The Art and Science of Molecular Docking.

Molecular docking has become an essential part of a structural biologist's and medicinal chemist's toolkits. Given a chemical compound and the three-dimensional structure of a molecular target-for example, a protein-docking methods fit the compound into the target, predicting the compound's bound structure and binding energy. Docking can be used to discover novel ligands for a target by screening large virtual compound libraries. Docking can also provide a useful starting point for structure-based ligand optimization or for investigating a ligand's mechanism of action. Advances in computational methods, including both physics-based and machine learning approaches, as well as in complementary experimental techniques, are making docking an even more powerful tool. We review how docking works and how it can drive drug discovery and biological research. We also describe its current limitations and ongoing efforts to overcome them.

Anteromedial thalamus gates the selection and stabilization of long-term memories.

Memories initially formed in hippocampus gradually stabilize to cortex over weeks-to-months for long-term storage. The mechanistic details of this brain re-organization remain poorly understood. We recorded bulk neural activity in circuits that link hippocampus and cortex as mice performed a memory-guided virtual-reality task over weeks. We identified a prominent and sustained neural correlate of memory in anterior thalamus, whose inhibition substantially disrupted memory consolidation. More strikingly, gain amplification enhanced consolidation of otherwise unconsolidated memories. To gain mechanistic insights, we developed a technology for simultaneous cellular-resolution imaging of hippocampus, thalamus, and cortex throughout consolidation. We found that whereas hippocampus equally encodes multiple memories, the anteromedial thalamus preferentially encodes salient memories, and gradually increases correlations with cortex to facilitate tuning and synchronization of cortical ensembles. We thus identify a thalamo-cortical circuit that gates memory consolidation and propose a mechanism suitable for the selection and stabilization of hippocampal memories into longer-term cortical storage.

A tridimensional atlas of the developing human head.

The evolution and development of the head have long captivated researchers due to the crucial role of the head as the gateway for sensory stimuli and the intricate structural complexity of the head. Although significant progress has been made in understanding head development in various vertebrate species, our knowledge of early human head ontogeny remains limited. Here, we used advanced whole-mount immunostaining and 3D imaging techniques to generate a comprehensive 3D cellular atlas of human head embryogenesis. We present detailed developmental series of diverse head tissues and cell types, including muscles, vasculature, cartilage, peripheral nerves, and exocrine glands. These datasets, accessible through a dedicated web interface, provide insights into human embryogenesis. We offer perspectives on the branching morphogenesis of human exocrine glands and unknown features of the development of neurovascular and skeletomuscular structures. These insights into human embryology have important implications for understanding craniofacial defects and neurological disorders and advancing diagnostic and therapeutic strategies.

Targeted Activation of Hippocampal Place Cells Drives Memory-Guided Spatial Behavior.

The hippocampus is crucial for spatial navigation and episodic memory formation. Hippocampal place cells exhibit spatially selective activity within an environment and have been proposed to form the neural basis of a cognitive map of space that supports these mnemonic functions. However, the direct influence of place cell activity on spatial navigation behavior has not yet been demonstrated. Using an 'all-optical' combination of simultaneous two-photon calcium imaging and two-photon optogenetics, we identified and selectively activated place cells that encoded behaviorally relevant locations in a virtual reality environment. Targeted stimulation of a small number of place cells was sufficient to bias the behavior of animals during a spatial memory task, providing causal evidence that hippocampal place cells actively support spatial navigation and memory.

Developmental Origin Governs CD8+ T Cell Fate Decisions during Infection.

Heterogeneity is a hallmark feature of the adaptive immune system in vertebrates. Following infection, naive T cells differentiate into various subsets of effector and memory T cells, which help to eliminate pathogens and maintain long-term immunity. The current model suggests there is a single lineage of naive T cells that give rise to different populations of effector and memory T cells depending on the type and amounts of stimulation they encounter during infection. Here, we have discovered that multiple sub-populations of cells exist in the naive CD8+ T cell pool that are distinguished by their developmental origin, unique transcriptional profiles, distinct chromatin landscapes, and different kinetics and phenotypes after microbial challenge. These data demonstrate that the naive CD8+ T cell pool is not as homogeneous as previously thought and offers a new framework for explaining the remarkable heterogeneity in the effector and memory T cell subsets that arise after infection.

A Map-like Micro-Organization of Grid Cells in the Medial Entorhinal Cortex.

How the topography of neural circuits relates to their function remains unclear. Although topographic maps exist for sensory and motor variables, they are rarely observed for cognitive variables. Using calcium imaging during virtual navigation, we investigated the relationship between the anatomical organization and functional properties of grid cells, which represent a cognitive code for location during navigation. We found a substantial degree of grid cell micro-organization in mouse medial entorhinal cortex: grid cells and modules all clustered anatomically. Within a module, the layout of grid cells was a noisy two-dimensional lattice in which the anatomical distribution of grid cells largely matched their spatial tuning phases. This micro-arrangement of phases demonstrates the existence of a topographical map encoding a cognitive variable in rodents. It contributes to a foundation for evaluating circuit models of the grid cell network and is consistent with continuous attractor models as the mechanism of grid formation.

Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex.

Neuronal representations change as associations are learned between sensory stimuli and behavioral actions. However, it is poorly understood whether representations for learned associations stabilize in cortical association areas or continue to change following learning. We tracked the activity of posterior parietal cortex neurons for a month as mice stably performed a virtual-navigation task. The relationship between cells' activity and task features was mostly stable on single days but underwent major reorganization over weeks. The neurons informative about task features (trial type and maze locations) changed across days. Despite changes in individual cells, the population activity had statistically similar properties each day and stable information for over a week. As mice learned additional associations, new activity patterns emerged in the neurons used for existing representations without greatly affecting the rate of change of these representations. We propose that dynamic neuronal activity patterns could balance plasticity for learning and stability for memory.

Changes in spatial self-consciousness elicit grid cell-like representation in the entorhinal cortex.

Grid cells in the entorhinal cortex (EC) encode an individual's location in space, integrating both environmental and multisensory bodily cues. Notably, body-derived signals are also primary signals for the sense of self. While studies have demonstrated that continuous application of visuo-tactile bodily stimuli can induce perceptual shifts in self-location, it remains unexplored whether these illusory changes suffice to trigger grid cell-like representation (GCLR) within the EC, and how this compares to GCLR during conventional virtual navigation. To address this, we systematically induced illusory drifts in self-location toward controlled directions using visuo-tactile bodily stimulation, while maintaining the subjects' visual viewpoint fixed (absent conventional virtual navigation). Subsequently, we evaluated the corresponding GCLR in the EC through functional MRI analysis. Our results reveal that illusory changes in perceived self-location (independent of changes in environmental navigation cues) can indeed evoke entorhinal GCLR, correlating in strength with the magnitude of perceived self-location, and characterized by similar grid orientation as during conventional virtual navigation in the same virtual room. These data demonstrate that the same grid-like representation is recruited when navigating based on environmental, mainly visual cues, or when experiencing illusory forward drifts in self-location, driven by perceptual multisensory bodily cues.

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators.

We address the challenge of acoustic simulations in three-dimensional (3D) virtual rooms with parametric source positions, which have applications in virtual/augmented reality, game audio, and spatial computing. The wave equation can fully describe wave phenomena such as diffraction and interference. However, conventional numerical discretization methods are computationally expensive when simulating hundreds of source and receiver positions, making simulations with parametric source positions impractical. To overcome this limitation, we propose using deep operator networks to approximate linear wave-equation operators. This enables the rapid prediction of sound propagation in realistic 3D acoustic scenes with parametric source positions, achieving millisecond-scale computations. By learning a compact surrogate model, we avoid the offline calculation and storage of impulse responses for all relevant source/listener pairs. Our experiments, including various complex scene geometries, show good agreement with reference solutions, with root mean squared errors ranging from 0.02 to 0.10 Pa. Notably, our method signifies a paradigm shift as-to our knowledge-no prior machine learning approach has achieved precise predictions of complete wave fields within realistic domains.

Observing heroic behavior and its influencing factors in immersive virtual environments.

Studying heroism in controlled settings presents challenges and ethical controversies due to its association with physical risk. Leveraging virtual reality (VR) technology, we conducted a three-study series with 397 participants from China to investigate heroic actions. Participants unexpectedly witnessed a criminal event in a simulated scenario, allowing observation of their tendency to physically intercept a thief. We examined situational factors (voluntariness, authority, and risk) and personal variables [gender, impulsivity, empathy, and social value orientation (SVO)] that may influence heroism. Also, the potential association between heroism and social conformity was explored. In terms of situational variables, voluntariness modulated participants' tendency to intercept the escaping thief, while perceived risk demonstrated its impact by interacting with gender. That is, in study 3 where the perceived risk was expected to be higher (as supported by an online study 5), males exhibited a greater inclination toward heroic behavior compared to females. Regarding other personal variables, the tendency to engage in heroic behavior decreased as empathy levels rose among males, whereas the opposite trend was observed for females. SVO influenced heroic behavior but without a gender interaction. Finally, an inverse relationship between heroism and social conformity was observed. The robustness of these findings was partly supported by the Chinese sample (but not the international sample) of an online study 4 that provided written descriptions of VR scenarios, indicating cultural variations. These results advance insights into motivational factors influencing heroism in the context of restoring order and highlight the power of VR technology in examining social psychological hypotheses beyond ethical constraints.

Protein language models are performant in structure-free virtual screening.

Hitherto virtual screening (VS) has been typically performed using a structure-based drug design paradigm. Such methods typically require the use of molecular docking on high-resolution three-dimensional structures of a target protein-a computationally-intensive and time-consuming exercise. This work demonstrates that by employing protein language models and molecular graphs as inputs to a novel graph-to-transformer cross-attention mechanism, a screening power comparable to state-of-the-art structure-based models can be achieved. The implications thereof include highly expedited VS due to the greatly reduced compute required to run this model, and the ability to perform early stages of computer-aided drug design in the complete absence of 3D protein structures.

Proteomics Impact on Cell Biology to Resolve Cell Structure and Function.

The acceleration of advances in proteomics has enabled integration with imaging at the EM and light microscopy levels, cryo-EM of protein structures, and artificial intelligence with proteins comprehensively and accurately resolved for cell structures at nanometer to subnanometer resolution. Proteomics continues to outpace experimentally based structural imaging, but their ultimate integration is a path toward the goal of a compendium of all proteins to understand mechanistically cell structure and function.

Skin-integrated systems for power efficient, programmable thermal sensations across large body areas.

Thermal sensations contribute to our ability to perceive and explore the physical world. Reproducing these sensations in a spatiotemporally programmable manner through wireless computer control could enhance virtual experiences beyond those supported by video, audio and, increasingly, haptic inputs. Flexible, lightweight and thin devices that deliver patterns of thermal stimulation across large areas of the skin at any location of the body are of great interest in this context. Applications range from those in gaming and remote socioemotional communications, to medical therapies and physical rehabilitation. Here, we present a set of ideas that form the foundations of a skin-integrated technology for power-efficient generation of thermal sensations across the skin, with real-time, closed-loop control. The systems exploit passive cooling mechanisms, actively switchable thermal barrier interfaces, thin resistive heaters and flexible electronics configured in a pixelated layout with wireless interfaces to portable devices, the internet and cloud data infrastructure. Systematic experimental studies and simulation results explore the essential mechanisms and guide the selection of optimized choices in design. Demonstration examples with human subjects feature active thermoregulation, virtual social interactions, and sensory expansion.

Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 macrodomain of SARS-CoV-2.

The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small-molecule inhibitors of Mac1 have great therapeutic potential, at the outset of the COVID-19 pandemic, there were no well-validated inhibitors for this protein nor, indeed, the macrodomain enzyme family, making this target a pharmacological orphan. Here, we report the structure-based discovery and development of several different chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high-resolution protein crystallography, and binding evaluation. Potent scaffolds were designed with in silico fragment linkage and by ultra-large library docking of over 450 million molecules. Both techniques leverage the computational exploration of tangible chemical space and are applicable to other pharmacological orphans. Overall, 160 ligands in 119 different scaffolds were discovered, and 153 Mac1-ligand complex crystal structures were determined, typically to 1 Å resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated conformational changes within the active site, and key inhibitor motifs that will template future drug development against Mac1.

Architectural experience influences the processing of others' body expressions.

The interplay between space and cognition is a crucial issue in Neuroscience leading to the development of multiple research fields. However, the relationship between architectural space and the movement of the inhabitants and their interactions has been too often neglected, failing to provide a unifying view of architecture's capacity to modulate social cognition broadly. We bridge this gap by requesting participants to judge avatars' emotional expression (high vs. low arousal) at the end of their promenade inside high- or low-arousing architectures. Stimuli were presented in virtual reality to ensure a dynamic, naturalistic experience. High-density electroencephalography (EEG) was recorded to assess the neural responses to the avatar's presentation. Observing highly aroused avatars increased Late Positive Potentials (LPP), in line with previous evidence. Strikingly, 250 ms before the occurrence of the LPP, P200 amplitude increased due to the experience of low-arousing architectures, reflecting an early greater attention during the processing of body expressions. In addition, participants stared longer at the avatar's head and judged the observed posture as more arousing. Source localization highlighted a contribution of the dorsal premotor cortex to both P200 and LPP. In conclusion, the immersive and dynamic architectural experience modulates human social cognition. In addition, the motor system plays a role in processing architecture and body expressions suggesting that the space and social cognition interplay is rooted in overlapping neural substrates. This study demonstrates that the manipulation of mere architectural space is sufficient to influence human social cognition.

Phase-contrast virtual chest radiography.

Respiratory X-ray imaging enhanced by phase contrast has shown improved airway visualization in animal models. Limitations in current X-ray technology have nevertheless hindered clinical translation, leaving the potential clinical impact an open question. Here, we explore phase-contrast chest radiography in a realistic in silico framework. Specifically, we use preprocessed virtual patients to generate in silico chest radiographs by Fresnel-diffraction simulations of X-ray wave propagation. Following a reader study conducted with clinical radiologists, we predict that phase-contrast edge enhancement will have a negligible impact on improving solitary pulmonary nodule detection (6 to 20 mm). However, edge enhancement of bronchial walls visualizes small airways (< 2 mm), which are invisible in conventional radiography. Our results show that phase-contrast chest radiography could play a future role in observing small-airway obstruction (e.g., relevant for asthma or early-stage chronic obstructive pulmonary disease), which cannot be directly visualized using current clinical methods, thereby motivating the experimental development needed for clinical translation. Finally, we discuss quantitative requirements on distances and X-ray source/detector specifications for clinical implementation of phase-contrast chest radiography.

Mentoring during Uncertain Times.

Scientific success is mainly supported by mentoring, which often occurs through face-to-face interactions. Changes to the research environment incurred by the Coronavirus 2019 (COVID-19) pandemic have necessitated mentorship adaptations. Here, we describe how mentors can broaden their mentorship to support trainee growth and provide reassurance about trainee development amid uncertain circumstances.

A genome sequencing system for universal newborn screening, diagnosis, and precision medicine for severe genetic diseases.

Newborn screening (NBS) dramatically improves outcomes in severe childhood disorders by treatment before symptom onset. In many genetic diseases, however, outcomes remain poor because NBS has lagged behind drug development. Rapid whole-genome sequencing (rWGS) is attractive for comprehensive NBS because it concomitantly examines almost all genetic diseases and is gaining acceptance for genetic disease diagnosis in ill newborns. We describe prototypic methods for scalable, parentally consented, feedback-informed NBS and diagnosis of genetic diseases by rWGS and virtual, acute management guidance (NBS-rWGS). Using established criteria and the Delphi method, we reviewed 457 genetic diseases for NBS-rWGS, retaining 388 (85%) with effective treatments. Simulated NBS-rWGS in 454,707 UK Biobank subjects with 29,865 pathogenic or likely pathogenic variants associated with 388 disorders had a true negative rate (specificity) of 99.7% following root cause analysis. In 2,208 critically ill children with suspected genetic disorders and 2,168 of their parents, simulated NBS-rWGS for 388 disorders identified 104 (87%) of 119 diagnoses previously made by rWGS and 15 findings not previously reported (NBS-rWGS negative predictive value 99.6%, true positive rate [sensitivity] 88.8%). Retrospective NBS-rWGS diagnosed 15 children with disorders that had been undetected by conventional NBS. In 43 of the 104 children, had NBS-rWGS-based interventions been started on day of life 5, the Delphi consensus was that symptoms could have been avoided completely in seven critically ill children, mostly in 21, and partially in 13. We invite groups worldwide to refine these NBS-rWGS conditions and join us to prospectively examine clinical utility and cost effectiveness.

ML-PLIC: a web platform for characterizing protein-ligand interactions and developing machine learning-based scoring functions.

Cracking the entangling code of protein-ligand interaction (PLI) is of great importance to structure-based drug design and discovery. Different physical and biochemical representations can be used to describe PLI such as energy terms and interaction fingerprints, which can be analyzed by machine learning (ML) algorithms to create ML-based scoring functions (MLSFs). Here, we propose the ML-based PLI capturer (ML-PLIC), a web platform that automatically characterizes PLI and generates MLSFs to identify the potential binders of a specific protein target through virtual screening (VS). ML-PLIC comprises five modules, including Docking for ligand docking, Descriptors for PLI generation, Modeling for MLSF training, Screening for VS and Pipeline for the integration of the aforementioned functions. We validated the MLSFs constructed by ML-PLIC in three benchmark datasets (Directory of Useful Decoys-Enhanced, Active as Decoys and TocoDecoy), demonstrating accuracy outperforming traditional docking tools and competitive performance to the deep learning-based SF, and provided a case study of the Serine/threonine-protein kinase WEE1 in which MLSFs were developed by using the ML-based VS pipeline in ML-PLIC. Underpinning the latest version of ML-PLIC is a powerful platform that incorporates physical and biological knowledge about PLI, leveraging PLI characterization and MLSF generation into the design of structure-based VS pipeline. The ML-PLIC web platform is now freely available at http://cadd.zju.edu.cn/plic/.

Uni-GBSA: an open-source and web-based automatic workflow to perform MM/GB(PB)SA calculations for virtual screening.

Binding free energy calculation of a ligand to a protein receptor is a fundamental objective in drug discovery. Molecular mechanics/Generalized-Born (Poisson-Boltzmann) surface area (MM/GB(PB)SA) is one of the most popular methods for binding free energy calculations. It is more accurate than most scoring functions and more computationally efficient than alchemical free energy methods. Several open-source tools for performing MM/GB(PB)SA calculations have been developed, but they have limitations and high entry barriers to users. Here, we introduce Uni-GBSA, a user-friendly automatic workflow to perform MM/GB(PB)SA calculations, which can perform topology preparation, structure optimization, binding free energy calculation and parameter scanning for MM/GB(PB)SA calculations. It also offers a batch mode that evaluates thousands of molecules against one protein target in parallel for efficient application in virtual screening. The default parameters are selected after systematic testing on the PDBBind-2011 refined dataset. In our case studies, Uni-GBSA produced a satisfactory correlation with the experimental binding affinities and outperformed AutoDock Vina in molecular enrichment. Uni-GBSA is available as an open-source package at https://github.com/dptech-corp/Uni-GBSA. It can also be accessed for virtual screening from the Hermite web platform at https://hermite.dp.tech. A free Uni-GBSA web server of a lab version is available at https://labs.dp.tech/projects/uni-gbsa/. This increases user-friendliness because the web server frees users from package installations and provides users with validated workflows for input data and parameter settings, cloud computing resources for efficient job completions, a user-friendly interface and professional support and maintenance.