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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.

Deriving reaction coordinates for the characterization of chemical reactions has long been a demanding task. In our previous work [ACS Cent. Sci. 3, 407 (2017)], the reaction coordinate of a (retro-) Claisen rearrangement in aqueous solution optimized through a Bayesian measure, a linear combination of bond lengths formation and breakage, was judged to be optimal among all trails. Here, considering the nonlinearity of the transition state, we use isometric mapping and locally linear embedding to obtain one reaction coordinate which is composed of a few collective variables. With these methods, we find a more reasonable and powerful one-dimensional reaction coordinate, which can well describe the reaction progression. To explore the reaction mechanism, we analyze the contribution of intrinsic molecular properties and the solvent-solute interactions to the nonlinear reaction coordinate. Furthermore, another coordinate is identified to characterize the heterogeneity of reaction mechanisms.

Sequence-specific recognition of transcription factor (TF) binding motifs in the target site of DNA over the vast amount of non-target DNA is of primary importance for the transcriptional regulation of gene expression by the TFs. Binding of TFs to the target site of DNA relies not only on the direct contact formation but also on the structural and conformational features of DNA. Recognition of DNA structural features or shape readout by proteins is an important factor in the context of TF-DNA interaction. Based on the atomistic molecular simulation, here we report the sequence-dependent unique structural features, solvation, and ion-binding properties of biologically relevant AT- and GC-rich human TF binding motifs in DNA. Counterion and water distribution around the motif is found to be sensitive to the motif sequence, which is accompanied with the DNA shape features. The motif sequence affects the electrostatic potential along the grooves, and cytosine methylation alters the DNA shape features. Characteristic solvation properties of TF binding motif DNA fragments infer that an ionic environment and hydration influences are essential to describe TF-DNA interactions.

Sequence-dependent binding between DNA and proteins in chromatin is an essential part of gene expression. Linker histone H1 is an important protein in the regulation of chromatin compartmentalization and compaction, and its binding with the nucleosome is sensitive to the DNA sequence. Although the interactions of H1 and DNA have been widely investigated, the mechanism of nucleosome conformation changes induced by the DNA-sequence-dependent binding with gH1 (globular H1.0) remains largely unclear at the atomic level. In the present molecular dynamics simulations, both linker and dyad DNAs were mutated to investigate the conformational changes of the nucleosome induced by the sequence-dependent binding of gH1 based on the on-dyad binding mode. Our results indicate that gH1 is insensitive to the DNA sequence of the dyad DNA but presents an apparent preference to linker DNA with an AT-rich sequence. Moreover, this specific binding induces the entry/exit region of a nucleosome to a tight conformation and regulates the accessibility of core histones. Considering that the entry/exit region of the nucleosome is a crucial binding site for many functional proteins related to gene expression, the conformational change at this region could represent an important gene regulation signal.

NMR experiments can detect in situ structures and dynamic interactions, but the NMR assignment process requires expertise and is time-consuming, thereby limiting its applicability. Deep learning algorithms have been employed to aid in experimental data analysis. In this work, we developed a RASP model which can enhance structure prediction with restraints. Based on the Evoformer and structure module architecture of AlphaFold, this model can predict structure based on sequence and a flexible number of input restraints. Moreover, it can evaluate the consistency between the predicted structure and the imposed restraints. Based on this model, we constructed an iterative NMR NOESY peak assignment pipeline named FAAST, to accelerate assignment process of NOESY restraints and obtaining high quality structure ensemble. The RASP model and FAAST pipeline not only allow for the leveraging of experimental restraints to improve model prediction, but can also facilitate and expedite experimental data analysis with their integrated capabilities.

The high‐order chromatin structure, together with DNA methylation and other epigenetic marks, plays a vital role in gene regulation and displays abnormal status in cancer cells. Theoretical analyses are expected to provide a more unified understanding of the multi‐omics data on the large variety of samples, and hopefully a common picture of carcinogenesis. In particular, we are interested in the question of whether an underlying origin DNA sequence exists for these epigenetic alterations. The human genome consists of two types of megabase‐sized domain based on the distribution of CpG islands (CGIs) that show distinct structural, epigenetic, and transcriptional properties: CGI‐rich and CGI‐poor domains. Through an integrated analysis of chromatin structure, DNA methylation, and RNA sequencing data, we found that, in carcinogenesis, the two different types of domain display different structural changes and have an increased number of DNA methylation differences and transcriptional‐level differences, compared with in noncancer cells. We also compared the structural features among carcinogenesis, senescence, and mitosis, showing the possible connection between chromatin structure and cell state, which could affect vital cancer‐related properties. In summary, chromatin structure, DNA methylation, and gene expression, as well as their changes observed in several types of cancers, show a dependence on multiscale DNA sequence heterogeneity., By employing an integrated analysis of multi‐omics data, we observed that the changes in chromatin structure, DNA methylation and gene expression related to carcinogenesis are dependent on multiscale DNA sequence heterogeneity. These concerted changes that we identified in several cancer types offer a new perspective in understanding the molecular mechanisms behind cancer development.

It is generally agreed that the nuclei of eukaryotic cells at interphase are partitioned into disjointed territories, with distinct regions occupied by certain chromosomes. However, the underlying mechanism for such territorialization is still under debate. Here we model chromosomes as coarse-grained block copolymers and to investigate the effect of loop domains (LDs) on the formation of compartments and territories based on dissipative particle dynamics. A critical length of LDs, which depends sensitively on the length of polymeric blocks, is obtained to minimize the degree of phase separation. This also applies to the two-polymer system: The critical length not only maximizes the degree of territorialization but also minimizes the degree of phase separation. Interestingly, by comparing with experimental data, we find the critical length for LDs and the corresponding length of blocks to be respectively very close to the mean length of topologically associating domains (TADs) and chromosomal segments with different densities of CpG islands for human chromosomes. The results indicate that topological constraints with optimal length can contribute to the formation of territories by weakening the degree of phase separation, which likely promotes the chromosomal flexibility in response to genetic regulations.

Understanding the reaction mechanism is required for better control of chemical reactions and is usually achieved by locating transition states (TSs) along a proper one-dimensional coordinate called reaction coordinate (RC). The identification of RC can be very difficult for high-dimensional realistic systems. A number of methods have been proposed to tackle this problem. A machine learning method is developed here to incorporate the influence of velocity on the reaction process. The method is also free of the unbalanced label problem resulting from the rather low fraction of configurations near the TS and can be easily extended to large systems. It locates the transition zone in the phase space and defines the dividing surface with a high transmission coefficient. Moreover, considering that the reaction environment can not only change the reaction path but also activate the reactive mode through energy transfer, we devise two measures to quantify the influence of these two factors on the reaction process and find that solvents can assist the reaction by directly doing work along the reactive mode. Not surprisingly, there is a positive correlation between the efficiency of energy transfer into the reactive mode and the reaction rate.

Water is one of the most abundant molecules on Earth. However, this common and "simple" material has more than 18 different phases, which poses a great challenge to theoretically study the nature of water and ice. We designed two reaction coordinates that can distinguish between water and various ice states and used them to efficiently sample all possible states of the system in all-atom molecular dynamics simulation at ambient temperature and pressure. Various structural and thermodynamics properties, including the water-ice phase diagrams, can thus be calculated. We also present a simple model that successfully explains the thermodynamic stability of different ice states. Our work provides effective methods and data for theoretical studies of different phases of water and ice.

Indoor propagation of airborne diseases is yet poorly understood. Here, we theoretically study a microscopic model based on the motions of virus particles in a respiratory microdroplet, responsible for airborne transmission of diseases, to understand their indoor propagation. The virus particles are driven by a driving force that mimics force due to gushing of air by devices like indoor air conditioning along with the gravity. A viral particle within the droplet experiences viscous drag due to the droplet medium, force due to interfacial tension at the droplet boundary, the thermal forces and mutual interaction forces with the other viral particles. We use Brownian Dynamics (BD) simulations and scaling arguments to study the motion of the droplet, given by that of the center of mass of the viral assembly. The BD simulations show that in presence of the gravity force alone, the time the droplet takes to reach the ground level, defined by the gravitational potential energy being zero, from a vertical height H,t

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 datasets and derived the chromatin contact networks (CCNs). We discovered significant decreases in information transmission efficiencies in CCNs of colorectal cancer (CRC) and T-lineage acute lymphoblastic leukemia (T-ALL) compared to corresponding normal controls through graph statistics. Through network embedding in Poincaré disk, the hierarchy depths of CCNs from CRC and T-ALL patients were found to be significantly shallower compared to their normal controls. A reverse trend of CCN change was observed for the transition from embryo to differentiated primary tissues. During early embryo developments of both human and mouse, the hierarchy of embedded chromatin structure gradually establishes. Additionally, we found tissue-specific conservation of hierarchy order in normal CCNs, which was disturbed in tumor. Our findings uncover the cell-state related hierarchical characteristics in chromatin structure, which reveal chromatin loci that play important roles in stabilizing the cellular conditions.

Gene expression is regulated by many factors, including transcription factors, chromatin three-dimensional topology, modifications of DNA and histone proteins, and non-coding RNAs. The execution of these complex mechanisms requires an effectively coordinated regulation system. In this review, we emphasize that the multi-scale heterogeneous DNA sequence plays a fundamental and important role for gene expression activity and usage of different means of epigenetic regulation. We illustrate here that the chromatin structure organization provides a stage for spatiotemporal regulation between different genes or gene modules and to realize their downstream functional cooperation. Such a perspective expands our understanding of the central dogma: In addition to one-dimensional sequence information, inter-gene interactions can also be transferred from DNA and RNA to protein levels.

High-throughput chromosome conformation capture (Hi-C) technique profiles the genomic structure in a genome-wide fashion. The reproducibility and consistency of Hi-C data are essential in characterizing dynamics of genomic structures. We developed a diffusion-based method, CTG (Hi-C To Geometry), to deal with the technical bias induced by insufficient sampling in sequencing and obtain reliable gemeotric information of the chromatin. CTG properly quantifies dubiously weak or even undetected interactions and produces a consistent and reproducible framework for the 3D genomic structure. CTG allows for a reliable genome-wide insight on the alteration of genomic structures under different cellular conditions and reveals correlations between genomic-proximal genes at both transcriptional and translational levels. Cell-specific correspondence between gene-gene and corresponding protein-protein physical interactions, as well as that with the transcription correlation reveals the coordinated inter-molecular structural and regulatory information passage in the central dogma.

Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach (RL$^\ddag$) to automatically unravel chemical reaction mechanisms. In RL$^\ddag$, locating the transition state of a chemical reaction is formulated as a game, where a virtual player is trained to shoot simulation trajectories connecting the reactant and product. The player utilizes two functions, one for value estimation and the other for policy making, to iteratively improve the chance of winning this game. We can directly interpret the reaction mechanism according to the value function. Meanwhile, the policy function enables efficient sampling of the transition paths, which can be further used to analyze the reaction dynamics and kinetics. Through multiple experiments, we show that RL{\ddag} can be trained tabula rasa hence allows us to reveal chemical reaction mechanisms with minimal subjective biases., Comment: version 1

The timescale involved in chemical reactions is quite often beyond that of normal molecular dynamics simulations. Here, we combine metadynamics with selective integrated tempering sampling to simulate an intra-molecular Diels-Alder reaction in explicit solvents. Based on a one-dimensional collective variable obtained from harmonic linear discriminant analysis, four chiral isomers of products were observed in the simulation. Analyses of reactive trajectories showed that this reaction follows a concerted mechanism in all four solvents. In addition, the hydrogen bond between the reactant and water solvent plays an important role in the water-accelerated reaction mechanism. The dynamics of chirality formation varies significantly with solvents. The chirality of products forms significantly before the transition state, especially in ionic liquid.

Gene regulatory functions of noncanonical i-motif DNA are associated with dynamic i-motif formation in the cellular environment and pH variation. With atomistic simulations, we show the dramatic influence of solvent pH on the conformational dynamics of biologically relevant telomeric i-motif DNA coupled with protonation of cytosine bases in different conformations. We rationalized the pH-dependent dynamics and conformational variability of the i-motif in terms of base pairing and specific loop motions. The human telomeric i-motif is found to acquire various metastable folded conformations at pH values near the p

High-order chromatin structure has been shown to play a vital role in gene regulation. Previously we identified two types of sequence domains, CGI (CpG island) forest and CGI prairie, which tend to spatially segregate, but to different extent in different tissues. Here we aim to further quantify the association of domain segregation with gene regulation and therefore differentiation. By means of the published RNA-seq and Hi-C data, we identified tissue-specific genes and quantitatively investigated how their regulation is relevant to chromatin structure. Besides, two types of gene networks were constructed and the association between gene pair co-regulation and genome organization is discussed. We show that compared to forests, tissue-specific genes tend to be enriched in prairies. Highly specific genes also tend to cluster according to their functions in a relatively small number of prairies. Furthermore, tissue-specific forest-prairie contact formation was associated with the regulation of tissue-specific genes, in particular those in the prairie domains, pointing to the important role of gene positioning, in the linear DNA sequence as well as in 3D chromatin structure, in gene regulatory network formation. We investigated how gene regulation is related to genome organization from the perspective of forest-prairie spatial interactions. Since unlike compartments A and B, forest and prairie are identified solely based on sequence properties. Therefore, the simple and uniform framework (forest-prairie domain segregation) provided here can be utilized to further understand the chromatin structure changes as well as the underlying biological significances in different stages, such as tumorgenesis.

Energy transfer and spatial diffusion are two significant sub-processes in chemical reaction. Traditional rate theory is based on two assumptions: (1) energy transfer is faster than chemical reaction so that specific energy transfer channel is not important. (2) The solvents can respond to the change of solute quickly resulting in equilibrium solvation. Then once system cross the barrier, it will enter the product basin. But when energy transfer is fast enough to distribute the energy into all degrees of freedom according to certain ratio, the specific energy transfer pathways should be considered. Our previous studies have found that the polar functional groups play the major role in the intermolecular energy transfer process. And the energy accumulation and dissipation are completed by the motion with specific frequency. Furthermore, the major energy transfer channel is different in different solutions. Moreover, the length of transition path is very short, leading to the existence of different transient solvation configurations with distinctly different strengths of interaction between solutes and solvents. Consequently, the motion of solvents modulates the reaction dynamics and results in heterogeneous reaction paths. Therefore, the dynamic effects are vital for understanding and controlling the chemical reaction.

A recent success in which the engineered iron-haem enzymes P411CHA′ aminate the intermolecular benzylic C–H bond with both high efficiency and stereoselectivity solves a long-standing challenge in ...

The delta values of the isotope composition of atmospheric ozone is ~100‰ (referenced to atmospheric O2). Previous photochemical models, which considered the isotope fractionation processes from both formation and photolysis of ozone, predicted δ49O3 and δ50O3 values, in δ49O3 versus δ50O3 space, that are >10‰ larger than the measurements. We propose that the difference between the model and observations could be explained either by the temperature variation, Chappuis band photolysis, or a combination of the two and examine them. The isotopic fractionation associated with ozone formation increases with temperature. Our model shows that a hypothetical reduction of ~20 K in the nominal temperature profile could reproduce the observations. However, this hypothesis is not consistent with temperatures obtained by in situ measurements and NCEP Reanalysis. Photolysis of O3 in the Chappuis band causes O3 to be isotopically depleted, which is supported by laboratory measurements for 18O18O18O but not by recent new laboratory data made at several wavelengths for 49O3 and 50O3. Cloud reflection can significantly enhance the photolysis rate and affect the spectral distribution of photons, which could influence the isotopic composition of ozone. Sensitivity studies that modify the isotopic composition of ozone by the above two mechanisms are presented. We conclude isotopic fractionation occurring in photolysis in the Chappuis band remains the most plausible solution to the model-observation discrepancy. Implications of our results for using the oxygen isotopic signature for constraining atmospheric chemical processes related to ozone, such as CO2, nitrate, and the hydroxyl radical, are discussed.

The CpG dinucleotide and its methylation behaviors play vital roles in gene regulation. Previous studies have divided genes into several categories based on the CpG intensity around transcription starting sites and found that housekeeping genes tend to possess high CpG density, whereas tissue-specific genes are generally characterized by low CpG density. In this study, we investigated how the CpG density distribution of a gene affects its transcription and regulation pattern. Based on the CpG density distribution around transcription starting site, by means of a semi-supervised neural network we designed, which took data augmentation into account, we divided the human genes into three categories, and genes within each cluster shared similar CpG density distribution. Not only sequence properties, these different clusters exhibited distinctly different structural features, regulatory mechanisms, correlation patterns between the expression level and CpG/TpG density, and expression and epigenetic mark variations during tumorigenesis. For instance, the activation of cluster 3 genes relies more on 3D genome reorganization, compared with cluster 1 and 2 genes, whereas cluster 2 genes showed the strongest correlation between gene expression and H3K27me3. Genes exhibiting uncoupled correlation between gene regulation and histone modifications are mainly in cluster 3. These results emphasized that the usage of epigenetic marks in gene regulation is partially rooted in the sequence property of genes such as their CpG density distribution and explained to some extent why the relation between epigenetic marks and gene expression is controversial.

The CpG dinucleotide and its methylation play vital roles in gene regulation as well as 3D genome organization. Previous studies have divided genes into several categories based on the CpG intensity around transcription starting sites (TSS) and found that housekeeping genes tend to possess high CpG density while tissue-specific genes are generally characterized by low CpG density. In this study, we investigated how the CpG density distribution of a gene affects its transcription and regulation pattern. Based on the CpG density distribution around TSS, the human genes are clearly divided into different categories. Not only sequence properties, these different clusters exhibited distinctly different structural features, regulatory mechanisms, and correlation patterns between expression level and CpG/TpG density. These results emphasized that the usage of epigenetic marks in gene regulation is partially rooted in the sequence property of genes, such as their CpG density distribution.

In this Letter, we analyzed the inductive bias underlying complex free-energy landscapes (FELs) and exploited it to train deep neural networks that yield reduced and clustered representation for the FEL. Our parametric method, called information distilling of metastability (IDM), is end-to-end differentiable and thus scalable to ultralarge data sets. IDM is able to perform clustering in the meantime of reducing the dimensionality. Besides, as an unsupervised learning method, IDM differs from many existing dimensionality reduction and clustering methods in that it requires neither a cherry-picked distance metric nor the ground-true number of clusters defined a priori, and it can be used to unroll and zoom in on the hierarchical FEL with respect to different time scales. Through multiple experiments, we show that IDM can achieve physically meaningful representations that partition the FEL into well-defined metastable states that hence are amenable for downstream tasks such as mechanism analysis and kinetic modeling.