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Many biological mechanisms rely on the precise control of conformational changes in proteins. Understanding such dynamic processes requires methods for determining structures and their temporal evolution. In this study, we introduce a novel approach to time-resolved ion mobility mass spectrometry. We validated the method on a simple photoreceptor model and applied it to a more complex system, the animal-like cryptochrome from Chlamydomonas reinhardtii ( Cr aCRY), to determine the role of specific amino acids affecting the conformational dynamics as reaction to blue light activation. In our setup, using a high-power LED mounted in the source region of an ion mobility mass spectrometer, we allow a time-resolved evaluation of mass and ion mobility spectra. Cryptochromes like Cr aCRY are a widespread type of blue light photoreceptors and mediate various light-triggered biological functions upon excitation of their inbuilt flavin chromophore. Another hallmark of cryptochromes is their flexible carboxy-terminal extension (CTE), whose structure and function as well as the details of its interaction with the photolyase homology region are not yet fully understood and differ among different cryptochromes types. Here, we addressed the highly conserved C-terminal domain of Cr aCRY, to study the effects of single mutations on the structural transition of the C-terminal helix α22 and the attached CTE upon lit-state formation. We show that D321, the putative proton acceptor of the terminal proton-coupled electron transfer event from Y373, is essential for triggering the large-scale conformational changes of helix α22 and the CTE in the lit state, while D323 influences the timing.

Cryptochromes are a ubiquitously occurring class of photoreceptors. Together with photolyases, they form the Photolyase Cryptochrome Superfamily (PCSf) by sharing a common protein architecture and binding mode of the FAD chromophore. Despite these similarities, PCSf members exert different functions. Photolyases repair UV-induced DNA damage by photocatalytically driven electron transfer between FADH ¯ and the DNA lesion, whereas cryptochromes are light-dependent signaling molecules and trigger various biological processes by photoconversion of their FAD redox and charge states. Given that most cryptochromes possess a C-terminal extension (CTE) of varying length, the functions of their CTE have not yet been fully elucidated and are hence highly debated. In this study, the role of the CTE was investigated for a novel subclass of the PCSf, the CryP-like cryptochromes, by hydrogen/deuterium exchange and mass-spectrometric analysis. Striking differences in the relative deuterium uptake were observed in different redox states of CryP from the diatom Phaeodactylum tricornutum. Based on these measurements we propose a model for light-triggered conformational changes in CryP-like cryptochromes that differs from other known cryptochrome families like the insect or plant cryptochromes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Machine learning (ML) models are poised to transform surgical pathology practice. The most successful use attention mechanisms to examine whole slides, identify which areas of tissue are diagnostic, and use them to guide diagnosis. Tissue contaminants, such as floaters, represent unexpected tissue. Although human pathologists are extensively trained to consider and detect tissue contaminants, we examined their impact on ML models. We trained 4 whole-slide models. Three operate in placenta for the following functions: (1) detection of decidual arteriopathy, (2) estimation of gestational age, and (3) classification of macroscopic placental lesions. We also developed a model to detect prostate cancer in needle biopsies. We designed experiments wherein patches of contaminant tissue are randomly sampled from known slides and digitally added to patient slides and measured model performance. We measured the proportion of attention given to contaminants and examined the impact of contaminants in the t-distributed stochastic neighbor embedding feature space. Every model showed performance degradation in response to one or more tissue contaminants. Decidual arteriopathy detection--balanced accuracy decreased from 0.74 to 0.69 ± 0.01 with addition of 1 patch of prostate tissue for every 100 patches of placenta (1% contaminant). Bladder, added at 10% contaminant, raised the mean absolute error in estimating gestational age from 1.626 weeks to 2.371 ± 0.003 weeks. Blood, incorporated into placental sections, induced false-negative diagnoses of intervillous thrombi. Addition of bladder to prostate cancer needle biopsies induced false positives, a selection of high-attention patches, representing 0.033 mm 2 , and resulted in a 97% false-positive rate when added to needle biopsies. Contaminant patches received attention at or above the rate of the average patch of patient tissue. Tissue contaminants induce errors in modern ML models. The high level of attention given to contaminants indicates a failure to encode biological phenomena. Practitioners should move to quantify and ameliorate this problem., (Copyright © 2024 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved.)