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Approaches to in silico prediction of protein structures have been revolutionized by AlphaFold2, while those to predict interfaces between proteins are relatively underdeveloped, owing to the overly complicated yet relatively limited data of protein–protein complexes. In short, proteins are 1D sequences of amino acids folding into 3D structures, and interact to form assemblies to function. We believe that such intricate scenarios are better modeled with additional indicative information that reflects their multi-modality nature and multi-scale functionality. To improve binary prediction of inter-protein residue-residue contacts, we propose to augment input features with multi-modal representations and to synergize the objective with auxiliary predictive tasks. (i) We first progressively add three protein modalities into models: protein sequences, sequences with evolutionary information, and structure-aware intra-protein residue contact maps. We observe that utilizing all data modalities delivers the best prediction precision. Analysis reveals that evolutionary and structural information benefit predictions on the difficult and rigid protein complexes, respectively, assessed by the resemblance to native residue contacts in bound complex structures. (ii) We next introduce three auxiliary tasks via self-supervised pre-training (binary prediction of protein-protein interaction (PPI)) and multi-task learning (prediction of inter-protein residue–residue distances and angles). Although PPI prediction is reported to benefit from predicting intercontacts (as causal interpretations), it is not found vice versa in our study. Similarly, the finer-grained distance and angle predictions did not appear to uniformly improve contact prediction either. This again reflects the high complexity of protein–protein complex data, for which designing and incorporating synergistic auxiliary tasks remains challenging. 

Simultaneous spatial localization and structural characterization of molecules in complex biological samples currently represents an analytical challenge for mass spectrometry imaging (MSI) techniques. In this study, we describe a novel experimental platform, which substantially expands the capabilities and enhances the depth of chemical information obtained in high spatial resolution MSI experiments performed using nanospray desorption electrospray ionization (nano-DESI). Specifically, we designed and constructed a portable nano-DESI MSI platform and coupled it with a drift tube ion mobility spectrometer-mass spectrometer (IM-MS). Separation of biomolecules observed in MSI experiments based on their drift times provides unique molecular descriptors necessary for their identification by comparison with databases. Furthermore, it enables isomer-specific imaging, which is particularly important for unraveling the complexity of biological systems. Imaging of day 4 pregnant mouse uterine sections using the newly developed nano-DESI-IM-MSI system demonstrates rapid isobaric and isomeric separation and reduced chemical noise in MSI experiments. A direct comparison of the performance of the new nano-DESI-MSI platform operated in the MS mode with the more established nano-DESI-Orbitrap platform indicates a comparable performance of these two systems. A spatial resolution of better than ~16 μm and similar molecular coverage was obtained using both platforms. The structural information provided by the ion mobility separation expands the molecular specificity of high-resolution MSI necessary for the detailed understanding of biological systems. 

Glucuronidation is a common phase II metabolic process for drugs and xenobiotics which increases their solubility for excretion. Acyl glucuronides (glucuronides of carboxylic acids) present concerns of toxicity as they have been implicated in gastrointestinal toxicity and hepatic failure. Despite the substantial success in the bulk analysis of these species, little is known about their localization in tissues. Herein, we used nanospray desorption electrospray ionization mass spectrometry imaging (nano-DESI-MSI) to examine the localization of diclofenac, a widely used nonsteroidal anti-inflammatory drug, and its metabolites in mouse kidney and liver tissues. Nano-DESI allows for label-free imaging with high spatial resolution and sensitivity without special sample pretreatment. Using nano-DESI-MSI, ion images for diclofenac and its major metabolites were produced. MSI data acquired over a broad m/z range showed fairly low signals of the drug and its metabolites. At least an order of magnitude improvement in the signals was obtained using selected ion monitoring (SIM), with m/z windows centered around the low-abundance ions of interest. Using nano-DESI MSI in SIM mode, we observed that diclofenac acyl glucuronide is localized to the inner medulla and hydroxydiclofenac to the cortex of the kidney. The distributions observed for both metabolites closely match the previously reported localization of enzymes that process diclofenac into its respective metabolites. The localization of diclofenac acyl glucuronide to medulla likely indicates that the toxic metabolite is being excreted from the tissue. In contrast, a uniform distribution of diclofenac, hydroxydiclofenac and the diclofenac acyl glucuronide metabolite was observed in the liver tissue. Semiquantitative analysis found the metabolite to diclofenac ratios calculated from nano-DESI in agreement to those calculated from liquid chromatography tandem mass spectrometry (LC-MS/MS) experiments. Collectively, our results demonstrate nano-DESI-MSI can be successfully used to image diclofenac and its primary metabolites in dosed liver and kidney tissues from mice and derive semi-quantitative data from localized tissue regions.