SPECIAL TOPIC — Modeling and simulations for the structures and functions of proteins and nucleic acids
Quorum sensing (QS) refers to the cell communication through signaling molecules that regulate many important biological functions of bacteria by monitoring their population density. Although a wide spectrum of studies on the QS system mechanisms have been carried out in experiments, mathematical modeling to explore the QS system has become a powerful approach as well. In this paper, we review the research progress of network modeling in bacterial QS to capture the system’s underlying mechanisms. There are four types of QS system models for bacteria: the Gram-negative QS system model, the Gram-positive QS system model, the model for both Gram-negative and Gram-positive QS system, and the synthetic QS system model. These QS system models are mostly described by the ordinary differential equations (ODE) or partial differential equations (PDE) to study the changes of signaling molecule dynamics in time and space and the cell population density variations. Besides the deterministic simulations, the stochastic modeling approaches have also been introduced to discuss the noise effects on kinetics in QS systems. Taken together, these current modeling efforts advance our understanding of the QS system by providing systematic and quantitative dynamics description, which can hardly be obtained in experiments.
RNAs carry out diverse biological functions, partly because different conformations of the same RNA sequence can play different roles in cellular activities. To fully understand the biological functions of RNAs requires a conceptual framework to investigate the folding kinetics of RNA molecules, instead of native structures alone. Over the past several decades, many experimental and theoretical methods have been developed to address RNA folding. The helix-based RNA folding theory is the one which uses helices as building blocks, to calculate folding kinetics of secondary structures with pseudoknots of long RNA in two different folding scenarios. Here, we will briefly review the helix-based RNA folding theory and its application in exploring regulation mechanisms of several riboswitches and self-cleavage activities of the hepatitis delta virus (HDV) ribozyme.
RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.
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 Cα 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.
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.
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.
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.
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.
Prion diseases are associated with the misfolding of the normal helical cellular form of prion protein (PrPC) into the β-sheet-rich scrapie form (PrPSc) and the subsequent aggregation of PrPSc into amyloid fibrils. Recent studies demonstrated that a naturally occurring variant V127 of human PrPC 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 PrPC 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 PrPC structure and inhibiting its conversion into PrPSc. 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.