cgRNASP-CN: a minimal coarse-grained representation-based statistical potential for RNA 3D structure evaluation

Knowledge of RNA 3-dimensional (3D) structures is critical to understand the important biological functions of RNAs, and various models have been developed to predict RNA 3D structures in silico. However, there is still lack of a reliable and efficient statistical potential for RNA 3D structure evaluation. For this purpose, we developed a statistical potential based on a minimal coarse-grained representation and residue separation, where every nucleotide is represented by C4' atom for backbone and N1 (or N9) atom for base. In analogy to the newly developed all-atom rsRNASP, cgRNASP-CN is composed of short-ranged and long-ranged potentials, and the short-ranged one was involved more subtly. The examination indicates that the performance of cgRNASP-CN is close to that of the all-atom rsRNASP and is superior to other top all-atom traditional statistical potentials and scoring functions trained from neural networks, for two realistic test datasets including the RNA-Puzzles dataset. Very importantly, cgRNASP-CN is about 100 times more efficient than existing all-atom statistical potentials/scoring functions including rsRNASP. cgRNASP-CN is available at website: https://github.com/Tan-group/cgRNASP-CN.     

A novel knowledge-based potential for RNA 3D structure evaluation

Ribonucleic acids(RNAs) play a vital role in biology, and knowledge of their three-dimensional(3D) structure is required to understand their biological functions. Recently structural prediction methods have been developed to address this issue, but a series of RNA 3D structures are generally predicted by most existing methods. Therefore, the evaluation of the predicted structures is generally indispensable. Although several methods have been proposed to assess RNA 3D structures, the existing methods are not precise enough. In this work, a new all-atom knowledge-based potential is developed for more accurately evaluating RNA 3D structures. The potential not only includes local and nonlocal interactions but also fully considers the specificity of each RNA by introducing a retraining mechanism. Based on extensive test sets generated from independent methods, the proposed potential correctly distinguished the native state and ranked near-native conformations to effectively select the best. Furthermore, the proposed potential precisely captured RNA structural features such as base-stacking and base-pairing. Comparisons with existing potential methods show that the proposed potential is very reliable and accurate in RNA 3D structure evaluation.     

Statistical potentials for 3D structure evaluation:From proteins to RNAs

Structure evaluation is critical to in silico 3-dimensional structure predictions for biomacromolecules such as proteins and RNAs.For proteins,structure evaluation has been paid attention over three decades along with protein folding problem,and statistical potentials have been shown to be effective and efficient in protein structure prediction and evaluation.In recent two decades,RNA folding problem has attracted much attention and several statistical potentials have been developed for RNA structure evaluation,partially with the aid of the progress in protein structure prediction.In this review,we will firstly give a brief overview on the existing statistical potentials for protein structure evaluation.Afterwards,we will introduce the recently developed statistical potentials for RNA structure evaluation.Finally,we will emphasize the perspective on developing new statistical potentials for RNAs in the near future.     

RNAGCN: RNA tertiary structure assessment with a graph convolutional network

RNAs play crucial and versatile roles in cellular biochemical reactions. Since experimental approaches of determining their three-dimensional (3D) structures are costly and less efficient, it is greatly advantageous to develop computational methods to predict RNA 3D structures. For these methods, designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges. In this study, we designed and trained a deep learning model to tackle this problem. The model was based on a graph convolutional network (GCN) and named RNAGCN. The model provided a natural way of representing RNA structures, avoided complex algorithms to preserve atomic rotational equivalence, and was capable of extracting features automatically out of structural patterns. Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions. Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions. RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.     

Structure search of two-dimensional systems using CALYPSO methodology

The dimensionality of structures allows materials to be classified into zero-, one-, two-, and threedimensional systems. Two-dimensional (2D) systems have attracted a great deal of attention and typically include surfaces, interfaces, and layered materials. Due to their varied properties, 2D systems hold promise for applications such as electronics, optoelectronics, magnetronics, and valleytronics. The design of 2D systems is an area of intensive research because of the rapid development of ab initiostructure-searching methods. In this paper, we highlight recent research progress on accelerating the design of 2D systems using the CALYPSO methodology. Challenges and perspectives for future developments in 2D structure prediction methods are also presented.     

Statistical model of the habit and arrangement of mineral crystals in the collagen of bone

Randomly colored space tesselations are considered as models for the mineral/organic structure of bone. First, it is shown that the structure function for such models is always proportional to the average form factor of the individual tiles and hence independent of the mineral density in the sample. Then the structure function is calculated for three such models: for model I, based on a hexagonal, and model 2, on a Poisson-Voronoi tesselation of the plane and for model 3, based on a random tesselation of the line. These results are compared to experimental structure functions measured by small-angle scattering and excellent agreement is obtained between model 2 and the bone from mice and rats, as well as between model 3 and calcified turkey leg tendon. Divergent conclusions following recent experiments by small-angle x-ray scattering and by electron microscopy are discussed in the light of these structural models and an explanation is proposed which might remove the discrepancy.Dedicated to Prof. Oliver Penrose on the occasion of his 65th birthday.     

Raman spectra and structure of a 25mer HCV RNA

We have employed Raman spectroscopy to investigate the conformation of an (Hepatitis C virus) HCV RNA 25mer (1–25 nucleotides) in solution. The principal findings of this study are (1) the A‐form secondary structure involving C3′‐ endo/anti ribofuranose pucker is predominant; (2) some uridine and guanosine nucleoside residues adopt the C2′‐ endo/anti and C3′‐ endo/syn conformations, respectively, which appear in looped nucleotide sequences; and (3) six out of nine guanine residues are base‐paired probably forming a stem. These results are interpreted as formation of a hairpin whose secondary structure is consistent with that proposed on the basis of phylogenetic comparisons with other viral RNAs. Copyright © 2009 John Wiley & Sons, Ltd.     

Force-constant-decayed anisotropic network model: An improved method for predicting RNA flexibility

RNA is an important biological macromolecule, which plays an irreplaceable role in many life activities. RNA functions are largely determined by its tertiary structure and the intrinsic dynamics encoded in the structure. Thus, how to effective extract structure-encoded dynamics is of great significance for understanding RNA functions. Anisotropic network model (ANM) is an efficient method to investigate macromolecular dynamical properties, which has been widely used in protein studies. However, the performance of the conventional ANM in describing RNA flexibility is not as good as that on proteins. In this study, we proposed a new approach, named force-constant-decayed anisotropic network model (fcd-ANM), to improve the performance in investigating the dynamical properties encoded in RNA structures. In fcd-ANM, nucleotide pairs in RNA structure were connected by springs and the force constant of springs was decayed exponentially based on the separation distance to describe the differences in the inter-nucleotide interaction strength. The performance of fcd-ANM in predicting RNA flexibility was evaluated using a non-redundant structure database composed of 51 RNAs. The results indicate that fcd-ANM significantly outperforms the conventional ANM in reproducing the experimental B-factors of nucleotides in RNA structures, and the Pearson correlation coefficient between the predicted and experimental nucleotide B-factors was distinctly improved by 21.05% compared to the conventional ANM. Fcd-ANM can serve as a more effective method for analysis of RNA dynamical properties.     

Three-dimensional spiral structure of tropical cyclone under four-force balance

The steady axis-symmetrical atmosphere dynamical equations are used for describing spiral structure of tropical cyclones under four-force （pressure gradient force, Coriolis force, centrifugal force, and friction force） balance, and the dynamical systems of three-dimensional （3D） velocity field are introduced. The qualitative analysis of the dynamical system shows that there are down 3D spiral structures in eye of tropical cyclone and tropical cyclone is 3D counterclockwise up spiral structure. These results are consistent with the observed tropical cyclone on the weather map.     

Enhancements of the Gaussian network model in describing nucleotide residue fluctuations for RNA

Gaussian network model (GNM) is an efficient method to investigate the structural dynamics of biomolecules. However, the application of GNM on RNAs is not as good as that on proteins, and there is still room to improve the model. In this study, two novel approaches, named the weighted GNM (wGNM) and the force-constant-decayed GNM (fcdGNM), were proposed to enhance the performance of ENM in investigating the structural dynamics of RNAs. In wGNM, the force constant for each spring is weighted by the number of interacting heavy atom pairs between two nucleotides. In fcdGNM, all the pairwise nucleotides were connected by springs and the force constant decayed exponentially with the separate distance of the nucleotide pairs. The performance of these two proposed models was evaluated by using a non-redundant RNA structure database composed of 51 RNA molecules. The calculation results show that both the proposed models outperform the conventional GNM in reproducing the experimental B-factors of RNA structures. Compared with the conventional GNM, the Pearson correlation coefficient between the predicted and experimental B-factors was improved by 9.85% and 6.76% for wGNM and fcdGNM, respectively. Our studies provide two candidate methods for better revealing the dynamical properties encoded in RNA structures.     

三维声传播模型BELLHOP3D的信息传递接口并行优化

近些年,我国对海洋不断深入的探索对复杂环境中声场的快速预报提出了越来越高的需求。BELLHOP3D是一种基于射线法的三维声传播计算模型,在海洋声学中应用十分广泛。BELLHOP3D的计算效率比其他常用模型高,但是仍然有非常大的提升空间。该文使用信息传递接口对BELLHOP3D进行粗粒度的并行优化,并行后的程序计算结果稳定可靠,并行效率高,更适合在实际应用中实现快速的声场预报。并行BELLHOP3D程序可以在https://github.com/nj-zyq/BELLHOP3D＿MPI.git下载。     

Scaling laws in high-energy electron-nuclear scattering

The approximate scaling behavior suggested by recent measurements of electron scattering form factors and inelastic structure functions of few-body nuclei (mass 2, 3, 4) is discussed in a relativistic impulse approximation model. The model is a straightforward extension incorporating spin of a nucleon parton model introduced in recent works. We present results for electric and magnetic form factors as well as inelastic structure functions near threshold. The important corrections to scaling which are present in the preasymptotic regions are found to be well accounted for by the type of binding effects included in the phenomenologically constructed infinite-momentum frame nuclear wave functions. While predicted form factors are very sensitive to the parameters in the wave functions it does not appear possible to associate unambiguous dynamical meaning to these parameters. We find that spin effects bring significant and useful corrections.     

Structural studies of large nucleoprotein particles,vaults

Vault is the largest nonicosahedral cytosolic nucleoprotein particle ever described. The widespread presence and evolutionary conservation of vaults suggest important biologic roles, although their functions have not been fully elucidated. X-ray structure of vault from rat liver was determined at 3.5 Å resolution. It exhibits an ovoid shape with a size of 40 × 40 × 67 nm³. The cage structure of vault consists of a dimer of half-vaults, with each half-vault comprising 39 identical major vault protein (MVP) chains. Each MVP monomer folds into 12 domains: nine structural repeat domains, a shoulder domain, a cap-helix domain and a cap-ring domain. Interactions between the 42-turn-long cap-helix domains are key to stabilizing the particle. The other components of vaults, telomerase-associated proteins, poly(ADP-ribose) polymerases and small RNAs, are in location in the vault particle by electron microscopy.     

Behavioral model for magnetorheological fluid under a magnetic field using Lekner summation method

Magnetorheological (MR) fluid is used for various applications due to its controllable viscosity. To predict the behavior of MR fluid under certain three-dimensional (3D) magnetic and shear strain fields, it is essential to model the fluid in an appropriate manner. The behavioral models used in the previous research, however, have serious limitations because most of them oversimplify the inter-particle interactions and employ assumptions valid only under specific geometric configurations and field conditions. In this study, a new model that can predict the behavior of MR fluid under arbitrary 3D magnetic and shear strain fields is proposed. The present work considers an MR fluid configured as a 3D infinite lattice structure. Using the proposed model, the shear stress components themselves, not the dipolar interaction energy, are calculated directly to avoid the mathematical singularity otherwise encountered. The resulting stress functions of the proposed model are transformed into rapidly convergent functions using the Lekner summation method. Finally, the characteristics of the stiffened MR fluid under a magnetic field are investigated using the transformed functions. Numerical computations on the original and transformed functions are performed and compared under selected conditions to ensure the validity and prove the high convergence efficiency of the proposed model.     

DLR-F6翼身组合体阻力计算

采用"亚跨超CFD软件平台"(TRIP2.0)数值模拟DLR-F6翼身组合体构型,采用的多块对接网格、测压和测力的试验结果均来自美国AIAA阻力计算小组,对比计算结果采用CFL3D的结果.详细研究网格密度、湍流模型对DLR-F6翼身组合体构型的总体气动特性和压力分布的影响,计算结果与相应的试验结果较一致.采用SST两方程模型得到网格收敛结果;不同的湍流模型对压差阻力影响较小,对摩擦阻力影响较大;不同的网格密度和湍流模型对压力分布影响较小.     

Recent progress on the prediction of two-dimensional materials using CALYPSO

In recent years, structure design and predictions based on global optimization approach as implemented in CALYPSO software have gained great success in accelerating the discovery of novel two-dimensional(2D) materials. Here we highlight some most recent research progress on the prediction of novel 2D structures, involving elements, metal-free and metal-containing compounds using CALYPSO package. Particular emphasis will be given to those 2D materials that exhibit unique electronic and magnetic properties with great potentials for applications in novel electronics, optoelectronics,magnetronics, spintronics, and photovoltaics. Finally, we also comment on the challenges and perspectives for future discovery of multi-functional 2D materials.     

A new form of liquid matter: Quantum droplets

This brief review summarizes recent theoretical and experimental results which predict and establish the existence of quantum droplets (QDs), i.e., robust two- and three-dimensional (2D and 3D) selftrapped states in Bose–Einstein condensates (BECs), which are stabilized by effective self-repulsion induced by quantum fluctuations around the mean-field (MF) states [alias the Lee–Huang–Yang (LHY) effect]. The basic models are presented, taking special care of the dimension crossover, 2D→3D. Recently reported experimental results, which exhibit stable 3D and quasi-2D QDs in binary BECs, with the inter-component attraction slightly exceeding the MF self-repulsion in each component, and in single-component condensates of atoms carrying permanent magnetic moments, are presented in some detail. The summary of theoretical results is focused, chiefly, on 3D and quasi-2D QDs with embedded vorticity, as the possibility to stabilize such states is a remarkable prediction. Stable vortex states are presented both for QDs in free space, and for singular but physically relevant 2D modes pulled to the center by the inverse-square potential, with the quantum collapse suppressed by the LHY effect.