Biology subjects

Vanella, R.

Publications and source records attributed to Vanella, R..

7 recordsLinked to original sources

bioRxiv2025

Discovery of Electron Hole-hopping Redox Mutations in Myoglobin by Deep Mutational Learning

In addition to storing molecular oxygen, myoglobin catalyzes peroxidase-like reactions involving high valency iron(IV)-oxo species that support one-electron oxidations on a range of substrates at an open active site. In select metalloenzymes, long-range electron transfer can be mediated by hole-hopping pathways composed of aromatic residues that act as relay stations for oxidative equivalents. However, it remains unclear how sequence variations could introduce or alter such catalytic mechanisms in myoglobin. Here we used enzyme proximity sequencing (EP-Seq) to measure the peroxidase activity levels of >6,000 human myoglobin variants. The resulting fitness landscape reveals how aromatic substitutions, in particular surface-exposed tryptophans, can enhance peroxidase activity. Using protein language models in tandem with feedforward neural networks, we trained an accurate fitness predictor on the experimental dataset, and applied it to screen >4M double mutant variants. The predictions suggested a beneficial role for electron-hole-hopping mutations in improving peroxidase activity. We experimentally tested 20 high scoring variants in a yeast display assay, all of which outperformed wild type myoglobin. Three selected variants were also tested in soluble format and similarly showed improved performance. A focused combinatorial library yielded a top double tryptophan variant (Q92W/F107W) with 4.9-fold higher catalytic efficiency than wild type. These results show that deep mutational learning can identify myoglobin variants with enhanced peroxidase activity that are consistent with the involvement of hole-hopping pathways, with broad implications for biocatalyst and redox enzyme design.

bioengineering↗

bioRxiv2025

Enzyme-responsive Hemostatic Elastin-like Polypeptides for Fibrin Stabilization and Enhanced Coagulation in Thrombocytopenia

Hemorrhage remains a leading cause of mortality in trauma and surgery, and treatment options are limited for thrombocytopenic patients with impaired platelet function. Current plasma-derived hemostatic products face challenges including limited supply, storage requirements, and infectious risk. Here we report a recombinant protein-based hemostat designed to enhance clot mechanics through enzyme responsiveness and self-assembly, which integrates biophysical design principles with clot-targeted drug delivery. We rationally designed a library of enzyme-responsive glutamine (Q)-containing block elastin-like polypeptides (Q-block-ELPs) that reinforce fibrin clots through phase separation and covalent cross-linking. Q-block-ELPs incorporate glutamine residues within a peptide motif recognized by coagulation factor XIIIa, enabling site-specific grafting into fibrin networks during clot formation. By tuning polymer length, Q-block valency, and lower critical solution temperature (LCST) behavior, we engineered Q-block-ELPs to phase separate at body temperature and integrate into the fibrin architecture. In vitro, Q-block-ELPs increase fibrin network density and stiffness. In a thrombocytopenic mouse model, systemic administration reduced blood loss and accelerated clot formation. This strategy delivers a programmable, pathogen-free platform for systemic bleeding control, bridging biophysical protein design with translational hemostatic therapy, addressing an urgent need for platelet-deficient bleeding disorders.

bioengineering↗

bioRxiv2025

Decoding Substrate Specificity in a Promiscuous Biocatalyst by Enzyme Proximity Sequencing

Substrate specificity is a defining feature of enzyme function, but its molecular underpinnings remain difficult to decode and engineer. Here, we leveraged enzyme proximity sequencing (EP-Seq) to systematically map how single-point and combinatorial mutations reshape the substrate preferences of D-amino acid oxidase (DAOx) from Rhodotorula gracilis, a model promiscuous enzyme. We generated [~]40,000 sequence-phenotype pairs, enabling us to profile the activities of [~]6,500 unique DAOx variants against five D-amino acid substrates with distinct physicochemical properties. Our analysis revealed that substrate-specific mutations are distributed throughout the enzyme structure. Mutations near the active site drive strong specificity shifts but also incur catalytic penalties, while distal mutations subtly rewire intramolecular contacts in order to modulate specificity with minimal loss of activity. We identified and validated positional hotspots that act allosterically to influence specificity, and characterized key variants that acquired exclusive substrate specificity or exhibited up to 230-fold changes in substrate preference. Combining mutations with complementary effects further sharpened substrate discrimination, enabling rational design of highly selective biocatalysts. This work provides a powerful framework for decoding enzyme specificity and provides unique foundational datasets to advance AI-guided enzyme engineering.

bioengineering↗

bioRxiv2024

Anchor Point Engineering on Anticalin Scaffolds for Enhanced Particle Adhesion to CTLA-4 Under Shear Stress

Achieving high binding strength and efficient delivery of molecular cargo to cells expressing biomarker targets is a significant challenge in drug delivery. Here, we investigated how altering the surface immobilization residue (i.e. anchor point) within a non-antibody binding scaffold called Anticalin can enhance particle adhesion to the immune checkpoint protein CTLA-4 on mammalian cells under shear stress. By introducing bio-orthogonal clickable amino acids into Anticalin at various positions and applying tension to the protein complex using single-molecule AFM force spectroscopy and bead-based adhesion assays, we elucidate the relationship between anchor point position and mechanostability of the Anticalin:(CTLA-4) complex. Multi-regression analysis of the physicochemical properties of the anchor points revealed that the distance from the anchor point on Anticalin to CTLA-4s center of mass was a major determinant of binding strength under shear flow. These results demonstrate how anchor point engineering can enhance particle adhesion and cellular delivery to CTLA-4 targets and provides a heuristic for choosing surface immobilization points of targeting proteins such that they withstand high mechanical forces.

biophysics↗

bioRxiv2024

Decoding Stability and Epistasis in Human Myoglobin by Deep Mutational Scanning and Codon-level Machine Learning

Engineering protein stability is a critical challenge in biotechnology. Here, we used massively parallel deep mutational scanning (DMS) to comprehensively explore the mutational stability landscape of human myoglobin (hMb) and identify key mutations that enhance stability. Our DMS approach involved screening over 10,000 hMb variants by yeast surface display, single-cell sorting and high-throughput DNA sequencing. We show how surface display levels serve as a proxy for thermostability of soluble hMb variants, and report strong correlations between DMS-derived display levels and top-performing machine learning stability prediction algorithms. This approach led to the discovery of a variant with a de novo disulfide bond between residues R32C and C111, which increased thermostability by >12 {degrees}C compared to wild-type hMb. By combining single stabilizing mutations with R32C, we engineered combinatorial variants that exhibited predominantly additive effects on stability with minimal epistasis. The most stable combinatorial variant exhibited a denaturation temperature exceeding 89 {degrees}C, representing a >17 {degrees}C improvement over wild-type hMb. Our findings demonstrate the capabilities in DMS-assisted combinatorial protein engineering to guide the discovery of thermostable variants, and highlight the potential of massively parallel mutational analysis for the development of proteins for industrial and biomedical applications.

bioengineering↗

bioRxiv2024

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure is a long standing goal with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost intensive. Computational methods have potential to accelerate epitope predictions, however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologs. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2 Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis alone each proved ineffectual in differentiating between two putative binding models referred to as parallel and perpendicular. However, our integrated approach based on dynamic network analysis showed that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols including cross linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Our research highlights the potential of deploying dynamic network analysis to refine AI-based structure predictions for precise predictions of protein-protein interaction interfaces.

biophysics↗

bioRxiv2023

Dissecting the Biophysical Origins of Activity-Stability Tradeoffs in D-amino Acid Oxidase with Enzyme Proximity-Seq

Understanding the complex relationships between enzyme sequence, folding stability and catalytic activity is crucial for applications in industry and biomedicine. However, current enzyme assay technologies are limited by an inability to simultaneously resolve both stability and activity phenotypes and to couple these to gene sequences at large scale. Here we developed Enzyme Proximity Sequencing (EP-Seq), a deep mutational scanning method that leverages peroxidase-mediated radical labeling with single cell fidelity to dissect the effects of thousands of mutations on stability and catalytic activity of oxidoreductase enzymes in a single experiment. We used EP-Seq to analyze how 6,399 missense mutations influence folding stability and catalytic activity in a D-amino acid oxidase (DAOx) from R.gracilis. The resulting datasets demonstrate activity-based constraints that limit folding stability during natural evolution, and identify hotspots distant from the active site as candidates for mutations that improve catalytic activity without sacrificing stability. EP-Seq can be extended to other enzyme classes and provides valuable insights into biophysical principles governing enzyme structure and function.

bioengineering↗