Influenza hemagglutinin (HA), a homotrimeric glycoprotein crucial for membrane fusion, undergoes a large-scale structural rearrangement during viral invasion. X-ray crystallography has shown that the pre-and postfusion configurations of HA 2 , the membranefusion subunit of HA, have disparate secondary, tertiary, and quaternary structures, where some regions are displaced by more than 100 Å. To explore structural dynamics during the conformational transition, we studied simulations of a minimally frustrated model based on energy landscape theory. The model combines structural information from both the pre-and postfusion crystallographic configurations of HA 2 . Rather than a downhill drive toward formation of the central coiled-coil, we discovered an order-disorder transition early in the conformational change as the mechanism for the release of the fusion peptides from their burial sites in the prefusion crystal structure. This disorder quickly leads to a metastable intermediate with a broken threefold symmetry. Finally, kinetic competition between the formation of the extended coiled-coil and C-terminal melting results in two routes from this intermediate to the postfusion structure. Our study reiterates the roles that cracking and disorder can play in functional molecular motions, in contrast to the downhill mechanical interpretations of the "springloaded" model proposed for the HA 2 conformational transition.protein folding | structure-based model H emagglutinin (HA) is a viral receptor-binding and membrane-fusion glycoprotein involved in the invasion of influenza virions into host cells (1). Structural rearrangements of HA during membrane fusion are crucial for the delivery of the viral genome. The postfusion conformation of HA shows considerable similarity to other viral fusion proteins and eukaryotic membrane receptors involved in intracellular vesicle trafficking (2), suggesting there may be common mechanisms in the function of these proteins. Therefore, HA may serve as a model system, allowing characterization of the molecular and energetic details that underlie its conformational transition to provide insights into general principles of membrane fusion (3).HA is a homotrimer consisting of two domains connected by disulfide bonds (4): a globular receptor binding domain (HA 1 ), and a coiled-coil membrane-fusion domain anchored to the viral membrane (HA 2 ). Recognized by the sialic acid receptor of a host cell, the intact virus enters the cell via endocytosis. Low pH in a late endosome then induces the dissociation of HA 1 from HA 2 (1) and an irreversible conformational transition of HA 2 . Experimentally, this conformational change can be triggered by either low pH, high temperature, or urea denaturation (5).Structures of HA in pre-and postfusion pH conformations have been solved by X-ray crystallography. The structure of the prefusion ectodomain contains both HA 1 and HA 2 , and was purified from influenza virions (4). A postfusion conformation of HA 1 and HA 2 were obtained from prefusion viral HA th...

Class I viral fusion proteins are α-helical proteins that facilitate membrane fusion between viral and host membranes through large conformational transitions. Although prefusion and postfusion crystal structures have been solved for many of these proteins, details about how they transition between these states have remained elusive. This work presents the first, to our knowledge, computational survey of transitions between pre- and postfusion configurations for several class I viral fusion proteins using structure-based models to analyze their dynamics. As suggested by their structural similarities, all proteins share common mechanistic features during their transitions that can be characterized by a diffusive rotational search followed by cooperative N- and C-terminal zipping. Instead of predicting a stable spring-loaded intermediate, our model suggests that helical bundle formation is mediated by N- and C-terminal interactions late in the transition. Shared transition features suggest a global mechanism in which fusion is activated by slow protein-core rotation.

Treatment of non-small cell lung cancer is increasingly biomarker driven with multiple genomic alterations, including those in the epidermal growth factor receptor (EGFR) gene, that benefit from targeted therapies. We developed a set of algorithms to assess EGFR status and morphology using a real-world advanced lung adenocarcinoma cohort of 2099 patients with hematoxylin and eosin (H&E) images exhibiting high morphological diversity and low tumor content relative to public datasets. The best performing EGFR algorithm was attention-based and achieved an area under the curve (AUC) of 0.870, a negative predictive value (NPV) of 0.954 and a positive predictive value (PPV) of 0.410 in a validation cohort reflecting the 15% prevalence of EGFR mutations in lung adenocarcinoma. The attention model outperformed a heuristic-based model focused exclusively on tumor regions, and we show that although the attention model also extracts signal primarily from tumor morphology, it extracts additional signal from non-tumor tissue regions. Further analysis of high-attention regions by pathologists showed associations of predicted EGFR negativity with solid growth patterns and higher peritumoral immune presence. This algorithm highlights the potential of deep learning tools to provide instantaneous rule-out screening for biomarker alterations and may help prioritize the use of scarce tissue for biomarker testing.

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