Fucosylation is important for the function of many proteins with biotechnical and medical applications. Alpha-fucosidases comprise a large enzyme family that recognizes fucosylated substrates with diverse α-linkages on these proteins. Lactobacillus casei produces an α-fucosidase, called AlfC, with specificity towards α(1,6)-fucose, the only linkage found in human N-glycan core fucosylation. AlfC and certain point mutants thereof have been used to add and remove fucose from monoclonal antibody N-glycans, with significant impacts on their effector functions. Despite the potential uses for AlfC, little is known about its mechanism. Here, we present crystal structures of AlfC, combined with mutational and kinetic analyses, hydrogen–deuterium exchange mass spectrometry, molecular dynamic simulations, and transfucosylation experiments to define the molecular mechanisms of the activities of AlfC and its transfucosidase mutants. Our results indicate that AlfC creates an aromatic subsite adjacent to the active site that specifically accommodates GlcNAc in α(1,6)-linkages, suggest that enzymatic activity is controlled by distinct open and closed conformations of an active-site loop, with certain mutations shifting the equilibrium towards open conformations to promote transfucosylation over hydrolysis, and provide a potentially generalizable framework for the rational creation of AlfC transfucosidase mutants.

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The serpin plasminogen activator inhibitor 1 (PAI-1) spontaneously undergoes a massive structural change from a metastable, active conformation, with a solvent accessible reactive center loop (RCL), to a stable, inactive or latent conformation in which the RCL has inserted into the central β sheet. Physiologically, conversion to the latent state is regulated by the binding of vitronectin which retards the rate of this latency transition approximately 2-fold. We investigated the effects of vitronectin on the PAI-1 latency transition using all-atom path sampling simulations in explicit solvent. In simulated latency transitions of free PAI-1, the RCL is quite mobile as is the gate, the region that impedes RCL access to the central β sheet. This mobility allows the formation of a transient salt bridge that facilitates the transition, and this finding rationalizes existing mutagenesis results. Vitronectin binding reduces RCL and gate mobility by allosterically rigidifying structural elements over 40 Å away from the binding site thus blocking the transition to the latent conformation. The effects of vitronectin are propagated by a network of dynamically correlated residues including a number of conserved sites that have previously been identified as important for PAI-1 stability. Simulations also revealed a transient pocket populated only in the vitronectin bound state which corresponds to a cryptic drug binding site identified by crystallography. Overall, these results shed new light on regulation of the PAI-1 latency transition by vitronectin and illustrate the potential of path sampling simulations for understanding functional conformational changes in proteins and for facilitating drug discovery.

Interleukin-1 (IL-1) family cytokines are potent mediators of inflammation, acting to coordinate local and systemic immune responses to a wide range of stimuli. Aberrant signaling by IL-1 family cytokine members, however, is linked to myriad inflammatory syndromes, autoimmune conditions and cancers. As such, blocking the inflammatory signals inherent to IL-1 family signaling is an established and expanding therapeutic strategy. While several FDA-approved IL-1 inhibitors exist, including an Fc fusion protein, a neutralizing antibody, and an antagonist cytokine, none specifically targets the co-receptor IL-1 receptor accessory protein (IL-1RAcP). Most IL-1 family cytokines form productive signaling complexes by binding first to their cognate receptors – IL-1RI for IL-1α and IL-1β; ST2 for IL-33; and IL-36R for IL-36α, IL-36β and IL-36γ – after which they recruit the shared secondary receptor IL-1RAcP to form a ternary cytokine/receptor/co-receptor complex. Recently, IL-1RAcP was identified as a biomarker for both AML and CML. IL-1RAcP has also been implicated in tumor progression in solid tumors and an anti-IL1RAP antibody (nadunolimab, CAN04) is in phase II clinical studies in pancreatic cancer and non-small cell lung cancer (NCT03267316). As IL-1RAcP is common to all of the abovementioned IL-1 family cytokines, targeting this co-receptor raises the possibility of selective signaling inhibition for different IL-1 family cytokines. Indeed, previous studies of IL-1β and IL-33 signaling complexes have revealed that these cytokines employ distinct mechanisms of IL-1RAcP recruitment even though their overall cytokine/receptor/co-receptor complexes are structurally similar. Here, using functional, biophysical, and structural analyses, we show that antibodies specific for IL-1RAcP can differentially block signaling by IL-1 family cytokines depending on the distinct IL-1RAcP epitopes that they engage. Our results indicate that targeting a shared cytokine receptor is a viable therapeutic strategy for selective cytokine signaling inhibition.

Hydrogen-deuterium exchange (HDX) is a comprehensive yet detailed probe of protein structure and dynamics and, coupled to mass spectrometry, has become a powerful tool for investigating an increasingly large array of systems. Computer simulations are often used to help rationalize experimental observations of exchange, but interpretations have frequently been limited to simple, subjective correlations between microscopic dynamical fluctuations and the observed macroscopic exchange behavior. With this in mind, we previously developed the HDX ensemble reweighting approach and associated software, HDXer, to aid the objective interpretation of HDX data using molecular simulations. HDXer has two main functions; first, to compute H-D exchange rates that describe each structure in a candidate ensemble of protein structures, for example from molecular simulations, and second, to objectively reweight the conformational populations present in a candidate ensemble to conform to experimental exchange data. In this article, we first describe the HDXer approach, theory, and implementation. We then guide users through a suite of tutorials that demonstrate the practical aspects of preparing experimental data, computing HDX levels from molecular simulations, and performing ensemble reweighting analyses. Finally we provide a practical discussion of the capabilities and limitations of the HDXer methods including recommendations for a user's own analyses. Overall, this article is intended to provide an up-to-date, pedagogical counterpart to the software, which is freely available at https://github.com/Lucy-Forrest-Lab/HDXer.