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.

PMC-IZ: A Simple Algorithm for the Electrostatics Calculation in Slab Geometric Molecular Dynamics Simulations.

Slab geometric systems are widely utilized in molecular simulations. However, an efficient, straightforward, and accurate method for calculating electrostatic interactions in these systems for molecular dynamics (MD) simulations is still needed. This review introduces a PME-like approach called PMC-IZ, specifically designed for slab geometric systems. Traditional approaches for long-range electrostatic interaction calculations in slab geometry typically involve Ewald summation, where the Gaussian charge density is summed within 3D unit cells and then integrated in the 2D periodic space. In the proposed approach here, the Poisson equation was solved for a single Gaussian charge density within 2Dl periodic space, followed by convolution within 3D unit cells using an effective potential as the convolution kernel for summation. The effective potential ensures that the solution within the region of interest adheres strictly to 2D periodic boundary conditions while inherently possessing 3D periodic boundary condition properties. The PMC-IZ method provides for such systems accurate treatment of electrostatic interactions, overcomes limitations associated with finite vacuum layers, and offers improved computational efficiency. We thus postulate that this method provides a valuable tool for studying electrostatic interactions in slab geometric system MD simulations. It has promising applications in various areas such as surface science, catalysis, and materials research, where accurate modeling of slab geometric electrostatic interactions is essential.

Nanoscale one-dimensional close packing of interfacial alkali ions driven by water-mediated attraction.

The permeability and selectivity of biological and artificial ion channels correlate with the specific hydration structure of single ions. However, fundamental understanding of the effect of ion-ion interaction remains elusive. Here, via non-contact atomic force microscopy measurements, we demonstrate that hydrated alkali metal cations (Na+ and K+) at charged surfaces could come into close contact with each other through partial dehydration and water rearrangement processes, forming one-dimensional chain structures. We prove that the interplay at the nanoscale between the water-ion and water-water interaction can lead to an effective ion-ion attraction overcoming the ionic Coulomb repulsion. The tendency for different ions to become closely packed follows the sequence K+ > Na+ > Li+, which is attributed to their different dehydration energies and charge densities. This work highlights the key role of water molecules in prompting close packing and concerted movement of ions at charged surfaces, which may provide new insights into the mechanism of ion transport under atomic confinement.

Unsupervisedly Prompting AlphaFold2 for Accurate Few-Shot Protein Structure Prediction.

Data-driven predictive methods that can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development. Determining an accurate folding landscape using coevolutionary information is fundamental to the success of modern protein structure prediction methods. As the state of the art, AlphaFold2 has dramatically raised the accuracy without performing explicit coevolutionary analysis. Nevertheless, its performance still shows strong dependence on available sequence homologues. Based on the interrogation on the cause of such dependence, we presented EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in the low-data regime and even achieve encouraging performance with single-sequence predictions. Being able to make accurate predictions with few-shot MSA not only generalizes AlphaFold2 better for orphan sequences but also democratizes its use for high-throughput applications. Besides, EvoGen combined with AlphaFold2 yields a probabilistic structure generation method that could explore alternative conformations of protein sequences, and the task-aware differentiable algorithm for sequence generation will benefit other related tasks including protein design.

Action at a distance: organic cation induced long range organization of interfacial water enhances hydrogen evolution and oxidation kinetics.

Engineering efficient electrode-electrolyte interfaces for the hydrogen evolution and oxidation reactions (HOR/HER) is central to the growing hydrogen economy. Existing descriptors for HOR/HER catalysts focused on species that could directly impact the immediate micro-environment of surface-mediated reactions, such as the binding energies of adsorbates. In this work, we demonstrate that bulky organic cations, such as tetrapropyl ammonium, are able to induce a long-range structure of interfacial water molecules and enhance the HOR/HER kinetics even though they are located outside the outer Helmholtz plane. Through a combination of electrokinetic analysis, molecular dynamics and in situ spectroscopic investigations, we propose that the structure-making ability of bulky hydrophobic cations promotes the formation of hydrogen-bonded water chains connecting the electrode surface to the bulk electrolyte. In alkaline electrolytes, the HOR/HER involve the activation of interfacial water by donating or abstracting protons. The structural diffusion mechanism of protons in aqueous electrolytes enables water molecules and cations located at a distance from the electrode to influence surface-mediated reactions. The findings reported in this work highlight the prospect of leveraging the nonlocal mechanism to enhance electrocatalytic performance.

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.

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.

Artificial Intelligence Enhanced Molecular Simulations.

Molecular simulations, which simulate the motions of particles according to fundamental laws of physics, have been applied to a wide range of fields from physics and materials science to biochemistry and drug discovery. Developed for computationally intensive applications, most molecular simulation software involves significant use of hard-coded derivatives and code reuse across various programming languages. In this Review, we first align the relationship between molecular simulations and artificial intelligence (AI) and reveal the coherence between the two. We then discuss how the AI platform can create new possibilities and deliver new solutions to molecular simulations, from the perspective of algorithms, programming paradigms, and even hardware. Rather than focusing solely on increasingly complex neural network models, we introduce various concepts and techniques brought about by modern AI and explore how they can be transacted to molecular simulations. To this end, we summarized several representative applications of molecular simulations enhanced by AI, including from differentiable programming and high-throughput simulations. Finally, we look ahead to promising directions that may help address existing issues in the current framework of AI-enhanced molecular simulations.

Investigating the Activation Mechanism Differences between Human and Mouse cGAS by Molecular Dynamics Simulations.

Cyclic GMP-AMP synthase (cGAS) has been widely investigated as a drug target for its crucial role in innate immunity. However, the inhibitors designed using mouse model were often shown to be ineffective for humans. This outcome indicates that the activation mechanisms of human and mouse cGAS (mcGAS) are different. The cGAS activation is achieved by dimerization via binding to DNA, the detailed mechanism of which, however, is not entirely clear. To investigate these mechanisms, molecular dynamics (MD) simulations were performed on several states of four types of cGAS, namely, the mcGAS, the wild-type and A- and C-type mutations of human cGAS (hcGAS). We find that sequence differences between hcGAS and mcGAS can directly affect the protein structure stability, especially that of the siteB domain. The sequence and structural differences also contribute to DNA-binding differences. In addition, the conformational fluctuations of cGAS are found to correlate with the regulation of catalytic capacity. More importantly, we illustrate that dimerization enhances the correlation among distant residues and significantly reinforces the allosteric signal transmission among the DNA-binding interfaces and the catalytic pocket, which facilitates rapid immune response to cytosolic DNA. We conclude that siteB domain plays a prominent role in mcGAS activation, while siteA domain is key to hcGAS activation.

Noncollinear and Spin-Flip TDDFT in Multicollinear Approach.

Time-dependent density functional theory (TDDFT) is one of the most important tools for investigating the excited states of electrons. The TDDFT calculation for spin-conserving excitation, where collinear functionals are sufficient, has obtained great success and has become routine. However, TDDFT for noncollinear and spin-flip excitations, where noncollinear functionals are needed, is less widespread and still a challenge nowadays. This challenge lies in the severe numerical instabilities that root in the second-order derivatives of commonly used noncollinear functionals. To be free from this problem radically, noncollinear functionals with numerical stable derivatives are desired, and our recently developed approach, called the multicollinear approach, provides an option. In this work, the multicollinear approach is implemented in noncollinear and spin-flip TDDFT, and prototypical tests are given.

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.

Microscopic model on indoor propagation of respiratory droplets.

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,tf∼γ-0.1 dependence, where γ is the interfacial tension. In presence of the driving force of magnitude F0 and duration τ0, the horizontal propagation length, Ymax from the source increase linearly with τ0, where the slope is steeper for larger F0. Our scaling analysis explains qualitatively well the simulation observations and show long-distance transmission of airborne respiratory droplets in the indoor conditions due to F0 ∼ nano-dyne.

Sequence-Specific Structural Features and Solvation Properties of Transcription Factor Binding DNA Motifs: Insights from Molecular Dynamics Simulation.

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.

Multi-scale gene regulation mechanism: Spatiotemporal transmission of genetic information.

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.

DNA Sequence-Dependent Binding of Linker Histone gH1 Regulates Nucleosome Conformations.

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.

Phase Transition between Crystalline Variants of Ordinary Ice.

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.

Locating Transition Zone in Phase Space.

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.

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.