Catálogo de publicaciones - revistas

<jats:p>Proteins are important biological molecules whose structures are closely related to their specific functions. Understanding how the protein folds under physical principles, known as the protein folding problem, is one of the main tasks in modern biophysics. Coarse-grained methods play an increasingly important role in the simulation of protein folding, especially for large proteins. In recent years, we proposed a novel coarse-grained method derived from the topological soliton model, in terms of the backbone C<jats:sub> <jats:italic>α</jats:italic> </jats:sub> chain. In this review, we will first systematically address the theoretical method of topological soliton. Then some successful applications will be displayed, including the thermodynamics simulation of protein folding, the property analysis of dynamic conformations, and the multi-scale simulation scheme. Finally, we will give a perspective on the development and application of topological soliton.</jats:p>

<jats:p>It is a central issue to find the slow dynamic modes of biological macromolecules via analyzing the large-scale data of molecular dynamics simulation (MD). While the MD data are high-dimensional time-successive series involving all-atomic details and sub-picosecond time resolution, a few collective variables which characterizing the motions in longer than nanoseconds are needed to be chosen for an intuitive understanding of the dynamics of the system. The trajectory map (TM) was presented in our previous works to provide an efficient method to find the low-dimensional slow dynamic collective-motion modes from high-dimensional time series. In this paper, we present a more straight understanding about the principle of TM via the slow-mode linear space of the conformational probability distribution functions of MD trajectories and more clearly discuss the relation between the TM and the current other similar methods in finding slow modes.</jats:p>

<jats:p>Protein–protein interactions (PPI) are important for many biological processes. Theoretical understanding of the structurally determining factors of interaction sites will help to understand the underlying mechanism of protein–protein interactions. At the same time, understanding the complex structure of proteins helps to explore their function. And accurately predicting protein complexes from PPI networks helps us understand the relationship between proteins. In the past few decades, scholars have proposed many methods for predicting protein interactions and protein complex structures. In this review, we first briefly introduce the methods and servers for predicting protein interaction sites and interface residue pairs, and then introduce the protein complex structure prediction methods including template-based prediction and template-free prediction. Subsequently, this paper introduces the methods of predicting protein complexes from the PPI network and the method of predicting missing links in the PPI network. Finally, it briefly summarizes the application of machine/deep learning models in protein structure prediction and action site prediction.</jats:p>

<jats:p>The RNA tertiary structure is essential to understanding the function and biological processes. Unfortunately, it is still challenging to determine the large RNA structure from direct experimentation or computational modeling. One promising approach is first to predict the tertiary contacts and then use the contacts as constraints to model the structure. The RNA structure modeling depends on the contact prediction accuracy. Although many contact prediction methods have been developed in the protein field, there are only several contact prediction methods in the RNA field at present. Here, we first review the theoretical basis and test the performances of recent RNA contact prediction methods for tertiary structure and complex modeling problems. Then, we summarize the advantages and limitations of these RNA contact prediction methods. We suggest some future directions for this rapidly expanding field in the last.</jats:p>

<jats:p>Plants and animals recognize microbial invaders by detecting pathogen-associated molecular patterns (PAMPs) through pattern-recognition receptors (PRRs). This recognition plays a crucial role in plant immunity. The newly discovered protein in plants that responds to bacterial flagellin, i.e., flagellin-sensitive 2 (FLS2), is ubiquitously expressed and present in many plants. The association of FLS2 and BAK1, facilitated by a highly conserved epitope flg22 of flagellin, triggers such downstream immune responses as activated MAPK pathway and elevated reactive oxygen species (ROS) for bacterial defense and plant immunity. Here we study the intrinsic dynamics and conformational change of FLS2 upon the formation of the FLS2–flg22–BAK1 complex. The top intrinsic normal modes and principal structural fluctuation components are very similar, showing two bending modes and one twisting mode. The twisting mode alone, however, accounts for most of the conformational change of FLS2 induced by binding with flg22 and BAK1. This study indicates that flg22 binding suppresses FLS2 conformational fluctuation, especially on the twisting motion, thus facilitating FLS2–BAK1 interaction. A detailed analysis of this sensing mechanism may aid better design on both PRR and peptide mimetics for plant immunity.</jats:p>

<jats:p>Prion diseases are associated with the misfolding of the normal helical cellular form of prion protein (PrP<jats:sup>C</jats:sup>) into the <jats:italic>β</jats:italic>-sheet-rich scrapie form (PrP<jats:sup>Sc</jats:sup>) and the subsequent aggregation of PrP<jats:sup>Sc</jats:sup> into amyloid fibrils. Recent studies demonstrated that a naturally occurring variant V127 of human PrP<jats:sup>C</jats:sup> is intrinsically resistant to prion conversion and aggregation, and can completely prevent prion diseases. However, the underlying molecular mechanism remains elusive. Herein we perform multiple microsecond molecular dynamics simulations on both wildtype (WT) and V127 variant of human PrP<jats:sup>C</jats:sup> to understand at atomic level the protective effect of V127 variant. Our simulations show that G127V mutation not only increases the rigidity of the S2–H2 loop between strand-2 (S2) and helix-2 (H2), but also allosterically enhances the stability of the H2 C-terminal region. Interestingly, previous studies reported that animals with rigid S2–H2 loop usually do not develop prion diseases, and the increase in H2 C-terminal stability can prevent misfolding and oligomerization of prion protein. The allosteric paths from G/V127 to H2 C-terminal region are identified using dynamical network analyses. Moreover, community network analyses illustrate that G127V mutation enhances the global correlations and intra-molecular interactions of PrP, thus stabilizing the overall PrP<jats:sup>C</jats:sup> structure and inhibiting its conversion into PrP<jats:sup>Sc</jats:sup>. This study provides mechanistic understanding of human V127 variant in preventing prion conversion which may be helpful for the rational design of potent anti-prion compounds.</jats:p>

<jats:p>In general relativity, Mercury’s orbit becomes approximately elliptical and the its perihelion has thus an additional advance. We demonstrate, meanwhile, that in comparison of those given by Newton’s theory of gravitation for the orbit of the Mercury, the circumference and the area are reduced by 40.39 km and 2.35 × 10<jats:sup>9</jats:sup> km<jats:sup>2</jats:sup>, respectively, besides the major-axis contraction pointed out recently, and all are produced by the curved space within Einstein's theory of gravitation. Since the resolution power of present astronomical distance measurement technology reaches one kilometer, the shrinkage of Mercury’s orbit can then be observable.</jats:p>

<jats:p>Passive filtering of neural networks with time-invariant delay and quantized output is considered. A criterion on the passivity of a filtering error system is proposed by means of the Lyapunov–Krasovskii functional and the Bessel–Legendre inequality. Based on the criterion, a design approach for desired passive filters is developed in terms of the feasible solution of a set of linear matrix inequalities. Then, analyses and syntheses are extended to the time-variant delay situation using the reciprocally convex combination inequality. Finally, a numerical example with simulations is used to illustrate the applicability and reduced conservatism of the present passive filter design approaches.</jats:p>

<jats:p>We study the ferromagnetic transition of a two-component homogeneous dipolar Fermi gas with 1D spin–orbit coupling (SOC) at finite temperature. The ferromagnetic transition temperature is obtained as functions of dipolar constant <jats:italic>λ</jats:italic> <jats:sub>d</jats:sub>, spin–orbit coupling constant <jats:italic>λ</jats:italic> <jats:sub>SOC</jats:sub> and contact interaction constant <jats:italic>λ</jats:italic> <jats:sub>s</jats:sub>. It increases monotonically with these three parameters. In the ferromagnetic phase, the Fermi surfaces of different components can be deformed differently. The phase diagrams at finite temperature are obtained.</jats:p>

<jats:p>We investigate the effect of impurities on the thermal entanglement in a spin-1/2 Ising–Heisenberg butterfly-shaped chain, where four interstitial Heisenberg spins are localized on the vertices of a rectangular plaquette in a unit block. By using the transfer-matrix approach, we numerically calculate the partition function and the reduced density matrix of this model. The bipartite thermal entanglement between different Heisenberg spin pairs is quantified by the concurrence. We also discuss the fluctuations caused by the impurities through the uniform distribution and the Gaussian distribution. Considering the effects of the external magnetic field, temperature, Heisenberg and Ising interactions as well as the parameter of anisotropy on the thermal entanglement, our results show that comparing with the case of the clean model, in both the two-impurity model and the impurity fluctuation model the entanglement is more robust within a certain range of anisotropic parameters and the region of the magnetic field where the entanglement occurred is also larger.</jats:p>