QM/MM model of the mouse olfactory receptor MOR244-3 validated by site-directed mutagenesis experiments.

Understanding structure/function relationships of olfactory receptors is challenging due to the lack of x-ray structural models. Here, we introduce a QM/MM model of the mouse olfactory receptor MOR244-3, responsive to organosulfur odorants such as (methylthio)methanethiol. The binding site consists of a copper ion bound to the heteroatoms of amino-acid residues H105, C109, and N202. The model is consistent with site-directed mutagenesis experiments and biochemical measurements of the receptor activation, and thus provides a valuable framework for further studies of the sense of smell at the molecular level.

Genomic landscape of copy number aberrations enables the identification of oncogenic drivers in hepatocellular carcinoma.

UNLABELLED: Cancer is a genetic disease with frequent somatic DNA alterations. Studying recurrent copy number aberrations (CNAs) in human cancers would enable the elucidation of disease mechanisms and the prioritization of candidate oncogenic drivers with causal roles in oncogenesis. We have comprehensively and systematically characterized CNAs and the accompanying gene expression changes in tumors and matched nontumor liver tissues from 286 hepatocellular carcinoma (HCC) patients. Our analysis identified 29 recurrently amplified and 22 recurrently deleted regions with a high level of copy number changes. These regions harbor established oncogenes and tumor suppressors, including CCND1 (cyclin D1), MET (hepatocyte growth factor receptor), CDKN2A (cyclin-dependent kinase inhibitor 2A) and CDKN2B (cyclin-dependent kinase inhibitor 2B), as well as many other genes not previously reported to be involved in liver carcinogenesis. Pathway analysis of cis-acting genes in the amplification and deletion peaks implicates alterations of core cancer pathways, including cell-cycle, p53 signaling, phosphoinositide 3-kinase signaling, mitogen-activated protein kinase signaling, Wnt signaling, and transforming growth factor beta signaling, in a large proportion of HCC patients. We further credentialed two candidate driver genes (BCL9 and MTDH) from the recurrent focal amplification peaks and showed that they play a significant role in HCC growth and survival. CONCLUSION: We have demonstrated that characterizing the CNA landscape in HCC will facilitate the understanding of disease mechanisms and the identification of oncogenic drivers that may serve as potential therapeutic targets for the treatment of this devastating disease.

Metabolic activity organizes olfactory representations.

Hearing and vision sensory systems are tuned to the natural statistics of acoustic and electromagnetic energy on earth and are evolved to be sensitive in ethologically relevant ranges. But what are the natural statistics of odors, and how do olfactory systems exploit them? Dissecting an accurate machine learning model (Lee et al., 2022) for human odor perception, we find a computable representation for odor at the molecular level that can predict the odor-evoked receptor, neural, and behavioral responses of nearly all terrestrial organisms studied in olfactory neuroscience. Using this olfactory representation (principal odor map [POM]), we find that odorous compounds with similar POM representations are more likely to co-occur within a substance and be metabolically closely related; metabolic reaction sequences (Caspi et al., 2014) also follow smooth paths in POM despite large jumps in molecular structure. Just as the brain's visual representations have evolved around the natural statistics of light and shapes, the natural statistics of metabolism appear to shape the brain's representation of the olfactory world.

A principal odor map unifies diverse tasks in olfactory perception.

Mapping molecular structure to odor perception is a key challenge in olfaction. We used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sample odorants, the model-generated odor profile more closely matched the trained panel mean than did the median panelist. By applying simple, interpretable, theoretically rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.

The active site of melanopsin: the biological clock photoreceptor.

The nonvisual ocular photoreceptor melanopsin, found in the neurons of vertebrate inner retina, absorbs blue light and triggers the "biological clock" of mammals by activating the suprachiasmatic nuclei (a small region of the brain that regulates the circadian rhythms of neuronal and hormonal activities over 24 h cycles). The structure of melanopsin, however, has yet to be established. Here, we propose for the first time a structural model of the active site of mouse melanopsin. The homology model is based on the crystal structure of squid rhodopsin (λ(max) = 490 nm) and shows a maximal absorbance (λ(max) = 447 nm) consistent with the observed absorption of the photoreceptor. The 43 nm spectral shift is due to an increased bond-length alternation of the protonated Schiff base of 11-cis-retinal chromophore, induced by N87Q mutation and water-mediated H-bonding interactions with the Schiff base linkage. These findings, analogous to spectral changes observed in the G89Q bovine rhodopsin mutant, suggest that single site mutations can convert photopigments into visual light sensors or nonvisual sensory photoreceptors.

Rapid Prediction of Electron-Ionization Mass Spectrometry Using Neural Networks.

When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the library's coverage by augmenting it with synthetic spectra that are predicted from candidate molecules using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules, averaging 5 ms per molecule with a recall-at-10 accuracy of 91.8%. Achieving high-accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine-learning-based work on spectrum prediction.

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.

We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds. A deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to discrete molecular representations. The predictor estimates chemical properties from the latent continuous vector representation of the molecule. Continuous representations of molecules allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the domain of drug-like molecules and also in a set of molecules with fewer that nine heavy atoms.

Neural Networks for the Prediction of Organic Chemistry Reactions.

Reaction prediction remains one of the major challenges for organic chemistry and is a prerequisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reagents and reactants, predicts the likely products. We test this method on problems from a popular organic chemistry textbook.

Phosphorylated S6 kinase and S6 ribosomal protein are diagnostic markers of antibody-mediated rejection in heart allografts.

BACKGROUND: Anti-MHC Class I alloantibodies have been implicated in the processes of acute and chronic rejection. These antibodies (Ab) bind to endothelial cells (EC) and transduce signals leading to the activation of cell survival and proliferation pathways, including Src, FAK and mTOR, as well as downstream targets ERK, S6 kinase (S6K) and S6 ribosomal protein (S6RP). We tested the hypothesis that phosphorylation of S6K, S6RP and ERK in capillary endothelium may serve as an adjunct diagnostic tool for antibody-mediated rejection (AMR) in heart allografts. METHODS: Diagnosis of AMR was based on histology or immunoperoxidase staining of paraffin-embedded tissue, consistent with 2013 ISHLT criteria. Diagnosis of acute cellular rejection (ACR) was based on ISHLT criteria. Endomyocardial biopsies from 67 heart transplant recipients diagnosed with acute rejection [33 with pAMR, 18 with ACR (15 with Grade 1R, 3 with Grade ≥2R), 16 with pAMR and ACR (13 with 1R and 3 with ≥2R)] and 40 age- and gender-matched recipients without rejection were tested for the presence of phosphorylated forms of ERK, S6RP and S6K by immunohistochemistry. RESULTS: Immunostaining of endomyocardial biopsies with evidence of pAMR showed a significant increase in expression of p-S6K and p-S6RP in capillary EC compared with controls. A weaker association was observed between pAMR and p-ERK. CONCLUSIONS: Biopsies diagnosed with pAMR often showed phosphorylation of S6K and S6RP, indicating that staining for p-S6K and p-S6RP is useful for the diagnosis of AMR. Our findings support a role for antibody-mediated HLA signaling in the process of graft injury.

Printing colour at the optical diffraction limit.

The highest possible resolution for printed colour images is determined by the diffraction limit of visible light. To achieve this limit, individual colour elements (or pixels) with a pitch of 250 nm are required, translating into printed images at a resolution of ∼100,000 dots per inch (d.p.i.). However, methods for dispensing multiple colourants or fabricating structural colour through plasmonic structures have insufficient resolution and limited scalability. Here, we present a non-colourant method that achieves bright-field colour prints with resolutions up to the optical diffraction limit. Colour information is encoded in the dimensional parameters of metal nanostructures, so that tuning their plasmon resonance determines the colours of the individual pixels. Our colour-mapping strategy produces images with both sharp colour changes and fine tonal variations, is amenable to large-volume colour printing via nanoimprint lithography, and could be useful in making microimages for security, steganography, nanoscale optical filters and high-density spectrally encoded optical data storage.