Using the Wimley-White Hydrophobicity Scale as a Direct Quantitative Test of Force Fields: The MARTINI Coarse-Grained Model

Chain-Length Dependence of Peptide-Lipid Bilayer Interaction Strength and Binding Kinetics: A Combined Theoretical and Experimental Approach

Physical interactions between polypeptide chains and lipid membranes underlie critical cellular processes. Yet, despite fundamental importance, key mechanistic aspects of these interactions remain elusive. Bulk experiments have revealed a linear relationship between free energy and peptide chain length in a model system, but does this linearity extend to the interaction strength and to the kinetics of lipid binding? To address these questions, we utilized a combination of coarse-grained molecular dynamics (CG MD) simulations, analytical modeling, and atomic force microscopy (AFM)-based single molecule force spectroscopy. Following previous bulk experiments, we focused on interactions between short hydrophobic peptides (WLn, n = 1, ..., 5) with 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC) bilayers, a simple system that probes peptide primary structure effects. Potentials of mean force extracted from CG MD recapitulated the linearity of free energy with the chain length. Simulation results were quantitatively connected to bulk biochemical experiments via a single scaling factor of order unity, corroborating the methodology. Additionally, CG MD revealed an increase in the distance to the transition state, a result that weakens the dependence of the dissociation force on the peptide chain length. AFM experiments elucidated rupture force distributions and, through modeling, intrinsic dissociation rates. Taken together, the analysis indicates a rupture force plateau in the WLn-POPC system, suggesting that the final rupture event involves the last 2 or 3 residues. In contrast, the linear dependence on chain length was preserved in the intrinsic dissociation rate. This study advances the understanding of peptide-lipid interactions and provides potentially useful insights for the design of peptides with tailored membrane-interacting properties.

Deep Learning Empowers the Discovery of Self-Assembling Peptides with Over 10 Trillion Sequences

Self-assembling of peptides is essential for a variety of biological and medical applications. However, it is challenging to investigate the self-assembling properties of peptides within the complete sequence space due to the enormous sequence quantities. Here, it is demonstrated that a transformer-based deep learning model is effective in predicting the aggregation propensity (AP) of peptide systems, even for decapeptide and mixed-pentapeptide systems with over 10 trillion sequence quantities. Based on the predicted AP values, not only the aggregation laws for designing self-assembling peptides are derived, but the transferability relation among the APs of pentapeptides, decapeptides, and mixed pentapeptides is also revealed, leading to discoveries of self-assembling peptides by concatenating or mixing, as consolidated by experiments. This deep learning approach enables speedy, accurate, and thorough search and design of self-assembling peptides within the complete sequence space of oligopeptides, advancing peptide science by inspiring new biological and medical applications.

Development, Validation, and Applications of Nonbonded Interaction Parameters between Coarse-Grained Amino Acid and Water Models

Interactions between amino acids and water play an important role in determining the stability and folding/unfolding, in aqueous solution, of many biological macromolecules, which affects their function. Thus, understanding the molecular-level interactions between water and amino acids is crucial to tune their function in aqueous solutions. Herein, we have developed nonbonded interaction parameters between the coarse-grained (CG) models of 20 amino acids and the one-site CG water model. The nonbonded parameters, represented using the 12-6 Lennard Jones (LJ) potential form, have been optimized using an artificial neural network (ANN)-assisted particle swarm optimization (PSO) (ANN-assisted PSO) method. All-atom (AA) molecular dynamics (MD) simulations of dipeptides in TIP3P water molecules were performed to calculate the Gibbs hydration free energies. The nonbonded force-field (FF) parameters between CG amino acids and the one-site CG water model were developed to accurately reproduce these energies. Furthermore, to test the transferability of these newly developed parameters, we calculated the hydration free energies of the analogues of the amino acid side chains, which showed good agreement with reported experimental data. Additionally, we show the applicability of these models by performing self-assembly simulations of peptide amphiphiles. Overall, these models are transferable and can be used to study the self-assembly of various biomaterials and biomolecules to develop a mechanistic understanding of these processes.

An implementation of the Martini coarse-grained force field in OpenMM

We describe a complete implementation of Martini 2 and Martini 3 in the OpenMM molecular dynamics software package. Martini is a widely used coarse-grained force field with applications in biomolecular simulation, materials, and broader areas of chemistry. It is implemented as a force field but makes extensive use of facilities unique to the GROMACS software, including virtual sites and bonded terms that are not commonly used in standard atomistic force fields. OpenMM is a flexible molecular dynamics package widely used for methods development and is competitive in speed on GPUs with other commonly used packages. OpenMM has facilities to easily implement new force field terms, external forces and fields, and other nonstandard features, which we use to implement all force field terms used in Martini 2 and Martini 3. This allows Martini simulations, starting with GROMACS topology files that are processed by custom scripts, with all the added flexibility of OpenMM. We provide a GitHub repository with test cases, compare accuracy and performance between GROMACS and OpenMM, and discuss the limitations of our implementation in terms of direct comparison with GROMACS. We describe a use case that implements the Modeling Employing Limited Data method to apply experimental constraints in a Martini simulation to efficiently determine the structure of a protein complex. We also discuss issues and a potential solution with the Martini 2 topology for cholesterol.

Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop

The amino acid sequences of peptides determine their self-assembling properties. Accurate prediction of peptidic hydrogel formation, however, remains a challenging task. This work describes an interactive approach involving the mutual information exchange between experiment and machine learning for robust prediction and design of (tetra)peptide hydrogels. We chemically synthesize more than 160 natural tetrapeptides and evaluate their hydrogel-forming ability, and then employ machine learning-experiment iterative loops to improve the accuracy of the gelation prediction. We construct a score function coupling the aggregation propensity, hydrophobicity, and gelation corrector Cg, and generate an 8,000-sequence library, within which the success rate of predicting hydrogel formation reaches 87.1%. Notably, the de novo-designed peptide hydrogel selected from this work boosts the immune response of the receptor binding domain of SARS-CoV-2 in the mice model. Our approach taps into the potential of machine learning for predicting peptide hydrogelator and significantly expands the scope of natural peptide hydrogels.

Short Peptide Self-Assembly in the Martini Coarse-Grain Force Field Family

ConspectusPivotal to the success of any computational experiment is the ability to make reliable predictions about the system under study and the time required to yield these results. Biomolecular interactions is one area of research that sits in every camp of resolution vs the time required, from the quantum mechanical level to in vivo studies. At an approximate midpoint, there is coarse-grained molecular dynamics, for which the Martini force fields have become the most widely used, fast enough to simulate the entire membrane of a mitochondrion though lacking atom-specific precision. While many force fields have been parametrized to account for a specific system under study, the Martini force field has aimed at casting a wider net with more generalized bead types that have demonstrated suitability for broad use and reuse in applications from protein-graphene oxide coassembly to polysaccharides interactions.In this Account, the progressive (Martini versions 1 through 3) and peripheral (Sour Martini, constant pH, Martini Straight, Dry Martini, etc.) developmental trajectory of the Martini force field will be analyzed in terms of self-assembling systems with a focus on short (two to three amino acids) peptide self-assembly in aqueous environments. In particular, this will focus on the effects of the Martini solvent model and compare how changes in bead definitions and mapping have effects on different systems. Considerable effort in the development of Martini has been expended to reduce the "stickiness" of amino acids to better simulate proteins in bilayers. We have included in this Account a short study of dipeptide self-assembly in water, using all mainstream Martini force fields, to examine their ability to reproduce this behavior. The three most recently released versions of Martini and variations in their solvents are used to simulate in triplicate all 400 dipeptides of the 20 gene-encoded amino acids. The ability of the force fields to model the self-assembly of the dipeptides in aqueoues environments is determined by the measurement of the aggregation propensity, and additional descriptors are used to gain further insight into the dipeptide aggregates.

Machine learning overcomes human bias in the discovery of self-assembling peptides

Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias. Here we introduce a machine learning workflow-AI-expert-that combines Monte Carlo tree search and random forest with molecular dynamics simulations to develop a fully autonomous computational search engine to discover peptide sequences with high potential for self-assembly. We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. The predictability of AI-expert performs on par or better than our human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.

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.

Effects of Linker Flexibility and Conformational Changes of IP3 Receptor on Split Luciferase Complementation Assay

Background:

IP3-induced Ca2+ release, mediated by IP3R, is one of the most momentous cellular signaling mechanisms that regulate in a wide variety of essential cellular functions. Involvement of disrupted IP3 signaling pathways in numerous pathophysiology conditions is implicated to find the best methods for its measurement. Hence, several different biosensors have developed to monitor temporal changes of IP3 by using the IP3-binding domain of IP3 receptors.

Objectives:

Based on a previous study, we developed and characterized a series of bioluminescent biosensors using the human type-II IP3 receptor ligand binding domain (residues 1–604), named LAIRE (luminescent analyzer for IP3 receptor element) to study the effect of flexible and rigid linkers on the luminescence intensity of split luciferase. The effect of a mutation in IP3 binding residues and suppressor domain in the IP3 binding domain on luciferase complementary assay is also investigated.

Materials and Methods:

In the present study, first IP3-binding domain (residues 1-604) of IP3-receptor type 2 (LAIRE) was fused between complementary non-functional fragments of firefly luciferase and then the rigid linker sequence (LLRAIEAQQHLL), selected by ProDA database, introduced between Nluc and ligand binding domain and compared with that of the flexible linker ((GGGGS)2) in LAIRE chimera. The IP3-insensitive mutant of the biosensor was constructed using the Stratagene QuikChange® procedure. In order to the analysis of the dynamical movements of selected structures in the large-scale, coarse-graining method of molecular dynamics simulation (1µs) was applied.

Results:

As expected, the flexible linker brings two inactive fragments of luciferase together relative to the rigid linker and leads to complementation of luciferase activity, which is detected using luciferin. However, this conformational flexibility in linker increases background to noise ratio and attenuates fold induction.

Conclusions:

It seems that the ligand binding properties of IP3 binding core make it more suitable for the design of biosensor than the ligand binding domain.

Molecular Dynamics Simulations of Membrane Proteins: An Overview

Simulations of membrane proteins have been rising in popularity in the past decade. Advancements in technology and force fields made it possible to simulate behavior of membrane proteins. Membrane protein simulations can now be used as supporting evidence for experimental findings, for elucidating protein mechanisms, and validating protein crystal structures. Unrelated to experimental data, these simulations can also serve to investigate larger scale processes like protein sorting, protein-membrane interactions, and more. In this review, the history as well as the state-of-the-art methodologies in membrane protein simulations will be summarized. An emphasis will be put on how to set up the system and on the current models for the different components of the simulation system. An overview of the available tools for membrane protein simulation will be given, and current limitations and prospects will also be discussed.

Refining amino acid hydrophobicity for dynamics simulation of membrane proteins

Coarse-grained (CG) models have been successful in simulating the chemical properties of lipid bilayers, but accurate treatment of membrane proteins and lipid-protein molecular interactions remains a challenge. The CgProt force field, original developed with the multiscale coarse graining method, is assessed by comparing the potentials of mean force for sidechain insertion in a DOPC bilayer to results reported for atomistic molecular dynamics simulations. Reassignment of select CG sidechain sites from the apolar to polar site type was found to improve the attractive interfacial behavior of tyrosine, phenylalanine and asparagine as well as charged lysine and arginine residues. The solvation energy at membrane depths of 0, 1.3 and 1.7 nm correlates with experimental partition coefficients in aqueous mixtures of cyclohexane, octanol and POPC, respectively, for sidechain analogs and Wimley-White peptides. These experimental values serve as important anchor points in choosing between alternate CG models based on their observed permeation profiles, particularly for Arg, Lys and Gln residues where the all-atom OPLS solvation energy does not agree well with experiment. Available partitioning data was also used to reparameterize the representation of the peptide backbone, which needed to be made less attractive for the bilayer hydrophobic core region. The newly developed force field, CgProt 2.4, correctly predicts the global energy minimum in the potentials of mean force for insertion of the uncharged membrane-associated peptides LS3 and WALP23. CgProt will find application in studies of lipid-protein interactions and the conformational properties of diverse membrane protein systems.

Parameterization of Palmitoylated Cysteine, Farnesylated Cysteine, Geranylgeranylated Cysteine, and Myristoylated Glycine for the Martini Force Field

Peripheral membrane proteins go through various post-translational modifications that covalently bind fatty acid tails to specific amino acids. These post-translational modifications significantly alter the lipophilicity of the modified proteins and allow them to anchor to biological membranes. Over 1000 different proteins have been identified to date that require such membrane-protein interactions to carry out their biological functions, including members of the Src and Ras superfamilies that play key roles in cell signaling and carcinogenesis. We have used all-atom simulations with the CHARMM36 force field to parameterize four of the most common post-translational modifications for the Martini 2.2 force field: palmitoylated cysteine, farnesylated cysteine, geranylgeranylated cysteine, and myristoylated glycine. The parameters reproduce the key features of clusters of configurations of the different anchors in lipid membranes as well as the water-octanol partitioning free energies of the anchors, which are crucial for the correct reproduction of the expected biophysical behavior of peripheral membrane proteins at the membrane-water interface. Implementation in existing Martini setup tools facilitates the use of the new parameters.

Critical Comparison of Biomembrane Force Fields: Protein-Lipid Interactions at the Membrane Interface

Molecular dynamics (MD) simulations offer the possibility to study biological processes at high spatial and temporal resolution often not reachable by experiments. Corresponding biomolecular force field parameters have been developed for a wide variety of molecules ranging from inorganic ligands and small organic molecules over proteins and lipids to nucleic acids. Force fields have typically been parametrized and validated on thermodynamic observables and structural characteristics of individual compounds, e.g. of soluble proteins or lipid bilayers. Less strictly, due to the added complexity and missing experimental data to compare to, force fields have hardly been tested on the properties of mixed systems, e.g. on protein-lipid systems. Their selection and combination for mixed systems is further complicated by the partially differing parametrization strategies. Additionally, the presence of other compounds in the system may shift the subtle balance of force field parameters. Here, we assessed the protein-lipid interactions as described in the four atomistic force fields GROMOS54a7, CHARMM36 and the two force field combinations Amber14sb/Slipids and Amber14sb/Lipid14. Four observables were compared, focusing on the membrane-water interface: the conservation of the secondary structure of transmembrane proteins, the positioning of transmembrane peptides relative to the lipid bilayer, the insertion depth of side chains of unfolded peptides absorbed at the membrane interface, and the ability to reproduce experimental insertion energies of Wimley-White peptides at the membrane interface. Significant differences between the force fields were observed that affect e.g. membrane insertion depths and tilting of transmembrane peptides.

Influence of membrane composition on the binding and folding of a membrane lytic peptide from the non-enveloped flock house virus

Using a combination of coarse-grained and atomistic molecular dynamics simulations we have investigated the membrane binding and folding properties of the membrane lytic peptide of Flock House virus (FHV). FHV is an animal virus and an excellent model system for studying cell entry mechanisms in non-enveloped viruses. FHV undergoes a maturation event where the 44 C-terminal amino acids are cleaved from the major capsid protein, forming the membrane lytic (γ) peptides. Under acidic conditions, γ is released from the capsid interior allowing the peptides to bind and disrupt membranes. The first 21 N-terminal residues of γ, termed γ₁, have been resolved in the FHV capsid structure and γ₁ has been the subject of in vitro studies. γ₁ is structurally dynamic as it adopts helical secondary structure inside the capsid and on membranes, but it is disordered in solution. In vitro studies have shown the binding free energies to POPC or POPG membranes are nearly equivalent, but binding to POPC is enthalpically driven, while POPG binding is entropically driven. Through coarse-grained and multiple microsecond all-atom simulations the membrane binding and folding properties of γ₁ are investigated against homogeneous and heterogeneous bilayers to elucidate the dependence of the microenvironment on the structural properties of γ₁. Our studies provide a rationale for the thermodynamic data and suggest binding of γ₁ to POPG bilayers occurs in a disordered state, but γ₁ must adopt a helical conformation when binding POPC bilayers.

Coarse-grained simulations of hemolytic peptide δ-lysin interacting with a POPC bilayer

δ-lysin, secreted by a Gram-positive bacterium Staphylococcus aureus, is a 26-residue membrane active peptide that shares many common features with antimicrobial peptides (AMPs). However, it possesses a few unique features that differentiate itself from typical AMPs. In particular, δ-lysin has zero net charge, even though it has many charged residues, and it preferentially lyses eukaryotic cells over bacterial cells. Here, we present the results of coarse-grained molecular dynamics simulations of δ-lysin interacting with a zwitterionic membrane over a wide range of peptide concentrations. When the peptides concentration is low, spontaneous dimerization of peptides is observed on the membrane surface, but deep insertion of peptides or pore formation was not observed. However, the calculated free energy of peptide insertion suggests that a small fraction of peptides is likely to be present inside the membrane at the peptide concentrations typically seen in dye efflux experiments. When the simulations with multiple peptides are carried out with a single pre-inserted transmembrane peptide, spontaneous pore formation occurs with a peptide-to-lipid ratio (P/L) as low as P/L=1:42. Inter-peptide salt bridges among the transmembrane peptides seem to play a role in creating compact pores with very low level of hydration. More importantly, the transmembrane peptides making up the pore are constantly pushed to the opposite side of the membrane when the mass imbalance between the two sides of membrane is significant. Thus, the pore is very dynamic, allowing multiple peptides to translocate across the membrane simultaneously.

Ganglioside-Lipid and Ganglioside-Protein Interactions Revealed by Coarse-Grained and Atomistic Molecular Dynamics Simulations

Gangliosides are glycolipids in which an oligosaccharide headgroup containing one or more sialic acids is connected to a ceramide. Gangliosides reside in the outer leaflet of the plasma membrane and play a crucial role in various physiological processes such as cell signal transduction and neuronal differentiation by modulating structures and functions of membrane proteins. Because the detailed behavior of gangliosides and protein-ganglioside interactions are poorly known, we investigated the interactions between the gangliosides GM1 and GM3 and the proteins aquaporin (AQP1) and WALP23 using equilibrium molecular dynamics simulations and potential of mean force calculations at both coarse-grained (CG) and atomistic levels. In atomistic simulations, on the basis of the GROMOS force field, ganglioside aggregation appears to be a result of the balance between hydrogen bond interactions and steric hindrance of the headgroups. GM3 clusters are slightly larger and more ordered than GM1 clusters due to the smaller headgroup of GM3. The different structures of GM1 and GM3 clusters from atomistic simulations are not observed at the CG level based on the Martini model, implying a difference in driving forces for ganglioside interactions in atomistic and CG simulations. For protein-ganglioside interactions, in the atomistic simulations, GM1 lipids bind to specific sites on the AQP1 surface, whereas they are depleted from WALP23. In the CG simulations, the ganglioside binding sites on the AQP1 surface are similar, but ganglioside aggregation and protein-ganglioside interactions are more prevalent than in the atomistic simulations. Using the polarizable Martini water model, results were closer to the atomistic simulations. Although experimental data for validation is lacking, we proposed modified Martini parameters for gangliosides to more closely mimic the sizes and structures of ganglioside clusters observed at the atomistic level.

Thermodynamics of cell-penetrating HIV1 TAT peptide insertion into PC/PS/CHOL model bilayers through transmembrane pores: the roles of cholesterol and anionic lipids

Efficient delivery of pharmaceutically active molecules across cellular membranes using cell penetrating peptides (CPPs), such as the cationic human immunodeficiency virus-1 trans-acting activator of transcription peptide (HIV-1 TAT), continues to attract scientific attention in drug design and disease treatment. Experimental results show that the TAT peptide is not only capable of directly penetrating the biological membrane in a passive manner, but also forming physical, membrane-spanning pores that may facilitate transport. Experiments further show that anionic lipids accelerate peptide permeation within a range of mole percentage composition. In this work, we explored the structures and translocation thermodynamics of the cationic TAT peptide across a series of DPPC/DPPS model membranes with the presence of 0-30 mol% cholesterol. We computed the potentials of the mean force by using umbrella sampling molecular dynamics simulations coupled to the Martini coarse-grained force field. We systematically investigated the roles of cholesterol and anionic lipids (membrane surface charge) in TAT peptide translocation. In qualitative agreement with experimental findings, the barrier heights were significantly reduced in the presence of anionic lipids. A toroidal hydrophilic pore was strongly suggested by membrane structure analysis. Cholesterol stabilizes the liquid-ordered (Lo) phase of membranes and increases the elastic stiffness of bilayers. Consequently, it hinders transmembrane pore formation and thus modulates solute permeability, since the liquid-ordered phase suppresses reorientation of the lipid molecules on simulation time scales. Though cholesterol contributes marginally to the total free energy associated with peptide permeation, the coordination of cholesterol to the peptide weakens more favorable peptide-lipid interactions. The addition of the anionic lipid DPPS to the neutral DPPC bilayer leads to the emergence and further enhancement of an interfacially stable state of the peptide due to the favorable peptide-anionic lipid interactions. Translocation free energy barriers decrease in lockstep with increasing DPPS composition in the model bilayers simulated. Finally, we investigated the size of hydrophilic pores emerging in our simulations, as well as the qualitative mobility of the peptide on the membrane surface.

Thermodynamics of Micelle Formation and Membrane Fusion Modulate Antimicrobial Lipopeptide Activity

Antimicrobial lipopeptides (AMLPs) are antimicrobial drug candidates that preferentially target microbial membranes. One class of AMLPs, composed of cationic tetrapeptides attached to an acyl chain, have minimal inhibitory concentrations in the micromolar range against a range of bacteria and fungi. Previously, we used coarse-grained molecular dynamics simulations and free energy methods to study the thermodynamics of their interaction with membranes in their monomeric state. Here, we extended the study to the biologically relevant micellar state, using, to our knowledge, a novel reaction coordinate based on hydrophobic contacts. Using umbrella sampling along this reaction coordinate, we identified the critical transition states when micelles insert into membranes. The results indicate that the binding of these AMLP micelles to membranes is thermodynamically favorable, but in contrast to the monomeric case, there are significant free energy barriers. The height of these free energy barriers depends on the membrane composition, suggesting that the AMLPs' ability to selectively target bacterial membranes may be as much kinetic as thermodynamic. This mechanism highlights the importance of considering oligomeric state in solution as criterion when optimizing peptides or lipopeptides as antibiotic leads.

Association of Peripheral Membrane Proteins with Membranes: Free Energy of Binding of GRP1 PH Domain with Phosphatidylinositol Phosphate-Containing Model Bilayers

Understanding the energetics of peripheral protein-membrane interactions is important to many areas of biophysical chemistry and cell biology. Estimating free-energy landscapes by molecular dynamics (MD) simulation is challenging for such systems, especially when membrane recognition involves complex lipids, e.g., phosphatidylinositol phosphates (PIPs). We combined coarse-grained MD simulations with umbrella sampling to quantify the binding of the well-explored GRP1 pleckstrin homology (PH) domain to model membranes containing PIP molecules. The experimentally observed preference of GRP1-PH for PIP3 over PIP2 was reproduced. Mutation of a key residue (K273A) within the canonical PIP-binding site significantly reduced the free energy of PIP binding. The presence of a noncanonical PIP-interaction site, observed experimentally in other PH domains but not previously in GRP1-PH, was also revealed. These studies demonstrate how combining coarse-grained simulations and umbrella sampling can unmask the molecular basis of the energetics of interactions between peripheral membrane proteins and complex cellular membranes.

Amphiphilic Peptides A6K and V6K Display Distinct Oligomeric Structures and Self-Assembly Dynamics: A Combined All-Atom and Coarse-Grained Simulation Study

Amphiphilic peptides can self-assemble into ordered nanostructures with different morphologies. However, the assembly mechanism and the structures of the early assemblies prior to nanostructure formation remain elusive. In this study, we investigated the oligomeric structures of two amphiphilic heptapeptides A6K and V6K by all-atom explicit-solvent replica-exchange molecular dynamics (REMD) simulations, and then examined the assembly dynamics of large aggregates by coarse-grained (CG) MD simulations. Our 200 ns REMD simulations show that A6K peptides predominantly adopt loosely packed disordered coil aggregates, whereas V6K peptides mostly assemble into compact β-sheet-rich conformations, consistent with the signal measured experimentally in aqueous solution. Well-organized β-sheet-rich conformations, albeit with low population, are also populated for V6K octamers, including bilayer β-sheets and β-barrels. These ordered β-sheet-rich conformations are observed for the first time for amphiphilic peptides. Our 10-μs CG-MD simulations on 200 peptide chains demonstrate that A6K and V6K peptides follow two different self-assembly processes, and the former form monolayer lamellas while the latter assemble into plate-like assemblies. CG-MD simulations also show that V6K peptides display higher assembly capability than A6K, in support of our all-atom REMD simulation results. Interpeptide interaction analyses reveal that the marked differences in oligomeric structures and assembly dynamics between A6K and V6K result from the subtle interplay of competition among hydrophobic, hydrogen-bonding, and electrostatic interactions of the two peptides. Our study provides structural and mechanistic insights into the initial self-assembly process of A6K and V6K at the molecular level.

Thermodynamics of antimicrobial lipopeptide binding to membranes: origins of affinity and selectivity

The development of novel antibiotic drugs is one of the most pressing biomedical problems due to the increasing number of antibiotic-resistant pathogens. Antimicrobial peptides and lipopeptides are a promising category of candidates, but the molecular origins of their antimembrane activity is unclear. Here we explore a series of recently developed antimicrobial lipopeptides, using coarse-grained molecular-dynamics simulations and free energy methods to uncover the thermodynamics governing their binding to membranes. Specifically, we quantify C16-KGGK's binding affinity to the two types of membrane by umbrella sampling. We also examined the origin of C16-KGGK's selectivity for bacterial versus mammalian membranes by systematically varying the peptide sequence and salt concentration. Our data showed that the C16 hydrophobic tail is the main contributor to its affinity to lipid membrane, whereas the peptide portion is mainly responsible for its selectivity. Furthermore, the electrostatic interaction between the cationic peptide and anionic bacterial membrane plays a significant role in the selectivity.

Exploring the sequence space for (tri-)peptide self-assembly to design and discover new hydrogels

Peptides that self-assemble into nanostructures are of tremendous interest for biological, medical, photonic and nanotechnological applications. The enormous sequence space that is available from 20 amino acids probably harbours many interesting candidates, but it is currently not possible to predict supramolecular behaviour from sequence alone. Here, we demonstrate computational tools to screen for the aqueous self-assembly propensity in all of the 8,000 possible tripeptides and evaluate these by comparison with known examples. We applied filters to select for candidates that simultaneously optimize the apparently contradicting requirements of aggregation propensity and hydrophilicity, which resulted in a set of design rules for self-assembling sequences. A number of peptides were subsequently synthesized and characterized, including the first reported tripeptides that are able to form a hydrogel at neutral pH. These tools, which enable the peptide sequence space to be searched for supramolecular properties, enable minimalistic peptide nanotechnology to deliver on its promise.

Reconciling structural and thermodynamic predictions using all-atom and coarse-grain force fields: the case of charged oligo-arginine translocation into DMPC bilayers

Using the translocation of short, charged cationic oligo-arginine peptides (mono-, di-, and triarginine) from bulk aqueous solution into model DMPC bilayers, we explore the question of the similarity of thermodynamic and structural predictions obtained from molecular dynamics simulations using all-atom and Martini coarse-grain force fields. Specifically, we estimate potentials of mean force associated with translocation using standard all-atom (CHARMM36 lipid) and polarizable and nonpolarizable Martini force fields, as well as a series of modified Martini-based parameter sets. We find that we are able to reproduce qualitative features of potentials of mean force of single amino acid side chain analogues into model bilayers. In particular, modifications of peptide-water and peptide-membrane interactions allow prediction of free energy minima at the bilayer-water interface as obtained with all-atom force fields. In the case of oligo-arginine peptides, the modified parameter sets predict interfacial free energy minima as well as free energy barriers in almost quantitative agreement with all-atom force field based simulations. Interfacial free energy minima predicted by a modified coarse-grained parameter set are -2.51, -4.28, and -5.42 for mono-, di-, and triarginine; corresponding values from all-atom simulations are -0.83, -3.33, and -3.29, respectively, all in units of kcal/mol. We found that a stronger interaction between oligo-arginine and the membrane components and a weaker interaction between oligo-arginine and water are crucial for producing such minima in PMFs using the polarizable CG model. The difference between bulk aqueous and bilayer center states predicted by the modified coarse-grain force field are 11.71, 14.14, and 16.53 kcal/mol, and those by the all-atom model are 6.94, 8.64, and 12.80 kcal/mol; those are of almost the same order of magnitude. Our simulations also demonstrate a remarkable similarity in the structural aspects of the ensemble of configurations generated using the all-atom and coarse-grain force fields. Both resolutions show that oligo-arginine peptides adopt preferential orientations as they translocate into the bilayer. The guiding theme centers on charged groups maintaining coordination with polar and charged bilayer components as well as local water. We also observe similar behaviors related with membrane deformations.

Recent advances in transferable coarse-grained modeling of proteins

Computer simulations are indispensable tools for studying the structure and dynamics of biological macromolecules. Biochemical processes occur on different scales of length and time. Atomistic simulations cannot cover the relevant spatiotemporal scales at which the cellular processes occur. To address this challenge, coarse-grained (CG) modeling of the biological systems is employed. Over the last few years, many CG models for proteins continue to be developed. However, many of them are not transferable with respect to different systems and different environments. In this review, we discuss those CG protein models that are transferable and that retain chemical specificity. We restrict ourselves to CG models of soluble proteins only. We also briefly review recent progress made in the multiscale hybrid all-atom/CG simulations of proteins.

The power of coarse graining in biomolecular simulations

Computational modeling of biological systems is challenging because of the multitude of spatial and temporal scales involved. Replacing atomistic detail with lower resolution, coarse grained (CG), beads has opened the way to simulate large-scale biomolecular processes on time scales inaccessible to all-atom models. We provide an overview of some of the more popular CG models used in biomolecular applications to date, focusing on models that retain chemical specificity. A few state-of-the-art examples of protein folding, membrane protein gating and self-assembly, DNA hybridization, and modeling of carbohydrate fibers are used to illustrate the power and diversity of current CG modeling.

Translocation thermodynamics of linear and cyclic nonaarginine into model DPPC bilayer via coarse-grained molecular dynamics simulation: implications of pore formation and nonadditivity

Structural mechanisms and underlying thermodynamic determinants of efficient internalization of charged cationic peptides (cell-penetrating peptides, CPPs) such as TAT, polyarginine, and their variants, into cells, cellular constructs, and model membrane/lipid bilayers (large and giant unilamellar or multilamelar vesicles) continue to garner significant attention. Two widely held views on the translocation mechanism center on endocytotic and nonendocytotic (diffusive) processes. Espousing the view of a purely diffusive internalization process (supported by recent experimental evidence, [Säälik, P.; et al. J. Controlled Release2011, 153, 117–125]), we consider the underlying free energetics of the translocation of a nonaarginine peptide (Arg₉) into a model DPPC bilayer. In the case of the Arg₉ cationic peptide, recent experiments indicate a higher internalization efficiency of the cyclic structure (cyclic Arg₉) relative to the linear conformer. Furthermore, recent all-atom resolution molecular dynamics simulations of cyclic Arg₉ [Huang, K.; et al. Biophys. J., 2013, 104, 412–420] suggested a critical stabilizing role of water- and lipid-constituted pores that form within the bilayer as the charged Arg₉ translocates deep into the bilayer center. Herein, we use umbrella sampling molecular dynamics simulations with coarse-grained Martini lipids, polarizable coarse-grained water, and peptide to explore the dependence of translocation free energetics on peptide structure and conformation via calculation of potentials of mean force along preselected reaction paths allowing and preventing membrane deformations that lead to pore formation. Within the context of the coarse-grained force fields we employ, we observe significant barriers for Arg₉ translocation from bulk aqueous solution to bilayer center. Moreover, we do not find free-energy minima in the headgroup–water interfacial region, as observed in simulations using all-atom force fields. The pore-forming paths systematically predict lower free-energy barriers (ca. 90 kJ/mol lower) than the non pore-forming paths, again consistent with all-atom force field simulations. The current force field suggests no preference for the more compact or covalently cyclic structures upon entering the bilayer. Decomposition of the PMF into the system’s components indicates that the dominant stabilizing contribution along the pore-forming path originates from the membrane as both layers of it deformed due to the formation of pore. Furthermore, our analysis revealed that although there is significant entropic stabilization arising from the enhanced configurational entropy exposing more states as the peptide moves through the bilayer, the enthalpic loss (as predicted by the interactions of this coarse-grained model) far outweighs any former stabilization, thus leading to significant barrier to translocation. Finally, we observe reduction in the translocation free-energy barrier for a second Arg₉ entering the bilayer in the presence of an initial peptide restrained at the center, again, in qualitative agreement with all-atom force fields.

Balancing bond, nonbond, and gō-like terms in coarse grain simulations of conformational dynamics

Characterization of the protein conformational landscape remains a challenging problem, whether it concerns elucidating folding mechanisms, predicting native structures or modeling functional transitions. Coarse-grained molecular dynamics simulation methods enable exhaustive sampling of the energetic landscape at resolutions of biological interest. The general utility of structure-based models is reviewed along with their differing levels of approximation. Simple Gō models incorporate attractive native interactions and repulsive nonnative contacts, resulting in an ideal smooth landscape. Non-Gō coarse-grained models reduce the parameter set as needed but do not include bias to any desired native structure. While non-Gō models have achieved limited success in protein coarse-graining, they can be combined with native structured-based potentials to create a balanced and powerful force field. Recent applications of such Gō-like models have yielded insight into complex folding mechanisms and conformational transitions in large macromolecules. The accuracy and usefulness of reduced representations are also revealed to be a function of the mathematical treatment of the intrinsic bonded topology.

Aggregation of lipid-anchored full-length H-Ras in lipid bilayers: simulations with the MARTINI force field

Lipid-anchored Ras oncoproteins assemble into transient, nano-sized substructures on the plasma membrane. These substructures, called nanoclusters, were proposed to be crucial for high-fidelity signal transmission in cells. However, the molecular basis of Ras nanoclustering is poorly understood. In this work, we used coarse-grained (CG) molecular dynamics simulations to investigate the molecular mechanism by which full-length H-ras proteins form nanoclusters in a model membrane. We chose two different conformations of H-ras that were proposed to represent the active and inactive state of the protein, and a domain-forming model bilayer made up of di16:0-PC (DPPC), di18:2-PC (DLiPC) and cholesterol. We found that, irrespective of the initial conformation, Ras molecules assembled into a single large aggregate. However, the two binding modes, which are characterized by the different orientation of the G-domain with respect to the membrane, differ in dynamics and organization during and after aggregation. Some of these differences involve regions of Ras that are important for effector/modulator binding, which may partly explain observed differences in the ability of active and inactive H-ras nanoclusters to recruit effectors. The simulations also revealed some limitations in the CG force field to study protein assembly in solution, which we discuss in the context of proposed potential avenues of improvement.

Effects of flanking loops on membrane insertion of transmembrane helices: a role for peptide conformational equilibrium

The ability of a transmembrane helix (TMH) to insert into a lipid bilayer has been mainly understood based on the total hydrophobicity of the peptide sequence. Recently, Hedin et al. investigated the influence of flanking loops on membrane insertion of a set of marginally hydrophobic TMHs using translocon-based membrane integration assays. While the flanking loops were found to facilitate the insertion in most cases, counter examples also emerged where the flanking loops hinder membrane insertion and contradict the hydrophobicity and charge distribution analyses. Here, coarse-grained free energy calculations and atomistic simulations were performed to investigate the energetics and conformational details of the membrane insertion of two representative marginally hydrophobic TMHs with (NhaL and EmrL) and without (NhaA and EmrD) the flanking loops. The simulations fail to directly recapitulate the contrasting effects of the flanking loops for these two TMHs, due to systematic overprediction of the stabilities of the transmembrane states that has also been consistently observed in previous studies. Nonetheless, detailed force decomposition and peptide conformation analyses suggest a novel mechanism on how the peptide conformational equilibrium in the aqueous phase may modulate the effects of flanking loops on membrane insertion. Specifically, the flanking loops in peptide EmrL interact strongly with the TMH segment and form stable compact conformations in the aqueous phase, which can hinder membrane absorption and insertion as these processes require extended conformations with minimal interactions between the flanking loops and TMH segment. This work also emphasizes the general importance of considering the peptide conformational equilibrium for understanding the mechanism and energetics of membrane insertion, an aspect that has not yet been sufficiently addressed in the literature.

Coarse-Grained Models for Protein-Cell Membrane Interactions

The physiological properties of biological soft matter are the product of collective interactions, which span many time and length scales. Recent computational modeling efforts have helped illuminate experiments that characterize the ways in which proteins modulate membrane physics. Linking these models across time and length scales in a multiscale model explains how atomistic information propagates to larger scales. This paper reviews continuum modeling and coarse-grained molecular dynamics methods, which connect atomistic simulations and single-molecule experiments with the observed microscopic or mesoscale properties of soft-matter systems essential to our understanding of cells, particularly those involved in sculpting and remodeling cell membranes.

Mechanism of bacterial signal transduction revealed by molecular dynamics of Tsr dimers and trimers of dimers in lipid vesicles

Bacterial chemoreceptors provide an important model for understanding signalling processes. In the serine receptor Tsr from E. coli, a binding event in the periplasmic domain of the receptor dimer causes a shift in a single transmembrane helix of roughly 0.15 nm towards the cytoplasm. This small change is propagated through the ∼22 nm length of the receptor, causing downstream inhibition of the kinase CheA. This requires interactions within a trimer of receptor dimers. Additionally, the signal is amplified across a 53,000 nm² array of chemoreceptor proteins, including ∼5,200 receptor trimers-of-dimers, at the cell pole. Despite a wealth of experimental data on the system, including high resolution structures of individual domains and extensive mutagenesis data, it remains uncertain how information is communicated across the receptor from the binding event to the downstream effectors. We present a molecular model of the entire Tsr dimer, and examine its behaviour using coarse-grained molecular dynamics and elastic network modelling. We observe a large bending in dimer models between the linker domain HAMP and coiled-coil domains, which is supported by experimental data. Models of the trimer of dimers, built from the dimer models, are more constrained and likely represent the signalling state. Simulations of the models in a 70 nm diameter vesicle with a biologically realistic lipid mixture reveal specific lipid interactions and oligomerisation of the trimer of dimers. The results indicate a mechanism whereby small motions of a single helix can be amplified through HAMP domain packing, to initiate large changes in the whole receptor structure.

To understand cell signalling events requires a physical model of the structure and behaviour of the signalling proteins involved. The methyl-accepting chemoreceptor proteins direct bacterial movement towards food sources and away from toxins. Based on experimental data we have built structural models of the serine chemoreceptor (Tsr) as a dimer, which is incapable of activating the downstream kinase CheA, and as a trimer of dimers, which can activate CheA. We have performed molecular dynamics simulation to reveal the behaviour of these two forms in a planar lipid bilayer and in a 70 nm diameter lipid vesicle with a mixture of lipids mimicking the E. coli inner membrane. We show that in isolation the dimers undergo a bending movement around the central HAMP domain, whereas the trimer-of-dimers model does not. Comparison with published experimental data suggests that these bending motions are real, and that they occur in the trimer of dimers only in response to ligand binding. Drawing together these observations with studies showing that the signalling event involves small piston motions in the transmembrane helices suggests that the bending motion is frustrated in the unliganded trimer of dimers, and that ligand binding induces bending by repacking the HAMP interface.

Improving Internal Peptide Dynamics in the Coarse-Grained MARTINI Model: Toward Large-Scale Simulations of Amyloid- and Elastin-like Peptides

We present an extension of the coarse-grained MARTINI model for proteins and apply this extension to amyloid- and elastin-like peptides. Atomistic simulations of tetrapeptides, octapeptides, and longer peptides in solution are used as a reference to parametrize a set of pseudodihedral potentials that describe the internal flexibility of MARTINI peptides. We assess the performance of the resulting model in reproducing various structural properties computed from atomistic trajectories of peptides in water. The addition of new dihedral angle potentials improves agreement with the contact maps computed from atomistic simulations significantly. We also address the question of which parameters derived from atomistic trajectories are transferable between different lengths of peptides. The modified coarse-grained model shows reasonable transferability of parameters for the amyloid- and elastin-like peptides. In addition, the improved coarse-grained model is also applied to investigate the self-assembly of β-sheet forming peptides on the microsecond time scale. The octapeptides SNNFGAIL and (GV)(4) are used to examine peptide aggregation in different environments, in water, and at the water-octane interface. At the interface, peptide adsorption occurs rapidly, and peptides spontaneously aggregate in favor of stretched conformers resembling β-strands.

Hydrophobicity scales: a thermodynamic looking glass into lipid-protein interactions

The partitioning of amino acid sidechains into the membrane is a key aspect of membrane protein folding. However, lipid bilayers exhibit rapidly changing physicochemical properties over their nanometer-scale thickness, which complicates understanding the thermodynamics and microscopic details of membrane partitioning. Recent data from diverse approaches, including protein insertion by the Sec translocon, folding of a small beta-barrel membrane protein and computer simulations of the exact distribution of a variety of small molecules and peptides, have joined older hydrophobicity scales for membrane protein prediction. We examine the correlations among the scales and find that they are remarkably correlated even though there are large differences in magnitude. We discuss the implications of these scales for understanding membrane protein structure and function.