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.

Small RNA(sRNA)-mediated post-transcriptional regulation differs from protein-mediated regulation. Through base-pairing, sRNA can regulate the target mRNA in a catalytic or stoichiometric manner. Some theoretical models were built for comparison of the protein-mediated and sRNA-mediated modes in the steady-state behaviors and noise properties. Many experiments demonstrated that a single sRNA can regulate several mRNAs, which causes crosstalk between the targets. Here, we focus on some models in which two target mRNAs are silenced by the same sRNA to discuss their crosstalk features. Additionally, the sequence-function relationship of sRNA and its role in the kinetic process of base-pairing have been highlighted in model building.

Bilayer membranes self-assembled from amphiphilic molecules such as lipids, surfactants, and block copolymers are ubiquitous in biological and physiochemical systems. The shape and structure of bilayer membranes depend crucially on their mechanical properties such as surface tension, bending moduli, and line tension. Understanding how the molecular properties of the amphiphiles determine the structure and mechanics of the self-assembled bilayers requires a molecularly detailed theoretical framework. The self-consistent field theory provides such a theoretical framework, which is capable of accurately predicting the mechanical parameters of self-assembled bilayer membranes. In this mini review we summarize the formulation of the self-consistent field theory, as exemplified by a model system composed of flexible amphiphilic chains dissolved in hydrophilic polymeric solvents, and its application to the study of self-assembled bilayer membranes.

Autapses are synapses that connect a neuron to itself in the nervous system. Previously, both experimental and theoretical studies have demonstrated that autaptic connections in the nervous system have a significant physiological function. Autapses in nature provide self-delayed feedback, thus introducing an additional timescale to neuronal activities and causing many dynamic behaviors in neurons. Recently, theoretical studies have revealed that an autapse provides a control option for adjusting the response of a neuron: e.g., an autaptic connection can cause the electrical activities of the Hindmarsh-Rose neuron to switch between quiescent, periodic, and chaotic firing patterns; an autapse can enhance or suppress the mode-locking status of a neuron injected with sinusoidal current; and the firing frequency and interspike interval distributions of the response spike train can also be modified by the autapse. In this paper, we review recent studies that showed how an autapse affects the response of a single neuron.

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.

Block copolymers are a class of soft matter that self-assemble to form ordered morphologies on the scale of nanometers, making them ideal materials for various applications. These applications directly depend on the shape and size of the self-assembled morphologies, and hence, a high degree of control over the self-assembly is desired. Grafting block copolymer chains onto a substrate to form copolymer brushes is a versatile method to fabricate functional surfaces. Such surfaces demonstrate a response to their environment, i.e., they change their surface topography in response to different external conditions. Furthermore, such surfaces may possess nanoscale patterns, which are important for some applications; however, such patterns may not form with spun-cast films under the same condition. In this review, we summarize the recent progress of the self-assembly of block copolymers grafted onto a flat substrate. We mainly concentrate on the self-assembled morphologies of end-grafted AB diblock copolymers, junction point-grafted AB diblock copolymers (i.e., Y-shaped brushes), and end-grafted ABA triblock copolymers. Special emphasis is placed on theoretical and simulation progress.

The structural flexibility of nucleic acids plays a key role in many fundamental life processes, such as gene replication and expression, DNA-protein recognition, and gene regulation. To obtain a thorough understanding of nucleic acid flexibility, extensive studies have been performed using various experimental methods and theoretical models. In this review, we will introduce the progress that has been made in understanding the flexibility of nucleic acids including DNAs and RNAs, and will emphasize the experimental findings and the effects of salt, temperature, and sequence. Finally, we will discuss the major unanswered questions in understanding the flexibility of nucleic acids.

Soluble peptides or proteins can self-aggregate into insoluble, ordered amyloid fibrils under appropriate conditions. These amyloid aggregates are the hallmarks of several human diseases ranging from neurodegenerative disorders to systemic amyloidoses. In this review, we first introduce the common structural features of amyloid fibrils and the amyloid fibrillation kinetics determined from experimental studies. Then, we discuss the structural models of Alzheimer's amyloid-β (Aβ) fibrils derived from solid-state nuclear magnetic resonance spectroscopy. On the computational side, molecular dynamics simulations can provide atomic details of structures and the underlying oligomerization mechanisms. We finally summarize recent progress in atomistic simulation studies on the oligomerization of Aβ (including full-length Aβ and its fragments) and the influence of carbon nanoparticles.

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.

Membrane curvature is no longer thought of as a passive property of the membrane; rather, it is considered as an active, regulated state that serves various purposes in the cell such as between cells and organelle definition. While transport is usually mediated by tiny membrane bubbles known as vesicles or membrane tubules, such communication requires complex interplay between the lipid bilayers and cytosolic proteins such as members of the Bin/Amphiphysin/Rvs (BAR) superfamily of proteins. With rapid developments in novel experimental techniques, membrane remodeling has become a rapidly emerging new field in recent years. Molecular dynamics (MD) simulations are important tools for obtaining atomistic information regarding the structural and dynamic aspects of biological systems and for understanding the physics-related aspects. The availability of more sophisticated experimental data poses challenges to the theoretical community for developing novel theoretical and computational techniques that can be used to better interpret the experimental results to obtain further functional insights. In this review, we summarize the general mechanisms underlying membrane remodeling controlled or mediated by proteins. While studies combining experiments and molecular dynamics simulations recall existing mechanistic models, concurrently, they extend the role of different BAR domain proteins during membrane remodeling processes. We review these recent findings, focusing on how multiscale molecular dynamics simulations aid in understanding the physical basis of BAR domain proteins, as a representative of membrane-remodeling proteins.

Metastasis is the leading cause of most cancer deaths, as opposed to dysregulated cell growth of the primary tumor. Molecular mechanisms of metastasis have been studied for decades and the findings have evolved our understanding of the progression of malignancy. However, most of the molecular mechanisms fail to address the causes of cancer and its evolutionary origin, demonstrating an inability to find a solution for complete cure of cancer. After being a neglected area of tumor biology for quite some time, recently several studies have focused on the impact of the tumor microenvironment on cancer growth. The importance of the tumor microenvironment is gradually gaining attention, particularly from the perspective of biophysics. In vitro three-dimensional (3-D) metastatic models are an indispensable platform for investigating the tumor microenvironment, as they mimic the in vivo tumor tissue. In 3-D metastatic in vitro models, static factors such as the mechanical properties, biochemical factors, as well as dynamic factors such as cell-cell, cell-ECM interactions, and fluid shear stress can be studied quantitatively. With increasing focus on basic cancer research and drug development, the in vitro 3-D models offer unique advantages in fundamental and clinical biomedical studies.

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 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 GB^OBC 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.