The Transcriptome and DNA Methylome Landscapes of Human Primordial Germ Cells.

Germ cells are vital for transmitting genetic information from one generation to the next and for maintaining the continuation of species. Here, we analyze the transcriptome of human primordial germ cells (PGCs) from the migrating stage to the gonadal stage at single-cell and single-base resolutions. Human PGCs show unique transcription patterns involving the simultaneous expression of both pluripotency genes and germline-specific genes, with a subset of them displaying developmental-stage-specific features. Furthermore, we analyze the DNA methylome of human PGCs and find global demethylation of their genomes. Approximately 10 to 11 weeks after gestation, the PGCs are nearly devoid of any DNA methylation, with only 7.8% and 6.0% of the median methylation levels in male and female PGCs, respectively. Our work paves the way toward deciphering the complex epigenetic reprogramming of the germline with the aim of restoring totipotency in fertilized oocytes.

Characterization of network hierarchy reflects cell state specificity in genome organization.

Dynamic chromatin structure acts as the regulator of transcription program in crucial processes including cancer and cell development, but a unified framework for characterizing chromatin structural evolution remains to be established. Here, we performed graph inferences on Hi-C data sets and derived the chromatin contact networks. We discovered significant decreases in information transmission efficiencies in chromatin of colorectal cancer (CRC) and T-cell acute lymphoblastic leukemia (T-ALL) compared to corresponding normal controls through graph statistics. Using network embedding in the Poincaré disk, the hierarchy depths of chromatin from CRC and T-ALL patients were found to be significantly shallower compared to their normal controls. A reverse trend of change in chromatin structure was observed during early embryo development. We found tissue-specific conservation of hierarchy order in chromatin contact networks. Our findings reveal the top-down hierarchy of chromatin organization, which is significantly attenuated in cancer.

Diffusion-enhanced characterization of 3D chromatin structure reveals its linkage to gene regulatory networks and the interactome.

The interactome networks at the DNA, RNA, and protein levels are crucial for cellular functions, and the diverse variations of these networks are heavily involved in the establishment of different cell states. We have developed a diffusion-based method, Hi-C to geometry (CTG), to obtain reliable geometric information on the chromatin from Hi-C data. CTG produces a consistent and reproducible framework for the 3D genomic structure and provides a reliable and quantitative understanding of the alterations of genomic structures under different cellular conditions. The genomic structure yielded by CTG serves as an architectural blueprint of the dynamic gene regulatory network, based on which cell-specific correspondence between gene-gene and corresponding protein-protein physical interactions, as well as transcription correlation, is revealed. We also find that gene fusion events are significantly enriched between genes of short CTG distances and are thus close in 3D space. These findings indicate that 3D chromatin structure is at least partially correlated with downstream processes such as transcription, gene regulation, and even regulatory networking through affecting protein-protein interactions.

Atomistic Insights into Sequence-Mediated Spontaneous Association of Short RNA Chains.

RNA-RNA association and phase separation appear to be essential for the assembly of stress granules and underlie RNA foci formation in repeat expansion disorders. RNA molecules are found to play a significant role in gene-regulatory functions via condensate formation among themselves or with RNA-binding proteins. The interplay between driven versus spontaneous processes is likely to be an important factor for controlling the formation of RNA-mediated biomolecular condensate. However, the sequence-specific interactions and molecular mechanisms that drive the spontaneous RNA-RNA association and help to form RNA-mediated phase-separated condensate remain unclear. With microseconds-long atomistic molecular simulations here, we report how essential aspects of RNA chains, namely, base composition, metal ion binding, and hydration properties, contribute to the association of the series of simplest biologically relevant homopolymeric and heteropolymeric short RNA chains. We show that spontaneous processes make the key contributions governed by the sequence-intrinsic properties of RNA chains, where the definite roles of base-specific hydrogen bonding and stacking interactions are prominent in the association of the RNA chains. Purine versus pyrimidine contents of RNA chains can directly influence the association properties of RNA chains by modulating hydrogen bonding and base stacking interactions. This study determines the impact of ionic environment in sequence-specific spontaneous association of short RNA chains, hydration features, and base-specific interactions of Na+, K+, and Mg2+ ions with RNA chains.

Unraveling the key role of chromatin structure in cancer development through epigenetic landscape characterization of oral cancer.

Epigenetic alterations, such as those in chromatin structure and DNA methylation, have been extensively studied in a number of tumor types. But oral cancer, particularly oral adenocarcinoma, has received far less attention. Here, we combined laser-capture microdissection and muti-omics mini-bulk sequencing to systematically characterize the epigenetic landscape of oral cancer, including chromatin architecture, DNA methylation, H3K27me3 modification, and gene expression. In carcinogenesis, tumor cells exhibit reorganized chromatin spatial structures, including compromised compartment structures and altered gene-gene interaction networks. Notably, some structural alterations are observed in phenotypically non-malignant paracancerous but not in normal cells. We developed transformer models to identify the cancer propensity of individual genome loci, thereby determining the carcinogenic status of each sample. Insights into cancer epigenetic landscapes provide evidence that chromatin reorganization is an important hallmark of oral cancer progression, which is also linked with genomic alterations and DNA methylation reprogramming. In particular, regions of frequent copy number alternations in cancer cells are associated with strong spatial insulation in both cancer and normal samples. Aberrant methylation reprogramming in oral squamous cell carcinomas is closely related to chromatin structure and H3K27me3 signals, which are further influenced by intrinsic sequence properties. Our findings indicate that structural changes are both significant and conserved in two distinct types of oral cancer, closely linked to transcriptomic alterations and cancer development. Notably, the structural changes remain markedly evident in oral adenocarcinoma despite the considerably lower incidence of genomic copy number alterations and lesser extent of methylation alterations compared to squamous cell carcinoma. We expect that the comprehensive analysis of epigenetic reprogramming of different types and subtypes of primary oral tumors can provide additional guidance to the design of novel detection and therapy for oral cancer.

Generating High-Precision Force Fields for Molecular Dynamics Simulations to Study Chemical Reaction Mechanisms Using Molecular Configuration Transformer.

Theoretical studies on chemical reaction mechanisms have been crucial in organic chemistry. Traditionally, calculating the manually constructed molecular conformations of transition states for chemical reactions using quantum chemical calculations is the most commonly used method. However, this way is heavily dependent on individual experience and chemical intuition. In our previous study, we proposed a research paradigm that used enhanced sampling in molecular dynamics simulations to study chemical reactions. This approach can directly simulate the entire process of a chemical reaction. However, the computational speed limited the use of high-precision potential energy functions for simulations. To address this issue, we presented a scheme for training high-precision force fields for molecular modeling using a previously developed graph-neural-network-based molecular model, molecular configuration transformer. This potential energy function allowed for highly accurate simulations at a low computational cost, leading to more precise calculations of the mechanism of chemical reactions. We applied this approach to study a Claisen rearrangement reaction and a carbonyl insertion reaction catalyzed by manganese.

The effect of hydration number on the interfacial transport of sodium ions.

Ion hydration and transport at interfaces are relevant to a wide range of applied fields and natural processes1-5. Interfacial effects are particularly profound in confined geometries such as nanometre-sized channels6-8, where the mechanisms of ion transport in bulk solutions may not apply9,10. To correlate atomic structure with the transport properties of hydrated ions, both the interfacial inhomogeneity and the complex competing interactions among ions, water and surfaces require detailed molecular-level characterization. Here we constructed individual sodium ion (Na+) hydrates on a NaCl(001) surface by progressively attaching single water molecules (one to five) to the Na+ ion using a combined scanning tunnelling microscopy and noncontact atomic force microscopy system. We found that the Na+ ion hydrated with three water molecules diffuses orders of magnitude more quickly than other ion hydrates. Ab initio calculations revealed that such high ion mobility arises from the existence of a metastable state, in which the three water molecules around the Na+ ion can rotate collectively with a rather small energy barrier. This scenario would apply even at room temperature according to our classical molecular dynamics simulations. Our work suggests that anomalously high diffusion rates for specific hydration numbers of ions are generally determined by the degree of symmetry match between the hydrates and the surface lattice.

Publisher Correction: The effect of hydration number on the interfacial transport of sodium ions.

In this Letter, the links to Supplementary Videos 5, 7, 9 and 10 were incorrect, and there were some formatting errors in the Supplementary Video legends. These errors have been corrected online.

Structural and dynamical aspect of DNA motif sequence specific binding of AP-1 transcription factor.

Activator protein-1 (AP-1) comprises one of the largest and most evolutionary conserved families of ubiquitous eukaryotic transcription factors that act as a pioneer factor. Diversity in DNA binding interaction of AP-1 through a conserved basic-zipper (bZIP) domain directs in-depth understanding of how AP-1 achieves its DNA binding selectivity and consequently gene regulation specificity. Here, we address the structural and dynamical aspects of the DNA target recognition process of AP-1 using microsecond-long atomistic simulations based on the structure of the human AP-1 FosB/JunD bZIP-DNA complex. Our results show the unique role of DNA shape features in selective base specific interactions, characteristic ion population, and solvation properties of DNA grooves to form the motif sequence specific AP-1-DNA complex. The TpG step at the two terminals of the AP-1 site plays an important role in the structural adjustment of DNA by modifying the helical twist in the AP-1 bound state. We addressed the role of intrinsic motion of the bZIP domain in terms of opening and closing gripper motions of DNA binding helices, in target site recognition and binding of AP-1 factors. Our observations suggest that binding to the cognate motif in DNA is mainly accompanied with the precise adjustment of closing gripper motion of DNA binding helices of the bZIP domain.

Steric repulsion introduced by loop constraints modulates the microphase separation of chromatins.

Within the confines of a densely populated cell nucleus, chromatin undergoes intricate folding, forming loops, domains, and compartments under the governance of topological constraints and phase separation. This coordinated process inevitably introduces interference between different folding strategies. In this study, we model interphase chromatins as block copolymers with hetero-hierarchical loops within a confined system. Employing dissipative particle dynamics simulations and scaling analysis, we aim to explain how the structure and distribution of loop domains modulate the microphase separation of chromatins. Our results highlight the correlation between the microphase separation of the copolymer and the length, heterogeneity, and hierarchically nested levels of the loop domains. This correlation arises from steric repulsion intrinsic to loop domains. The steric repulsion induces variations in chain stiffness (including local orientation correlations and the persistence length), thereby influencing the degree of phase separation. Through simulations of block copolymers with distinct groups of hetero-hierarchical loop anchors, we successfully reproduce changes in phase separation across diverse cell lines, under fixed interaction parameters. These findings, in qualitative alignment with Hi-C data, suggest that the variations of loop constraints alone possess the capacity to regulate higher-order structures and the gene expressions of interphase chromatins.

DSDP: A Blind Docking Strategy Accelerated by GPUs.

Virtual screening, including molecular docking, plays an essential role in drug discovery. Many traditional and machine-learning-based methods are available to fulfill the docking task. However, the traditional docking methods are normally extensively time-consuming, and their performance in blind docking remains to be improved. Although the runtime of docking based on machine learning is significantly decreased, their accuracy is still limited. In this study, we take advantage of both traditional and machine-learning-based methods and present a method, deep site and docking pose (DSDP), to improve the performance of blind docking. For traditional blind docking, the entire protein is covered by a cube, and the initial positions of ligands are randomly generated in this cube. In contrast, DSDP can predict the binding site of proteins and provide an accurate searching shape and initial positions for further conformational sampling. The sampling task of DSDP makes use of the score function and a similar but modified searching strategy of AutoDock Vina, accelerated by implementation in GPUs. We systematically compare its performance in redocking, blind docking, and virtual screening tasks with state-of-the-art methods, including AutoDock Vina, GNINA, QuickVina, SMINA, and DiffDock. In the blind docking task, DSDP reaches a 29.8% top-1 success rate (root-mean-squared deviation < 2 Å) on an unbiased and challenging test dataset with 1.2 s wall-clock computational time per system. Its performances on the DUD-E dataset and the time-split PDBBind dataset used in EquiBind, TANKBind, and DiffDock are also evaluated, presenting a 57.2 and 41.8% top-1 success rate with 0.8 and 1.0 s per system, respectively.

Superresolution imaging reveals spatiotemporal propagation of human replication foci mediated by CTCF-organized chromatin structures.

Mammalian DNA replication is initiated at numerous replication origins, which are clustered into thousands of replication domains (RDs) across the genome. However, it remains unclear whether the replication origins within each RD are activated stochastically or preferentially near certain chromatin features. To understand how DNA replication in single human cells is regulated at the sub-RD level, we directly visualized and quantitatively characterized the spatiotemporal organization, morphology, and in situ epigenetic signatures of individual replication foci (RFi) across S-phase at superresolution using stochastic optical reconstruction microscopy. Importantly, we revealed a hierarchical radial pattern of RFi propagation dynamics that reverses directionality from early to late S-phase and is diminished upon caffeine treatment or CTCF knockdown. Together with simulation and bioinformatic analyses, our findings point to a "CTCF-organized REplication Propagation" (CoREP) model, which suggests a nonrandom selection mechanism for replication activation at the sub-RD level during early S-phase, mediated by CTCF-organized chromatin structures. Collectively, these findings offer critical insights into the key involvement of local epigenetic environment in coordinating DNA replication across the genome and have broad implications for our conceptualization of the role of multiscale chromatin architecture in regulating diverse cell nuclear dynamics in space and time.

Accelerating supramolecular aggregation by molecular sliding.

Diffusion-based translocation along DNA or RNA molecules is essential for genome regulatory proteins to execute their biological functions. The reduced dimensionality of the searching process makes the proteins bind specific target sites at a "faster-than-diffusion-controlled rate". We herein report a photoresponsive slider-track diffusion system capable of self-assembly rate acceleration, which consists of (-)-camphorsulfonic acid, 4-(4'-n-octoxylphenylazo)benzenesulfonic acid, and isotactic poly(2-vinylpyridine). The protonated pyridine rings act as the footholds for anionic azo sliders to diffusively bind and slide along polycationic tracks via electrostatic interactions. Ultraviolet light triggers the trans to cis isomerization and aggregation of azo sliders, which can be monitored by multiple spectroscopic methods without labeling. The presence of vinyl polymer track increases the aggregation rate of cis azobenzene up to ∼20 times, depending on the stereoregularity of the polymer chain, the acid/base ratio and the addition of salt. This system has a feature of simplicity, monitorability, controllability, and could find applications in designing molecular machines with desired functionalities.

Deep reinforcement learning of transition states.

Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach, called RL‡, to automatically unravel chemical reaction mechanisms. In RL‡, locating the transition state of a chemical reaction is formulated as a game, and two functions are optimized, one for value estimation and the other for policy making, to iteratively improve our chance of winning this game. Both functions can be approximated by deep neural networks. By virtue of RL‡, one can directly interpret the reaction mechanism according to the value function. Meanwhile, the policy function allows efficient sampling of the transition path ensemble, which can be further used to analyze reaction dynamics and kinetics. Through multiple experiments, we show that RL‡ can be trained tabula rasa hence allowing us to reveal chemical reaction mechanisms with minimal subjective biases.

Comparison of the Microsolvation of CaX2 (X = F, Cl, Br, I) in Water: Size-Selected Anion Photoelectron Spectroscopy and Theoretical Calculations.

To understand the microsolvation of alkaline-earth dihalides in water and provide information about the dependence of solvation processes on different halides, we investigated CaBr2(H2O)n-, CaI2(H2O)n-, and CaF2(H2O)n- (n = 0-6) clusters using size-selected anion photoelectron spectroscopy and conducted theoretical calculations on these clusters and their neutrals. The results are compared with those of CaCl2(H2O)n-/0 clusters reported previously. It is found that the vertical detachment energies (VDEs) of CaCl2(H2O)n-, CaBr2(H2O)n-, and CaI2(H2O)n- show a similar trend with increasing cluster size, while the VDEs of CaF2(H2O)n- show a different trend. The VDEs of CaF2(H2O)n- are much lower than those of CaCl2(H2O)n-, CaBr2(H2O)n-, and CaI2(H2O)n-. A detailed probing of the structures shows that a significant increase of the Ca-X distance (separation of Ca2+-X- ion pair) in CaCl2(H2O)n-/0, CaBr2(H2O)n-/0, and CaI2(H2O)n-/0 clusters occurred at about n = 5. However, for CaF2(H2O)n-/0, no abrupt change of the Ca-F distance with the increasing cluster size has been observed. In CaCl2(H2O)6-/0, CaBr2(H2O)6-/0, and CaI2(H2O)6-/0, the Ca atom coordinates directly with 5 H2O molecules. However, in CaF2(H2O)n-/0, the Ca atom coordinates directly with only 2 or 3 H2O molecules. The similarity or differences in the structures and coordination numbers are consistent with the fact that CaCl2, CaBr2, and CaI2 have similar solubility, while CaF2 has much lower solubility.

A DNA Sequence Based Polymer Model for Chromatin Folding.

The recent development of sequencing technology and imaging methods has provided an unprecedented understanding of the inter-phase chromatin folding in mammalian nuclei. It was found that chromatin folds into topological-associated domains (TADs) of hundreds of kilo base pairs (kbps), and is further divided into spatially segregated compartments (A and B). The compartment B tends to be located near to the periphery or the nuclear center and interacts with other domains of compartments B, while compartment A tends to be located between compartment B and interacts inside the domains. These spatial domains are found to highly correlate with the mosaic CpG island (CGI) density. High CGI density corresponds to compartments A and small TADs, and vice versa. The variation of contact probability as a function of sequential distance roughly follows a power-law decay. Different chromosomes tend to segregate to occupy different chromosome territories. A model that can integrate these properties at multiple length scales and match many aspects is highly desired. Here, we report a DNA-sequence based coarse-grained block copolymer model that considers different interactions between blocks of different CGI density, interactions of TAD formation, as well as interactions between chromatin and the nuclear envelope. This model captures the various single-chromosome properties and partially reproduces the formation of chromosome territories.

Polymer models for the mechanisms of chromatin 3D folding: review and perspective.

Recent experiments have provided unprecedented details on the hierarchical organization of the chromatin 3D structure and thus a great opportunity for understanding the mechanisms behind chromatin folding. As a bridge between experimental results and physical theory, coarse-grained polymer models of chromatin are of great value. Here, we review several popular models of chromatin folding, including the fractal globule model, loop models (the random loop model, the dynamic loop model, and the loop extrusion model), the string-and-binder switch model, and the block copolymer model. Physical models are still in great need to explain a larger variety of chromatin folding properties, especially structural features at different scales, their relation to the heterogeneous nature of the DNA sequence, and the highly dynamic nature of chromatin folding.

A Perspective on Deep Learning for Molecular Modeling and Simulations.

Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vision and natural language processing, its development in molecular modeling and simulations is still at an early stage, largely because the inductive biases of molecules are completely different from those of images or texts. Footed on these differences, we first reviewed the limitations of traditional deep learning models from the perspective of molecular physics and wrapped up some relevant technical advancement at the interface between molecular modeling and deep learning. We do not focus merely on the ever more complex neural network models; instead, we introduce various useful concepts and ideas brought by modern deep learning. We hope that transacting these ideas into molecular modeling will create new opportunities. For this purpose, we summarized several representative applications, ranging from supervised to unsupervised and reinforcement learning, and discussed their connections with the emerging trends in deep learning. Finally, we give an outlook for promising directions which may help address the existing issues in the current framework of deep molecular modeling.

Deep learning for variational multiscale molecular modeling.

Molecular simulations are widely applied in the study of chemical and bio-physical problems. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atomistic level and performing enhanced sampling or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper, we propose a machine-learning approach to ally both strategies so that simulations on different scales can benefit mutually from their crosstalks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations through deep generative learning; in turn, FG simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method defines a variational and adaptive training objective, which allows end-to-end training of parametric molecular models using deep neural networks. Through multiple experiments, we show that our method is efficient and flexible and performs well on challenging chemical and bio-molecular systems.

Hydration processes of barium chloride: Size-selected anion photoelectron spectroscopy and theoretical calculations of BaCl2-water clusters.

In order to understand the hydration processes of BaCl2, we investigated BaCl2(H2O)n - (n = 0-5) clusters using size-selected anion photoelectron spectroscopy and theoretical calculations. The structures of neutral BaCl2(H2O)n clusters up to n = 8 were also investigated by theoretical calculations. It is found that in BaCl2(H2O)n -/0, the Ba-Cl distances increase very slowly with the cluster size. The hydration process is not able to induce the breaking of a Ba-Cl bond in the cluster size range (n = 0-8) studied in this work. In small BaCl2(H2O)n clusters with n ≤ 5, the Ba atom has a coordination number of n + 2; however, in BaCl2(H2O)6-8 clusters, the Ba atom coordinates with two Cl atoms and (n - 1) water molecules, and it has a coordination number of n + 1. Unlike the previously studied MgCl2(H2O)n - and CaCl2(H2O)n -, negative charge-transfer-to-solvent behavior has not been observed for BaCl2(H2O)n -, and the excess electron of BaCl2(H2O)n - is mainly localized on the Ba atom rather on the water molecules. No observation of Ba2+-Cl- separation in current work is consistent with the lower solubility of BaCl2 compared to MgCl2 and CaCl2. Considering the BaCl2/H2O mole ratio in the saturated solution, one would expect that about 20-30 H2O molecules are needed to break the first Ba-Cl bond in BaCl2.