The hydrogen bond (HB) is an important type of intermolecular interaction, which is generally weak, ubiquitous, and essential to life on earth. The small mass of hydrogen means that many properties of HBs are quantum mechanical in nature. In recent years, because of the development of computer simulation methods and computational power, the influence of nuclear quantum effects (NQEs) on the structural and energetic properties of some hydrogen bonded systems has been intensively studied. Here, we present a review of these studies by focussing on the explanation of the principles underlying the simulation methods, i.e., the ab initio path-integral molecular dynamics. Its extension in combination with the thermodynamic integration method for the calculation of free energies will also be introduced. We use two examples to show how this influence of NQEs in realistic systems is simulated in practice.
Anesthetics are extremely important in modern surgery to greatly reduce the patient's pain. The understanding of anesthesia at molecular level is the preliminary step for the application of anesthetics in clinic safely and effectively. Inert gases, with low chemical activity, have been found to cause anesthesia for centuries, but the mechanism is unclear yet. In this review, we first summarize the progress of theories about general anesthesia, especially for inert gas narcosis, and then propose a new hypothesis that the aggregated rather than the dispersed inert gas molecules are the key to trigger the narcosis to explain the steep dose-response relationship of anesthesia.
In this review, we explore the physical mechanisms of biological processes such as protein folding and recognition, ligand binding, and systems biology, including cell cycle, stem cell, cancer, evolution, ecology, and neural networks. Our approach is based on the landscape and flux theory for nonequilibrium dynamical systems. This theory provides a unifying principle and foundation for investigating the underlying mechanisms and physical quantification of biological systems.
In order to understand the electric interfacial behavior, mean field based electric double layer (EDL) theory has been continuously developed over the past 150 years. In this article, we briefly review the development of the EDL model, from the dimensionless Gouy-Chapman model to the symmetric Bikerman-Freise model, and finally toward size-asymmetric mean field theory models. We provide the general derivations within the framework of Helmholtz free energy of the lattice-gas model, and it can be seen that the above-mentioned models are consistent in the sense that the interconversion among them can be achieved by reducing the basic assumptions.
We herein review our studies on simulating the thermal unfolding Fourier transform infrared and two-dimensional infrared spectra of peptides. The peptide-water configuration ensembles, required forspectrum modeling, aregenerated at a series of temperatures using the GBOBC implicit solvent model and the integrated tempering sampling technique. The fluctuating vibrational Hamiltonians of the amide I vibrational band are constructed using the Frenkel exciton model. The signals are calculated using nonlinear exciton propagation. The simulated spectral features such as the intensity and ellipticity are consistent with the experimental observations. Comparing the signals for two beta-hairpin polypeptides with similar structures suggests that this technique is sensitive to peptide folding landscapes.
Under appropriate physicochemical conditions, short peptide fragments and their synthetic mimics have been shown to form elongated cross-β nanostructures through self-assembly. The self-assembly process and the resultant peptide nanostructures are not only related to neurodegenerative diseases but also provide inspiration for the development of novel bionanomaterials. Both experimental and theoretical studies on peptide self-assembly have shown that the self-assembly process spans multiple time and length scales and is hierarchical. β -sheet self-assembly consists of three sub-processes from the microscopic to the mesoscopic level: β -sheet locking, lateral stacking, and morphological transformation. Detailed atomistic simulation studies have provided insight into the early stages of peptide nanostructure formation and the interplay between different non-covalent interactions at the microscopic level. This review gives a brief introduction of the hierarchical peptide self-assembly process and focuses on the roles of various non-covalent interactions in the sub-processes based on recent simulation, experimental, and theoretical studies.
Computational design of proteins is a relatively new field, where scientists search the enormous sequence space for sequences that can fold into desired structure and perform desired functions. With the computational approach, proteins can be designed, for example, as regulators of biological processes, novel enzymes, or as biotherapeutics. These approaches not only provide valuable information for understanding of sequence-structure-function relations in proteins, but also hold promise for applications to protein engineering and biomedical research. In this review, we briefly introduce the rationale for computational protein design, then summarize the recent progress in this field, including de novo protein design, enzyme design, and design of protein-protein interactions. Challenges and future prospects of this field are also discussed.
Modeling of biomolecular systems plays an essential role in understanding biological processes, such as ionic flow across channels, protein modification or interaction, and cell signaling. The continuum model described by the Poisson-Boltzmann (PB)/Poisson-Nernst-Planck (PNP) equations has made great contributions towards simulation of these processes. However, the model has shortcomings in its commonly used form and cannot capture (or cannot accurately capture) some important physical properties of the biological systems. Considerable efforts have been made to improve the continuum model to account for discrete particle interactions and to make progress in numerical methods to provide accurate and efficient simulations. This review will summarize recent main improvements in continuum modeling for biomolecular systems, with focus on the size-modified models, the coupling of the classical density functional theory and the PNP equations, the coupling of polar and nonpolar interactions, and numerical progress.
Polymerases are protein enzymes that move along nucleic acid chains and catalyze template-based polymerization reactions during gene transcription and replication. The polymerases also substantially improve transcription or replication fidelity through the non-equilibrium enzymatic cycles. We briefly review computational efforts that have been made toward understanding mechano-chemical coupling and fidelity control mechanisms of the polymerase elongation. The polymerases are regarded as molecular information motors during the elongation process. It requires a full spectrum of computational approaches from multiple time and length scales to understand the full polymerase functional cycle. We stay away from quantum mechanics based approaches to the polymerase catalysis due to abundant former surveys, while addressing statistical physics modeling approaches along with all-atom molecular dynamics simulation studies. We organize this review around our own modeling and simulation practices on a single subunit T7 RNA polymerase, and summarize commensurate studies on structurally similar DNA polymerases as well. For multi-subunit RNA polymerases that have been actively studied in recent years, we leave systematical reviews of the simulation achievements to latest computational chemistry surveys, while covering only representative studies published very recently, including our own work modeling structure-based elongation kinetic of yeast RNA polymerase II. In the end, we briefly go through physical modeling on elongation pauses and backtracking activities of the multi-subunit RNAPs. We emphasize on the fluctuation and control mechanisms of the polymerase actions, highlight the non-equilibrium nature of the operation system, and try to build some perspectives toward understanding the polymerase impacts from the single molecule level to a genome-wide scale.
With recent breakthroughs in camera and image processing technologies single-particle electron cryo-microscopy (CryoEM) has suddenly gained the attention of structural biologists as a powerful tool able to solve the atomic structures of biological complexes and assemblies. Compared with x-ray crystallography, CryoEM can be applied to partially flexible structures in solution and without the necessity of crystallization, which is especially important for large complexes and assemblies. This review briefly explains several key bottlenecks for atomic resolution CryoEM, and describes the corresponding solutions for these bottlenecks based on the recent technical advancements. The review also aims to provide an overview about the technical differences between its applications in biology and those in material science.
Modern computer simulations of biological systems often involve an explicit treatment of the complex interactions among a large number of molecules. While it is straightforward to compute the short-ranged Van der Waals interaction in classical molecular dynamics simulations, it has been a long-lasting issue to develop accurate methods for the long-ranged Coulomb interaction. In this short review, we discuss three types of methodologies for the accurate treatment of electrostatics in simulations of explicit molecules: truncation-type methods, Ewald-type methods, and mean-field-type methods. Throughout the discussion, we brief the formulations and developments of these methods, emphasize the intrinsic connections among the three types of methods, and focus on the existing problems which are often associated with the boundary conditions of electrostatics. This brief survey is summarized with a short perspective on future trends along the method developments and applications in the field of biological simulations.
The intensive concern over the biosafety of nanomaterials demands the systematic study of the mechanisms underlying their biological effects. Many of the effects of nanomaterials can be attributed to their interactions with proteins and their impacts on protein function. On the other hand, nanomaterials show potential for a variety of biomedical applications, many of which also involve direct interactions with proteins. In this paper, we review some recent computational studies on this subject, especially those investigating the interactions of carbon and gold nanomaterials. Beside hydrophobic and π-stacking interactions, the mode of interaction of carbon nanomaterials can also be regulated by their functional groups. The coatings of gold nanomaterials similarly adjust their mode of interaction, in addition to coordination interactions with the sulfur groups of cysteine residues and the imidazole groups of histidine residues. Nanomaterials can interact with multiple proteins and their impacts on protein activity are attributed to a wide spectrum of mechanisms. These findings on the mechanisms of nanomaterial-protein interactions can further guide the design and development of nanomaterials to realize their application in disease diagnosis and treatment.
Elucidating the structure of large biomolecules such as multi-domain proteins or protein complexes is challenging due to their high flexibility in solution. Recently, an “integrative structural biology” approach has been proposed, which aims to determine the protein structure and characterize protein flexibility by combining complementary high-and low-resolution experimental data using computer simulations. Small-angle x-ray scattering (SAXS) is an efficient technique that can yield low-resolution structural information, including protein size and shape. Here, we review computational methods that integrate SAXS with other experimental datasets for structural modeling. Finally, we provide a case study of determination of the structure of a protein complex formed between the tandem SH3 domains in c-Cb1-associated protein and the proline-rich loop in human vinculin.
Biological raw data are growing exponentially, providing a large amount of information on what life is. It is believed that potential functions and the rules governing protein behaviors can be revealed from analysis on known native structures of proteins. Many knowledge-based potentials for proteins have been proposed. Contrary to most existing review articles which mainly describe technical details and applications of various potential models, the main foci for the discussion here are ideas and concepts involving the construction of potentials, including the relation between free energy and energy, the additivity of potentials of mean force and some key issues in potential construction. Sequence analysis is briefly viewed from an energetic viewpoint.
DNA condensation is an important process in many fields including life sciences, polymer physics, and applied technology. In the nucleus, DNA is condensed into chromosomes. In polymer physics, DNA is treated as a semi-flexible molecule and a polyelectrolyte. Many agents, including multi-valent cations, surfactants, and neutral poor solvents, can cause DNA condensation, also referred to as coil-globule transition. Moreover, DNA condensation has been used for extraction and gene delivery in applied technology. Many physical theories have been presented to elucidate the mechanism underlying DNA condensation, including the counterion correlation theory, the electrostatic zipper theory, and the hydration force theory. Recently several single-molecule studies have focused on DNA condensation, shedding new light on old concepts. In this document, the multi-field concepts and theories related to DNA condensation are introduced and clarified as well as the advances and considerations of single-molecule DNA condensation experiments are introduced.
