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Site-directed Cu(II)-labelling in pulsed electron paramagnetic resonance (EPR) spectroscopy has demonstrated narrow Cu(II)-Cu(II) distance distributions suitable to resolve subtle protein conformational changes. The high precision derives from a double histidine (dHis) mutation that effectively locks a Cu(II)-nitrilotriacetic acid (Cu(II)-NTA) moiety in place. To date, no structures featuring the dHis-Cu(II)-NTA motif have been resolved. This work presents the atomic-resolution X-ray crystal structures of seven α-helical dHis sites of T4 lysozyme (T4L) in the presence and absence of Cu(II)-NTA. Our research captured the rigid octahedral coordination of the dHis-Cu(II)-NTA complex as well as non-conventional binding modes, which provide valuable insight into dHis site selection. Pulsed EPR experiments on double dHis T4L mutants displayed remarkable agreement to the crystallography-derived distances. This research showcases the rigid configuration of the dHis-Cu(II)-NTA motif, providing geometric constraints that can be leveraged in modeling and molecular dynamics programs to extract protein structural details from EPR experiments.

Antibodies are naturally evolved molecular recognition scaffolds that can bind a variety of surfaces. Their designability is crucial to the development of biologics, with computational methods holding promise in accelerating the delivery of medicines to the clinic. Modeling antibody-antigen recognition is prohibitively difficult, with data paucity being one of the biggest hurdles. Current affinity datasets comprise a small number of experimental measurements, which are often not standardized between molecules. Here, we address these issues by creating a dataset of seven antigens with two antibodies each, for which we introduce a heterogeneous set of mutations to the CDR-H3 measured by ELISA. Each of the parental complexes has a known crystal structure. We perform benchmarking of state-of-the-art affinity prediction algorithms to gauge their effectiveness. Current computational methods exhibit substantial limitations in accurately predicting the effects of single-point mutations. In contrast, the older empirical, physics-based method FoldX performs well in identifying mutants that retain binding. These findings highlight the need for more resources like the one presented here, i.e. large, molecularly diverse, and experimentally consistent datasets.

The SARS-CoV-2 spike protein is highly antigenic, with epitopes in three distinct regions of the receptor binding domain (RBD) alone that have known mechanisms of neutralization. In previous work, we predicted a fourth RBD epitope based on allosteric conformational perturbations measured by hydrogen-deuterium exchange mass spectrometry (HDX-MS) upon complexation with the canonical spike protein target, human angiotensin-converting enzyme 2 (hACE2). We subsequently identified a pan-neutralizing antibody (ICO-hu104) with the predicted epitope, however, as the epitope was somewhat distant from the hACE2 binding interface, and our previous work limited to the spike RBD, the neutralization mechanism was unclear. Using HDX-MS, we investigated the binding of ICO-hu104 to the full-length SARS-CoV-2 spike protein from Wuhan, Delta and Omicron variants. We demonstrate that binding of ICO-hu104 results in an increase in deuterium uptake in the distant HR1 domain in latter variants, which in a biological context could be indicative of destabilisation of the helices within this region, promoting premature S1 shedding or failure of helical extension during S2-mediated fusion. This is supported by our computational modelling, highlighting propagation of allosteric effects to the S2 coiled-coil region upon rigidification of the ICO-hu104 epitope. Collectively, this work demonstrates an alternative neutralization mechanism for ICO-hu104 which is distinct from its first-generation predecessors and thus opens alternative avenues targeting non-RBD epitopes through allosteric perturbations.