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Solvent structures that surround active sites reorganize during catalysis and influence the stability of surface intermediates. Within zeolite pores, H 2 O molecules form hydrogen-bonded structures that differ substantially from bulk H 2 O. Here, we show by spectroscopic measurements and molecular dynamics simulations that H 2 O molecules form bulk-like three-dimensional structures within 1.3 nm cages, whereas H 2 O molecules coalesce into oligomeric one-dimensional chains when the pore diameter falls below 0.65 nm. The differences between these solvent structure motifs provide opportunities to manipulate enthalpy–entropy compensation relationships and greatly increase the rates of catalysis. We describe how the reorganization of these pore-size-dependent H 2 O structures during alkene epoxidation catalysis gives rise to entropy gains that increase the turnover rates by up to 400-fold. Collectively, this work shows that solvent molecules form distinct structures with a highly correlated motion within microporous environments, and the reorganization of these structures may be controlled to confer stability to the desired reactive intermediates. Solvent structuring affects the energy landscape of catalytic reactions, but the quantitative understanding of such effects remains difficult. Now, the structure of water within the micropores of different zeolites is disclosed together with the effects that its reorganization has over alkene epoxidation catalysis.

Integrins are heterodimeric receptors important for cell adhesion and signaling. Integrin α5β1 is a key mediator of angiogenesis and its dysregulation is associated with tumor progression and metastasis. Despite numerous efforts, α5β1-targeting therapeutics have been unsuccessful due to poor efficacy and off-target effects. A contributing factor is our limited understanding of how integrin conformation influences interactions with therapeutics. Using cell-based functional assays, patient derived xenografts, biophysics, and electron microscopy, we shed light on these relationships by characterizing two anti-α5β1 antibodies, BIIG2 and MINT1526A. We show that both antibodies bind α5β1 with nanomolar affinity, reduce angiogenesis , and bind overlapping epitopes that block fibronectin binding. However, using cryoEM, we reveal that while BIIG2 binding doesn't alter the conformational states, MINT1526A restricts α5β1's range of flexibility. These insights can guide which aspects to prioritize and improve the design of future integrin-targeted therapeutics.

A potential therapeutic candidate for neutralizing SARS-CoV-2 infection is engineering high- affinity soluble ACE2 decoy proteins to compete for binding of the viral spike (S) protein. Previously, a deep mutational scan of ACE2 was performed and has led to the identification of a triple mutant ACE2 variant, named ACE22.v.2.4, that exhibits nanomolar affinity binding to the RBD domain of S. Using a recently developed transfer learning algorithm, TLmutation, we sought to identified other ACE2 variants, namely double mutants, that may exhibit similar binding affinity with decreased mutational load. Upon training a TLmutation model on the effects of single mutations, we identified several ACE2 double mutants that bind to RBD with tighter affinity as compared to the wild type, most notably, L79V;N90D that binds RBD with similar affinity to ACE22.v.2.4. The successful experimental validation of the double mutants demonstrated the use transfer and supervised learning approaches for engineering protein-protein interactions and identifying high affinity ACE2 peptides for targeting SARS-CoV-2. Competing Interest Statement E.P. is an inventor on a patent filing by the University of Illinois covering the use of engineered peptides targeting coronaviruses. E.P. and K.K.C. are cofounders of Orthogonal Biologics, which licenses the intellectual property and is in a business partnership with Cyrus Biotechnology.

Integrins are a large family of heterodimeric receptors important for cell adhesion and signaling. Integrin α5β1, also known as the fibronectin receptor, is a key mediator of angiogenesis and its dysregulation is associated with tumor proliferation, progression, and metastasis. Despite numerous efforts, α5β1-targeting therapeutics have been unsuccessful in large part due to efficacy and off-target effects. To mediate activation and signaling, integrins undergo drastic conformational changes. However, how therapeutics influence or are affected by integrin conformation remains incompletely characterized. Using cell biology, biophysics, and electron microscopy, we shed light on these relationships by characterizing two potentially therapeutic anti-α5β1 antibodies, BIIG2 and MINT1526A. We show that both antibodies bind α5β1 with nanomolar affinity and reduce angiogenesis in vitro. We demonstrate BIIG2 reduces tumor growth in two human xenograft mouse models and exhibits a strong specificity for connective tissue-resident fibroblasts and melanoma cells. Using electron microscopy, we map out the molecular interfaces mediating the integrin-antibody interactions and reveal that although both antibodies have overlapping epitopes and block fibronectin binding via steric hindrance, the effect on the conformational equilibrium is drastically different. While MINT1526A constricts α5β1's range of flexibility, BIIG2 binds without restricting the available conformational states. These mechanistic insights, coupled with the functional analysis, guide which aspects should be prioritized to avoid off-target effects or partial agonism in the design of future integrin-targeted therapeutics.Integrins are a large family of heterodimeric receptors important for cell adhesion and signaling. Integrin α5β1, also known as the fibronectin receptor, is a key mediator of angiogenesis and its dysregulation is associated with tumor proliferation, progression, and metastasis. Despite numerous efforts, α5β1-targeting therapeutics have been unsuccessful in large part due to efficacy and off-target effects. To mediate activation and signaling, integrins undergo drastic conformational changes. However, how therapeutics influence or are affected by integrin conformation remains incompletely characterized. Using cell biology, biophysics, and electron microscopy, we shed light on these relationships by characterizing two potentially therapeutic anti-α5β1 antibodies, BIIG2 and MINT1526A. We show that both antibodies bind α5β1 with nanomolar affinity and reduce angiogenesis in vitro. We demonstrate BIIG2 reduces tumor growth in two human xenograft mouse models and exhibits a strong specificity for connective tissue-resident fibroblasts and melanoma cells. Using electron microscopy, we map out the molecular interfaces mediating the integrin-antibody interactions and reveal that although both antibodies have overlapping epitopes and block fibronectin binding via steric hindrance, the effect on the conformational equilibrium is drastically different. While MINT1526A constricts α5β1's range of flexibility, BIIG2 binds without restricting the available conformational states. These mechanistic insights, coupled with the functional analysis, guide which aspects should be prioritized to avoid off-target effects or partial agonism in the design of future integrin-targeted therapeutics.

Heterodimeric integrin proteins transmit signals through conformational changes upon ligand binding between their alpha (α) and beta (β) subunits. Early in chordate evolution, some α subunits acquired an \"inserted\" (I) domain, which expanded their ligand binding capacity but simultaneously obstructed the ancestral ligand-binding pocket. While this would seemingly impede conventional ligand-mediated integrin activation, it was proposed that the I domain itself could serve both as a ligand replacement and an activation trigger. Here, we provide compelling evidence in support of this longstanding hypothesis using high-resolution cryo-electron microscopy structures of two distinct integrin complexes: the ligand-free and E-cadherin-bound states of the αEβ7 integrin with the I domain, as well as the α4β7 integrin lacking the I domain in both a ligand-free state and bound to MadCAM-1. We trace the evolutionary origin of the I domain to an ancestral collagen-collagen interaction domain. Our analyses illuminate how the I domain intrinsically mimics an extrinsic ligand, enabling integrins to undergo the canonical allosteric cascade of conformational activation and dramatically expanding the range of cellular communication mechanisms in vertebrates.

In this thesis, we present proof-of-concept studies that describe how data-driven techniques can be applied to observational data in order to detect and estimate properties of galaxy clusters, which are the largest gravitationally bound objects to have assembled in the Universe. Given the significance of clusters in astrophysics and cosmology, it is important to develop automated methods that are able to efficiently detect and process a large sample of clusters from existing photometric datasets, in preparation for upcoming large-scale galaxy surveys. This can be achieved by employing machine learning algorithms that are suited at solving the tasks at hand. In particular, algorithms that can self-learn the importance of features from the labelled data of known clusters, which minimises the amount of manual input required to make accurate predictions.Initially, we demonstrate how a popular object detection algorithm can be applied to wide-field colour images to identify and predict the astronomical coordinates of clusters. We then demonstrate how a novel ensemble regression algorithm can be applied to line-of-sight galaxies within colour-magnitude space to estimate the photometric redshift of clusters. Finally, we present a hybrid empirical and analytical model that performs background subtraction of field galaxies along the line-of-sight of clusters within colour-magnitude space and then estimates the richness of clusters within a characteristic radius.We also compare our findings with the results of existing conventional techniques to examine the overall predictive performance of our methods at generalising to unseen instances. Furthermore, we note that our methods can be combined together into a sequential data pipeline to create a comprehensive catalogue that contains key characteristics (e.g. position, distance, mass) of observed clusters for conducting astrophysical and cosmological research.

We introduce AutoEnRichness, a hybrid approach that combines empirical and analytical strategies to determine the richness of galaxy clusters (in the redshift range of \\(0.1 \\leq z \\leq 0.35\\)) using photometry data from the Sloan Digital Sky Survey Data Release 16, where cluster richness can be used as a proxy for cluster mass. In order to reliably estimate cluster richness, it is vital that the background subtraction is as accurate as possible when distinguishing cluster and field galaxies to mitigate severe contamination. AutoEnRichness is comprised of a multi-stage machine learning algorithm that performs background subtraction of interloping field galaxies along the cluster line-of-sight and a conventional luminosity distribution fitting approach that estimates cluster richness based only on the number of galaxies within a magnitude range and search area. In this proof-of-concept study, we obtain a balanced accuracy of \\(83.20\\) per cent when distinguishing between cluster and field galaxies as well as a median absolute percentage error of \\(33.50\\) per cent between our estimated cluster richnesses and known cluster richnesses within \\(r_{200}\\). In the future, we aim for AutoEnRichness to be applied on upcoming large-scale optical surveys, such as the Legacy Survey of Space and Time and \\(\\textit{Euclid}\\), to estimate the richness of a large sample of galaxy groups and clusters from across the halo mass function. This would advance our overall understanding of galaxy evolution within overdense environments as well as enable cosmological parameters to be further constrained.

We introduce Z-Sequence, a novel empirical model that utilises photometric measurements of observed galaxies within a specified search radius to estimate the photometric redshift of galaxy clusters. Z-Sequence itself is composed of a machine learning ensemble based on the k-nearest neighbours algorithm. We implement an automated feature selection strategy that iteratively determines appropriate combinations of filters and colours to minimise photometric redshift prediction error. We intend for Z-Sequence to be a standalone technique but it can be combined with cluster finders that do not intrinsically predict redshift, such as our own DEEP-CEE. In this proof-of-concept study we train, fine-tune and test Z-Sequence on publicly available cluster catalogues derived from the Sloan Digital Sky Survey. We determine the photometric redshift prediction error of Z-Sequence via the median value of \\(|\\Delta z|/(1+z)\\) (across a photometric redshift range of \\(0.05 \\le \\textit{z} \\le 0.6\\)) to be \\(\\sim0.01\\) when applying a small search radius. The photometric redshift prediction error for test samples increases by 30-50 per cent when the search radius is enlarged, likely due to line-of-sight interloping galaxies. Eventually, we aim to apply Z-Sequence to upcoming imaging surveys such as the Legacy Survey of Space and Time to provide photometric redshift estimates for large samples of as yet undiscovered and distant clusters.

Membrane transporters of the solute carrier 6 (SLC6) family mediate various physiological processes by facilitating the translocation of amino acids, neurotransmitters, and other metabolites. In the human body, these transporters are tightly controlled through various post-translational modifications with implications on protein expression, stability, membrane trafficking, and dynamics. While N-linked glycosylation is a universal regulatory mechanism among eukaryotes, the exact molecular mechanism of how glycosylation affects the SLC6 transporter family. It is generally believed that glycans influence transporter stability and membrane trafficking, however, the role of glycosylation on transporter dynamics remains inconsistent, with differing conclusions among individual transporters across the SLC6 family. In this study, we collected over 1 millisecond of aggregated all-atom molecular dynamics (MD) simulation data to identify the impact of N-glycans of four human SLC6 transporters: the serotonin transporter, dopamine transporter, glycine transporter, and neutral amino acid transporter B0AT1. We designed our computational study by first simulating all possible combination of a glycan attached to each glycosylation sites followed by investigating the effect of larger, oligo-N-linked glycans to each transporter. Our simulations reveal that glycosylation does not significantly affect transporter structure, but alters the dynamics of the glycosylated extracellular loop. The structural consequences of glycosylation on the loop dynamics are further emphasized in the presence of larger glycan molecules. However, no apparent trend in ligand stability or movement of gating helices was observed. In all, the simulations suggest that glycosylation does not consistently affect transporter structure and dynamics among the collective SLC6 family and should be characterized at a per-transporter level to further elucidate the underlining mechanisms of in vivo regulation.