Coarse grained protein-lipid model with application to lipoprotein particles

The structure, self-assembly and dynamics of lipid nanodiscs revealed by computational approaches

Nanodisc technology is increasingly being used in structural, biochemical and biophysical studies of membrane proteins. The computational approaches have revealed many important features of nanodisc assembly, structures and dynamics. Therefore, we reviewed the application of computational approaches, especially molecular modeling and molecular dyncamics (MD) simulations, to characterize nanodiscs, including the structural models, assembly and disassembly, protocols for modeling, structural properties and dynamics, and protein-lipid interactions in nanodiscs. More amazing computational studies about nanodiscs are looked forward to in the future.

An atomistic characterization of high-density lipoproteins and the conserved "LN" region of apoA-I

The physicochemical characteristics of the various subpopulations of high-density lipoproteins (HDLs) and, in particular, their surface properties determine their ability to scavenge lipids and interact with specific receptors and peptides. Five representative spheroidal HDL subpopulation models were mapped from a previously reported equilibrated coarse-grained (CG) description to an atomistic representation for subsequent molecular dynamics simulation. For each HDL model a range of finer-level analyses was undertaken, including the component-wise characterization of HDL surfaces, the average size and composition of hydrophobic surface patches, dynamic protein secondary structure monitoring, and the proclivity for solvent exposure of the proposed β-amyloid (Aβ) binding region of apolipoprotein A-I (apoA-I), "LN." This study reveals that previously characterized ellipsoidal HDL3a and HDL2a models revert to a more spherical geometry in an atomistic representation due to the enhanced conformational flexibility afforded to the apoA-I protein secondary structure, allowing for enhanced surface lipid packing and lower overall surface hydrophobicity. Indeed, the proportional surface hydrophobicity and apoA-I exposure reduced with increasing HDL size, consistent with previous characterizations. Furthermore, solvent exposure of the "LN" region of apoA-I was exclusively limited to the smallest HDL3c model within the timescale of the simulations, and typically corresponded to a distinct loss in secondary structure across the "LN" region to form part of a significant contiguous hydrophobic patch on the HDL surface. Taken together, these findings provide preliminary evidence for a subpopulation-specific interaction between HDL3c particles and circulating hydrophobic species such as Aβ via the exposed "LN" region of apoA-I.

Dynamic framework for large-scale modeling of membranes and peripheral proteins

In this chapter, we present a novel computational framework to study the dynamic behavior of extensive membrane systems, potentially in interaction with peripheral proteins, as an alternative to conventional simulation methods. The framework effectively describes the complex dynamics in protein-membrane systems in a mesoscopic particle-based setup. Furthermore, leveraging the hydrodynamic coupling between the membrane and its surrounding solvent, the coarse-grained model grounds its dynamics in macroscopic kinetic properties such as viscosity and diffusion coefficients, marrying the advantages of continuum- and particle-based approaches. We introduce the theoretical background and the parameter-space optimization method in a step-by-step fashion, present the hydrodynamic coupling method in detail, and demonstrate the application of the model at each stage through illuminating examples. We believe this modeling framework to hold great potential for simulating membrane and protein systems at biological spatiotemporal scales, and offer substantial flexibility for further development and parametrization.

pSPICA Force Field Extended for Proteins and Peptides

Many coarse-grained (CG) molecular dynamics (MD) studies have been performed to investigate biological processes involving proteins and lipids. CG force fields (FFs) in these MD studies often use implicit or nonpolar water models to reduce computational costs. CG-MD using water models cannot properly describe electrostatic screening effects owing to the hydration of ionic segments and thus cannot appropriately describe molecular events involving water channels and pores through lipid membranes. To overcome this issue, we developed a protein model in the pSPICA FF, in which a polar CG water model showing the proper dielectric response was adopted. The developed CG model greatly improved the transfer free energy profiles of charged side chain analogues across the lipid membrane. Application studies on melittin-induced membrane pores and mechanosensitive channels in lipid membranes demonstrated that CG-MDs using the pSPICA FF correctly reproduced the structure and stability of the pores and channels. Furthermore, the adsorption behavior of the highly charged nona-arginine peptides on lipid membranes changed with salt concentration, indicating the pSPICA FF is also useful for simulating protein adsorption on membrane surfaces.

Applications of Molecular Dynamics Simulation in Protein Study

Molecular Dynamics (MD) Simulations is increasingly used as a powerful tool to study protein structure-related questions. Starting from the early simulation study on the photoisomerization in rhodopsin in 1976, MD Simulations has been used to study protein function, protein stability, protein-protein interaction, enzymatic reactions and drug-protein interactions, and membrane proteins. In this review, we provide a brief review for the history of MD Simulations application and the current status of MD Simulations applications in protein studies.

Replenishing HDL with synthetic HDL has multiple protective effects against sepsis in mice

Sepsis is a major health issue with mortality exceeding 30% and few treatment options. We found that high-density lipoprotein cholesterol (HDL-C) abundance was reduced by 45% in septic patients compared to that in nonseptic patients. Furthermore, HDL-C abundance in nonsurviving septic patients was substantially lower than in those patients who survived. We therefore hypothesized that replenishing HDL might be a therapeutic approach for treating sepsis and found that supplementing HDL with synthetic HDL (sHDL) provided protection against sepsis in mice. In mice subjected to cecal ligation and puncture (CLP), infusing the sHDL ETC-642 increased plasma HDL-C amounts and improved the 7-day survival rate. Septic mice treated with sHDL showed improved kidney function and reduced inflammation, as indicated by marked decreases in the plasma concentrations of blood urea nitrogen (BUN) and the cytokines interleukin-6 (IL-6) and IL-10, respectively. We found that sHDL inhibited the ability of the endotoxins LPS and LPA to activate inflammatory pathways in RAW264.7 cells and HEK-Blue cells expressing the receptors TLR4 or TLR2 and NF-κB reporters. In addition, sHDL inhibited the activation of HUVECs by LPS, LTA, and TNF-α. Together, these data indicate that sHDL treatment protects mice from sepsis in multiple ways and that it might be an effective therapy for patients with sepsis.

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI.

Estimating the binding affinities of protein-protein interactions (PPIs) is crucial to understand protein function and design new functional proteins. Since the experimental measurement in wet-labs is labor-intensive and time-consuming, fast and accurate in silico approaches have received much attention. Although considerable efforts have been made in this direction, predicting the effects of mutations on the protein-protein binding affinity is still a challenging research problem. In this work, we introduce GeoPPI, a novel computational approach that uses deep geometric representations of protein complexes to predict the effects of mutations on the binding affinity. The geometric representations are first learned via a self-supervised learning scheme and then integrated with gradient-boosting trees to accomplish the prediction. We find that the learned representations encode meaningful patterns underlying the interactions between atoms in protein structures. Also, extensive tests on major benchmark datasets show that GeoPPI has made an important improvement over the existing methods in predicting the effects of mutations on the binding affinity.

Biomolecular response to hour-long ultralow field microwave radiation: An effective coarse-grained model simulation

Various electronic devices, which we commonly use, radiate microwaves. Such external perturbation influences the functionality of biomolecules. In an ultralow field, the cumulative response of a molecule is expected only over a time scale of hours. To study the structural dynamics of biomolecules over hours, we adopt a simple methodology for constructing the coarse-grained structure of the protein molecule and solve the Langevin equation under different working potentials. In this approach, each amino acid residue of a biomolecule is mapped onto a number of beads, a few for the backbone, and few for the side chain, depending on the complexity of its chemical structure. We choose the force field in such a way that the dynamics of the protein molecule in the presence of ultralow radiation field of microvolt/nm could be followed over the time frame of 2 h. We apply the model to describe a biomolecule, hen egg white lysozyme, and simulate its structural evolution under ultralow strength electromagnetic radiation. The simulation revealed the finer structural details, like the extent of exposure of bioactive residues and the state of the secondary structures of the molecule, further confirmed from spectroscopic measurements [details are available in Phys. Rev. E 97, 052416 (2018)10.1103/PhysRevE.97.052416 and briefly described here]. Though tested for a specific system, the model is quite general. We believe that it harnesses the potential in studying the structural dynamics of any biopolymer under external perturbation over an extended time scale.

Perspective on multi-scale simulation of thermal transport in solids and interfaces

Phonon-mediated thermal transport is inherently multi-scale. The wave-length of phonons (considering phonons as waves) is typically at the nanometer scale; the typical size of a phonon wave energy packet is tens of nanometers, while the phonon mean free path (MFP) can be as long as microns. At different length scales, the phonons will interact with structures of different feature sizes, which can be as small as 0D defects (point defects), short to medium range linear defects (dislocations), medium to large range 2D planar defects (stacking faults and twin boundaries), and large scale 3D defects (voids, inclusions, and various microstructures). The nature of multi-scale thermal transport is that there are different heat transfer physics across different length scales and in the meantime the physics crossing the different scales is interdependent and coupled. Since phonon behavior is usually mode dependent, thermal transport in materials with a combined micro-/nano-structure complexity becomes complicated, making modeling this kind of transport process very challenging. In this perspective, we first summarize the advantages and disadvantages of computational methods for mono-scale heat transfer and the state-of-the-art multi-scale thermal transport modeling. We then discuss a few important aspects of the future development of multi-scale modeling, in particular with the aid of modern machine learning and uncertainty quantification techniques. As more sophisticated theoretical and computational methods continue to advance thermal transport predictions, novel heat transfer physics and thermally functional materials will be discovered for the pertaining energy systems and technologies.

Formation and Properties of a Self-Assembled Nanoparticle-Supported Lipid Bilayer Probed through Molecular Dynamics Simulations

We have carried out coarse-grained molecular dynamics (MD) simulations to study the self-assembly procedure of a system of randomly placed lipid molecules, water beads, and a nanoparticle (NP). The self-assembly results in the formation of the nanoparticle-supported lipid bilayer (NPSLBL), with the self-assembly mechanism being driven by events such as the formation of small lipid clusters, merging of the lipid clusters in the vicinity of the NP to form NP-embedded vesicle with a pore, and collapsing of that pore to eventually form the equilibrated NPSLBL system overcoming a large free-energy barrier. Subsequently, we quantify the properties and the configurations of this NPSLBL system. We reveal that unlike our proposition of an equal number of lipid molecules occupying the inner and outer leaflets in a recent report studying the properties of a preassembled lipid bilayer, the equilibrated self-assembled NPSLBL system demonstrates a much larger number of lipid molecules occupying the outer leaflet as compared to the inner leaflet. Second, the thickness of the water layer entrapped between the NP and the inner leaflet shows similar values as predicted by experiments and our previous study. Finally, we reveal that, similar to our previous study, the diffusivity of the lipid molecules in the outer leaflet is larger than that in the inner leaflet but, due to higher temperature employed during our simulations, are even larger than that predicted by our previous study.

Computational Nanoscopy of Tight Junctions at the Blood-Brain Barrier Interface

The selectivity of the blood-brain barrier (BBB) is primarily maintained by tight junctions (TJs), which act as gatekeepers of the paracellular space by blocking blood-borne toxins, drugs, and pathogens from entering the brain. The BBB presents a significant challenge in designing neurotherapeutics, so a comprehensive understanding of the TJ architecture can aid in the design of novel therapeutics. Unraveling the intricacies of TJs with conventional experimental techniques alone is challenging, but recently developed computational tools can provide a valuable molecular-level understanding of TJ architecture. We employed the computational methods toolkit to investigate claudin-5, a highly expressed TJ protein at the BBB interface. Our approach started with the prediction of claudin-5 structure, evaluation of stable dimer conformations and nanoscale assemblies, followed by the impact of lipid environments, and posttranslational modifications on these claudin-5 assemblies. These led to the study of TJ pores and barriers and finally understanding of ion and small molecule transport through the TJs. Some of these in silico, molecular-level findings, will need to be corroborated by future experiments. The resulting understanding can be advantageous towards the eventual goal of drug delivery across the BBB. This review provides key insights gleaned from a series of state-of-the-art nanoscale simulations (or computational nanoscopy studies) performed on the TJ architecture.

mol2sphere: spherical decomposition of multi-domain molecules for visualization and coarse grained spatial modeling

Motivation

Proteins, especially those involved in signaling pathways are composed of functional modules connected by linker domains with varying degrees of flexibility. To understand the structure-function relationships in these macromolecules, it is helpful to visualize the geometric arrangement of domains. Furthermore, accurate spatial representation of domain structure is necessary for coarse-grain models of the multi-molecular interactions that comprise signaling pathways.

Results

We introduce a new tool, mol2sphere, that transforms the atomistic structure of a macromolecule into a series of linked spheres corresponding to domains. It does this with a k-means clustering algorithm. It may be used for visualization or for coarse grain modeling and simulation.

Availability and implementation

PyMOL plugin, source, and documentation.

https://nmrbox.org/registry/mol2sphere. SpringSaLaD executables and documentation: http://vcell.org/ssalad, SpringSaLaD v.2 source: https://github.com/jmasison/SpringSaLaD.

Characterization of Lipid-Protein Interactions and Lipid-Mediated Modulation of Membrane Protein Function through Molecular Simulation

The cellular membrane constitutes one of the most fundamental compartments of a living cell, where key processes such as selective transport of material and exchange of information between the cell and its environment are mediated by proteins that are closely associated with the membrane. The heterogeneity of lipid composition of biological membranes and the effect of lipid molecules on the structure, dynamics, and function of membrane proteins are now widely recognized. Characterization of these functionally important lipid-protein interactions with experimental techniques is however still prohibitively challenging. Molecular dynamics (MD) simulations offer a powerful complementary approach with sufficient temporal and spatial resolutions to gain atomic-level structural information and energetics on lipid-protein interactions. In this review, we aim to provide a broad survey of MD simulations focusing on exploring lipid-protein interactions and characterizing lipid-modulated protein structure and dynamics that have been successful in providing novel insight into the mechanism of membrane protein function.

An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications

In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness.

CHARMM-GUI Nanodisc Builder for modeling and simulation of various nanodisc systems

Nanodiscs are discoidal protein-lipid complexes that have wide applications in membrane protein studies. Modeling and simulation of nanodiscs are challenging due to the absence of structures of many membrane scaffold proteins (MSPs) that wrap around the membrane bilayer. We have developed CHARMM-GUI Nanodisc Builder (http://www.charmm-gui.org/input/nanodisc) to facilitate the setup of nanodisc simulation systems by modeling the MSPs with defined size and known structural features. A total of 11 different nanodiscs with a diameter from 80 to 180 Å are made available in both the all-atom CHARMM and two coarse-grained (PACE and Martini) force fields. The usage of the Nanodisc Builder is demonstrated with various simulation systems. The structures and dynamics of proteins and lipids in these systems were analyzed, showing similar behaviors to those from previous all-atom and coarse-grained nanodisc simulations. We expect the Nanodisc Builder to be a convenient and reliable tool for modeling and simulation of nanodisc systems. © 2019 Wiley Periodicals, Inc.

A Practical View of the Martini Force Field

Martini is a coarse-grained (CG) force field suitable for molecular dynamics (MD) simulations of (bio)molecular systems. It is based on mapping of two to four heavy atoms to one CG particle. The effective interactions between the CG particles are parametrized to reproduce partitioning free energies of small chemical compounds between polar and apolar phases. In this chapter, a summary of the key elements of this CG force field is presented, followed by an example of practical application: a lipoplex-membrane fusion experiment. Formulated as hands-on practice, this chapter contains guidelines to build CG models of important biological systems, such as asymmetric bilayers and double-stranded DNA. Finally, a series of notes containing useful information, limitations, and tips are described in the last section.

Molecular dynamics simulations of lipid nanodiscs

A lipid nanodisc is a discoidal lipid bilayer stabilized by proteins, peptides, or polymers on its edge. Nanodiscs have two important connections to structural biology. The first is associated with high-density lipoprotein (HDL), a particle with a variety of functionalities including lipid transport. Nascent HDL (nHDL) is a nanodisc stabilized by Apolipoprotein A-I (APOA1). Determining the structure of APOA1 and its mimetic peptides in nanodiscs is crucial to understanding pathologies related to HDL maturation and designing effective therapies. Secondly, nanodiscs offer non-detergent membrane-mimicking environments and greatly facilitate structural studies of membrane proteins. Although seemingly similar, natural and synthetic nanodiscs are different in that nHDL is heterogeneous in size, due to APOA1 elasticity, and gradually matures to become spherical. Synthetic nanodiscs, in contrast, should be homogenous, stable, and size-tunable. This report reviews previous molecular dynamics (MD) simulation studies of nanodiscs and illustrates convergence and accuracy issues using results from new multi-microsecond atomistic MD simulations. These new simulations reveal that APOA1 helices take 10-20 μs to rearrange on the nanodisc, while peptides take 2 μs to migrate from the disc surfaces to the edge. These systems can also become kinetically trapped depending on the initial conditions. For example, APOA1 was trapped in a biologically irrelevant conformation for the duration of a 10 μs trajectory; the peptides were similarly trapped for 5 μs. It therefore remains essential to validate MD simulations of these systems with experiments due to convergence and accuracy issues. This article is part of a Special Issue entitled: Emergence of Complex Behavior in Biomembranes edited by Marjorie Longo.

Antimicrobial action of the cationic peptide, chrysophsin-3: a coarse-grained molecular dynamics study

Antimicrobial peptides (AMPs) are small cationic proteins that are able to destabilize a lipid bilayer structure through one or more modes of action. In this study, we investigate the processes of peptide aggregation and pore formation in lipid bilayers and vesicles by the highly cationic AMP, Chrysophsin-3 (chrys-3), using coarse-grained molecular dynamics (CG-MD) simulations and potential of mean force calculations. We study long 50 μs simulations of chrys-3 at different concentrations, both at the surface of dipalmitoylphosphatidylcholine (DPPC) and palmitoyloleoylphosphatidylcholine (POPC) bilayers, and also interacting within the interior of the lipid membrane. We show that aggregation of peptides at the surface, leads to pronounced deformation of lipid bilayers, leading in turn to lipid protrusions for peptide : ligand ratios > 1 : 12. In addition, aggregation of chrys-3 peptides within the centre of a lipid bilayer leads to spontaneous formation of pores and aggregates. Both mechanisms of interaction are consistent with previously reported experimental data for chrys-3. Similar results are observed also in POPC vesicles and mixed lipid bilayers composed of the zwitterionic lipid palmitoyloleoylphosphatidylethanolamine (POPE) and the negatively charged lipid palmitoyloleoylphosphatidylglycerol (POPG). The latter are employed as models of the bacterial membrane of Escherichia coli.

Polymer Nanodiscs: Discoidal Amphiphilic Block Copolymer Membranes as a New Platform for Membrane Proteins

Lipid nanodiscs are playing increasingly important roles in studies of the structure and function of membrane proteins. Development of lipid nanodiscs as a membrane-protein-supporting platform, or a drug targeting and delivery vehicle in general, is undermined by the fluidic and labile nature of lipid bilayers. Here, we report the discovery of polymer nanodiscs, i.e., discoidal amphiphilic block copolymer membrane patches encased within membrane scaffold proteins, as a novel two-dimensional nanomembrane that maintains the advantages of lipid nanodiscs while addressing their weaknesses. Using MsbA, a bacterial ATP-binding cassette transporter as a membrane protein prototype, we show that the protein can be reconstituted into the polymer nanodiscs in an active state. As with lipid nanodiscs, reconstitution of detergent-solubilized MsbA into the polymer nanodiscs significantly enhances its activity. In contrast to lipid nanodiscs that undergo time- and temperature-dependent structural changes, the polymer nanodiscs experience negligible structural evolution under similar environmental stresses, revealing a critically important property for the development of nanodisc-based characterization methodologies or biotechnologies. We expect that the higher mechanical and chemical stability of block copolymer membranes and their chemical versatility for adaptation will open new opportunities for applications built upon diverse membrane protein functions, or involved with drug targeting and delivery.

Adaptive Splitting Integrators for Enhancing Sampling Efficiency of Modified Hamiltonian Monte Carlo Methods in Molecular Simulation

The modified Hamiltonian Monte Carlo (MHMC) methods, i.e., importance sampling methods that use modified Hamiltonians within a Hybrid Monte Carlo (HMC) framework, often outperform in sampling efficiency standard techniques such as molecular dynamics (MD) and HMC. The performance of MHMC may be enhanced further through the rational choice of the simulation parameters and by replacing the standard Verlet integrator with more sophisticated splitting algorithms. Unfortunately, it is not easy to identify the appropriate values of the parameters that appear in those algorithms. We propose a technique, that we call MAIA (Modified Adaptive Integration Approach), which, for a given simulation system and a given time step, automatically selects the optimal integrator within a useful family of two-stage splitting formulas. Extended MAIA (or e-MAIA) is an enhanced version of MAIA, which additionally supplies a value of the method-specific parameter that, for the problem under consideration, keeps the momentum acceptance rate at a user-desired level. The MAIA and e-MAIA algorithms have been implemented, with no computational overhead during simulations, in MultiHMC-GROMACS, a modified version of the popular software package GROMACS. Tests performed on well-known molecular models demonstrate the superiority of the suggested approaches over a range of integrators (both standard and recently developed), as well as their capacity to improve the sampling efficiency of GSHMC, a noticeable method for molecular simulation in the MHMC family. GSHMC combined with e-MAIA shows a remarkably good performance when compared to MD and HMC coupled with the appropriate adaptive integrators.

Multi-scale simulations of biological systems using the OPEP coarse-grained model

Biomolecules are complex machines that are optimized by evolution to properly fulfill or contribute to a variety of biochemical tasks in the cellular environment. Computer simulations based on quantum mechanics and atomistic force fields have been proven to be a powerful microscope for obtaining valuable insights into many biological, physical, and chemical processes. Many interesting phenomena involve, however, a time scale and a number of degrees of freedom, notably if crowding is considered, that cannot be explored at an atomistic resolution. To bridge the gap between reality and simulation, many different advanced computational techniques and coarse-grained (CG) models have been developed. Here, we report some applications of the CG OPEP protein model to amyloid fibril formation, the response of catch-bond proteins to two types of fluid flow, and interactive simulations to fold peptides with well-defined 3D structures or with intrinsic disorder.

Effects of Coarse Graining and Saturation of Hydrocarbon Chains on Structure and Dynamics of Simulated Lipid Molecules

Molecular dynamics simulations are used extensively to study the processes on biological membranes. The simulations can be conducted at different levels of resolution: all atom (AA), where all atomistic details are provided; united atom (UA), where hydrogen atoms are treated inseparably of corresponding heavy atoms; and coarse grained (CG), where atoms are grouped into larger particles. Here, we study the behavior of model bilayers consisting of saturated and unsaturated lipids DOPC, SOPC, OSPC and DSPC in simulations performed using all atom CHARMM36 and coarse grained Martini force fields. Using principal components analysis, we show that the structural and dynamical properties of the lipids are similar, both in AA and CG simulations, although the unsaturated molecules are more dynamic and favor more extended conformations. We find that CG simulations capture 75 to 100% of the major collective motions, overestimate short range ordering, result in more flexible molecules and 5–7 fold faster sampling. We expect that the results reported here will be useful for comprehensive quantitative comparisons of simulations conducted at different resolution levels and for further development and improvement of CG force fields.

Interplay between Conformational Heterogeneity and Hydration in the Folding Landscape of a Designed Three-Helix Bundle

Water is known to play a critical role in protein folding and stability. Here we develop and employ a coarse-grained (CG) model to directly explore the role of water in shaping the conformational landscape explored during protein folding. For this purpose, we simulate a designed sequence with binary patterning of neutral and hydrophobic residues, which is capable of folding to a three-helix bundle in explicit water. We find two folded states of this sequence, with rotation of the helices occurring to trade between hydrophobic packing and water expulsion from the core. This work provides insight into the role of water and hydrophobicity in generating competing folded states for a protein.

Nanodiscs in Membrane Biochemistry and Biophysics

Membrane proteins play a most important part in metabolism, signaling, cell motility, transport, development, and many other biochemical and biophysical processes which constitute fundamentals of life on the molecular level. Detailed understanding of these processes is necessary for the progress of life sciences and biomedical applications. Nanodiscs provide a new and powerful tool for a broad spectrum of biochemical and biophysical studies of membrane proteins and are commonly acknowledged as an optimal membrane mimetic system that provides control over size, composition, and specific functional modifications on the nanometer scale. In this review we attempted to combine a comprehensive list of various applications of nanodisc technology with systematic analysis of the most attractive features of this system and advantages provided by nanodiscs for structural and mechanistic studies of membrane proteins.

Interplay of G Protein-Coupled Receptors with the Membrane: Insights from Supra-Atomic Coarse Grain Molecular Dynamics Simulations

G protein-coupled receptors (GPCRs) are central to many fundamental cellular signaling pathways. They transduce signals from the outside to the inside of cells in physiological processes ranging from vision to immune response. It is extremely challenging to look at them individually using conventional experimental techniques. Recently, a pseudo atomistic molecular model has emerged as a valuable tool to access information on GPCRs, more specifically on their interactions with their environment in their native cell membrane and the consequences on their supramolecular organization. This approach uses the Martini coarse grain (CG) model to describe the receptors, lipids, and solvent in molecular dynamics (MD) simulations and in enough detail to allow conserving the chemical specificity of the different molecules. The elimination of unnecessary degrees of freedom has opened up large-scale simulations of the lipid-mediated supramolecular organization of GPCRs. Here, after introducing the Martini CGMD method, we review these studies carried out on various members of the GPCR family, including rhodopsin (visual receptor), opioid receptors, adrenergic receptors, adenosine receptors, dopamine receptor, and sphingosine 1-phosphate receptor. These studies have brought to light an interesting set of novel biophysical principles. The insights range from revealing localized and heterogeneous deformations of the membrane bilayer at the surface of the protein, specific interactions of lipid molecules with individual GPCRs, to the effect of the membrane matrix on global GPCR self-assembly. The review ends with an overview of the lessons learned from the use of the CGMD method, the biophysical-chemical findings on lipid-protein interplay.

Reverse Coarse-Graining for Equation-Free Modeling: Application to Multiscale Molecular Dynamics

Constructing atom-resolved states from low-resolution data is of practical importance in many areas of science and engineering. This problem is addressed in this article in the context of multiscale factorization methods for molecular dynamics. These methods capture the crosstalk between atomic and coarse-grained scales arising in macromolecular systems. This crosstalk is accounted for by Trotter factorization, which is used to separate the all-atom from the coarse-grained phases of the computation. In this approach, short molecular dynamics runs are used to advance in time the coarse-grained variables, which in turn guide the all-atom state. To achieve this coevolution, an all-atom microstate consistent with the updated coarse-grained variables must be recovered. This recovery is cast here as a nonlinear optimization problem that is solved with a quasi-Newton method. The approach yields a Boltzmann-relevant microstate whose coarse-grained representation and some of its fine-scale features are preserved. Embedding this algorithm in multiscale factorization is shown to be accurate and scalable for simulating proteins and their assemblies.

Coarse-Grained Simulations of Membrane Insertion and Folding of Small Helical Proteins Using the CABS Model

The CABS coarse-grained model is a well-established tool for modeling globular proteins (predicting their structure, dynamics, and interactions). Here we introduce an extension of the CABS representation and force field (CABS-membrane) to the modeling of the effect of the biological membrane environment on the structure of membrane proteins. We validate the CABS-membrane model in folding simulations of 10 short helical membrane proteins not using any knowledge about their structure. The simulations start from random protein conformations placed outside the membrane environment and allow for full flexibility of the modeled proteins during their spontaneous insertion into the membrane. In the resulting trajectories, we have found models close to the experimental membrane structures. We also attempted to select the correctly folded models using simple filtering followed by structural clustering combined with reconstruction to the all-atom representation and all-atom scoring. The CABS-membrane model is a promising approach for further development toward modeling of large protein-membrane systems.

Binding kinetics of membrane-anchored receptors and ligands: Molecular dynamics simulations and theory

The adhesion of biological membranes is mediated by the binding of membrane-anchored receptor and ligand proteins. Central questions are how the binding kinetics of these proteins is affected by the membranes and by the membrane anchoring of the proteins. In this article, we (i) present detailed data for the binding of membrane-anchored proteins from coarse-grained molecular dynamics simulations and (ii) provide a theory that describes how the binding kinetics depends on the average separation and thermal roughness of the adhering membranes and on the anchoring, lengths, and length variations of the proteins. An important element of our theory is the tilt of bound receptor-ligand complexes and transition-state complexes relative to the membrane normals. This tilt results from an interplay of the anchoring energy and rotational entropy of the complexes and facilitates the formation of receptor-ligand bonds at membrane separations smaller than the preferred separation for binding. In our simulations, we have considered both lipid-anchored and transmembrane receptor and ligand proteins. We find that the binding equilibrium constant and binding on-rate constant of lipid-anchored proteins are considerably smaller than the binding constant and on-rate constant of rigid transmembrane proteins with identical binding domains.

Fast Photochemical Oxidation of Proteins Maps the Topology of Intrinsic Membrane Proteins: Light-Harvesting Complex 2 in a Nanodisc

Although membrane proteins are crucial participants in photosynthesis and other biological processes, many lack high-resolution structures. Prior to achieving a high-resolution structure, we are investigating whether MS-based footprinting can provide coarse-grained protein structure by following structural changes that occur upon ligand binding, pH change, and membrane binding. Our platform probes topology and conformation of membrane proteins by combining MS-based footprinting, specifically fast photochemical oxidation of proteins (FPOP), and lipid Nanodiscs, which are more similar to the native membrane environment than are the widely used detergent micelles. We describe here results that show a protein's outer membrane regions are more heavily footprinted by OH radicals whereas the regions spanning the lipid bilayer remain inert to the labeling. Nanodiscs generally exhibit more protection of membrane proteins compared to detergent micelles and less shielding to those protein residues that exist outside the membrane. The combination of immobilizing the protein in Nanodiscs and footprinting with FPOP is a feasible approach to map extra-membrane protein surfaces, even at the amino-acid level, and to illuminate intrinsic membrane protein topology.

Binding equilibrium and kinetics of membrane-anchored receptors and ligands in cell adhesion: Insights from computational model systems and theory

The adhesion of cell membranes is mediated by the binding of membrane-anchored receptor and ligand proteins. In this article, we review recent results from simulations and theory that lead to novel insights on how the binding equilibrium and kinetics of these proteins is affected by the membranes and by the membrane anchoring and molecular properties of the proteins. Simulations and theory both indicate that the binding equilibrium constant [Formula: see text] and the on- and off-rate constants of anchored receptors and ligands in their 2-dimensional (2D) membrane environment strongly depend on the membrane roughness from thermally excited shape fluctuations on nanoscales. Recent theory corroborated by simulations provides a general relation between [Formula: see text] and the binding constant [Formula: see text] of soluble variants of the receptors and ligands that lack the membrane anchors and are free to diffuse in 3 dimensions (3D).

Water ordering controls the dynamic equilibrium of micelle-fibre formation in self-assembly of peptide amphiphiles

Understanding the role of water in governing the kinetics of the self-assembly processes of amphiphilic peptides remains elusive. Here, we use a multistage atomistic-coarse-grained approach, complemented by circular dichroism/infrared spectroscopy and dynamic light scattering experiments to highlight the dual nature of water in driving the self-assembly of peptide amphiphiles (PAs). We show computationally that water cage formation and breakage near the hydrophobic groups control the fusion dynamics and aggregation of PAs in the micellar stage. Simulations also suggest that enhanced structural ordering of vicinal water near the hydrophilic amino acids shifts the equilibrium towards the fibre phase and stimulates structure and order during the PA assembly into nanofibres. Experiments validate our simulation findings; the measured infrared O–H bond stretching frequency is reminiscent of an ice-like bond which suggests that the solvated water becomes increasingly ordered with time in the assembled peptide network, thus shedding light on the role of water in a self-assembly process.

The role of water in the kinetics of the self-assembly process of amphiphilic peptides still remains unknown. Sankaranarayanan et al. have shown through computational study that water has a dual nature when dictating the mechanism and dynamics of self-assembly of peptide amphiphiles.

On the dynamics of molecular self-assembly and the structural analysis of bilayer membranes using coarse-grained molecular dynamics simulations

We present a molecular dynamics simulation study of the self-assembly of coarse-grained lipid molecules from unbiased random initial configurations. Our lipid model is based on a well-tried CG polymer model with an additional potential that mimics the hydrophobic properties of lipid tails. We find that several stages of self-organization of lipid clusters are involved in the dynamics of bilayer formation and that the resulting equilibrium structures sensitively depend on the strength of hydrophobic interactions hc of the lipid tails and on temperature T. The obtained stable lipid membranes are quantitatively analyzed with respect to their local structure and their degree of order. At equilibrium, we obtain self-stabilizing bilayer membrane structures that exhibit a bending stiffness κB and compression modulus KC comparable to experimental measurements under physiological conditions. We present a phase diagram of our lipid model which covers a sol-gel transition, a liquid (or gel-like) phase including stable bilayer structures and vesicle formation, as well as a quasi-crystalline phase. We also determine the exact conditions for temperature T and degree of hydrophobicity hc for stable bilayer formation including closed vesicles.

Encapsulated membrane proteins: A simplified system for molecular simulation

Over the past 50years there has been considerable progress in our understanding of biomolecular interactions at an atomic level. This in turn has allowed molecular simulation methods employing full atomistic modelling at ever larger scales to develop. However, some challenging areas still remain where there is either a lack of atomic resolution structures or where the simulation system is inherently complex. An area where both challenges are present is that of membranes containing membrane proteins. In this review we analyse a new practical approach to membrane protein study that offers a potential new route to high resolution structures and the possibility to simplify simulations. These new approaches collectively recognise that preservation of the interaction between the membrane protein and the lipid bilayer is often essential to maintain structure and function. The new methods preserve these interactions by producing nano-scale disc shaped particles that include bilayer and the chosen protein. Currently two approaches lead in this area: the MSP system that relies on peptides to stabilise the discs, and SMALPs where an amphipathic styrene maleic acid copolymer is used. Both methods greatly enable protein production and hence have the potential to accelerate atomic resolution structure determination as well as providing a simplified format for simulations of membrane protein dynamics. This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg.

Force Field Development for Lipid Membrane Simulations

With the rapid development of computer power and wide availability of modelling software computer simulations of realistic models of lipid membranes, including their interactions with various molecular species, polypeptides and membrane proteins have become feasible for many research groups. The crucial issue of the reliability of such simulations is the quality of the force field, and many efforts, especially in the latest several years, have been devoted to parametrization and optimization of the force fields for biomembrane modelling. In this review, we give account of the recent development in this area, covering different classes of force fields, principles of the force field parametrization, comparison of the force fields, and their experimental validation. This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg.

Patch formation of a viral channel forming protein within a lipid membrane – Vpu of HIV-1

Dimer-first formation leads to larger assemblies with potentially relevant structures.

Effects of protein crowding on membrane systems

Cellular membranes are typically decorated with a plethora of embedded and adsorbed macromolecules, e.g. proteins, that participate in numerous vital processes. With typical surface densities of 30,000 proteins per μm(2) cellular membranes are indeed crowded places that leave only few nanometers of private space for individual proteins. Here, we review recent advances in our understanding of protein crowding in membrane systems. We first give a brief overview on state-of-the-art approaches in experiment and simulation that are frequently used to study crowded membranes. After that, we review how crowding can affect diffusive transport of proteins and lipids in membrane systems. Next, we discuss lipid and protein sorting in crowded membrane systems, including effects like protein cluster formation, phase segregation, and lipid droplet formation. Subsequently, we highlight recent progress in uncovering crowding-induced conformational changes of membranes, e.g. membrane budding and vesicle formation. Finally, we give a short outlook on potential future developments in the field of crowded membrane systems. This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg.

Bicelles and Other Membrane Mimics: Comparison of Structure, Properties, and Dynamics from MD Simulations

The increased interest in studying membrane proteins has led to the development of new membrane mimics such as bicelles and nanodiscs. However, only limited knowledge is available of how these membrane mimics are affected by embedded proteins and how well they mimic a lipid bilayer. Herein, we present molecular dynamics simulations to elucidate structural and dynamic properties of small bicelles and compare them to a large alignable bicelle, a small nanodisc, and a lipid bilayer. Properties such as lipid packing and properties related to embedding both an α-helical peptide and a transmembrane protein are investigated. The small bicelles are found to be very dynamic and mainly assume a prolate shape substantiating that small bicelles cannot be regarded as well-defined disclike structures. However, addition of a peptide results in an increased tendency to form disc-shaped bicelles. The small bicelles and the nanodiscs show increased peptide solvation and difference in peptide orientation compared to embedding in a bilayer. The large bicelle imitated a bilayer well with respect to both curvature and peptide solvation, although peripheral binding of short tailed lipids to the embedded proteins is observed, which could hinder ligand binding or multimer formation.

Coarse-Grained Protein Model Coupled with a Coarse-Grained Water Model: Molecular Dynamics Study of Polyalanine-Based Peptides

The coupling of a coarse-grained (CG) protein model with the CG water model developed by Marrink et al. (J. Phys. Chem. B 2004, 108, 750) is presented. The model was used in the molecular dynamics studies of Ac-(Ala)6-Xaa-(Ala)7-NHMe, Xaa = Ala, Leu, Val, and Gly. A Gly mutation in the middle of polyalanine is found to destabilize the helix and stabilize the hairpin by favoring a type-II' turn and probably to speed up hairpin folding. The simulations allow us to derive thermodynamic parameters of, in particular, the helical propensities (s) of amino acids in these polyalanine-based peptides. The calculated s values are 1.18 (Ala), 0.84 (Leu), 0.30 (Val), and <0.02 (Gly) at 291 K, in excellent agreement with experimental values (R(2)=0.970). Analyses using a structural approach method show that the helical propensity difference of these amino acids mainly comes from solvation effect. Leu and Val have lower helical propensities than Ala mainly because the larger side chains shield the solvation of helical structures, while Gly has a much poorer helical propensity mainly due to the much better solvation for the coil structures than for the helical structures. Overall, the model is at least about 10(2) times faster than current all-atom MD methods with explicit solvent.