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.

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.

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.

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.

Enhanced Sampling Simulation Reveals How Solvent Influences Chirogenesis of the Intra-Molecular Diels-Alder Reaction.

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.

Two-step fitness selection for intra-host variations in SARS-CoV-2.

Spontaneous mutations introduce uncertainty into coronavirus disease 2019 (COVID-19) control procedures and vaccine development. Here, we perform a spatiotemporal analysis on intra-host single-nucleotide variants (iSNVs) in 402 clinical samples from 170 affected individuals, which reveals an increase in genetic diversity over time after symptom onset in individuals. Nonsynonymous mutations are overrepresented in the pool of iSNVs but underrepresented at the single-nucleotide polymorphism (SNP) level, suggesting a two-step fitness selection process: a large number of nonsynonymous substitutions are generated in the host (positive selection), and these substitutions tend to be unfixed as SNPs in the population (negative selection). Dynamic iSNV changes in subpopulations with different gender, age, illness severity, and viral shedding time displayed a varied fitness selection process among populations. Our study highlights that iSNVs provide a mutational pool shaping the rapid global evolution of the virus.

Efficient Sr-90 removal from highly alkaline solution by an ultrastable crystalline zirconium phosphonate.

We report here a distinct case of strontium removal under 1 M NaOH solution by an ultrastable crystalline zirconium phosphonate framework (SZ-7) with high adsorption capacity (183 mg g-1) and in-depth removal performance (Kd = 3.9 × 105 mL g-1), demonstrating the potential application of SZ-7 for 90Sr removal in highly alkaline nuclear waste.

A perspective on the molecular simulation of DNA from structural and functional aspects.

As genetic material, DNA not only carries genetic information by sequence, but also affects biological functions ranging from base modification to replication, transcription and gene regulation through its structural and dynamic properties and variations. The motion and structural properties of DNA involved in related biological processes are also multi-scale, ranging from single base flipping to local DNA deformation, TF binding, G-quadruplex and i-motif formation, TAD establishment, compartmentalization and even chromosome territory formation, just to name a few. The sequence-dependent physical properties of DNA play vital role in all these events, and thus it is interesting to examine how simple sequence information affects DNA and the formation of the chromatin structure in these different hierarchical orders. Accordingly, molecular simulations can provide atomistic details of interactions and conformational dynamics involved in different biological processes of DNA, including those inaccessible by current experimental methods. In this perspective, which is mainly based on our recent studies, we provide a brief overview of the atomistic simulations on how the hierarchical structure and dynamics of DNA can be influenced by its sequences, base modifications, environmental factors and protein binding in the context of the protein-DNA interactions, gene regulation and structural organization of chromatin. We try to connect the DNA sequence, the hierarchical structures of DNA and gene regulation.

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.

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.

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.

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.

Enhanced sampling in molecular dynamics.

Although molecular dynamics simulations have become a useful tool in essentially all fields of chemistry, condensed matter physics, materials science, and biology, there is still a large gap between the time scale which can be reached in molecular dynamics simulations and that observed in experiments. To address the problem, many enhanced sampling methods were introduced, which effectively extend the time scale being approached in simulations. In this perspective, we review a variety of enhanced sampling methods. We first discuss collective-variables-based methods including metadynamics and variationally enhanced sampling. Then, collective variable free methods such as parallel tempering and integrated tempering methods are presented. At last, we conclude with a brief introduction of some newly developed combinatory methods. We summarize in this perspective not only the theoretical background and numerical implementation of these methods but also the new challenges and prospects in the field of the enhanced sampling.

The Exploration of a New Stable G-Triplex DNA and Its Novel Function in Electrochemical Biosensing.

A G-triplex, a new kind of DNA structure, has been identified as an intermediate in the folding of G-quadruplexes. However, the studies on G-triplexes are still very limited, and the functions and applications of G-triplexes need to be further developed. In this paper, a new G-triplex sequence (5'-CTGGGAGGGAGGGA-3', G3), obtained by truncating four bases (GGGA) from the 3' end of an 18-base G-quadruplex sequence (G4), was found to significantly decrease the diffusion current of methylene blue (MB). In particular, we proved that (a) MB stabilized the structure of G3 and increased the Tm of G3 considerably based on circular dichroism; and (b) MB formed a 1:1 noncovalent complex with G3 based on electrospray ionization mass spectrometry. Moreover, molecular dynamics simulations established reliable speculation in the folding topology of G3 and interaction sites between G3 and MB. Based on the strong affinity of G3 with MB, we further developed a novel function of G3 as an electrochemical signal read-out and applied it in the fabrication of a sensitive homogeneous electrochemical aptasensor for cocaine. The features we observed in the G3/MB complex will serve as a new inspiring guideline for developing functional short G-rich ligands.

Selective enhanced sampling in dihedral energy facilitates overcoming the dihedral energy increase in protein folding and accelerates the searching for protein native structure.

The dihedral energy function is the most influential parameter in molecular mechanics (MM) force field parameter optimization. A selective enhanced sampling of dihedral energy could effectively reflect the influence of dihedral energy settings on protein secondary structure representation, which in turn testifies the availability of the force field in folding simulation. Here, a Dihedral-based Selective Integrated-Tempering-Sampling Molecular Dynamics (D-SITSMD) simulation method is shown to provide a selective enhanced sampling of dihedral energy without introducing large energetic noise. Its capabilities of searching for protein natively folded structure and providing the underlying folding pathway are evaluated through the folding tests of three peptides (chignolin, TC5b, and HP35) with multiple AMBER force fields (FF14SBonlysc, FF99SBildn, or FF03) and the comparison to presented experimental data and REMD simulations. Both above-mentioned capabilities are improved, displaying the potential of D-SITSMD in the studies of in silico protein folding and structure refinement. Additionally, it is commonly observed among the test simulation systems that their folding processes are thermodynamically favorable for non-bonded vdW and electrostatic energies but unfavorable for dihedral energy, such that the folding barrier height correlated with the dihedral energy increase from the unfolded to folded state whereas the unfolding free energy barrier correlated with the combined increase of vdW and electrostatic energies in the unfolding process. It is speculated that the influence of a force field on the folding barrier of a protein is fulfilled mainly through regulating the contribution of dihedral energy to determine the secondary structure formation in the global folding process.

Structure of water confined between two parallel graphene plates.

We study, in this paper, the physical properties of water confined between two parallel graphene plates with different slit widths to understand the effects of confinement on the water structure and how bulk properties are reached as the water layer thickens. It was found that the microscopic structures of the interfacial liquid layer close to graphene vary with the slit width. Water tends to locate at the center of the six-membered ring of graphene planes to form triangular patterns, as found by others. The narrower the slit width is, the more pronounced this pattern is, except for the slit width of 9.5 Å, for which a well-defined two-layer structure of water forms. On the other hand, squared structures can be clearly seen in single snapshots at small (6.5 Å and 7.5 Å) but not large slit widths. Even at small slit widths, the square-like geometry is observed only when an average is taken for a short trajectory, and averaging over a long time yields a triangular pattern dictated by the graphene geometry. We estimate the length of time needed to observe two patterns, respectively. We also used the two-phase thermodynamic model to study the variation of entropy of confined water and found that at 8.5 Å, the entropy of confined water is larger than that of bulk water. The rotational entropy of confined water is higher than that of bulk water for all slit widths due to the reduction of the hydrogen bond in the confined space.

Role of Conformational Fluctuations of Protein toward Methylation in DNA by Cytosine-5-methyltransferase.

Methylation of cytosine is the common epigenetic modification in genomes ranging from bacteria to mammals, and aberrant methylation leads to human diseases including cancer. Recognition of a cognate DNA sequence by DNA methyltransferases and flipping of a target base into the enzyme active site pocket are the key steps in DNA methylation. Using molecular dynamics simulations and enhanced sampling techniques here we elucidate the role of conformational fluctuations of protein and active or passive involvement of protein elements that mediate base flipping and formation of the closed catalytic complex. The free energy profiles for the flipping of target cytosine into the enzyme active site support the major groove base eversion pathway; and the results show that the closed state of enzyme increases the free energy barrier, whereas the open state reduces it. We found that the interactions of the key loop residues of protein with cognate DNA altered the protein motions, and modulation of protein fluctuations relates to the closed catalytic complex formation. Methylation of cytosine in the active site of the closed complex destabilizes the interactions of catalytic loop residues with cognate DNA and reduces the stability of the closed state. Our study provides microscopic insights on the base flipping mechanism coupled with enzyme's loop motions and provides evidence for the role of conformational fluctuations of protein in the enzyme-catalyzed DNA processing mechanism.

Combined Molecular Dynamics and Coordinate Driving Method for Automatic Reaction Pathway Search of Reactions in Solution.

The combined molecular dynamics and coordinate driving (MD/CD) method is further refined in this work to automatically search reaction pathways for chemical reactions in solution. In this refined MD/CD method, the selective integrated tempering sampling based MD (SITS-MD) simulations are performed to efficiently sample conformers of the reactants in a realistic solution environment. Then, dozens of the reactant/solvent clusters from SITS-MD simulations were used in the subsequent CD step with the quantum mechanics/molecular mechanics (QM/MM) method to generate the reaction pathways. The present MD/CD method is able to search reaction pathways, in which solvent molecules may directly participate. This approach is applied to investigate two reactions without any prior knowledge of the reaction mechanism: Cope elimination of amine oxide in aqueous and dimethyl sulfoxide solutions, and dehydration of methanediol in aqueous solution. For both reactions, our calculations can locate a large number of low-energy reaction pathways. For the dehydration reaction in aqueous solution, free energy barriers for several reaction modes located by the MD/CD method have been obtained from the potential of mean force calculations. Our results show that the most likely reaction mode is the dehydration of methanediol catalyzed by two water molecules. These two illustrative applications demonstrate that the refined MD/CD method is a promising tool in predicting low-energy reaction pathways for reactions in solution.

De novo assembly and comparative transcriptome characterization of Poecilobdella javanica provide insight into blood feeding of medicinal leeches.

Leeches (family Hirudinidae) are classic model invertebrates used in diverse clinical treatments, such as reconstructive microsurgery, hypertension, and gangrene treatment. The blood-feeding habit is essential for these therapies, yet the molecular mechanisms underlying the process are poorly understood. In the present study, the transcriptome of Poecilobdella javanica from five time points (days 0, 1, 10, 20, and 30 separately) of blood feeding was sequenced with short paired-end reads. After stringent quality control, ∼380 million high-quality reads were assembled using SOAPdenovo-Trans with optimal parameters into a non-redundant set of 48 784 transcripts (≥100 base pairs), representing about 38 Mb of unique transcriptome sequence. The average length of the transcripts was 570 bp with N50 lengths of 5751 to 7413 bp among different time points. We have assessed the effect of sequence quality and various assembly parameters on the final assembly output. Functional categorization revealed the conservation of genes involved in various biological processes, such as basal transcription factors and ribosome biogenesis in eukaryotes. In addition, we found that DNA/RNA related pathways were predominantly expressed in the starving state while fatty acid metabolism, the anticoagulant pathway, and amino acid biosynthesis were activated during blood feeding. The leech transcriptome provides a resource for gene discovery and development of functional molecular markers during clinical applications.