A Small Helical Bundle Prepares Primer Synthesis by Binding Two Nucleotides that Enhance Sequence-Specific Recognition of the DNA Template.

Primases have a fundamental role in DNA replication. They synthesize a primer that is then extended by DNA polymerases. Archaeoeukaryotic primases require for synthesis a catalytic and an accessory domain, the exact contribution of the latter being unresolved. For the pRN1 archaeal primase, this domain is a 115-amino acid helix bundle domain (HBD). Our structural investigations of this small HBD by liquid- and solid-state nuclear magnetic resonance (NMR) revealed that only the HBD binds the DNA template. DNA binding becomes sequence-specific after a major allosteric change in the HBD, triggered by the binding of two nucleotide triphosphates. The spatial proximity of the two nucleotides and the DNA template in the quaternary structure of the HBD strongly suggests that this small domain brings together the substrates to prepare the first catalytic step of primer synthesis. This efficient mechanism is likely general for all archaeoeukaryotic primases.

COVID-19 vaccine mRNA-1273 elicits a protective immune profile in mice that is not associated with vaccine-enhanced disease upon SARS-CoV-2 challenge.

Vaccine-associated enhanced respiratory disease (VAERD) was previously observed in some preclinical models of severe acute respiratory syndrome (SARS) and MERS coronavirus vaccines. We used the SARS coronavirus 2 (SARS-CoV-2) mouse-adapted, passage 10, lethal challenge virus (MA10) mouse model of acute lung injury to evaluate the immune response and potential for immunopathology in animals vaccinated with research-grade mRNA-1273. Whole-inactivated virus or heat-denatured spike protein subunit vaccines with alum designed to elicit low-potency antibodies and Th2-skewed CD4+ T cells resulted in reduced viral titers and weight loss post challenge but more severe pathological changes in the lung compared to saline-immunized animals. In contrast, a protective dose of mRNA-1273 induced favorable humoral and cellular immune responses that protected from viral replication in the upper and lower respiratory tract upon challenge. A subprotective dose of mRNA-1273 reduced viral replication and limited histopathological manifestations compared to animals given saline. Overall, our findings demonstrate an immunological signature associated with antiviral protection without disease enhancement following vaccination with mRNA-1273.

Advances in computational methods for ligand binding kinetics.

Binding kinetic parameters can be correlated with drug efficacy, which in recent years led to the development of various computational methods for predicting binding kinetic rates and gaining insight into protein-drug binding paths and mechanisms. In this review, we introduce and compare computational methods recently developed and applied to two systems, trypsin-benzamidine and kinase-inhibitor complexes. Methods involving enhanced sampling in molecular dynamics simulations or machine learning can be used not only to predict kinetic rates, but also to reveal factors modulating the duration of residence times, selectivity, and drug resistance to mutations. Methods which require less computational time to make predictions are highlighted, and suggestions to reduce the error of computed kinetic rates are presented.

Needs for a conceptual bridge between biological domestication and early food globalization.

The past 15 y has seen much development in documentation of domestication of plants and animals as gradual traditions spanning millennia. There has also been considerable momentum in understanding the dispersals of major domesticated taxa across continents spanning thousands of miles. The two processes are often considered within different theoretical strains. What is missing from our repertoire of explanations is a conceptual bridge between the protracted process over millennia and the multiregional, globally dispersed nature of domestication. The evidence reviewed in this paper bears upon how we conceptualize domestication as an episode or a process. By bringing together the topics of crop domestication and crop movement, those complex, protracted, and continuous outcomes come more clearly into view.

Classical vs. quantum plasmon-induced molecular transformations at metallic nanojunctions.

Chemical transformations near plasmonic metals have attracted increasing attention in the past few years. Specifically, reactions occurring within plasmonic nanojunctions that can be detected via surface and tip-enhanced Raman (SER and TER) scattering were the focus of numerous reports. In this context, even though the transition between localized and nonlocal (quantum) plasmons at nanojunctions is documented, its implications on plasmonic chemistry remain poorly understood. We explore the latter through AFM-TER-current measurements. We use two molecules: i) 4-mercaptobenzonitrile (MBN) that reports on the (non)local fields and ii) 4-nitrothiophenol (NTP) that features defined signatures of its neutral/anionic forms and dimer product, 4,4'-dimercaptoazobenzene (DMAB). The transition from classical to quantum plasmons is established through our optical measurements: It is marked by molecular charging and optical rectification. Simultaneously recorded force and current measurements support our assignments. In the case of NTP, we observe the parent and DMAB product beneath the probe in the classical regime. Further reducing the gap leads to the collapse of DMAB to form NTP anions. The process is reversible: Anions subsequently recombine into DMAB. Our results have significant implications for AFM-based TER measurements and their analysis, beyond the scope of this work. In effect, when precise control over the junction is not possible (e.g., in SER and ambient TER), both classical and quantum plasmons need to be considered in the analysis of plasmonic reactions.

Enhanced weathering in the US Corn Belt delivers carbon removal with agronomic benefits.

Terrestrial enhanced weathering (EW) of silicate rocks, such as crushed basalt, on farmlands is a promising scalable atmospheric carbon dioxide removal (CDR) strategy that urgently requires performance assessment with commercial farming practices. We report findings from a large-scale replicated EW field trial across a typical maize-soybean rotation on an experimental farm in the heart of the United Sates Corn Belt over 4 y (2016 to 2020). We show an average combined loss of major cations (Ca2+ and Mg2+) from crushed basalt applied each fall over 4 y (50 t ha-1 y-1) gave a conservative time-integrated cumulative CDR potential of 10.5 ± 3.8 t CO2 ha-1. Maize and soybean yields increased significantly (P < 0.05) by 12 to 16% with EW following improved soil fertility, decreased soil acidification, and upregulation of root nutrient transport genes. Yield enhancements with EW were achieved with significantly (P < 0.05) increased key micro- and macronutrient concentrations (including potassium, magnesium, manganese, phosphorus, and zinc), thus improving or maintaining crop nutritional status. We observed no significant increase in the content of trace metals in grains of maize or soybean or soil exchangeable pools relative to controls. Our findings suggest that widespread adoption of EW across farming sectors has the potential to contribute significantly to net-zero greenhouse gas emissions goals while simultaneously improving food and soil security.

Adaptive CVgen: Leveraging reinforcement learning for advanced sampling in protein folding and chemical reactions.

Enhanced sampling techniques have traditionally encountered two significant challenges: identifying suitable reaction coordinates and addressing the exploration-exploitation dilemma, particularly the difficulty of escaping local energy minima. Here, we introduce Adaptive CVgen, a universal adaptive sampling framework designed to tackle these issues. Our approach utilizes a set of collective variables (CVs) to comprehensively cover the system's potential evolutionary phase space, generating diverse reaction coordinates to address the first challenge. Moreover, we integrate reinforcement learning strategies to dynamically adjust the generated reaction coordinates, thereby effectively balancing the exploration-exploitation dilemma. We apply this framework to sample the conformational space of six proteins transitioning from completely disordered states to folded states, as well as to model the chemical synthesis process of C60, achieving conformations that perfectly match the standard C60 structure. The results demonstrate Adaptive CVgen's effectiveness in exploring new conformations and escaping local minima, achieving both sampling efficiency and exploration accuracy. This framework holds potential for extending to various related challenges, including protein folding dynamics, drug targeting, and complex chemical reactions, thereby opening promising avenues for application in these fields.

Enhanced charge-carrier dynamics and efficient solar-to-urea conversion on Si-based photocathodes.

Photoelectrochemical (PEC) coupling of CO2 and nitrate can provide a useful and green source of urea, but the process is affected by the photocathodes with poor charge-carrier dynamics and low conversion efficiency. Here, a NiFe diatomic catalysts/TiO2 layer/nanostructured n+p-Si photocathode is rationally designed, achieving a good charge-separation efficiency of 78.8% and charge-injection efficiency of 56.9% in the process of PEC urea synthesis. Compared with the electrocatalytic urea synthesis by using the same catalysts, the Si-based photocathode shows a similar urea yield rate (81.1 mg·h-1·cm-2) with a higher faradic efficiency (24.2%, almost twice than the electrocatalysis) at a lower applied potential under 1 sun illumination, meaning that a lower energy-consumption method acquires more aimed productions. Integrating the PEC measurements and characterization results, the synergistic effect of hierarchical structure is the dominating factor for enhancing the charge-carrier separation, transfer, and injection by the matched band structure and favorable electron-migration channels. This work provides a direct and efficient route of solar-to-urea conversion.

Solar-enhanced lithium extraction with self-sustaining water recycling from salt-lake brines.

Lithium is an emerging strategic resource for modern energy transformation toward electrification and decarbonization. However, current mainstream direct lithium extraction technology via adsorption suffers from sluggish kinetics and intensive water usage, especially in arid/semiarid and cold salt-lake regions (natural land brines). Herein, an efficient proof-of-concept integrated solar microevaporator system is developed to realize synergetic solar-enhanced lithium recovery and water footprint management from hypersaline salt-lake brines. The 98% solar energy harvesting efficiency of the solar microevaporator system, elevating its local temperature, greatly promotes the endothermic Li+ extraction process and solar steam generation. Benefiting from the photothermal effect, enhanced water flux, and enriched local Li+ supply in nanoconfined space, a double-enhanced Li+ recovery capacity was delivered (increase from 12.4 to 28.7 mg g-1) under one sun, and adsorption kinetics rate (saturated within 6 h) also reached twice of that at 280 K (salt-lake temperature). Additionally, the self-assembly rotation feature endows the microevaporator system with distinct self-cleaning desalination ability, achieving near 100% water recovery from hypersaline brines for further self-sufficient Li+ elution. Outdoor comprehensive solar-powered experiment verified the feasibility of basically stable lithium recovery ability (>8 mg g-1) directly from natural hypersaline salt-lake brines with self-sustaining water recycling for Li+ elution (440 m3 water recovery per ton Li2CO3). This work offers an integrated solution for sustainable lithium recovery with near zero water/carbon consumption toward carbon neutrality.

Exploiting enzyme evolution for computational protein design.

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.

GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction.

Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric information (i.e. bond angles), leading to difficulties in accurately distinguishing different molecular structures. In addition, these methods also pose limitations in representing the binding process of protein-ligand complexes. To address these issues, we propose a novel geometry-enhanced mid-fusion network, named GEMF, to learn comprehensive molecular geometry and interaction patterns. Specifically, the GEMF consists of a graph embedding layer, a message passing phase, and a multi-scale fusion module. GEMF can effectively represent protein-ligand complexes as graphs, with graph embeddings based on physicochemical and geometric properties. Moreover, our dual-stream message passing framework models both covalent and non-covalent interactions. In particular, the edge-update mechanism, which is based on line graphs, can fuse both distance and angle information in the covalent branch. In addition, the communication branch consisting of multiple heterogeneous interaction modules is developed to learn intricate interaction patterns. Finally, we fuse the multi-scale features from the covalent, non-covalent, and heterogeneous interaction branches. The extensive experimental results on several benchmarks demonstrate the superiority of GEMF compared with other state-of-the-art methods.

Systematic Discovery of RNA Binding Proteins that Regulate MicroRNA Levels.

RNA binding proteins (RBPs) interact with primary, precursor, and mature microRNAs (miRs) to influence mature miR levels, which in turn affect critical aspects of human development and disease. To understand how RBPs contribute to miR biogenesis, we analyzed human enhanced UV crosslinking followed by immunoprecipitation (eCLIP) datasets for 126 RBPs to discover miR-encoding genomic loci that are statistically enriched for RBP binding. We find that 92% of RBPs interact directly with at least one miR locus, and that some interactions are cell line specific despite expression of the miR locus in both cell lines evaluated. We validated that ILF3 and BUD13 directly interact with and stabilize miR-144 and that BUD13 suppresses mir-210 processing to the mature species. We also observed that DDX3X regulates primary miR-20a, while LARP4 stabilizes precursor mir-210. Our approach to identifying regulators of miR loci can be applied to any user-defined RNA annotation, thereby guiding the discovery of uncharacterized regulators of RNA processing.

Driving and characterizing nucleation of urea and glycine polymorphs in water.

Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases, its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights into the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here, we employ the machine learning-augmented molecular dynamics framework "reweighted autoencoded variational Bayes for enhanced sampling (RAVE)." We study two molecular systems-urea and glycine-in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe multiple back-and-forth nucleation events of different polymorphs from homogeneous solution; from these trajectories, we calculate the relative ranking of finite-sized polymorph crystals embedded in solution, in terms of the free-energy difference between the finite-sized crystal polymorph and the original solution state. We further observe that the obtained reaction coordinates and transitions are highly nonclassical.

The role of dynamics in heterogeneous catalysis: Surface diffusivity and N2 decomposition on Fe(111).

Dynamics has long been recognized to play an important role in heterogeneous catalytic processes. However, until recently, it has been impossible to study their dynamical behavior at industry-relevant temperatures. Using a combination of machine learning potentials and advanced simulation techniques, we investigate the cleavage of the N[Formula: see text] triple bond on the Fe(111) surface. We find that at low temperatures our results agree with the well-established picture. However, if we increase the temperature to reach operando conditions, the surface undergoes a global dynamical change and the step structure of the Fe(111) surface is destabilized. The catalytic sites, traditionally associated with this surface, appear and disappear continuously. Our simulations illuminate the danger of extrapolating low-temperature results to operando conditions and indicate that the catalytic activity can only be inferred from calculations that take dynamics fully into account. More than that, they show that it is the transition to this highly fluctuating interfacial environment that drives the catalytic process.

Integrating comparative modeling and accelerated simulations reveals conformational and energetic basis of actomyosin force generation.

Muscle contraction is performed by arrays of contractile proteins in the sarcomere. Serious heart diseases, such as cardiomyopathy, can often be results of mutations in myosin and actin. Direct characterization of how small changes in the myosin-actin complex impact its force production remains challenging. Molecular dynamics (MD) simulations, although capable of studying protein structure-function relationships, are limited owing to the slow timescale of the myosin cycle as well as a lack of various intermediate structures for the actomyosin complex. Here, employing comparative modeling and enhanced sampling MD simulations, we show how the human cardiac myosin generates force during the mechanochemical cycle. Initial conformational ensembles for different myosin-actin states are learned from multiple structural templates with Rosetta. This enables us to efficiently sample the energy landscape of the system using Gaussian accelerated MD. Key myosin loop residues, whose substitutions are related to cardiomyopathy, are identified to form stable or metastable interactions with the actin surface. We find that the actin-binding cleft closure is allosterically coupled to the myosin motor core transitions and ATP-hydrolysis product release from the active site. Furthermore, a gate between switch I and switch II is suggested to control phosphate release at the prepowerstroke state. Our approach demonstrates the ability to link sequence and structural information to motor functions.

Actuation-enhanced multifunctional sensing and information recognition by magnetic artificial cilia arrays.

Artificial cilia integrating both actuation and sensing functions allow simultaneously sensing environmental properties and manipulating fluids in situ, which are promising for environment monitoring and fluidic applications. However, existing artificial cilia have limited ability to sense environmental cues in fluid flows that have versatile information encoded. This limits their potential to work in complex and dynamic fluid-filled environments. Here, we propose a generic actuation-enhanced sensing mechanism to sense complex environmental cues through the active interaction between artificial cilia and the surrounding fluidic environments. The proposed mechanism is based on fluid-cilia interaction by integrating soft robotic artificial cilia with flexible sensors. With a machine learning-based approach, complex environmental cues such as liquid viscosity, environment boundaries, and distributed fluid flows of a wide range of velocities can be sensed, which is beyond the capability of existing artificial cilia. As a proof of concept, we implement this mechanism on magnetically actuated cilia with integrated laser-induced graphene-based sensors and demonstrate sensing fluid apparent viscosity, environment boundaries, and fluid flow speed with a reconfigurable sensitivity and range. The same principle could be potentially applied to other soft robotic systems integrating other actuation and sensing modalities for diverse environmental and fluidic applications.

Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics.

The chemical equilibrium between self-ionized and molecular water dictates the acid-base chemistry in aqueous solutions, yet understanding the microscopic mechanisms of water self-ionization remains experimentally and computationally challenging. Herein, Density Functional Theory (DFT)-based deep neural network (DNN) potentials are combined with enhanced sampling techniques and a global acid-base collective variable to perform extensive atomistic simulations of water self-ionization for model systems of increasing size. The explicit inclusion of long-range electrostatic interactions in the DNN potential is found to be crucial to accurately reproduce the DFT free energy profile of solvated water ion pairs in small (64 and 128 H2O) cells. The reversible work to separate the hydroxide and hydronium to a distance [Formula: see text] is found to converge for simulation cells containing more than 500 H2O, and a distance of [Formula: see text] 8 Å is the threshold beyond which the work to further separate the two ions becomes approximately zero. The slow convergence of the potential of mean force with system size is related to a restructuring of water and an increase of the local order around the water ions. Calculation of the dissociation equilibrium constant illustrates the key role of long-range electrostatics and entropic effects in the water autoionization process.

Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?

Binding affinity prediction largely determines the discovery efficiency of lead compounds in drug discovery. Recently, machine learning (ML)-based approaches have attracted much attention in hopes of enhancing the predictive performance of traditional physics-based approaches. In this study, we evaluated the impact of structural dynamic information on the binding affinity prediction by comparing the models trained on different dimensional descriptors, using three targets (i.e. JAK1, TAF1-BD2 and DDR1) and their corresponding ligands as the examples. Here, 2D descriptors are traditional ECFP4 fingerprints, 3D descriptors are the energy terms of the Smina and NNscore scoring functions and 4D descriptors contain the structural dynamic information derived from the trajectories based on molecular dynamics (MD) simulations. We systematically investigate the MD-refined binding affinity prediction performance of three classical ML algorithms (i.e. RF, SVR and XGB) as well as two common virtual screening methods, namely Glide docking and MM/PBSA. The outcomes of the ML models built using various dimensional descriptors and their combinations reveal that the MD refinement with the optimized protocol can improve the predictive performance on the TAF1-BD2 target with considerable structural flexibility, but not for the less flexible JAK1 and DDR1 targets, when taking docking poses as the initial structure instead of the crystal structures. The results highlight the importance of the initial structures to the final performance of the model through conformational analysis on the three targets with different flexibility.

Predicting microbe-drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy.

MOTIVATION: Predicting the associations between human microbes and drugs (MDAs) is one critical step in drug development and precision medicine areas. Since discovering these associations through wet experiments is time-consuming and labor-intensive, computational methods have already been an effective way to tackle this problem. Recently, graph contrastive learning (GCL) approaches have shown great advantages in learning the embeddings of nodes from heterogeneous biological graphs (HBGs). However, most GCL-based approaches don't fully capture the rich structure information in HBGs. Besides, fewer MDA prediction methods could screen out the most informative negative samples for effectively training the classifier. Therefore, it still needs to improve the accuracy of MDA predictions. RESULTS: In this study, we propose a novel approach that employs the Structure-enhanced Contrastive learning and Self-paced negative sampling strategy for Microbe-Drug Association predictions (SCSMDA). Firstly, SCSMDA constructs the similarity networks of microbes and drugs, as well as their different meta-path-induced networks. Then SCSMDA employs the representations of microbes and drugs learned from meta-path-induced networks to enhance their embeddings learned from the similarity networks by the contrastive learning strategy. After that, we adopt the self-paced negative sampling strategy to select the most informative negative samples to train the MLP classifier. Lastly, SCSMDA predicts the potential microbe-drug associations with the trained MLP classifier. The embeddings of microbes and drugs learning from the similarity networks are enhanced with the contrastive learning strategy, which could obtain their discriminative representations. Extensive results on three public datasets indicate that SCSMDA significantly outperforms other baseline methods on the MDA prediction task. Case studies for two common drugs could further demonstrate the effectiveness of SCSMDA in finding novel MDA associations. AVAILABILITY: The source code is publicly available on GitHub https://github.com/Yue-Yuu/SCSMDA-master.

Phanto-IDP: compact model for precise intrinsically disordered protein backbone generation and enhanced sampling.

The biological function of proteins is determined not only by their static structures but also by the dynamic properties of their conformational ensembles. Numerous high-accuracy static structure prediction tools have been recently developed based on deep learning; however, there remains a lack of efficient and accurate methods for exploring protein dynamic conformations. Traditionally, studies concerning protein dynamics have relied on molecular dynamics (MD) simulations, which incur significant computational costs for all-atom precision and struggle to adequately sample conformational spaces with high energy barriers. To overcome these limitations, various enhanced sampling techniques have been developed to accelerate sampling in MD. Traditional enhanced sampling approaches like replica exchange molecular dynamics (REMD) and frontier expansion sampling (FEXS) often follow the MD simulation approach and still cost a lot of computational resources and time. Variational autoencoders (VAEs), as a classic deep generative model, are not restricted by potential energy landscapes and can explore conformational spaces more efficiently than traditional methods. However, VAEs often face challenges in generating reasonable conformations for complex proteins, especially intrinsically disordered proteins (IDPs), which limits their application as an enhanced sampling method. In this study, we presented a novel deep learning model (named Phanto-IDP) that utilizes a graph-based encoder to extract protein features and a transformer-based decoder combined with variational sampling to generate highly accurate protein backbones. Ten IDPs and four structured proteins were used to evaluate the sampling ability of Phanto-IDP. The results demonstrate that Phanto-IDP has high fidelity and diversity in the generated conformation ensembles, making it a suitable tool for enhancing the efficiency of MD simulation, generating broader protein conformational space and a continuous protein transition path.