A transferable coarse-grained model for diphenylalanine: How to represent an environment driven conformational transition

Multiscale, Multiresolution Coarse-Grained Model via a Hybrid Approach: Solvation, Structure, and Self-Assembly of Aromatic Tripeptides

Short aromatic peptides have been observed to assemble into diverse nanostructures, including fibers, tubes, and vesicles, using computational techniques. However, the computational studies have employed top-down coarse-grained (CG) models, which are unable to capture the assembly along with the conformation, packing, and organization of the peptides within the aggregates in a manner that is consistent with the all atom (AA) representation of the molecules. In this study, a hybrid structure- and force-based approach is adapted to develop a bottom-up CG force field of triphenylalanine using reference data from AA trajectories. This approach follows a flexible methodology to approximate the chemical complexity of the underlying AA representation with the chosen CG representation. Two CG models are developed with distinct representations of the aromatic side chains. The first uses a simple single-bead representation, while the second uses a three-bead representation to more accurately represent the planarity of the ring. The one-bead model yields nanorods, while the three-bead model results in nanospheres. The role of different chemical groups in the assembly of nanostructures is identified, along with the importance of steric effects on the packing of the peptides within assemblies.

Gaussian representation of coarse-grained interactions of liquids: Theory, parametrization, and transferability

Coarse-grained (CG) interactions determined via bottom-up methodologies can faithfully reproduce the structural correlations observed in fine-grained (atomistic resolution) systems, yet they can suffer from limited extensibility due to complex many-body correlations. As part of an ongoing effort to understand and improve the applicability of bottom-up CG models, we propose an alternative approach to address both accuracy and transferability. Our main idea draws from classical perturbation theory to partition the hard sphere repulsive term from effective CG interactions. We then introduce Gaussian basis functions corresponding to the system's characteristic length by linking these Gaussian sub-interactions to the local particle densities at each coordination shell. The remaining perturbative long-range interaction can be treated as a collective solvation interaction, which we show exhibits a Gaussian form derived from integral equation theories. By applying this numerical parametrization protocol to CG liquid systems, our microscopic theory elucidates the emergence of Gaussian interactions in common phenomenological CG models. To facilitate transferability for these reduced descriptions, we further infer equations of state to determine the sub-interaction parameter as a function of the system variables. The reduced models exhibit excellent transferability across the thermodynamic state points. Furthermore, we propose a new strategy to design the cross-interactions between distinct CG sites in liquid mixtures. This involves combining each Gaussian in the proper radial domain, yielding accurate CG potentials of mean force and structural correlations for multi-component systems. Overall, our findings establish a solid foundation for constructing transferable bottom-up CG models of liquids with enhanced extensibility.

Machine Learning-Guided Adaptive Parametrization for Coupling Terms in a Mixed United-Atom/Coarse-Grained Model for Diphenylalanine Self-Assembly in Aqueous Ionic Liquids

Precise regulation of the peptide self-assembly into ordered nanostructures with intriguing properties has attracted intense attention. However, predicting peptide assembly at atomic resolution is a challenge due to both the structural flexibility of peptides and the associated huge computational costs. A machine learning-guided adaptive parametrization method was proposed for developing a mixed atomic and coarse-grained (CG) model through a multiobjective optimization strategy. Our model incorporates the united-atom (UA) model for diphenylalanine (P) and the polarizable electrostatic-variable coarse-grained (VaCG) model for aqueous ionic liquid [BMIM]+[BF4]- solution. In this mixed model, the coupling van der Waals (vdW) interaction is addressed by introducing virtual sites (VS) in the UA model to interact with solvent CG beads. The coupling parameters, including the electrostatic parameter and vdW parameters, are automatically optimized through ML-guided adaptive parametrization. The performance of this model was tested by some microstructural properties, e.g., the average number of P-P intermolecular hydrogen bonds (HBs) and radius distribution functions (RDFs) between P and different fragments of IL, in comparison with all-atom (AA) simulations. The computational cost is significantly reduced using such a parametrization scheme, which could search tens of thousands of force-field parameter sets, while needing only a small fraction of them to be assessed with molecular dynamics (MD) simulations. We used such a mixed resolution model to investigate the self-assembly in IL-water mixtures with variants of IL concentration (X). The long-range-ordered fibril structure is formed in a pure water system (X = 0). With an increase of IL concentrations, the formation of an ordered self-assembly nanostructure is prohibited, instead forming branched fibril at X = 2 mol % or amorphous aggregates when X > 10 mol %, resulting from the interplay between π-stacking and HB interactions between P and IL. The qualitative agreement between the simulated structures and the observed morphologies in experiments indicates the applicability of ML-guided parametrization strategy in the study of complex systems, such as polymers, lipid bilayers, and polysaccharides.

Coarse grained simulation of the aggregation and structure control of polyethylene nanocrystals

Polyethylene (PE) telechelics with carboxylate functional groups at both ends have been shown to assemble into hexagonal nanocrystal platelets with a height defined by their chain length in basic CsOH-solution. In this coarse grained (CG) simulation study we show how properties of the functional groups alter the aggregation and crystallization behavior of those telechelics. Systematic variation of the parameters of the CG model showed that important factors which control nanoparticle stability and structure are the PE chain length and the hydrophilicity and the steric demand of the head groups. To characterize the aggregation process we analyzed the number and size of the obtained aggregates as well as intramolecular order and intermolecular alignment of the polymer chains. By comparison of CG and atomistic simulation data, it could be shown that atomistic simulations representing different chemical systems can be emulated with specific, different CG parameter sets. Thus, the results from the (generic) CG simulation models can be used to explain the effect of different head groups and different counterions on the aggregation of PE telechelics and the order of the obtained nanocrystals.

Effect of the air-water interface on the conformation of amyloid beta

It has long been recognized that liquid interfaces, such as the air-water interface (AWI), can enhance the formation of protein fibrils. This makes liquid interfaces attractive templates for fibril formation but fully realizing this requires knowledge of protein behavior at interfaces, which is currently lacking. To address this, molecular dynamics simulation is used to investigate fragments of amyloid beta, a model fibril forming protein, at the air-water interface. At the air-water interface, the enrichment of aggregation-prone helical conformations provides a mechanism for the enhancement of fibrillation at interfaces. The conformational ensemble at the air-water interface was also considerably reduced compared to bulk solution due to the tendency of hydrophobic side chains partitioning into the air restricting the range of conformations. Little overlap between the conformational ensembles at the AWI and in the bulk solution was found, suggesting that AWI induces the formation of a different set of structures compared to bulk solution. The smaller Aβ(16-22) and Aβ(25-35) fragments show an increase in the propensity for an ordered secondary structure at the air-water interface but with a increased propensity for turn over other motifs, illustrating the importance of intra-protein interactions for stabilizing helical and extended conformations.

Bottom-up coarse-grained models for external fields and interfaces

Bottom-up coarse-grained (CG) models accurately describe the structure of homogeneous systems but sometimes provide limited transferability and a poor description of thermodynamic properties. Consequently, inhomogeneous systems present a severe challenge for bottom-up models. In this work, we examine bottom-up CG models for interfaces and inhomogeneous systems. We first analyze the effect of external fields upon the many-body potential of mean force. We also demonstrate that the multiscale CG (MS-CG) variational principle for modeling the external field corresponds to a generalization of the first Yvon-Born-Green equation. This provides an important connection with liquid state theory, as well as physical insight into the structure of interfaces and the resulting MS-CG models. We then develop and assess MS-CG models for a film of liquid methanol that is adsorbed on an attractive wall and in coexistence with its vapor phase. While pair-additive potentials provide unsatisfactory accuracy and transferability, the inclusion of local-density (LD) potentials dramatically improves the accuracy and transferability of the MS-CG model. The MS-CG model with LD potentials quite accurately describes the wall-liquid interface, the bulk liquid density, and the liquid-vapor interface while simultaneously providing a much improved description of the vapor phase. This model also provides an excellent description of the pair structure and pressure-density equation of state for the bulk liquid. Thus, LD potentials hold considerable promise for transferable bottom-up models that accurately describe the structure and thermodynamic properties of both bulk and interfacial systems.

Temperature and Phase Transferable Bottom-up Coarse-Grained Models

Despite the high fidelity of bottom-up coarse-grained (CG) approaches to recapitulate the structural correlations in atomistic simulations, the general use of bottom-up CG methods is limited because of the nontransferable nature of these CG models under different thermodynamic conditions. Because bottom-up CG potentials usually correspond to configuration-dependent free energies of the system, recent studies have focused on adjusting enthalpic or entropic contributions to account for issues with transferability. However, these approaches can require a manual adjustment of the CG interaction a priori and are usually limited to constant volume ensembles. To overcome these limitations, we construct temperature and phase transferable CG models under constant pressure by developing the ultra-coarse-graining (UCG) methodology in the mean-field limit. In the mean-field ansatz, an embedded semi-global order parameter recapitulates global changes to the system by automatically adjusting the effective CG interactions, thus bridging free energy decompositions with UCG theory. The method presented is designed to faithfully capture structural correlations under different thermodynamic conditions, using a single UCG model. Specifically, we test the applicability of the developed theory in three distinct cases: (1) different temperatures at constant pressure in liquids, (2) different temperatures across thermodynamic phases, and (3) liquid/vapor interfaces. We demonstrate that the systematic construction of both temperature and phase transferable bottom-up CG models is possible using this generalized UCG theory. Based on our findings, this approach significantly extends the transferability and applicability of the bottom-up CG theory and method.

Initial Aggregation and Ordering Mechanism of Diphenylalanine from Microsecond All-Atom Molecular Dynamics Simulations

Self-assembled diphenylalanine (FF) nanostructures have recently been demonstrated to be interesting materials for antibacterial and anticancer applications. These applications, among others, seek to take advantage of the high-order and resulting appealing physical properties of FF nanostructures by modifying the peptide in some way to achieve specific functionality. To rationally design modifications to the dipeptide that allow for this behavior, the driving forces of FF self-assembly must be understood. Molecular simulations have been utilized to assess these properties but have yielded conflicting conclusions due to inconsistencies in models chosen as well as the lack of quantitative analyses on the specific driving forces. Here, we present an all-atom explicit solvent molecular dynamics-based study on different length scales of FF aggregation. We utilize a free energy decomposition analysis as well as a dimer cluster analysis to identify the initial aggregation driving force to be FF intermolecular electrostatics, whereas solvent-mediated forces drive crystal growth. These data are consistent with the hypothesis that all hydrophobic dipeptides will have a similar initial aggregation mechanism until a critical aggregate size is reached, at which point crystallization occurs and subsequent crystal growth is dominated by solvent-mediated forces. We demonstrate that this proposed mechanism is testable by infrared spectroscopy focusing on the blueshift of the amide I peak as well as the ordering of the carboxylate peak.

Molecular dynamics studies on conformational thermodynamics of Orai1-calmodulin complex

Molecular understanding of bio-macromolecular binding is a challenging task due to large sizes of the molecules and presence of variety of interactions. Here, we study the molecular mechanism of calmodulin (CaM) binding to Orai1 that regulates Ca²⁺-dependent inactivation process in eukaryotic cells. Although experimental observations indicate that Orai1 binds to the C-terminal of Ca²⁺-loaded CaM, it is not decisive if N-domain of CaM interacts with Orai1. We address the issue of interaction of different domains of CaM with Orai1 using conformational thermodynamic changes, computed from histograms of dihedral angles over simulated trajectories of CaM, CaM-binding domain of Orai1 and complexes of CaM with Orai1. The changes for all residues of both C and N terminal domains of CaM upon Orai1 binding are compared. Our analysis shows that Orai1binds to both C-terminal and N-terminal domains of CaM, indicating 1:2 stoichiometry. The Orai1 binding to N-terminal domain of CaM is less stable than that to the C-terminal domain. The binding residues are primarily hydrophobic. These observations are in qualitative agreement to the experiments. The conformational thermodynamic changes thus provide a useful computational tool to provide atomic details of interactions in bio-macromolecular binding.

Assembly of Triblock Amphiphilic Peptides into One-Dimensional Aggregates and Network Formation

Peptide assembly plays a key role in both neurological diseases and development of novel biomaterials with well-defined nanostructures. Synthetic model peptides provide a unique platform to explore the role of intermolecular interactions in the assembly process. A triblock peptide architecture designed by the Hartgerink group is a versatile system which relies on Coulomb interactions, hydrogen bonding, and hydrophobicity to guide these peptides' assembly at three different length scales: β-sheets, double-wall ribbon-like aggregates, and finally a highly porous network structure which can support gels with ≤1% by weight peptide concentration. In this study, by using molecular dynamics simulations of a structure based implicit solvent coarse grained model, we analyzed this hierarchical assembly process. Parametrization of our CG model is based on multiple-state points from atomistic simulations, which enables this model to represent the conformational adaptability of the triblock peptide molecule based on the surrounding medium. Our results indicate that emergence of the double-wall β-sheet packing mechanism, proposed in light of the experimental evidence, strongly depends on the subtle balance of the intermolecular forces. We demonstrate that, even though backbone hydrogen bonding dominates the early nucleation stages, depending on the strength of the hydrophobic and Coulomb forces, alternative structures such as zero-dimensional aggregates with two β-sheets oriented orthogonally (which we refer to as a cross-packed structure) and β-sheets with misoriented hydrophobic side chains are also feasible. We discuss the implications of these competing structures for the three different length scales of assembly by systematically investigating the influence of density, counterion valency, and hydrophobicity.

Conformation and Aggregation of LKα14 Peptide in Bulk Water and at the Air/Water Interface

Historically, the protein folding problem has mainly been associated with understanding the relationship between amino acid sequence and structure. However, it is known that both the conformation of individual molecules and their aggregation strongly depend on the environmental conditions. Here, we study the aggregation behavior of the model peptide LKα14 (with amino acid sequence LKKLLKLLKKLLKL) in bulk water and at the air/water interface. We start by a quantitative analysis of the conformational space of a single LKα14 in bulk water. Next, in order to analyze the aggregation tendency of LKα14, by using the umbrella sampling technique we calculate the potential of mean force for pulling a single peptide from an n-molecule aggregate. In agreement with the experimental results, our calculations yield the optimal aggregate size as four. This equilibrium state is achieved by two opposing forces: Coulomb repulsion between the lysine side chains and the reduction of solvent accessible hydrophobic surface area upon aggregation. At the vacuum/water interface, however, even dimers of LKα14 become marginally stable, and any larger aggregate falls apart instantaneously. Our results indicate that even though the interface is highly influential in stabilizing the α-helix conformation for a single molecule, it significantly reduces the attraction between two LKα14 peptides, along with their aggregation tendency.

Potential of mean force analysis of the self-association of leucine-rich transmembrane α-helices: difference between atomistic and coarse-grained simulations

Interaction of transmembrane (TM) proteins is important in many biological processes. Large-scale computational studies using coarse-grained (CG) simulations are becoming popular. However, most CG model parameters have not fully been calibrated with respect to lateral interactions of TM peptide segments. Here, we compare the potential of mean forces (PMFs) of dimerization of TM helices obtained using a MARTINI CG model and an atomistic (AT) Berger lipids-OPLS/AA model (AT(OPLS)). For helical, tryptophan-flanked, leucine-rich peptides (WL15 and WALP15) embedded in a parallel configuration in an octane slab, the AT(OPLS) PMF profiles showed a shallow minimum (with a depth of approximately 3 kJ/mol; i.e., a weak tendency to dimerize). A similar analysis using the CHARMM36 all-atom model (AT(CHARMM)) showed comparable results. In contrast, the CG analysis generally showed steep PMF curves with depths of approximately 16-22 kJ/mol, suggesting a stronger tendency to dimerize compared to the AT model. This CG > AT discrepancy in the propensity for dimerization was also seen for dilauroylphosphatidylcholine (DLPC)-embedded peptides. For a WL15 (and WALP15)/DLPC bilayer system, AT(OPLS) PMF showed a repulsive mean force for a wide range of interhelical distances, in contrast to the attractive forces observed in the octane system. The change from the octane slab to the DLPC bilayer also mitigated the dimerization propensity in the CG system. The dimerization energies of CG (AALALAA)3 peptides in DLPC and dioleoylphosphatidylcholine bilayers were in good agreement with previous experimental data. The lipid headgroup, but not the length of the lipid tails, was a key causative factor contributing to the differences between octane and DLPC. Furthermore, the CG model, but not the AT model, showed high sensitivity to changes in amino acid residues located near the lipid-water interface and hydrophobic mismatch between the peptides and membrane. These findings may help interpret CG and AT simulation results on membrane proteins.

Tipping the Scale from Disorder to Alpha-helix: Folding of Amphiphilic Peptides in the Presence of Macroscopic and Molecular Interfaces

Secondary amphiphilicity is inherent to the secondary structural elements of proteins. By forming energetically favorable contacts with each other these amphiphilic building blocks give rise to the formation of a tertiary structure. Small proteins and peptides, on the other hand, are usually too short to form multiple structural elements and cannot stabilize them internally. Therefore, these molecules are often found to be structurally ambiguous up to the point of a large degree of intrinsic disorder in solution. Consequently, their conformational preference is particularly susceptible to environmental conditions such as pH, salts, or presence of interfaces. In this study we use molecular dynamics simulations to analyze the conformational behavior of two synthetic peptides, LKKLLKLLKKLLKL (LK) and EAALAEALAEALAE (EALA), with built-in secondary amphiphilicity upon forming an alpha-helix. We use these model peptides to systematically study their aggregation and the influence of macroscopic and molecular interfaces on their conformational preferences. We show that the peptides are neither random coils in bulk water nor fully formed alpha helices, but adopt multiple conformations and secondary structure elements with short lifetimes. These provide a basis for conformation-selection and population-shift upon environmental changes. Differences in these peptides' response to macroscopic and molecular interfaces (presented by an aggregation partner) can be linked to their inherent alpha-helical tendencies in bulk water. We find that the peptides' aggregation behavior is also strongly affected by presence or absence of an interface, and rather subtly depends on their surface charge and hydrophobicity.

A microscopic insight from conformational thermodynamics to functional ligand binding in proteins

We show that the thermodynamics of metal ion-induced conformational changes aid to understand the functions of protein complexes. This is illustrated in the case of a metalloprotein, alpha-lactalbumin (aLA), a divalent metal ion binding protein. We use the histograms of dihedral angles of the protein, generated from all-atom molecular dynamics simulations, to calculate conformational thermodynamics. The thermodynamically destabilized and disordered residues in different conformational states of a protein are proposed to serve as binding sites for ligands. This is tested for β-1,4-galactosyltransferase (β4GalT) binding to the Ca(2+)-aLA complex, in which the binding residues are known. Among the binding residues, the C-terminal residues like aspartate (D) 116, glutamine (Q) 117, tryptophan (W) 118 and leucine (L) 119 are destabilized and disordered and can dock β4GalT onto Ca(2+)-aLA. No such thermodynamically favourable binding residues can be identified in the case of the Mg(2+)-aLA complex. We apply similar analysis to oleic acid binding and predict that the Ca(2+)-aLA complex can bind to oleic acid through the basic histidine (H) 32 of the A2 helix and the hydrophobic residues, namely, isoleucine (I) 59, W60 and I95, of the interfacial cleft. However, the number of destabilized and disordered residues in Mg(2+)-aLA are few, and hence, the oleic acid binding to Mg(2+)-bound aLA is less stable than that to the Ca(2+)-aLA complex. Our analysis can be generalized to understand the functionality of other ligand bound proteins.