Flexible scaffold-based cheminformatics approach for polypharmacological drug design.

Effective treatments for complex central nervous system (CNS) disorders require drugs with polypharmacology and multifunctionality, yet designing such drugs remains a challenge. Here, we present a flexible scaffold-based cheminformatics approach (FSCA) for the rational design of polypharmacological drugs. FSCA involves fitting a flexible scaffold to different receptors using different binding poses, as exemplified by IHCH-7179, which adopted a "bending-down" binding pose at 5-HT2AR to act as an antagonist and a "stretching-up" binding pose at 5-HT1AR to function as an agonist. IHCH-7179 demonstrated promising results in alleviating cognitive deficits and psychoactive symptoms in mice by blocking 5-HT2AR for psychoactive symptoms and activating 5-HT1AR to alleviate cognitive deficits. By analyzing aminergic receptor structures, we identified two featured motifs, the "agonist filter" and "conformation shaper," which determine ligand binding pose and predict activity at aminergic receptors. With these motifs, FSCA can be applied to the design of polypharmacological ligands at other receptors.

Structure of the Nanobody-Stabilized Active State of the Kappa Opioid Receptor.

The κ-opioid receptor (KOP) mediates the actions of opioids with hallucinogenic, dysphoric, and analgesic activities. The design of KOP analgesics devoid of hallucinatory and dysphoric effects has been hindered by an incomplete structural and mechanistic understanding of KOP agonist actions. Here, we provide a crystal structure of human KOP in complex with the potent epoxymorphinan opioid agonist MP1104 and an active-state-stabilizing nanobody. Comparisons between inactive- and active-state opioid receptor structures reveal substantial conformational changes in the binding pocket and intracellular and extracellular regions. Extensive structural analysis and experimental validation illuminate key residues that propagate larger-scale structural rearrangements and transducer binding that, collectively, elucidate the structural determinants of KOP pharmacology, function, and biased signaling. These molecular insights promise to accelerate the structure-guided design of safer and more effective κ-opioid receptor therapeutics.

The Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders.

Although gene discovery in neuropsychiatric disorders, including autism spectrum disorder, intellectual disability, epilepsy, schizophrenia, and Tourette disorder, has accelerated, resulting in a large number of molecular clues, it has proven difficult to generate specific hypotheses without the corresponding datasets at the protein complex and functional pathway level. Here, we describe one path forward-an initiative aimed at mapping the physical and genetic interaction networks of these conditions and then using these maps to connect the genomic data to neurobiology and, ultimately, the clinic. These efforts will include a team of geneticists, structural biologists, neurobiologists, systems biologists, and clinicians, leveraging a wide array of experimental approaches and creating a collaborative infrastructure necessary for long-term investigation. This initiative will ultimately intersect with parallel studies that focus on other diseases, as there is a significant overlap with genes implicated in cancer, infectious disease, and congenital heart defects.

Crystal Structure of an LSD-Bound Human Serotonin Receptor.

The prototypical hallucinogen LSD acts via serotonin receptors, and here we describe the crystal structure of LSD in complex with the human serotonin receptor 5-HT2B. The complex reveals conformational rearrangements to accommodate LSD, providing a structural explanation for the conformational selectivity of LSD's key diethylamide moiety. LSD dissociates exceptionally slow from both 5-HT2BR and 5-HT2AR-a major target for its psychoactivity. Molecular dynamics (MD) simulations suggest that LSD's slow binding kinetics may be due to a "lid" formed by extracellular loop 2 (EL2) at the entrance to the binding pocket. A mutation predicted to increase the mobility of this lid greatly accelerates LSD's binding kinetics and selectively dampens LSD-mediated ß-arrestin2 recruitment. This study thus reveals an unexpected binding mode of LSD; illuminates key features of its kinetics, stereochemistry, and signaling; and provides a molecular explanation for LSD's actions at human serotonin receptors. PAPERCLIP.

A whole-slide foundation model for digital pathology from real-world data.

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1-3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context4. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data6. With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision-language pretraining for pathology7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling.

The rise of baobab trees in Madagascar.

The baobab trees (genus Adansonia) have attracted tremendous attention because of their striking shape and distinctive relationships with fauna1. These spectacular trees have also influenced human culture, inspiring innumerable arts, folklore and traditions. Here we sequenced genomes of all eight extant baobab species and argue that Madagascar should be considered the centre of origin for the extant lineages, a key issue in their evolutionary history2,3. Integrated genomic and ecological analyses revealed the reticulate evolution of baobabs, which eventually led to the species diversity seen today. Past population dynamics of Malagasy baobabs may have been influenced by both interspecific competition and the geological history of the island, especially changes in local sea levels. We propose that further attention should be paid to the conservation status of Malagasy baobabs, especially of Adansonia suarezensis and Adansonia grandidieri, and that intensive monitoring of populations of Adansonia za is required, given its propensity for negatively impacting the critically endangered Adansonia perrieri.

Recognition of methamphetamine and other amines by trace amine receptor TAAR1.

Trace amine-associated receptor 1 (TAAR1), the founding member of a nine-member family of trace amine receptors, is responsible for recognizing a range of biogenic amines in the brain, including the endogenous ß-phenylethylamine (ß-PEA)1 as well as methamphetamine2, an abused substance that has posed a severe threat to human health and society3. Given its unique physiological role in the brain, TAAR1 is also an emerging target for a range of neurological disorders including schizophrenia, depression and drug addiction2,4,5. Here we report structures of human TAAR1-G-protein complexes bound to methamphetamine and ß-PEA as well as complexes bound to RO5256390, a TAAR1-selective agonist, and SEP-363856, a clinical-stage dual agonist for TAAR1 and serotonin receptor 5-HT1AR (refs. 6,7). Together with systematic mutagenesis and functional studies, the structures reveal the molecular basis of methamphetamine recognition and underlying mechanisms of ligand selectivity and polypharmacology between TAAR1 and other monoamine receptors. We identify a lid-like extracellular loop 2 helix/loop structure and a hydrogen-bonding network in the ligand-binding pockets, which may contribute to the ligand recognition in TAAR1. These findings shed light on the ligand recognition mode and activation mechanism for TAAR1 and should guide the development of next-generation therapeutics for drug addiction and various neurological disorders.

Efficient Fizeau drag from Dirac electrons in monolayer graphene.

Fizeau demonstrated in 1850 that the speed of light can be modified when it is propagating in moving media1. However, such control of the light speed has not been achieved efficiently with a fast-moving electron media by passing an electrical current. Because the strong electromagnetic coupling between the electron and light leads to the collective excitation of plasmon polaritons, it is hypothesized that Fizeau drag in electron flow systems manifests as a plasmonic Doppler effect. Experimental observation of the plasmonic Doppler effect in electronic systems has been challenge because the plasmon propagation speed is much faster than the electron drift velocity in conventional noble metals. Here we report direct observation of Fizeau drag of plasmon polaritons in strongly biased monolayer graphene by exploiting the high electron mobility and the slow plasmon propagation of massless Dirac electrons. The large bias current in graphene creates a fast-drifting Dirac electron medium hosting the plasmon polariton. This results in non-reciprocal plasmon propagation, where plasmons moving with the drifting electron media propagate at an enhanced speed. We measure the Doppler-shifted plasmon wavelength using cryogenic near-field infrared nanoscopy, which directly images the plasmon polariton mode in the biased graphene at low temperature. We observe a plasmon wavelength difference of up to 3.6 per cent between a plasmon moving with and a plasmon moving against the drifting electron media. Our findings on the plasmonic Doppler effect provide opportunities for electrical control of non-reciprocal surface plasmon polaritons in non-equilibrium systems.

Aging and comprehensive molecular profiling in acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aging-related and heterogeneous hematopoietic malignancy. In this study, a total of 1,474 newly diagnosed AML patients with RNA sequencing data were enrolled, and targeted or whole exome sequencing data were obtained in 94% cases. The correlation of aging-related factors including age and clonal hematopoiesis (CH), gender, and genomic/transcriptomic profiles (gene fusions, genetic mutations, and gene expression networks or pathways) was systematically analyzed. Overall, AML patients aged 60 y and older showed an apparently dismal prognosis. Alongside age, the frequency of gene fusions defined in the World Health Organization classification decreased, while the positive rate of gene mutations, especially CH-related ones, increased. Additionally, the number of genetic mutations was higher in gene fusion-negative (GF-) patients than those with GF. Based on the status of CH- and myelodysplastic syndromes (MDS)-related mutations, three mutant subgroups were identified among the GF- AML cohort, namely, CH-AML, CH-MDS-AML, and other GF- AML. Notably, CH-MDS-AML demonstrated a predominance of elderly and male cases, cytopenia, and significantly adverse clinical outcomes. Besides, gene expression networks including HOXA/B, platelet factors, and inflammatory responses were most striking features associated with aging and poor prognosis in AML. Our work has thus unraveled the intricate regulatory circuitry of interactions among different age, gender, and molecular groups of AML.

Pleiotropy of autism-associated chromatin regulators.

Gene ontology analyses of high-confidence autism spectrum disorder (ASD) risk genes highlight chromatin regulation and synaptic function as major contributors to pathobiology. Our recent functional work in vivo has additionally implicated tubulin biology and cellular proliferation. As many chromatin regulators, including the ASD risk genes ADNP and CHD3, are known to directly regulate both tubulins and histones, we studied the five chromatin regulators most strongly associated with ASD (ADNP, CHD8, CHD2, POGZ and KMT5B) specifically with respect to tubulin biology. We observe that all five localize to microtubules of the mitotic spindle in vitro in human cells and in vivo in Xenopus. Investigation of CHD2 provides evidence that mutations present in individuals with ASD cause a range of microtubule-related phenotypes, including disrupted localization of the protein at mitotic spindles, cell cycle stalling, DNA damage and cell death. Lastly, we observe that ASD genetic risk is significantly enriched among tubulin-associated proteins, suggesting broader relevance. Together, these results provide additional evidence that the role of tubulin biology and cellular proliferation in ASD warrants further investigation and highlight the pitfalls of relying solely on annotated gene functions in the search for pathological mechanisms.

A new paradigm for applying deep learning to protein-ligand interaction prediction.

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

Serotonin 2A Receptor (5-HT2AR) Agonists: Psychedelics and Non-Hallucinogenic Analogues as Emerging Antidepressants.

Psychedelics make up a group of psychoactive compounds that induce hallucinogenic effects by activating the serotonin 2A receptor (5-HT2AR). Clinical trials have demonstrated the traditional psychedelic substances like psilocybin as a class of rapid-acting and long-lasting antidepressants. However, there is a pressing need for rationally designed 5-HT2AR agonists that possess optimal pharmacological profiles in order to fully reveal the therapeutic potential of these agonists and identify safer drug candidates devoid of hallucinogenic effects. This Perspective provides an overview of the structure-activity relationships of existing 5-HT2AR agonists based on their chemical classifications and discusses recent advancements in understanding their molecular pharmacology at a structural level. The encouraging clinical outcomes of psychedelics in depression treatment have sparked drug discovery endeavors aimed at developing novel 5-HT2AR agonists with improved subtype selectivity and signaling bias properties, which could serve as safer and potentially nonhallucinogenic antidepressants. These efforts can be significantly expedited through the utilization of structure-based methods and functional selectivity-directed screening.

Nucleic Acid Sensing by STING Induces an IFN-like Antiviral Response in a Marine Invertebrate.

The cytosolic detection of pathogen-derived nucleic acids has evolved as an essential strategy for host innate immune defense in mammals. One crucial component in this process is the stimulator of IFN genes (STING), which acts as a vital signaling adaptor, connecting the cytosolic detection of DNA by cyclic GMP-AMP (cGAMP) synthase (cGAS) to the downstream type I IFN signaling pathway. However, this process remains elusive in invertebrates. In this study, we present evidence demonstrating that STING, an ortholog found in a marine invertebrate (shrimp) called Litopenaeus vannamei, can directly detect DNA and initiate an IFN-like antiviral response. Unlike its homologs in other eukaryotic organisms, which exclusively function as sensors for cyclic dinucleotides, shrimp STING has the ability to bind to both double-stranded DNA and cyclic dinucleotides, including 2'3'-cGAMP. In vivo, shrimp STING can directly sense DNA nucleic acids from an infected virus, accelerate IFN regulatory factor dimerization and nuclear translocation, induce the expression of an IFN functional analog protein (Vago4), and finally establish an antiviral state. Taken together, our findings unveil a novel double-stranded DNA-STING-IKKε-IRF-Vago antiviral axis in an arthropod, providing valuable insights into the functional origins of DNA-sensing pathways in evolution.

Bioorthogonal labeling and profiling of N6-isopentenyladenosine (i6A) modified RNA.

Chemical modifications in RNAs play crucial roles in diversifying their structures and regulating numerous biochemical processes. Since the 1990s, several hydrophobic prenyl-modifications have been discovered in various RNAs. Prenyl groups serve as precursors for terpenes and many other biological molecules. The processes of prenylation in different macromolecules have been extensively studied. We introduce here a novel chemical biology toolkit that not only labels i6A, a prenyl-modified RNA residue, by leveraging the unique reactivity of the prenyl group, but also provides a general strategy to incorporate fluorescence functionalities into RNAs for molecular tracking purposes. Our findings revealed that iodine-mediated cyclization reactions of the prenyl group occur rapidly, transforming i6A from a hydrogen-bond acceptor to a donor. Based on this reactivity, we developed an Iodine-Mediated Cyclization and Reverse Transcription (IMCRT) tRNA-seq method, which can profile all nine endogenous tRNAs containing i6A residues in Saccharomyces cerevisiae with single-base resolution. Furthermore, under stress conditions, we observed a decline in i6A levels in budding yeast, accompanied by significant decrease of mutation rate at A37 position. Thus, the IMCRT tRNA-seq method not only permits semi-quantification of i6A levels in tRNAs but also holds potential for transcriptome-wide detection and analysis of various RNA species containing i6A modifications.

Closed-loop recyclable membranes enabled by covalent adaptable networks for water purification.

In the state-of-the-art membrane industry, membranes have linear life cycles and are commonly disposed of by landfill or incineration, sacrificing their sustainability. To date, little or no thought is given in the design phase to the end-of-life management of membranes. For the first time, we have innovated high-performance sustainable membranes, which can be closed-loop recycled after long-term usage for water purification. By synergizing membrane technology and dynamic covalent chemistry, covalent adaptable networks (CANs) with thermally reversible Diels-Alder (DA) adducts were synthesized and employed to fabricate integrally skinned asymmetric membranes via the nonsolvent-induced phase separation technique. Due to the stable and reversible features of CAN, the closed-loop recyclable membranes exhibit excellent mechanical properties and thermal and chemical stabilities as well as separation performance, which are comparable to or even higher than the state-of-the-art nonrecyclable membranes. Moreover, the used membranes can be closed-loop recycled with consistent properties and separation performance by depolymerization to remove contaminants, followed by refabrication into new membranes through the dissociation and reformation of DA adducts. This study may fill in the gaps in closed-loop recycling of membranes and inspire the advancement of sustainable membranes for a green membrane industry.

Structural insights into constitutive activity of 5-HT6 receptor.

While most therapeutic research on G-protein-coupled receptors (GPCRs) focuses on receptor activation by (endogenous) agonists, significant therapeutic potential exists through agonist-independent intrinsic constitutive activity that can occur in various physiological and pathophysiological settings. For example, inhibiting the constitutive activity of 5-HT6R-a receptor that is found almost exclusively in the brain and mediates excitatory neurotransmission-has demonstrated a therapeutic effect on cognitive/memory impairment associated with several neuropsychiatric disorders. However, the structural basis of such constitutive activity remains unclear. Here, we present a cryo-EM structure of serotonin-bound human 5-HT6R-Gs heterotrimer at 3.0-Å resolution. Detailed analyses of the structure complemented by comprehensive interrogation of signaling illuminate key structural determinants essential for constitutive 5-HT6R activity. Additional structure-guided mutagenesis leads to a nanobody mimic Gαs for 5-HT6R that can reduce its constitutive activity. Given the importance of 5-HT6R for a large number of neuropsychiatric disorders, insights derived from these studies will accelerate the design of more effective medications, and shed light on the molecular basis of constitutive activity.

ComplexQA: a deep graph learning approach for protein complex structure assessment.

MOTIVATION: In recent years, the end-to-end deep learning method for single-chain protein structure prediction has achieved high accuracy. For example, the state-of-the-art method AlphaFold, developed by Google, has largely increased the accuracy of protein structure predictions to near experimental accuracy in some of the cases. At the same time, there are few methods that can evaluate the quality of protein complexes at the residue level. In particular, evaluating the quality of residues at the interface of protein complexes can lead to a wide range of applications, such as protein function analysis and drug design. In this paper, we introduce a new deep graph neural network-based method ComplexQA, to evaluate the local quality of interfaces for protein complexes by utilizing the residue-level structural information in 3D space and the sequence-level constraints. RESULTS: We benchmark our method to other state-of-the-art quality assessment approaches on the HAF2 and DBM55-AF2 datasets (high-quality structural models predicted by AlphaFold-Multimer), and the BM5 docking dataset. The experimental results show that our proposed method achieves better or similar performance compared with other state-of-the-art methods, especially on difficult targets which only contain a few acceptable models. Our method is able to suggest a score for each interfac e residue, which demonstrates a powerful assessment tool for the ever-increasing number of protein complexes. AVAILABILITY: https://github.com/Cao-Labs/ComplexQA.git. Contact: caora@plu.edu.

Biological knowledge graph-guided investigation of immune therapy response in cancer with graph neural network.

The determination of transcriptome profiles that mediate immune therapy in cancer remains a major clinical and biological challenge. Despite responses induced by immune-check points inhibitors (ICIs) in diverse tumor types and all the big breakthroughs in cancer immunotherapy, most patients with solid tumors do not respond to ICI therapies. It still remains a big challenge to predict the ICI treatment response. Here, we propose a framework with multiple prior knowledge networks guided for immune checkpoints inhibitors prediction-DeepOmix-ICI (or ICInet for short). ICInet can predict the immune therapy response by leveraging geometric deep learning and prior biological knowledge graphs of gene-gene interactions. Here, we demonstrate more than 600 ICI-treated patients with ICI response data and gene expression profile to apply on ICInet. ICInet was used for ICI therapy responses prediciton across different cancer types-melanoma, gastric cancer and bladder cancer, which includes 7 cohorts from different data sources. ICInet is able to robustly generalize into multiple cancer types. Moreover, the performance of ICInet in those cancer types can outperform other ICI biomarkers in the clinic. Our model [area under the curve (AUC = 0.85)] generally outperformed other measures, including tumor mutational burden (AUC = 0.62) and programmed cell death ligand-1 score (AUC = 0.74). Therefore, our study presents a prior-knowledge guided deep learning method to effectively select immunotherapy-response-associated biomarkers, thereby improving the prediction of immunotherapy response for precision oncology.

A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function.

The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein-ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein-ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https://github.com/zchwang/DeepRMSD-Vina_Optimization.

Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model.

MOTIVATION: High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops and other stochastic contacts. RESULTS: We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17% in mean squared error and 26% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 14% relative to existing methods. We further show that our multiview idea can also be used to improve several existing methods, HiCARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix. AVAILABILITY AND IMPLEMENTATION: Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.