme design, success has been largely limited to low energy barrier model reactions. Amide bonds such as those linking amino acids along the peptide backbone are stable for hundreds of years in neutral aqueous solution because of the high energy barrier to hydrolysis. Here we describe the de novo design of enzymes which utilize an activated cysteine nucleophile to hydrolyze the polypeptide backbone in a sequence-dependent manner, with a success rate of 13/69=19% and rate enhancements over the background reaction (kcat/kuncat) of up to 3 x 10^7. The designed proteases have folds very different from proteases in nature (TM score < 0.50), and six crystal structures are very close to the design models (Ca RMSDs < 1.2 A), highlighting the capacity for generalization and the accuracy of the design methodology. Experimental and computational analyses suggest that the remaining gap in activity to the most active native cysteine proteases arises from imperfections in active site preorganization and substrate positioning. The designed proteases efficiently cleave their targets in mammalian cells, opening the door to a wide range of synthetic biology applications.

biochemistry↗

bioRxiv2025

Computational design of metalloproteases

Although significant progress has been made in creating de novo metalloenzymes that hydrolyze activated esters, the energetically demanding cleavage of amide bonds has remained a major challenge for enzyme design: amide bonds are significantly more stable than ester bonds, the amine leaving groups in proteins are not activated, and peptide substrates are flexible making them difficult to bind precisely. Here, we report the de novo design of zinc proteases from minimal catalytic motifs using RoseTTAFold Diffusion 2 for Molecular Interfaces, optimized for both enzyme and protein-protein interaction design. Of 135 computational designs experimentally tested, 36% had activity and cleaved precisely at the intended site. The most active design accelerates peptide bond hydrolysis by more than 10^8-fold relative to the uncatalyzed reaction, and by over 10^10-fold following the introduction of four point mutations that enhanced active-site preorganization4. Building on these capabilities, we designed metalloproteases that specifically cleave human TDP-43, the amyloid-{beta} peptide, and serum amyloid A with rate accelerations up to 9.2x10^8-fold over background. We illustrate the potential of our approach for bio-orthogonal control over cell state by generating caged cytokines and caged receptor antagonists that are selectively unmasked by our designed proteases. These results demonstrate that de novo enzyme design has advanced well beyond model reactions with activated substrates and open the door to design of proficient metallohydrolases for a wide range of applications in medicine and bioremediation.

biochemistry↗

bioRxiv2025

Targeting peptide-MHC complexes with designed T cell receptors and antibodies

Class I major histocompatibility complexes (MHCs), expressed on the surface of all nucleated cells, present peptides derived from intracellular proteins for surveillance by T cells. The precise recognition of foreign or mutated peptide-MHC (pMHC) complexes by T cell receptors (TCRs) is central to immune defense against pathogens and tumors. Although patient-derived TCRs specific for cancer-associated antigens have been used to engineer tumor-targeting therapies, their reactivity toward self- or near-self antigens may be constrained by negative selection in the thymus. Here, we introduce a structure-based deep learning framework, ADAPT (Antigen-receptor Design Against Peptide-MHC Targets), for the design of TCRs and antibodies that bind to pMHC targets of interest. We evaluate the ADAPT pipeline by designing and characterizing TCRs and antibodies against a diverse panel of pMHCs. Cryogenic electron microscopy structures of two designed antibodies bound to their respective pMHC targets demonstrate atomic-level accuracy at the recognition interface, supporting the robustness of our structure-based approach. Computationally designed TCRs and antibodies targeting pMHC complexes could enable a broad range of therapeutic applications, from cancer immunotherapy to autoimmune disease treatment, and insights gained from TCR-pMHC design should advance predictive understanding of TCR specificity with implications for basic immunology and clinical diagnostics.

immunology↗

bioRxiv2025

From data to publication in a browser with BRC-Analytics: Evolutionary dynamics of coding overlaps in measles virus

The analytical landscape of pathogen research is often fragmented, hindering transparency and reproducibility due to diverse genomic data sources, numerous software tools, and suboptimal integration methods. Here we introduce BRC-analytics, a novel browser-based environment that unifies authoritative sources of genomic data with community-curated best analysis practices on a freely accessible public computational infrastructure. We demonstrate its capabilities by analyzing the evolutionary dynamics within the P/V/C locus of the measles virus, a complex system involving overlapping coding regions and RNA editing. Our analysis, conducted entirely within BRC-analytics, reveals asymmetric evolution of the locuss reading frames under distinct selective pressures. BRC-analytics streamlines the entire research process--from data collection and primary analysis (e.g., variant calling) to interpretation (e.g., using integrated JupyterLite notebooks and LLMs) and publication--into a single web browser session. This eliminates the need for local installations and manual data transfers, implicitly tracking provenance and ensuring reproducibility. The platforms goal is to provide true data-to-publication functionality, making advanced pathogen genomics accessible to a broader research community regardless of their computational expertise or infrastructure access.

genomics↗

bioRxiv2025

Geometric Tuning of Cytokine Receptor Association Modulates Synthetic Agonist Signaling

Cytokines signal by bringing receptor subunits together, but the role of receptor geometry in shaping signaling remains unclear because natural ligands enforce fixed assemblies. Here, we present a de novo protein design platform that rigidly scaffolds receptor-binding domains into defined spatial arrangements. Applying this across IL-7, type I and III interferons, IL-10, gp130, {beta} common, and synthetic receptor pairs, we show that by varying geometry, we can bias pSTAT pathway usage and tune functional outcomes. Geometric control allowed us to decouple pSTAT1 from pSTAT5 in IL-7, separate antigen presentation (MHC-I) from checkpoint induction (PD-L1) in type I interferons, and suppress pro-inflammatory IFN{gamma} secretion while retaining anti-inflammatory activity in IL-10. We further created minimal IL-6 and IL-3 agonists and strengthened synthetic receptor pairings inaccessible with present cytokines. These results establish receptor geometry as a central determinant of cytokine activity and provide a platform for programmable immune modulation.

synthetic biology↗

bioRxiv2025

Direct cell reprogramming by a designed agonist inducing HER2-FGFR proximity

Growth factor induced receptor dimerization and activation of downstream pathways can modulate cell fate decisions. Here, we investigate the potential of de novo designed synthetic ligands, termed Novokines, to reprogram cell identity by inducing proximity of novel pairs of receptor subunits. We find that a design, H2F, that brings together HER2 (which has no known natural ligand) and the FGF receptor has potent signaling activity. H2F induces robust signaling and reprograms fibroblasts into myogenic cells. Unlike native FGF ligands, H2F selectively activates the MAPK pathway without engaging PLC{gamma}-mediated Ca{superscript 2} signaling. FRET assays confirm H2F-mediated HER2-FGFR proximity, and phosphoproteomic analysis reveals activation of MAPK effectors. H2F-induced ERK phosphorylation is abolished in cells expressing a kinase-dead FGFR1 (K514M) mutant, confirming the requirement for FGFR catalytic activity. H2F treatment significantly increases myofiber formation from adult patient-derived primary myoblasts, demonstrating its capacity to promote myogenic regeneration. Our findings demonstrate that synthetic receptor pairings can rewire signaling outputs to drive regeneration, providing a programmable platform for cell fate engineering.

biochemistry↗

bioRxiv2025

High-Throughput De Novo Protein Design Yields Novel Immunomodulatory Agonists

Cytokines regulate cell behavior by bringing together specific receptor subunits to trigger downstream signaling. Designed molecules that bring together non-natural receptor pairs could have novel signaling responses and cell specificities. We present a high-throughput de-novo design approach to create novel cytokines by generating and fusing pairs of computationally designed binders. By combining 33 designed receptor-binding domains, we generated over a thousand potential de novo designed "Novokines", of which 75 activated pSTAT signaling in peripheral blood mononuclear cells. We characterized 18 of these, including new pairings of established common receptors, cross-family pairings such as TrkA-{gamma}common, and a series of pairings with interferon receptor-1 (IFNAR1), revealing that IFNAR1 can function as a versatile common receptor similar to {gamma}common or {beta}common. We identify novokines that drive monocyte proliferation, T cell survival and CD4+ T cell-specific proliferation. Our framework provides a blueprint for expanding the understanding of cytokine signaling and generating novel therapeutic proteins.

synthetic biology↗

bioRxiv2025

Multiplex design and discovery of proximity handles for programmable proteome editing

Although we now have a rich toolset for genome editing, an equivalent framework for manipulating the proteome with a comparable flexibility and specificity remains elusive. A promising strategy for "proteome editing" is to use bifunctional molecules (e.g. PROteolysis-Targeting Chimeras or PROTACs1) that bring a target protein into proximity with a degradation or stabilization effector, but their broader application is constrained by a limited repertoire of well-characterized target or effector "handles". We asked whether coupling de novo protein design to a multiplex screening framework could address this gap by accelerating the discovery of effector handles for intracellular protein degradation, stabilization, or relocalization. Using LABEL-seq2, a sequencing-based assay that enables multiplex, quantitative measurement of protein abundance, we screened 9,715 de novo designed candidate effector handles for their ability to recruit a target protein to components of the ubiquitin-proteasome system3 (UPS) (FBXL12, TRAF2, UCHL1, USP38) or the autophagy pathway4 (GABARAP, GABARAPL2, MAP1LC3A). In a single experiment, we discovered hundreds of de novo designed effector handles that reproducibly drove either intracellular degradation (n = 277) or stabilization (n = 204) of a reporter protein. Validation of a subset of these hits in an orthogonal assay confirmed that sequencing-based measurements from the primary screen reliably reflected changes in intracellular abundance of the target protein. Successful effector handles were discovered for both the UPS (n = 194) and autophagy (n = 287) pathways, which provide complementary routes for programmable proteome editing. Autophagy-recruiting effector handles generalized to endogenous targets, as substituting the reporter-specific target handle with a high-affinity MCL1 binder5 reduced endogenous levels of this intracellular oncoprotein6. Moreover, directing autophagy-recruiting effector handles to the outer mitochondrial membrane dramatically perturbed mitochondrial networks in a manner consistent with synthetic tethering and sequestration7,8. Beyond generating a diverse repertoire of protein abundance or localization effector handles, our results establish a scalable, low-cost platform that links deep learning-guided protein design to functional cellular readouts, and chart a course toward a general framework for programmable proteome editing.

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↗

bioRxiv2025

De-novo design of a random protein walker

Molecular machines hold great potential. Design of static monomeric and oligomeric protein structures has advanced tremendously, but few dynamic protein systems have been designed. Here we present the design and characterization of a random protein walker that diffuses along a designed protein track. For the track, we designed micro-meter long fibres and developed a method to rigidly decorate them with arbitrary proteins. The walkers consist of homo-oligomers that reversibly bind the track using heterodimeric feet; we tested multiple heterodimer interfaces for reversibility and designed six walkers with different numbers of feet. Cryo-EM experiments confirmed the structure of the track and walkers. We performed detailed single molecule tracking and kinetics characterisation and found that the walkers with more feet diffuse along the track faster. The system represents a tuneable starting point for future powered protein molecular robots.

synthetic biology↗

bioRxiv2025

De novo design of phospho-tyrosine peptide binders

Phosphorylation on tyrosine is a key step in many signaling pathways. Despite recent progress in de novo design of protein binders, there are no current methods for designing binders that recognize phosphorylated proteins and peptides; this is a challenging problem as phosphate groups are highly charged, and phosphorylation often occurs within unstructured regions. Here we introduce RoseTTAFold Diffusion 2 for Molecular Interfaces (RFD2-MI), a deep generative framework for the design of binders for protein, ligand, and covalently modified protein targets. We demonstrate the power and versatility of this method by designing binders for four critical phosphotyrosine sites on three clinically relevant targets: Cluster of Differentiation 3 (CD3{varepsilon}), Epidermal Growth Factor Receptor (EGFR), Insulin Receptor (INSR) and Signal Transducer and Activator of Transcription 5 (STAT5). Experimental characterization shows that the designs bind their phosphotyrosine containing targets with affinities comparable to native binding sites and have negligible binding to non-phosphorylated targets or phosphopeptides with different sequences. X-ray crystal structures of generated binders to CD3{varepsilon} and EGFR are very close to the design models, demonstrating the accuracy of the design approach. A designed binder to an EGFR intracellular region phosphorylated upon EGF activation co-localizes with the receptor following EGF stimulation in single-particle tracking (SPT) experiments, demonstrating pY specific recognition in living cells. RFD2-MI provides a generalizable all-atom diffusion framework for probing and modulating phosphorylation-dependent signaling, and more generally, for developing research tools and targeted therapeutics against post-translationally modified proteins.

bioengineering↗