Surface-Tension Replica-Exchange Molecular Dynamics Method for Enhanced Sampling of Biological Membrane Systems

Hybrid neMD/MC lipid swapping algorithm to equilibrate membrane simulation with thermodynamic reservoir

Molecular dynamics (MD) simulations based on detailed all-atom models offer a powerful approach to study the structure and dynamics of biological membranes. However, the complexity of biological membranes in terms of chemical diversity presents an outstanding challenge. Particularly, difficulties are encountered when a given lipid type is present at very low abundance. While considering a very large simulation system with a small number of the low abundance lipid may offer a practical solution in some cases, resorting to increasingly large system rapidly becomes computationally costly and impractical. More fundamentally, an additional issue may be encountered if the low abundance lipid displays a high affinity for some protein in the simulation system. What is needed is to treat the simulation box as an open system in which the number of lipids can naturally fluctuate, as in the Grand Canonical Monte Carlo (MC) algorithm. However, this approach, in which a whole lipid molecule needs to be inserted or annihilated, is essentially impractical in the context of an all-atom simulation. To enforce equilibrium between a simulated system and an infinite surrounding bath, we propose a hybrid non-equilibrium (neMD)-MC algorithm, in which a randomly chosen lipid molecule in the simulated system is swapped with a lipid picked in a separate system standing as a thermodynamic "reservoir" with the desired mole fraction for all lipid components. The neMD/MC algorithm consists in driving the system via short non-equilibrium trajectories to generate a new state of the system that are subsequently accepted or rejected via a Metropolis MC step. The probability of exchanges in the context of an infinite reservoir with the desired mole fraction for all lipid components is derived and tested with a few illustrative systems for phosphatidylcholine and phosphatidylglycerol lipid mixtures.

Improved Highly Mobile Membrane Mimetic Model for Investigating Protein-Cholesterol Interactions

Cholesterol (CHL) plays an integral role in modulating the function and activity of various mammalian membrane proteins. Due to the slow dynamics of lipids, conventional computational studies of protein-CHL interactions rely on either long-time scale atomistic simulations or coarse-grained approximations to sample the process. A highly mobile membrane mimetic (HMMM) has been developed to enhance lipid diffusion and thus used to facilitate the investigation of lipid interactions with peripheral membrane proteins and, with customized in silico solvents to replace phospholipid tails, with integral membrane proteins. Here, we report an updated HMMM model that is able to include CHL, a nonphospholipid component of the membrane, henceforth called HMMM-CHL. To this end, we had to optimize the effect of the customized solvents on CHL behavior in the membrane. Furthermore, the new solvent is compatible with simulations using force-based switching protocols. In the HMMM-CHL, both improved CHL dynamics and accelerated lipid diffusion are integrated. To test the updated model, we have applied it to the characterization of protein-CHL interactions in two membrane protein systems, the human β2-adrenergic receptor (β2AR) and the mitochondrial voltage-dependent anion channel 1 (VDAC-1). Our HMMM-CHL simulations successfully identified CHL binding sites and captured detailed CHL interactions in excellent consistency with experimental data as well as other simulation results, indicating the utility of the improved model in applications where an enhanced sampling of protein-CHL interactions is desired.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Configurational Sampling of All-Atom Solvated Membranes Using Hybrid Nonequilibrium Molecular Dynamics Monte Carlo Simulations

All-atom simulations are a powerful approach to study the structure and dynamics of biological membranes. However, sampling the atomic configurations of inhomogeneous membranes can be challenging due to the slow lateral diffusion of the various constituents. To address this issue, a hybrid nonequilibrium molecular dynamics Monte Carlo (neMD/MC) simulation method is proposed in which randomly chosen lipid molecules are swapped to generate configurations that are subsequently accepted or rejected according to a Metropolis criterion based on the alchemical work for the attempted swap calculated via a short trajectory. A dual-topology framework constraining the common atoms of the exchanging molecules yields a good acceptance probability using switching trajectories as short as 10 ps. The performance of the hybrid neMD/MC algorithm and its ability to sample the distribution of lipids near a transmembrane helix carrying a net charge are illustrated for a binary mixture of charged and zwitterionic lipids.

Towards de novo design of transmembrane α-helical assemblies using structural modelling and molecular dynamics simulation

Computational de novo protein design involves iterative processes consisting of amino acid sequence design, structural modelling and scoring, and design validation by synthesis and experimental characterisation. Recent advances in protein structure prediction and modelling methods have enabled the highly efficient and accurate design of water-soluble proteins. However, the design of membrane proteins remains a major challenge. To advance membrane protein design, considering the higher complexity of membrane protein folding, stability, and dynamic interactions between water, ions, lipids, and proteins is an important task. For introducing explicit solvents and membranes to these design methods, all-atom molecular dynamics (MD) simulations of designed proteins provide useful information that cannot be obtained experimentally. In this review, we first describe two major approaches to designing transmembrane α-helical assemblies, consensus and de novo design. We further illustrate recent MD studies of membrane protein folding related to protein design, as well as advanced treatments in molecular models and conformational sampling techniques in the simulations. Finally, we discuss the possibility to introduce MD simulations after the existing static modelling and screening of design decoys as an additional step for refinement of the design, which considers membrane protein folding dynamics and interactions with explicit membranes.

Free-Energy Profiles for Membrane Permeation of Compounds Calculated Using Rare-Event Sampling Methods

The free-energy profile of a compound is an essential measurement in evaluating the membrane permeation process by means of theoretical methods. Computationally, molecular dynamics (MD) simulation allows the free-energy profile calculation. However, MD simulations frequently fail to sample membrane permeation because they are rare events induced in longer timescales than the accessible timescale of MD, leading to an insufficient conformational search to calculate an incorrect free-energy profile. To achieve a sufficient conformational search, several enhanced sampling methods have been developed and elucidated the membrane permeation process. In addition to these enhanced sampling methods, we proposed a simple yet powerful free-energy calculation of a compound for the membrane permeation process based on originally rare-event sampling methods developed by us. Our methods have a weak dependency on external biases and their optimizations to promote the membrane permeation process. Based on distributed computing, our methods only require the selection of initial structures and their conformational resampling, whereas the enhanced sampling methods may be required to adjust external biases. Furthermore, our methods efficiently search membrane permeation processes with simple scripts without modifying any MD program. As demonstrations, we calculated the free-energy profiles of seven linear compounds for their membrane permeation based on a hybrid conformational search using two rare-event sampling methods, that is, (1) parallel cascade selection MD (PaCS-MD) and (2) outlier flooding method (OFLOOD), combined with a Markov state model (MSM) construction. In the first step, PaCS-MD generated initial membrane permeation paths of a compound. In the second step, OFLOOD expanded the unsearched conformational area around the initial paths, allowing for a broad conformational search. Finally, the trajectories were employed to construct reliable MSMs, enabling correct free-energy profile calculations. Furthermore, we estimated the membrane permeability coefficients of all compounds by constructing the reliable MSMs for their membrane permeation. In conclusion, the calculated coefficients were qualitatively correlated with the experimental measurements (correlation coefficient (R²) = 0.8689), indicating that the hybrid conformational search successfully calculated the free-energy profiles and membrane permeability coefficients of the seven compounds.

Druggable Lipid Binding Sites in Pentameric Ligand-Gated Ion Channels and Transient Receptor Potential Channels

Lipids modulate the function of many ion channels, possibly through direct lipid-protein interactions. The recent outpouring of ion channel structures by cryo-EM has revealed many lipid binding sites. Whether these sites mediate lipid modulation of ion channel function is not firmly established in most cases. However, it is intriguing that many of these lipid binding sites are also known sites for other allosteric modulators or drugs, supporting the notion that lipids act as endogenous allosteric modulators through these sites. Here, we review such lipid-drug binding sites, focusing on pentameric ligand-gated ion channels and transient receptor potential channels. Notable examples include sites for phospholipids and sterols that are shared by anesthetics and vanilloids. We discuss some implications of lipid binding at these sites including the possibility that lipids can alter drug potency or that understanding protein-lipid interactions can guide drug design. Structures are only the first step toward understanding the mechanism of lipid modulation at these sites. Looking forward, we identify knowledge gaps in the field and approaches to address them. These include defining the effects of lipids on channel function in reconstituted systems using asymmetric membranes and measuring lipid binding affinities at specific sites using native mass spectrometry, fluorescence binding assays, and computational approaches.

Modeling lipid-protein interactions for coarse-grained lipid and Cα protein models

Biological membranes that play major roles in diverse functions are composed of numerous lipids and proteins, making them an important target for coarse-grained (CG) molecular dynamics (MD) simulations. Recently, we have developed the CG implicit solvent lipid force field (iSoLF) that has a resolution compatible with the widely used Cα protein representation [D. Ugarte La Torre and S. Takada, J. Chem. Phys. 153, 205101 (2020)]. In this study, we extended it and developed a lipid-protein interaction model that allows the combination of the iSoLF and the Cα protein force field, AICG2+. The hydrophobic-hydrophilic interaction is modeled as a modified Lennard-Jones potential in which parameters were tuned partly to reproduce the experimental transfer free energy and partly based on the free energy profile normal to the membrane surface from previous all-atom MD simulations. Then, the obtained lipid-protein interaction is tested for the configuration and placement of transmembrane proteins, water-soluble proteins, and peripheral proteins, showing good agreement with prior knowledge. The interaction is generally applicable and is implemented in the publicly available software, CafeMol.

Mechanical Activation of MscL Revealed by a Locally Distributed Tension Molecular Dynamics Approach

Membrane tension perceived by mechanosensitive (MS) proteins mediates cellular responses to mechanical stimuli and osmotic stresses, and it also guides multiple biological functions including cardiovascular control and development. In bacteria, MS channels function as tension-activated pores limiting excessive turgor pressure, with MS channel of large conductance (MscL) acting as an emergency release valve preventing cell lysis. Previous attempts to simulate gating transitions in MscL by either directly applying steering forces to the protein or by increasing the whole-system tension were not fully successful and often disrupted the integrity of the system. We present a novel, to our knowledge, locally distributed tension molecular dynamics (LDT-MD) simulation method that allows application of forces continuously distributed among lipids surrounding the channel using a specially constructed collective variable. We report reproducible and reversible transitions of MscL to the open state with measured parameters of lateral expansion and conductivity that exactly satisfy experimental values. The LDT-MD method enables exploration of the MscL-gating process with different pulling velocities and variable tension asymmetry between the inner and outer membrane leaflets. We use LDT-MD in combination with well-tempered metadynamics to reconstruct the tension-dependent free-energy landscape for the opening transition in MscL. The flexible definition of the LDT collective variable allows general application of our method to study mechanical activation of any membrane-embedded protein.

Pressure control using stochastic cell rescaling

Molecular dynamics simulations require barostats to be performed at a constant pressure. The usual recipe is to employ the Berendsen barostat first, which displays a first-order volume relaxation efficient in equilibration but results in incorrect volume fluctuations, followed by a second-order or a Monte Carlo barostat for production runs. In this paper, we introduce stochastic cell rescaling, a first-order barostat that samples the correct volume fluctuations by including a suitable noise term. The algorithm is shown to report volume fluctuations compatible with the isobaric ensemble and its anisotropic variant is tested on a membrane simulation. Stochastic cell rescaling can be straightforwardly implemented in the existing codes and can be used effectively in both equilibration and production phases.

Computer simulations of protein-membrane systems

The interactions between proteins and membranes play critical roles in signal transduction, cell motility, and transport, and they are involved in many types of diseases. Molecular dynamics (MD) simulations have greatly contributed to our understanding of protein-membrane interactions, promoted by a dramatic development of MD-related software, increasingly accurate force fields, and available computer power. In this chapter, we present available methods for studying protein-membrane systems with MD simulations, including an overview about the various all-atom and coarse-grained force fields for lipids, and useful software for membrane simulation setup and analysis. A large set of case studies is discussed.

Molecular Dynamics Simulations of Membrane Permeability

This Review illustrates the evaluation of permeability of lipid membranes from molecular dynamics (MD) simulation primarily using water and oxygen as examples. Membrane entrance, translocation, and exit of these simple permeants (one hydrophilic and one hydrophobic) can be simulated by conventional MD, and permeabilities can be evaluated directly by Fick's First Law, transition rates, and a global Bayesian analysis of the inhomogeneous solubility-diffusion model. The assorted results, many of which are applicable to simulations of nonbiological membranes, highlight the limitations of the homogeneous solubility diffusion model; support the utility of inhomogeneous solubility diffusion and compartmental models; underscore the need for comparison with experiment for both simple solvent systems (such as water/hexadecane) and well-characterized membranes; and demonstrate the need for microsecond simulations for even simple permeants like water and oxygen. Undulations, subdiffusion, fractional viscosity dependence, periodic boundary conditions, and recent developments in the field are also discussed. Last, while enhanced sampling methods and increasingly sophisticated treatments of diffusion add substantially to the repertoire of simulation-based approaches, they do not address directly the critical need for force fields with polarizability and multipoles, and constant pH methods.

Replica-Exchange Methods for Biomolecular Simulations

In this study, a replica-exchange method was developed to overcome conformational sampling difficulties in computer simulations of spin glass or other systems with rugged free-energy landscapes. This method was then applied to the protein-folding problem in combination with molecular dynamics (MD) simulation. Owing to its simplicity and sampling efficiency, the replica-exchange method has been applied to many other biological problems and has been continuously improved. The method has often been combined with other sampling techniques, such as umbrella sampling, free-energy perturbation, metadynamics, and Gaussian accelerated MD (GaMD). In this chapter, we first summarize the original replica-exchange molecular dynamics (REMD) method and discuss how new algorithms related to the original method are implemented to add new features. Heterogeneous and flexible structures of an N-glycan in a solution are simulated as an example of applications by REMD, replica exchange with solute tempering, and GaMD. The sampling efficiency of these methods on the N-glycan system and the convergence of the free-energy changes are compared. REMD simulation protocols and trajectory analysis using the GENESIS software are provided to facilitate the practical use of advanced simulation methods.

Acceleration of cryo-EM Flexible Fitting for Large Biomolecular Systems by Efficient Space Partitioning

Flexible fitting is a powerful technique to build the 3D structures of biomolecules from cryoelectron microscopy (cryo-EM) density maps. One popular method is a cross-correlation coefficient-based approach, where the molecular dynamics (MD) simulation is carried out with the biasing potential that includes the cross-correlation coefficient between the experimental and simulated density maps. Here, we propose efficient parallelization schemes for the calculation of the cross-correlation coefficient to accelerate flexible fitting. Our schemes are tested for small, medium, and large biomolecules using CPU and hybrid CPU + GPU architectures. The scheme for the atomic decomposition MD is suitable for small proteins such as Ca²⁺-ATPase with the all-atom Go model, while that for the domain decomposition MD is better for larger systems such as ribosome with the all-atom Go or the all-atom explicit solvent models. Our methods allow flexible fitting for various biomolecules with reasonable computational cost. This approach also connects high-resolution structure refinements with investigation of protein structure-function relationship.

A mixed alchemical and equilibrium dynamics to simulate heterogeneous dense fluids: Illustrations for Lennard-Jones mixtures and phospholipid membranes

An algorithm to efficiently simulate multi-component fluids is proposed and illustrated. The focus is on biological membranes that are heterogeneous and challenging to investigate quantitatively. To achieve rapid equilibration of spatially inhomogeneous fluids, we mix conventional molecular dynamics simulations with alchemical trajectories. The alchemical trajectory switches the positions of randomly selected pairs of molecules and plays the role of an efficient Monte Carlo move. It assists in accomplishing rapid spatial de-correlations. Examples of phase separation and mixing are given in two-dimensional binary Lennard-Jones fluid and a DOPC-POPC membrane. The performance of the algorithm is analyzed, and tools to maximize its efficiency are provided. It is concluded that the algorithm is vastly superior to conventional molecular dynamics for the equilibrium study of biological membranes.

Influence of various parameters in the replica-exchange molecular dynamics method: Number of replicas, replica-exchange frequency, and thermostat coupling time constant

The replica-exchange molecular dynamics (REMD) method has been used for conformational sampling of various biomolecular systems. To maximize sampling efficiency, some adjustable parameters must be optimized. Although it is agreed that shorter intervals between the replica-exchange attempts enhance traversals in the temperature space, details regarding the artifacts caused by these short intervals are controversial. In this study, we revisit this problem by performing REMD simulations on an alanine octapeptide in an implicit solvent. Fifty different sets of conditions, which are a combination of five replica-exchange periods, five different numbers of replicas, and two thermostat coupling time constants, were investigated. As a result, although short replica-exchange intervals enhanced the traversals in the temperature space, they led to artifacts in the ensemble average of the temperature, potential energy, and helix content. With extremely short replica-exchange intervals, i.e., attempted at every time step, the ensemble average of the temperature deviated from the thermostat temperature by ca. 7 K. Differences in the ensembles were observed even for larger replica-exchange intervals (between 100 and 1,000 steps). In addition, the shorter thermostat coupling time constant reduced the artifacts found when short replica-exchange intervals were used, implying that these artifacts are caused by insufficient thermal relaxation between the replica-exchange events. Our results will be useful to reduce the artifacts found in REMD simulations by adjusting some key parameters.

GENESIS 1.1: A hybrid-parallel molecular dynamics simulator with enhanced sampling algorithms on multiple computational platforms

GENeralized-Ensemble SImulation System (GENESIS) is a software package for molecular dynamics (MD) simulation of biological systems. It is designed to extend limitations in system size and accessible time scale by adopting highly parallelized schemes and enhanced conformational sampling algorithms. In this new version, GENESIS 1.1, new functions and advanced algorithms have been added. The all-atom and coarse-grained potential energy functions used in AMBER and GROMACS packages now become available in addition to CHARMM energy functions. The performance of MD simulations has been greatly improved by further optimization, multiple time-step integration, and hybrid (CPU + GPU) computing. The string method and replica-exchange umbrella sampling with flexible collective variable choice are used for finding the minimum free-energy pathway and obtaining free-energy profiles for conformational changes of a macromolecule. These new features increase the usefulness and power of GENESIS for modeling and simulation in biological research. © 2017 Wiley Periodicals, Inc.

Enhanced Sampling of Phase Transitions in Coarse-Grained Lipid Bilayers

Freezing and melting of dipalmitoylphosphatidylcholine (DPPC) bilayers are simulated in both the explicit (Wet) and implicit solvent (Dry) coarse-grained MARTINI force fields with enhanced sampling, via the isobaric, molecular dynamics version of the generalized replica exchange method (gREM). Phase transitions are described with the entropic viewpoint, based upon the statistical temperature as a function of enthalpy, T_S(H) = 1/(dS(H)/dH), where S is the configurational entropy. Bilayer thickness, area per lipid, and the second-rank order parameter (P2) are calculated vs temperature in the transition range. In a 32-lipid Wet MARTINI system, transitions in the lipid and water subsystems are strongly coupled, giving rise to considerable structure in T_S(H) and the need to specify the state of the water when reporting a lipid transition temperature. For gel lipid + liquid water → fluid lipid + liquid water, we find 292.4 K. The small system is influenced by finite-size effects, but it is argued that the entropic approach is well suited to revealing them, which will be particularly relevant for studies of finite nanosystems where there is no thermodynamic limit. In a 390-lipid Dry MARTINI system, two-dimensional analogues of the topographies of coexisting states ("subphases") seen in pure fluids are found. They are not seen in the 32-lipid Wet or Dry system, but the Dry lipids show a new type of state with gel in one leaflet and tilted gel in the other. Dry bilayer transition temperatures are 333.3 K (390 lipids) and 338 K (32 lipids), indicating that the 32-lipid system is not too small for a qualitative study of the transition. Physical arguments are given for Dry lipid system size dependence and for the difference between Wet and Dry systems.

Replica exchange molecular dynamics study of the amyloid beta (11–40) trimer penetrating a membrane

The transmembrane Aβ_11–40 trimer is investigated for the first time using REMD and FEP.

CHARMM-GUI HMMM Builder for Membrane Simulations with the Highly Mobile Membrane-Mimetic Model

Slow diffusion of the lipids in conventional all-atom simulations of membrane systems makes it difficult to sample large rearrangements of lipids and protein-lipid interactions. Recently, Tajkhorshid and co-workers developed the highly mobile membrane-mimetic (HMMM) model with accelerated lipid motion by replacing the lipid tails with small organic molecules. The HMMM model provides accelerated lipid diffusion by one to two orders of magnitude, and is particularly useful in studying membrane-protein associations. However, building an HMMM simulation system is not easy, as it requires sophisticated treatment of the lipid tails. In this study, we have developed CHARMM-GUI HMMM Builder (http://www.charmm-gui.org/input/hmmm) to provide users with ready-to-go input files for simulating HMMM membrane systems with/without proteins. Various lipid-only and protein-lipid systems are simulated to validate the qualities of the systems generated by HMMM Builder with focus on the basic properties and advantages of the HMMM model. HMMM Builder supports all lipid types available in CHARMM-GUI and also provides a module to convert back and forth between an HMMM membrane and a full-length membrane. We expect HMMM Builder to be a useful tool in studying membrane systems with enhanced lipid diffusion.

myPresto/omegagene: a GPU-accelerated molecular dynamics simulator tailored for enhanced conformational sampling methods with a non-Ewald electrostatic scheme

Molecular dynamics (MD) is a promising computational approach to investigate dynamical behavior of molecular systems at the atomic level. Here, we present a new MD simulation engine named "myPresto/omegagene" that is tailored for enhanced conformational sampling methods with a non-Ewald electrostatic potential scheme. Our enhanced conformational sampling methods, e.g., the virtual-system-coupled multi-canonical MD (V-McMD) method, replace a multi-process parallelized run with multiple independent runs to avoid inter-node communication overhead. In addition, adopting the non-Ewald-based zero-multipole summation method (ZMM) makes it possible to eliminate the Fourier space calculations altogether. The combination of these state-of-the-art techniques realizes efficient and accurate calculations of the conformational ensemble at an equilibrium state. By taking these advantages, myPresto/omegagene is specialized for the single process execution with Graphics Processing Unit (GPU). We performed benchmark simulations for the 20-mer peptide, Trp-cage, with explicit solvent. One of the most thermodynamically stable conformations generated by the V-McMD simulation is very similar to an experimentally solved native conformation. Furthermore, the computation speed is four-times faster than that of our previous simulation engine, myPresto/psygene-G. The new simulator, myPresto/omegagene, is freely available at the following URLs: http://www.protein.osaka-u.ac.jp/rcsfp/pi/omegagene/ and http://presto.protein.osaka-u.ac.jp/myPresto4/.

Atomic-level description of protein-lipid interactions using an accelerated membrane model

Peripheral membrane proteins are structurally diverse proteins that are involved in fundamental cellular processes. Their activity of these proteins is frequently modulated through their interaction with cellular membranes, and as a result techniques to study the interfacial interaction between peripheral proteins and the membrane are in high demand. Due to the fluid nature of the membrane and the reversibility of protein-membrane interactions, the experimental study of these systems remains a challenging task. Molecular dynamics simulations offer a suitable approach to study protein-lipid interactions; however, the slow dynamics of the lipids often prevents sufficient sampling of specific membrane-protein interactions in atomistic simulations. To increase lipid dynamics while preserving the atomistic detail of protein-lipid interactions, in the highly mobile membrane-mimetic (HMMM) model the membrane core is replaced by an organic solvent, while short-tailed lipids provide a nearly complete representation of natural lipids at the organic solvent/water interface. Here, we present a brief introduction and a summary of recent applications of the HMMM to study different membrane proteins, complementing the experimental characterization of the presented systems, and we offer a perspective of future applications of the HMMM to study other classes of membrane proteins. This article is part of a Special Issue entitled: Membrane proteins edited by J.C. Gumbart and Sergei Noskov.

Peripheral membrane proteins: Tying the knot between experiment and computation

Experimental biology has contributed to answer questions about the morphology of a system and how molecules organize themselves to maintain a healthy functional cell. Single-molecule techniques, optical and magnetic experiments, and fluorescence microscopy have come a long way to probe structural and dynamical information at multiple scales. However, some details are simply too small or the processes are too short-lived to detect by experiments. Computational biology provides a bridge to understand experimental results at the molecular level, makes predictions that have not been seen in vivo, and motivates new fields of research. This review focuses on the advances on peripheral membrane proteins (PMPs) studies; what is known about their interaction with membranes, their role in cell biology, and some limitations that both experiment and computation still have to overcome to gain better structural and functional understanding of these PMPs. As many recent reviews have acknowledged, interdisciplinary efforts between experiment and computation are needed in order to have useful models that lead future directions in the study of PMPs. We present new results of a case study on a PMP that behaves as an intricate machine controlling lipid homeostasis between cellular organelles, Osh4 in yeast Saccharomyces cerevisiae. Molecular dynamics simulations were run to examine the interaction between the protein and membrane models that reflect the lipid diversity of the endoplasmic reticulum and trans-Golgi membranes. Our study is consistent with experimental data showing several residues that interact to smaller or larger extent with the bilayer upon stable binding (~200 ns into the trajectory). We identified PHE239 as a key residue stabilizing the protein-membrane interaction along with two other binding regions, the ALPS-like motif and the β6-β7 loops in the mouth region of the protein. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms

This paper reviews various enhanced conformational sampling methods and explicit/implicit solvent/membrane models, as well as their recent applications to the exploration of the structure and dynamics of membranes and membrane proteins. Molecular dynamics simulations have become an essential tool to investigate biological problems, and their success relies on proper molecular models together with efficient conformational sampling methods. The implicit representation of solvent/membrane environments is reasonable approximation to the explicit all-atom models, considering the balance between computational cost and simulation accuracy. Implicit models can be easily combined with replica-exchange molecular dynamics methods to explore a wider conformational space of a protein. Other molecular models and enhanced conformational sampling methods are also briefly discussed. As application examples, we introduce recent simulation studies of glycophorin A, phospholamban, amyloid precursor protein, and mixed lipid bilayers and discuss the accuracy and efficiency of each simulation model and method. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

Greedy replica exchange algorithm for heterogeneous computing grids

Replica exchange molecular dynamics (REMD) has become a valuable tool in studying complex biomolecular systems. However, its application on distributed computing grids is limited by the heterogeneity of this environment. In this study, we propose a REMD implementation referred to as greedy REMD (gREMD) suitable for computations on heterogeneous grids. To decentralize replica management, gREMD utilizes a precomputed schedule of exchange attempts between temperatures. Our comparison of gREMD against standard REMD suggests four main conclusions. First, gREMD accelerates grid REMD simulations by as much as 40 %. Second, gREMD increases CPU utilization rates in grid REMD by up to 60 %. Third, we argue that gREMD is expected to maintain approximately constant CPU utilization rates and simulation wall-clock times with the increase in the number of replicas. Finally, we show that gREMD correctly implements the REMD algorithm and reproduces the conformational ensemble of a short peptide sampled in our previous standard REMD simulations. We believe that gREMD can find its place in large-scale REMD simulations on heterogeneous computing grids.

GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations

GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310–323. doi: 10.1002/wcms.1220

Computational study of bacterial membrane disruption by cationic biocides: structural basis for water pore formation

The development of biocides as disinfectants that do not induce bacterial resistance is crucial to health care since hospital-acquired infections afflict millions of patients every year. Recent experimental studies of a class of cationic biocides based on the phenylene ethynylene backbone, known as OPEs, have revealed that their biocidal activity is accompanied by strong morphology changes to bacterial cell membranes. In vitro studies of bacterial membrane mimics have shown changes to the lipid phase that are dependent on the length and orientation of the cationic moieties on the backbone. This study uses classical molecular dynamics to conduct a comprehensive survey of how oligomers with different chemical structures interact with each other and with a bacterial cell membrane mimic. In particular, the ability of OPEs to disrupt membrane structure is studied as a function of the length of the biocides and the orientation of their cationic moieties along the backbone of the molecule. The simulation results show that the structure of OPEs radically affects their interactions with a lipid bilayer. Biocides with branched cationic groups form trans-membrane water pores regardless of their backbone length, while only 1-1.5 nm of membrane thinning is observed with biocides with cationic groups on their terminal ends. The molecular dynamics simulations provide mechanistic details at the molecular level of the interaction of these biocidal oligomers and the lipid bilayer and corroborate experimental findings regarding observed differences in membrane disruption by OPEs with different chemical structures.