Michael A. Nash

This study identifies Scf1 as a C. auris adhesin that binds multiple human proteins and shows that shear stress influences its ligand preference.

The paper develops a psychrophilic orthogonal translation system for expanding the genetic code in E. coli, showing improved efficiency and versatility compared to mesophilic and thermophilic systems.

This paper develops enzyme-responsive elastin-like polypeptides to stabilize fibrin clots and restore coagulation in thrombocytopenia by enhancing clot mechanics and reducing blood loss.

This study investigates how environmental factors and mutations affect the stability and scalability of catch bond complexes between SdrG and fibrinogen beta chain using single-molecule and multi-valent adhesion assays.

This study uses deep mutational learning to identify myoglobin variants with enhanced peroxidase activity, showing the role of electron-hole hopping in catalytic efficiency.

The paper investigates the mechanical properties of the Izumo1:Juno complex using force spectroscopy and simulations, revealing its mechanostability and how mutations affect it.

This study investigates ADGRG1 activation mechanisms through mechanical and allosteric pathways, showing force direction determines signaling via GAIN domain dynamics and introducing engineered variants with tailored sensitivity.

The paper uses enzyme proximity sequencing to map how mutations affect substrate specificity of DAOx, identifying key positions and combinations that enhance selectivity.

This study shows how modifying anchor points on Anticalin scaffolds improves binding strength and adhesion to CTLA-4 under shear stress, enhancing targeted drug delivery.

This study uses deep mutational scanning to identify mutations that enhance thermostability in human myoglobin, discovering a de novo disulfide bond and combinatorial mutations that significantly increase stability.

The paper maps antibody binding epitopes on human arginase-1 using amber codon mutational scanning and bioorthogonal PEGylation, revealing SAC incorporation efficiency and residue reactivity.

The paper investigates how anchor point selection affects the mechanical stability of an Affibody/PD-L1 complex using multiple experimental and computational methods.

The paper investigates how interval censoring affects data accuracy and machine learning performance in biological high-throughput screening and proposes a metric to optimize experimental parameters.

The paper develops an integrated method combining dynamic network analysis with AI to predict epitopes in PD-L1:Affibody interactions, showing the perpendicular binding mode is more stable.

The paper demonstrates how modifying anchor geometry can generate catch bonding behavior in the DocG:CohE adhesion complex, showing mechanical anisotropy enables engineered catch bonds.

This paper introduces FUSION, a machine learning workflow for classifying and discovering protein unfolding trajectories from force spectroscopy data, achieving high accuracy and identifying new unfolding pathways.

This study investigates how fluorination affects the mechanical stability of a bacterial adhesion complex using single-molecule force spectroscopy, revealing distinct effects on equilibrium and non-equilibrium stability.