Molecular dynamics simulations of membrane proteins

Membrane proteins control the traffic across cell membranes and thereby play an essential role in cell function from transport of various solutes to immune response via molecular recognition. Because it is very difficult to determine the structures of membrane proteins experimentally, computational methods have been increasingly used to study their structure and function. Here we focus on two classes of membrane proteins—ion channels and transporters—which are responsible for the generation of action potentials in nerves, muscles, and other excitable cells. We describe how computational methods have been used to construct models for these proteins and to study the transport mechanism. The main computational tool is the molecular dynamics (MD) simulation, which can be used for everything from refinement of protein structures to free energy calculations of transport processes. We illustrate with specific examples from gramicidin and potassium channels and aspartate transporters how the function of these membrane proteins can be investigated using MD simulations.     

Membrane protein structural bioinformatics

Despite the increasing number of recently solved membrane protein structures, coverage of membrane protein fold space remains relatively sparse. This necessitates the use of computational strategies to investigate membrane protein structure, allowing us to further our understanding of how membrane proteins carry out their diverse range of functions, while aiding the development of novel predictive tools with which to probe uncharacterised folds. Analysis of known structures, the application of machine learning techniques, molecular dynamics simulations and protein structure prediction have enabled significant advances to be made in the field of membrane protein research. In this communication, the key bioinformatic methods that allow the characterisation of membrane proteins are reviewed, the tools available for the structural analysis of membrane proteins are presented and the contribution these tools have made to expanding our understanding of membrane protein structure, function and stability is discussed.     

Novel computational biology methods and their applications to drug discovery

Computational biology methods are now firmly entrenched in the drug discovery process. These methods focus on modeling and simulations of biological systems to complement and direct conventional experimental approaches. Two important branches of computational biology include protein homology modeling and the computational biophysics method of molecular dynamics. Protein modeling methods attempt to accurately predict three-dimensional (3D) structures of uncrystallized proteins for subsequent structure-based drug design applications. Molecular dynamics methods aim to elucidate the molecular motions of the static representations of crystallized protein structures. In this review we highlight recent novel methodologies in the field of homology modeling and molecular dynamics. Selected drug discovery applications using these methods conclude the review.     

Computer simulations of membrane proteins

Computer simulations are rapidly becoming a standard tool to study the structure and dynamics of lipids and membrane proteins. Increasing computer capacity allows unbiased simulations of lipid and membrane-active peptides. With the increasing number of high-resolution structures of membrane proteins, which also enables homology modelling of more structures, a wide range of membrane proteins can now be simulated over time spans that capture essential biological processes. Longer time scales are accessible by special computational methods. We review recent progress in simulations of membrane proteins.     

Recent developments in structural proteomics for protein structure determination

The major challenges in structural proteomics include identifying all the proteins on the genome-wide scale, determining their structure-function relationships, and outlining the precise three-dimensional structures of the proteins. Protein structures are typically determined by experimental approaches such as X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. However, the knowledge of three-dimensional space by these techniques is still limited. Thus, computational methods such as comparative and de novo approaches and molecular dynamic simulations are intensively used as alternative tools to predict the three-dimensional structures and dynamic behavior of proteins. This review summarizes recent developments in structural proteomics for protein structure determination; including instrumental methods such as X-ray crystallography and NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulations.     

Coarse-grained MD simulations of membrane protein-bilayer self-assembly

Complete determination of a membrane protein structure requires knowledge of the protein position within the lipid bilayer. As the number of determined structures of membrane proteins increases so does the need for computational methods which predict their position in the lipid bilayer. Here we present a coarse-grained molecular dynamics approach to lipid bilayer self-assembly around membrane proteins. We demonstrate that this method can be used to predict accurately the protein position in the bilayer for membrane proteins with a range of different sizes and architectures.     

Structure-based analyses of gut microbiome-related proteins by neural networks and molecular dynamics simulations

An imbalance in the gut microbiome is linked to immune disorders, such as autoimmune, allergic, and chronic inflammatory disorders. Elucidation of disease mechanisms is a matter of urgency. It requires precise elucidation of the structure-based mechanisms of protein interactions involved in disease onset. In addition, an understanding of the protein dynamics is vital because these fluctuations affect the function and interaction of disease-associated proteins. Experimental evaluation of not only protein interactions, functions, and structures but also the dynamics are time-consuming; therefore, computational predictions are necessary to elucidate disease mechanisms. Here, we introduce recent studies on structure-based analyses of proteins using computational approaches, particularly artificial intelligence (AI) and molecular dynamics (MD) simulations.     

Constant electric field simulations of the membrane potential illustrated with simple systems

Advances in modern computational methods and technology make it possible to carry out extensive molecular dynamics simulations of complex membrane proteins based on detailed atomic models. The ultimate goal of such detailed simulations is to produce trajectories in which the behavior of the system is as realistic as possible. A critical aspect that requires consideration in the case of biological membrane systems is the existence of a net electric potential difference across the membrane. For meaningful computations, it is important to have well validated methodologies for incorporating the latter in molecular dynamics simulations. A widely used treatment of the membrane potential in molecular dynamics consists of applying an external uniform electric field E perpendicular to the membrane. The field acts on all charged particles throughout the simulated system, and the resulting applied membrane potential V is equal to the applied electric field times the length of the periodic cell in the direction perpendicular to the membrane. A series of test simulations based on simple membrane-slab models are carried out to clarify the consequences of the applied field. These illustrative tests demonstrate that the constant-field method is a simple and valid approach for accounting for the membrane potential in molecular dynamics studies of biomolecular systems. This article is part of a Special Issue entitled: Membrane protein structure and function.     

Transmembrane helix-helix interactions: comparative simulations of the glycophorin a dimer

The glycophorin helix dimer is a paradigm for the exploration of helix-helix interactions in integral membrane proteins. Two NMR structures of the dimer are known, one in a detergent micelle and one in a lipid bilayer. Multiple (4 x 50 ns) molecular dynamics simulations starting from each of the two NMR structures, with each structure in either a dodecyl phosphocholine (DPC) micelle or a dimyristoyl phosphatidylcholine (DMPC) bilayer, have been used to explore the conformational dynamics of the helix dimer. Analysis of the helix-helix interaction, mediated by the GxxxG sequence motif, suggests convergence of the simulations to a common model. This is closer to the NMR structure determined in a bilayer than to micelle structure. The stable dimer interface in the final simulation model is characterized by (i) Gly/Gly packing and (ii) Thr/Thr interhelix H-bonds. These results demonstrate the ability of extended molecular dynamics simulations in a lipid bilayer environment to refine membrane protein structures or models derived from experimental data obtained in protein/detergent micelles.     

Piecewise All-Atom SMD Simulations Reveal Key Secondary Structures in Luciferase Unfolding Pathway

Although the folding of single-domain proteins is well characterized theoretically and experimentally, the folding of large multidomain proteins is less well known. Firefly luciferase, a 550 residue three-domain protein, has been commonly used as a substrate to study chaperone reactions and as a model system for the study of folding of long polypeptide chains, including related phenomena such as cotranslational folding. Despite being characterized by various experimental techniques, the atomic-level contributions of various secondary structures of luciferase to its fold’s mechanical stability remain unknown. Here, we developed a piecewise approach for all-atom steered molecular dynamics simulations to examine specific secondary structures that resist mechanical unfolding while minimizing the amount of computational resources required by the large water box of standard all-atom steered molecular dynamics simulations. We validated the robustness of this approach with a small NI3C protein and used our approach to elucidate the specific secondary structures that provide the largest contributions to luciferase mechanostability. In doing so, we show that piecewise all-atom steered molecular dynamics simulations can provide novel atomic resolution details regarding mechanostability and can serve as a platform for novel mutagenesis studies as well as a point for comparison with high-resolution force spectroscopy experiments.     

Structure refinement of membrane proteins via molecular dynamics simulations

A refinement protocol based on physics‐based techniques established for water soluble proteins is tested for membrane protein structures. Initial structures were generated by homology modeling and sampled via molecular dynamics simulations in explicit lipid bilayer and aqueous solvent systems. Snapshots from the simulations were selected based on scoring with either knowledge‐based or implicit membrane‐based scoring functions and averaged to obtain refined models. The protocol resulted in consistent and significant refinement of the membrane protein structures similar to the performance of refinement methods for soluble proteins. Refinement success was similar between sampling in the presence of lipid bilayers and aqueous solvent but the presence of lipid bilayers may benefit the improvement of lipid‐facing residues. Scoring with knowledge‐based functions (DFIRE and RWplus) was found to be as good as scoring using implicit membrane‐based scoring functions suggesting that differences in internal packing is more important than orientations relative to the membrane during the refinement of membrane protein homology models.     

Setting up and running molecular dynamics simulations of membrane proteins

Molecular dynamics simulations have become a popular and powerful technique to study lipids and membrane proteins. We present some general questions and issues that should be considered prior to embarking on molecular dynamics simulation studies of membrane proteins and review common simulation methods. We suggest a practical approach to setting up and running simulations of membrane proteins, and introduce two new (related) methods to embed a protein in a lipid bilayer. Both methods rely on placing lipids and the protein(s) on a widely spaced grid and then 'shrinking' the grid until the bilayer with the protein has the desired density, with lipids neatly packed around the protein. When starting from a grid based on a single lipid structure, or several potentially different lipid structures (method 1), the bilayer will start well-packed but requires more equilibration. When starting from a pre-equilibrated bilayer, either pure or mixed, most of the structure of the bilayer stays intact, reducing equilibration time (method 2). The main advantages of these methods are that they minimize equilibration time and can be almost completely automated, nearly eliminating one time consuming step in MD simulations of membrane proteins.     

A topological data analytic approach for discovering biophysical signatures in protein dynamics

Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution.     

Solid-State NMR-Restrained Ensemble Dynamics of a Membrane Protein in Explicit Membranes

Solid-state NMR has been used to determine the structures of membrane proteins in native-like lipid bilayer environments. Most structure calculations based on solid-state NMR observables are performed using simulated annealing with restrained molecular dynamics and an energy function, where all nonbonded interactions are represented by a single, purely repulsive term with no contributions from van der Waals attractive, electrostatic, or solvation energy. To our knowledge, this is the first application of an ensemble dynamics technique performed in explicit membranes that uses experimental solid-state NMR observables to obtain the refined structure of a membrane protein together with information about its dynamics and its interactions with lipids. Using the membrane-bound form of the fd coat protein as a model membrane protein and its experimental solid-state NMR data, we performed restrained ensemble dynamics simulations with different ensemble sizes in explicit membranes. For comparison, a molecular dynamics simulation of fd coat protein was also performed without any restraints. The average orientation of each protein helix is similar to a structure determined by traditional single-conformer approaches. However, their variations are limited in the resulting ensemble of structures with one or two replicas, as they are under the strong influence of solid-state NMR restraints. Although highly consistent with all solid-state NMR observables, the ensembles of more than two replicas show larger orientational variations similar to those observed in the molecular dynamics simulation without restraints. In particular, in these explicit membrane simulations, Lys⁴⁰, residing at the C-terminal side of the transmembrane helix, is observed to cause local membrane curvature. Therefore, compared to traditional single-conformer approaches in implicit environments, solid-state NMR restrained ensemble simulations in explicit membranes readily characterize not only protein dynamics but also protein-lipid interactions in detail.     

A reinterpretation of neutron scattering experiments on a lipidated Ras peptide using replica exchange molecular dynamics

The Ras family of proteins plays crucial roles in a variety of cell signaling networks where they have the function of a molecular switch. Their particular medical relevance arises from mutations in these proteins that are implicated in ~30% of human cancers. The various Ras proteins exhibit a high degree of homology in their soluble domains but extremely high variability in the membrane anchoring regions that are crucial for protein function and are the focus of this study. We have employed replica exchange molecular dynamics computer simulations to study a doubly lipidated heptapeptide, corresponding to the C-terminus of the human N-Ras protein, incorporated into a dimyristoylphosphatidylcholine lipid bilayer. This same system has previously been investigated experimentally utilizing a number of techniques, including neutron scattering. Here we present results of well converged simulations that describe the subtle changes in scattering density in terms of the location of the peptide and its lipid modifications and in terms of changes in phospholipid density arising from the incorporation of the peptide into the membrane bilayer. The detailed picture that emerges from the combination of experimental and computational data exemplifies the power of combining isotopic substitution neutron scattering with atomistic molecular dynamics simulation. This article is part of a Special Issue entitled: Membrane protein structure and function.     

Comparison of molecular dynamics simulation methods for amyloid β(1-42) monomers containing D-aspartic acid residues for predicting retention times in chromatography

Molecular dynamics simulations of amyloid β(1-42) containing D-aspartic acid residues were performed using several continuous solvent models to investigate the usefulness of simulation methods for D-amino acid-containing proteins and peptides. Normal molecular dynamics simulations and replica exchange molecular dynamics simulations, which are one of the generalized-ensemble algorithms, were performed. Because the β-structure contents of amyloid β(1-42) peptides obtained by replica exchange molecular dynamics simulations with Onufriev-Bashford-Case generalized Born implicit solvent were qualitatively consistent with experimental data, replica exchange molecular dynamics rather than other methods appeared to be more reasonable for calculations of amyloid β(1-42) containing D-aspartic acid residues. Computational results revealed that peptides with stereoinversion of Asp23 tend to form β-sheet structures by themselves, in contrast to the wild-type peptides that form β-sheet structures only after aggregation. These results are expected to be useful for computational investigations of proteins and peptides such as prediction of retention time of peptides and proteins containing D-aspartic acid residues.     

Water in protein cavities: A procedure to identify internal water and exchange pathways and application to fatty acid-binding protein

A computational approach based on Delaunay triangulation is presented to identify internal water molecules in proteins and to capture pathways of exchange with the bulk. The implemented procedure is computationally efficient and can easily be applied to long molecular dynamics trajectories of protein simulations. In an application to fatty acid-binding protein in apo-form and with bound palmitate, several protein orifices known from crystal structures have been confirmed to be major portals of solvent exchange. Differences between the two forms of the protein are observed and discussed.     

Energy Landscapes of Protein Aggregation and Conformation Switching in Intrinsically Disordered Proteins

The protein folding problem was apparently solved recently by the advent of a deep learning method for protein structure prediction called AlphaFold. However, this program is not able to make predictions about the protein folding pathways. Moreover, it only treats about half of the human proteome, as the remaining proteins are intrinsically disordered or contain disordered regions. By definition these proteins differ from natively folded proteins and do not adopt a properly folded structure in solution. However these intrinsically disordered proteins (IDPs) also systematically differ in amino acid composition and uniquely often become folded upon binding to an interaction partner. These factors preclude solving IDP structures by current machine-learning methods like AlphaFold, which also cannot solve the protein aggregation problem, since this meta-folding process can give rise to different aggregate sizes and structures. An alternative computational method is provided by molecular dynamics simulations that already successfully explored the energy landscapes of IDP conformational switching and protein aggregation in multiple cases. These energy landscapes are very different from those of ‘simple’ protein folding, where one energy funnel leads to a unique protein structure. Instead, the energy landscapes of IDP conformational switching and protein aggregation feature a number of minima for different competing low-energy structures. In this review, I discuss the characteristics of these multifunneled energy landscapes in detail, illustrated by molecular dynamics simulations that elucidated the underlying conformational transitions and aggregation processes.