Discrete molecular dynamics simulations of peptide aggregation

Computational exploration of the self-aggregation mechanisms of phenol-soluble modulins β1 and β2 in Staphylococcus aureus biofilms

The formation of functional bacterial amyloids by phenol-soluble modulins (PSMs) in Staphylococcus aureus is a critical component of biofilm-associated infections, providing robust protective barriers against antimicrobial agents and immune defenses. Clarifying the molecular mechanisms of PSM self-assembly within the biofilm matrix is essential for developing strategies to disrupt biofilm integrity and combat biofilm-related infections. In this study, we analyzed the self-assembly dynamics of PSM-β1 and PSM-β2 by examining their folding and dimerization through long-timescale atomistic discrete molecular dynamics simulations. Our findings revealed that both peptides primarily adopt helical structures as monomers but shift to β-sheets upon dimerization. Monomeric state, PSM-β1 exhibited frequent transitions between helical and β-sheet forms, while PSM-β2 largely retained a helical structure. Upon dimerization, both peptides showed pronounced β-sheet formation around conserved C-terminal residues 21-44. Residues 21-33, largely unstructured as monomers, demonstrated strong tendencies for β-sheet formation and intermolecular interactions, underscoring their central role in the self-assembly of both peptides. Additionally, the PSM-β1 N-terminus formed β-sheets only when interacting with the C-terminus, whereas the PSM-β2 N-terminus remained helical and uninvolved in β-sheet formation. These distinct aggregation behaviors likely contribute to biofilm dynamics, with C-terminal regions facilitating biofilm formation and N-terminal regions influencing stability. Targeting residues 21-33 in PSM-β1 and PSM-β2 offers a promising therapeutic approach for disrupting biofilm integrity. This study advances our understanding of PSM-β1 and PSM-β2 self-assembly and presents new targets for drug design against biofilm-associated diseases.

Unveiling the inhibition mechanism of host-defense peptide cathelicidin LL-37 on the amyloid aggregation of the human islet amyloid polypeptide

The aberrant aggregation of the human islet amyloid polypeptide (hIAPP) is a hallmark of type II diabetes. LL37, the only cathelicidin host-defense peptide in humans, plays essential roles in antimicrobial and immunomodulatory activities. Mounting evidence indicates that LL37 can inhibit the amyloid aggregation of hIAPP, suggesting possible interplays between infections and amyloid diseases while the mechanism remains unclear. In this paper, we explored the molecular interactions between hIAPP and LL37 using all-atom discrete molecular dynamics (DMD), a novel and predictive molecular dynamics engine with improved sampling efficiencies. We found that the LL37 peptides can effectively interact with hIAPP in monomer, oligomer, and fibril states driven by hydrophobic associations and pi-pi interactions. Specifically, the hydrophobic residues in the N- and C-termini of LL37 peptides can firmly bind with the monomeric and oligomeric hIAPP, especially in the amyloidogenic regions, to prevent the self-interactions of amyloidogenic regions and thus hinder the formation of amyloid fibrils. Furthermore, LL37 can bind to the elongation surfaces of the hIAPP fibril seeds with geometric incompatibility for monomer addition to block the fibril growth. Together, we identified the crucial residues and key driving forces for the interactions between LL37 and hIAPP peptides and revealed the related dynamics and conformational changes. The uncovered mechanism can contribute to a better understanding of the pathological links between microbial infections and amyloid diseases and guide the designs of novel therapies combining antimicrobial and anti-amyloid functions.

Optogenetically engineered Septin-7 enhances immune cell infiltration of tumor spheroids

Chimeric antigen receptor T cell therapies have achieved great success in eradicating some liquid tumors, whereas the preclinical results in treating solid tumors have proven less decisive. One of the principal challenges in solid tumor treatment is the physical barrier composed of a dense extracellular matrix, which prevents immune cells from penetrating the tissue to attack intratumoral cancer cells. Here, we improve immune cell infiltration into solid tumors by manipulating septin-7 functions in cells. Using protein allosteric design, we reprogram the three-dimensional structure of septin-7 and insert a blue light-responsive light-oxygen-voltage-sensing domain 2 (LOV2), creating a light-controllable septin-7-LOV2 hybrid protein. Blue light inhibits septin-7 function in live cells, inducing extended cell protrusions and cell polarization, enhancing cell transmigration efficiency through confining spaces. We genetically edited human natural killer cell line (NK92) and mouse primary CD8+ T-cells expressing the engineered protein, and we demonstrated improved penetration and cytotoxicity against various tumor spheroid models. Our proposed strategy to enhance immune cell infiltration is compatible with other methodologies and therefore, could be used in combination to further improve cell-based immunotherapies against solid tumors.

Uncovering Intermolecular Interactions Driving the Liquid-Liquid Phase Separation of the TDP-43 Low-Complexity Domain via Atomistic Dimerization Simulations

Liquid-liquid phase separation (LLPS) of transactive response DNA-binding protein of 43 kDa (TDP-43), which exerts multiple functions in the splicing, trafficking, and stabilization of RNA, mediates the formation of membraneless condensates with crucial physiological roles, while its aberrant LLPS is linked to multiple neurodegenerative diseases. However, due to the heterogeneous and dynamic nature of LLPS, major gaps remain in understanding the precise intermolecular interactions driving LLPS and how specific mutations alter LLPS dynamics. Here, we investigated the molecular mechanisms underlying the LLPS of the TDP-43 low-complexity domain (LCD) by simulating the dimerization process using all-atom discrete molecular dynamics with microsecond-long simulations. Our results showed that the TDP-43 LCD was intrinsically disordered, with helical structures consistent with prior nuclear magnetic resonance studies. Phase separation propensity was assessed by simulating the dimerization of the TDP-43 LCD and four mutants, showing that A321G, W334G, and M337V inhibited self-association, while G335D promoted it, fully consistent with experimental reports. During the dimerization process, two peptides experienced both elastic and nonelastic collisions, and the self-associated dimer featured both high- and low-contact states. These results suggested that the dimerization process of the TDP-43 LCD was accordingly dynamic and heterogeneous. Additionally, we identified crucial regions containing hydrophobic clusters and aromatic residues in the N-terminus, central region, and C-terminus that were essential for the self-association of the TDP-43 LCD. These residues with high binding affinities can act as stickers to form peptide networks in LLPS. Together, our simulation provides a comprehensive picture of the intermolecular interactions driving the phase separation of the TDP-43 LCD, offering insights into both physiological functions and pathological mechanisms.

Oligomer Formation by Physiologically Relevant C-Terminal Isoforms of Amyloid β-Protein

Alzheimer's disease (AD) is a neurological disorder associated with amyloid β-protein (Aβ) assembly into toxic oligomers. In addition to the two predominant alloforms, Aβ1-40 and Aβ1-42, other C-terminally truncated Aβ peptides, including Aβ1-38 and Aβ1-43, are produced in the brain. Here, we use discrete molecular dynamics (DMD) and a four-bead protein model with amino acid-specific hydropathic interactions, DMD4B-HYDRA, to examine oligomer formation of Aβ1-38, Aβ1-40, Aβ1-42, and Aβ1-43. Self-assembly of 32 unstructured monomer peptides into oligomers is examined using 32 replica DMD trajectories for each of the four peptides. In a quasi-steady state, Aβ1-38 and Aβ1-40 adopt similar unimodal oligomer size distributions with a maximum at trimers, whereas Aβ1-42 and Aβ1-43 oligomer size distributions are multimodal with the dominant maximum at trimers or tetramers, and additional maxima at hexamers and unidecamers (for Aβ1-42) or octamers and pentadecamers (for Aβ1-43). The free energy landscapes reveal isoform- and oligomer-order specific structural and morphological features of oligomer ensembles. Our results show that oligomers of each of the four isoforms have unique features, with Aβ1-42 alone resulting in oligomers with disordered and solvent-exposed N-termini. Our findings help unravel the structure-function paradigm governing oligomers formed by various Aβ isoforms.

Molecular Insights into the Effects of F16L and F19L Substitutions on the Conformation and Aggregation Dynamics of Human Calcitonin

Human calcitonin (hCT) regulates calcium-phosphorus metabolism, but its amyloid aggregation disrupts physiological activity, increases thyroid carcinoma risk, and hampers its clinical use for bone-related diseases like osteoporosis and Paget's disease. Improving hCT with targeted modifications to mitigate amyloid formation while maintaining its function holds promise as a strategy. Understanding how each residue in hCT's amyloidogenic core affects its structure and aggregation dynamics is crucial for designing effective analogues. Mutants F16L-hCT and F19L-hCT, where Phe residues in the core are replaced with Leu as in nonamyloidogenic salmon calcitonin, showed different aggregation kinetics. However, the molecular effects of these substitutions in hCT are still unclear. Here, we systematically investigated the folding and self-assembly conformational dynamics of hCT, F16L-hCT, and F19L-hCT through multiple long-time scale independent atomistic discrete molecular dynamics (DMD) simulations. Our results indicated that the hCT monomer primarily assumed unstructured conformations with dynamic helices around residues 4-12 and 14-21. During self-assembly, the amyloidogenic core of hCT14-21 converted from dynamic helices to β-sheets. However, substituting F16L did not induce significant conformational changes, as F16L-hCT exhibited characteristics similar to those of wild-type hCT in both monomeric and oligomeric states. In contrast, F19L-hCT exhibited substantially more helices and fewer β-sheets than did hCT, irrespective of their monomers or oligomers. The substitution of F19L significantly enhanced the stability of the helical conformation for hCT14-21, thereby suppressing the helix-to-β-sheet conformational conversion. Overall, our findings elucidate the molecular mechanisms underlying hCT aggregation and the effects of F16L and F19L substitutions on the conformational dynamics of hCT, highlighting the critical role of F19 as an important target in the design of amyloid-resistant hCT analogs for future clinical applications.

Molecular Insights into the Misfolding and Dimerization Dynamics of the Full-Length α-Synuclein from Atomistic Discrete Molecular Dynamics Simulations

The misfolding and pathological aggregation of α-synuclein forming insoluble amyloid deposits is associated with Parkinson's disease, the second most common neurodegenerative disease in the world population. Characterizing the self-assembly mechanism of α-synuclein is critical for discovering treatments against synucleinopathies. The intrinsically disordered property, high degrees of freedom, and macroscopic timescales of conformational conversion make its characterization extremely challenging in vitro and in silico. Here, we systematically investigated the dynamics of monomer misfolding and dimerization of the full-length α-synuclein using atomistic discrete molecular dynamics simulations. Our results suggested that both α-synuclein monomers and dimers mainly adopted unstructured formations with partial helices around the N-terminus (residues 8-32) and various β-sheets spanning the residues 35-56 (N-terminal tail) and residues 61-95 (NAC region). The C-terminus mostly assumed an unstructured formation wrapping around the lateral surface and the elongation edge of the β-sheet core formed by an N-terminal tail and NAC regions. Dimerization enhanced the β-sheet formation along with a decrease in the unstructured content. The inter-peptide β-sheets were mainly formed by the N-terminal tail and NACore (residues 68-78) regions, suggesting that these two regions played critical roles in the amyloid aggregation of α-synuclein. Interactions of the C-terminus with the N-terminal tail and the NAC region were significantly suppressed in the α-synuclein dimer, indicating that the interaction of the C-terminus with the N-terminal tail and NAC regions could prevent α-synuclein aggregation. These results on the structural ensembles and early aggregation dynamics of α-synuclein will help understand the nucleation and fibrillization of α-synuclein.

Molecular insights into the oligomerization dynamics and conformations of amyloidogenic and non-amyloidogenic amylin from discrete molecular dynamics simulations

The amyloid aggregation of human islet amyloid polypeptide (hIAPP) is associated with pancreatic β-cell death in type 2 diabetes. The S20G substitution of hIAPP (hIAPP(S20G)), found in Japanese and Chinese people, is more amyloidogenic and cytotoxic than wild-type hIAPP. Rat amylin (rIAPP) does not have aggregation propensity or cytotoxicity. Mounting evidence suggests that soluble low-molecular-weight amyloid oligomers formed during early aggregation are more cytotoxic than mature fibrils. The self-assembly dynamics and oligomeric conformations remain unknown because the oligomers are heterogeneous and transient. The molecular mechanism of sequence-variation rendering dramatically different aggregation propensity and cytotoxicity is also elusive. Here, we investigate the oligomerization dynamics and conformations of amyloidogenic hIAPP, hIAPP(S20G), and non-amyloidogenic rIAPP using atomistic discrete molecular dynamics (DMD) simulations. Our simulation results demonstrated that all three monomeric amylin peptides mainly adopted an unstructured formation with partial dynamical helices near the N-terminus. Relatively transient β-hairpins were more abundant in hIAPP and hIAPP(S20G) than in rIAPP. The S20G-substituting mutant of hIAPP altered the turn region of the β-hairpin motif, resulting in more hydrophobic residue-pairwise contacts within the β-hairpin. Oligomerization dynamic investigation revealed that all three peptides spontaneously accumulated into helix-populated oligomers. The conformational conversion to form β-sheet-rich oligomers was only observed in hIAPP and hIAPP(S20G). The population of high-β-sheet-content oligomers was enhanced by S20G substitution. Interestingly, both hIAPP and hIAPP(S20G) could form β-barrel formations, and the β-barrel propensity of hIAPP(S20G) was three times larger than that of hIAPP. No β-sheet-rich or β-barrel formations were observed in rIAPP. Our direct observation of the correlation between β-barrel oligomer formation and cytotoxicity suggests that β-barrels might play a critically important role in the cytotoxicity of amyloidosis.

Hydrophobic/Hydrophilic Ratio of Amphiphilic Helix Mimetics Determines the Effects on Islet Amyloid Polypeptide Aggregation

Amyloid depositions of human islet amyloid polypeptides (hIAPP) are associated with type II diabetes (T2D) impacting millions of people globally. Accordingly, strategies against hIAPP aggregation are essential for the prevention and eventual treatment of the disease. Helix mimetics, which modulate the protein-protein interaction by mimicking the side chain residues of a natural α-helix, were found to be a promising strategy for inhibiting hIAPP aggregation. Here, we applied molecular dynamics simulations to investigate two helix mimetics reported to have opposite effects on hIAPP aggregation in solution, the oligopyridylamide-based scaffold 1e promoted, whereas naphthalimide-appended oligopyridylamide scaffold DM 1 inhibited the aggregation of hIAPP in solution. We found that 1e promoted hIAPP aggregation because of the recruiting effects through binding with the N-termini of hIAPP peptides. In contrast, DM 1 with a higher hydrophobic/hydrophilic ratio effectively inhibited hIAPP aggregation by strongly binding with the C-termini of hIAPP peptides, which competed for the interpeptide contacts between amyloidogenic regions in the C-termini and impaired the fibrillization of hIAPP. Structural analyses revealed that DM 1 formed the core of hIAPP-DM 1 complexes and stabilized the off-pathway oligomers, whereas 1e formed the corona outside the hIAPP-1e complexes and remained active in recruiting free hIAPP peptides. The distinct interaction mechanisms of DM 1 and 1e, together with other reported potent antagonists in the literature, emphasized the effective small molecule-based amyloid inhibitors by disrupting peptide interactions that should reach a balanced hydrophobic/hydrophilic ratio, providing a viable and generic strategy for the rational design of novel anti-amyloid nanomedicine.

Ultrasmall Molybdenum Disulfide Quantum Dots Cage Alzheimer's Amyloid Beta to Restore Membrane Fluidity

Alzheimer's disease (AD) is a major cause of dementia characterized by the overexpression of transmembrane amyloid precursor protein and its neurotoxic byproduct amyloid beta (Aβ). A small peptide of considerable hydrophobicity, Aβ is aggregation prone catalyzed by the presence of cell membranes, among other environmental factors. Accordingly, current AD mitigation strategies often aim at breaking down the Aβ-membrane communication, yet no data is available concerning the cohesive interplay of the three key entities of the cell membrane, Aβ, and its inhibitor. Using a lipophilic Laurdan dye and confocal fluorescence microscopy, we observed cell membrane perturbation and actin reorganization induced by Aβ oligomers but not by Aβ monomers or amyloid fibrils. We further revealed recovery of membrane fluidity by ultrasmall MoS2 quantum dots, also shown in this study as a potent inhibitor of Aβ amyloid aggregation. Using discrete molecular dynamics simulations, we uncovered the binding of MoS2 and Aβ monomers as mediated by hydrophilic interactions between the quantum dots and the peptide N-terminus. In contrast, Aβ oligomers and fibrils were surface-coated by the ultrasmall quantum dots in distinct testudo-like, reverse protein-corona formations to prevent their further association with the cell membrane and adverse effects downstream. This study offers a crucial new insight and a viable strategy for regulating the amyloid aggregation and membrane-axis of AD pathology with multifunctional nanomedicine.

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

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

Protein aggregation: in silico algorithms and applications

Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.

Amyloid Aggregation under the Lens of Liquid-Liquid Phase Separation

Increasing experiments suggest that amyloid peptides can undergo liquid-liquid phase separation (LLPS) before the formation of amyloid fibrils. However, the exact role of LLPS in amyloid aggregation at the molecular level remains elusive. Here, we investigated the LLPS and amyloid fibrillization of a coarse-grained peptide, capable of capturing fundamental properties of amyloid aggregation over a wide range of concentrations in molecular dynamics simulations. On the basis of the Flory-Huggins theory of polymer solutions, we determined the binodal and spinodal concentrations of LLPS in the low-concentration regime, ϕ_BL and ϕ_SL, respectively. Only at concentrations above ϕ_BL, peptides formed metastable or stable oligomers corresponding to the high-density liquid phase (HDLP) in LLPS, out of which the nucleated conformational conversion to fibril seeds occurred. Below ϕ_SL, the HDLP was metastable and transient, and the subsequent fibrillization process followed the traditional nucleation and elongation mechanisms. Only above ϕ_SL, the HDLP became stable, and the initial fibril nucleation and growth were governed by the high local peptide concentrations. The predicted saturation of amyloid aggregation half-times with increasing peptide concentration to a constant, instead of the traditional power-law scaling to zero, was confirmed by simulations and by a thioflavin-T kinetic assay and the transmission electron microscopy of islet amyloid polypeptide (IAPP) aggregation. Our study provides a unified picture of amyloid aggregation for a wide range of concentrations within the framework of LLPS, which may help us better understand the etiology of amyloid diseases, where the amyloid protein concentration can vary by ∼9 orders of magnitude depending on the organ location and facilitate the engineering of novel amyloid-based functional materials.

αB-Crystallin Chaperone Inhibits Aβ Aggregation by Capping the β-Sheet-Rich Oligomers and Fibrils

Inhibiting the cytotoxicity of amyloid aggregation by endogenous proteins is a promising strategy against degenerative amyloid diseases due to their intrinsically high biocompatibility and low immunogenicity. In this study, we investigated the inhibition mechanism of the structured core region of αB-crystallin (αBC) against Aβ fibrillization using discrete molecular dynamics simulations. Our computational results recapitulated the experimentally observed Aβ binding sites in αBC and suggested that αBC could bind to various Aβ aggregate species during the aggregation process-including monomers, dimers, and likely other high molecular weight oligomers, protofibrils, and fibrils-by capping the exposed β-sheet elongation surfaces. Thus, the nucleation of Aβ oligomers into fibrils and the fibril growth could be inhibited. Mechanistic insights obtained from our systematic computational studies may aid in the development of novel therapeutic strategies to modulate the aggregation of pathological, amyloidogenic protein in degenerative diseases.

Thermo- and pH-responsive fibrillization of squid suckerin A1H1 peptide

Stimuli-responsive smart materials have attracted considerable attention with numerous applications in nanotechnology, sensing, and biomedicine. Suckerin family proteins found in squid ring teeth represent such a class of peptide-based smart materials with their self-assemblies featuring excellent thermo-plasticity and pH-dependence. Similar to block copolymers, suckerin proteins are comprised of two repeating sequence motifs, where M1 motifs are abundant in alanine and histidine residues and M2 are rich in glycine. Experimental studies of suckerin assemblies suggested that M1 regions mainly formed nano-confined β-sheets within an amorphous matrix made of M2 modules stabilizing these β-rich nano-assemblies. The histidine-containing M1 modules are believed to govern the pH- and temperature-sensitive properties of suckerin assemblies. To better understand the stimuli-responsive properties of suckerin assemblies at the molecular level, we systematically studied the self-assembly dynamics of A1H1 peptides - a representative M1 sequence - at different temperatures and pH conditions with atomistic discrete molecular dynamic simulations. Our simulations with twenty A1H1 peptides demonstrated that below the transition temperature Tagg, they could readily self-assemble from isolated monomers into well-defined β-sheet nanostructures by both primary and secondary nucleation of β-sheets and subsequent aggregation growth via elongation and coagulation. Interestingly, the dissociation of pre-formed A1H1 β-sheet nanostructures featured a melting temperature Tm higher than Tagg, exhibiting the thermal hysteresis that is characteristic of first-order phase transitions with high energy barriers. In acidic environments where all histidine residues were protonated, the stability of the A1H1 β-sheet nano-assemblies was reduced and the β-rich assemblies easily dissociated into unstructured monomers at significantly lower temperatures than in the neutral solution. The computationally derived molecular mechanisms for pH- and temperature-dependent A1H1 self-assembly will help to understand the supramolecular assembly structures and functions of the large suckerin family and aid in the future design of peptide-based stimuli-responsive smart materials.

Amphiphilic surface chemistry of fullerenols is necessary for inhibiting the amyloid aggregation of alpha-synuclein NACore

Featuring small sizes, caged structures, low cytotoxicity and the capability to cross biological barriers, fullerene hydroxy derivatives named fullerenols have been explored as nanomedicinal candidates for amyloid inhibition. Understanding the surface chemistry effect of hydroxylation extents and the corresponding amyloid inhibition mechanisms is necessary for enabling applications of fullerenols and also future designs of nanomedicines in mitigating amyloid aggregation. Here, we investigated effects of C60(OH)n with n = 0-40 on the aggregation of NACore (the amyloidogenic core region of the non-amyloid-β component in α-synuclein), the amyloidogenic core of α-synuclein, by computational simulations, transmission electron microscopy (TEM), Fourier transform infrared (FTIR) spectroscopy, thioflavin-T (ThT) fluorescence kinetics and viability assays. Computationally, NACore assembled into cross-β aggregates via intermediates including β-barrels, which are postulated as toxic oligomers of amyloid aggregation. Hydrophobic C60 preferred to self-assemble, and NACore bound to the surface of C60 nano-clusters formed β-sheet rich aggregates - i.e., having little inhibition effect. Amphiphilic C60(OH)n with n = 4-20 displayed significant inhibition effects on NACore aggregation, where hydrogen bonding between hydroxyls and peptide backbones interrupted the formation of β-sheets between peptides adsorbed onto the surfaces of fullerenols or fullerenol nano-assemblies due to hydrophobic interactions. Thus, both cross-β aggregates and β-barrel intermediates were significantly suppressed. With hydroxyls increased to 40, fullerenols became highly hydrophilic with reduced peptide binding and thus an inhibition effect on amyloid aggregation. ThT, FTIR and TEM characterization of C60(OH)n with n = 0, 24, & 40 confirmed the computational predictions. Our results and others underscore the importance of amphiphilic surface chemistry and the capability of polar groups in forming hydrogen bonds with peptide backbones to render amyloid inhibition, offering a new insight for de-novo design of anti-amyloid inhibitors.

Wang-Landau simulation of Gō model molecules

Gō-like models are one of the oldest protein modeling concepts in computational physics and have proven their value over and over for forty years. The essence of a Gō model is to define a native contact matrix for a well-defined low-energy polymer configuration, e.g., the native state in the case of proteins or peptides. Many different potential shapes and many different cut-off distances in the definition of this native contact matrix have been proposed and applied. We investigate here the physical consequences of the choice for this cut-off distance in the Gō models derived for a square-well tangent sphere homopolymer chain. For this purpose we are performing flat-histogram Monte Carlo simulations of Wang-Landau type, obtaining the thermodynamic and structural properties of such models over the complete temperature range. Differences and similarities with Gō models for proteins and peptides are discussed.

Contrasting effects of nanoparticle-protein attraction on amyloid aggregation

Nanoparticles (NPs) have been experimentally found to either promote or inhibit amyloid aggregation of proteins, but the molecular mechanisms for such complex behaviors remain unknown. Using coarse-grained molecular dynamics simulations, we investigated the effects of varying the strength of nonspecific NP-protein attraction on amyloid aggregation of a model protein, the amyloid-beta peptide implicated in Alzheimer's disease. Specifically, with increasing NP-peptide attraction, amyloid aggregation on the NP surface was initially promoted due to increased local protein concentration on the surface and destabilization of the folded state. However, further increase of NP-peptide attraction decreased the stability of amyloid fibrils and reduced their lateral diffusion on the NP surface necessary for peptide conformational changes and self-association, thus prohibiting amyloid aggregation. Moreover, we found that the relative concentration between protein and NPs also played an important role in amyloid aggregation. With a high NP/protein ratio, NPs that intrinsically promote protein aggregation may display an inhibitive effect by depleting the proteins in solution while having a low concentration of the proteins on each NP's surface. Our coarse-grained molecular dynamics simulation study offers a molecular mechanism for delineating the contrasting and seemingly conflicting effects of NP-protein attraction on amyloid aggregation and highlights the potential of tailoring anti-aggregation nanomedicine against amyloid diseases.

Structural diversity of Alzheimer's disease amyloid-β dimers and their role in oligomerization and fibril formation

Alzheimer's disease (AD) is associated with the formation of toxic amyloid-β (Aβ)42 oligomers, and recent evidence supports a role for Aβ dimers as building blocks for oligomers. Molecular dynamics simulation studies have identified clans for the dominant conformations of Aβ42 forming dimers; however, it is unclear if a larger spectrum of dimers is involved and which set(s) of dimers might evolve to oligomers verse fibrils. Therefore, for this study we generated multiple structural conformations of Aβ42, using explicit all-atom molecular dynamics, and then clustering the different structures based on key conformational similarities. Those matching a selection threshold were then used to model a process of oligomerization. Remarkably, we showed a greater diversity in Aβ dimers than previously described. Depending on the clan family, different types of Aβ dimers were obtained. While some had the tendency to evolve into oligomeric rings, others formed fibrils of diverse characteristics. Then we selected the dimers that would evolve to membranephilic annular oligomers. Nearly one third of the 28 evaluated annular oligomers had the dimer interfaces between the neighboring Aβ42 monomers with possible salt bridges between the residue K28 from one side and either residue E22 or D23 on the other. Based on these results, key amino acids were identified for point mutations that either enhanced or suppressed the formation and toxicity of oligomer rings. Our studies suggest a greater diversity of Aβ dimers. Understanding the structure of Aβ dimers might be important for the rationale design of small molecules that block formation of toxic oligomers.

A unified coarse-grained model of biological macromolecules based on mean-field multipole-multipole interactions

A unified coarse-grained model of three major classes of biological molecules—proteins, nucleic acids, and polysaccharides—has been developed. It is based on the observations that the repeated units of biopolymers (peptide groups, nucleic acid bases, sugar rings) are highly polar and their charge distributions can be represented crudely as point multipoles. The model is an extension of the united residue (UNRES) coarse-grained model of proteins developed previously in our laboratory. The respective force fields are defined as the potentials of mean force of biomacromolecules immersed in water, where all degrees of freedom not considered in the model have been averaged out. Reducing the representation to one center per polar interaction site leads to the representation of average site–site interactions as mean-field dipole–dipole interactions. Further expansion of the potentials of mean force of biopolymer chains into Kubo’s cluster-cumulant series leads to the appearance of mean-field dipole–dipole interactions, averaged in the context of local interactions within a biopolymer unit. These mean-field interactions account for the formation of regular structures encountered in biomacromolecules, e.g., α-helices and β-sheets in proteins, double helices in nucleic acids, and helicoidally packed structures in polysaccharides, which enables us to use a greatly reduced number of interacting sites without sacrificing the ability to reproduce the correct architecture. This reduction results in an extension of the simulation timescale by more than four orders of magnitude compared to the all-atom representation. Examples of the performance of the model are presented.

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The OPEP protein model: from single molecules, amyloid formation, crowding and hydrodynamics to DNA/RNA systems

The OPEP coarse-grained protein model has been applied to a wide range of applications since its first release 15 years ago. The model, which combines energetic and structural accuracy and chemical specificity, allows the study of single protein properties, DNA-RNA complexes, amyloid fibril formation and protein suspensions in a crowded environment. Here we first review the current state of the model and the most exciting applications using advanced conformational sampling methods. We then present the current limitations and a perspective on the ongoing developments.

Computational approaches to understanding protein aggregation in neurodegeneration

The generation of toxic non-native protein conformers has emerged as a unifying thread among disorders such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Atomic-level detail regarding dynamical changes that facilitate protein aggregation, as well as the structural features of large-scale ordered aggregates and soluble non-native oligomers, would contribute significantly to current understanding of these complex phenomena and offer potential strategies for inhibiting formation of cytotoxic species. However, experimental limitations often preclude the acquisition of high-resolution structural and mechanistic information for aggregating systems. Computational methods, particularly those combine both all-atom and coarse-grained simulations to cover a wide range of time and length scales, have thus emerged as crucial tools for investigating protein aggregation. Here we review the current state of computational methodology for the study of protein self-assembly, with a focus on the application of these methods toward understanding of protein aggregates in human neurodegenerative disorders.

Amyloid β-protein oligomers and Alzheimer's disease

The oligomer cascade hypothesis, which states that oligomers are the initiating pathologic agents in Alzheimer’s disease, has all but supplanted the amyloid cascade hypothesis, which suggested that fibers were the key etiologic agents in Alzheimer’s disease. We review here the results of in vivo, in vitro and in silico studies of amyloid β-protein oligomers, and discuss important caveats that should be considered in the evaluation of these results. This article is divided into four sections that mirror the main approaches used in the field to better understand oligomers: (1) attempts to locate and examine oligomers in vivo in situ; that is, without removing these species from their environment; (2) studies involving oligomers extracted from human or animal tissues and the subsequent characterization of their properties ex vivo; (3) studies of oligomers that have been produced synthetically and studied using a reductionist approach in relatively simple in vitro biophysical systems; and (4) computational studies of oligomers in silico. These multiple orthogonal approaches have revealed much about the molecular and cell biology of amyloid β-protein. However, as informative as these approaches have been, the amyloid β-protein oligomer system remains enigmatic.

Statistical mechanical treatments of protein amyloid formation

Protein aggregation is an important field of investigation because it is closely related to the problem of neurodegenerative diseases, to the development of biomaterials, and to the growth of cellular structures such as cyto-skeleton. Self-aggregation of protein amyloids, for example, is a complicated process involving many species and levels of structures. This complexity, however, can be dealt with using statistical mechanical tools, such as free energies, partition functions, and transfer matrices. In this article, we review general strategies for studying protein aggregation using statistical mechanical approaches and show that canonical and grand canonical ensembles can be used in such approaches. The grand canonical approach is particularly convenient since competing pathways of assembly and dis-assembly can be considered simultaneously. Another advantage of using statistical mechanics is that numerically exact solutions can be obtained for all of the thermodynamic properties of fibrils, such as the amount of fibrils formed, as a function of initial protein concentration. Furthermore, statistical mechanics models can be used to fit experimental data when they are available for comparison.

Random coil to globular thermal response of a protein (H3.1) with three knowledge-based coarse-grained potentials

The effect of temperature on the conformation of a histone (H3.1) is studied by a coarse-grained Monte Carlo simulation based on three knowledge-based contact potentials (MJ, BT, BFKV). Despite unique energy and mobility profiles of its residues, the histone H3.1 undergoes a systematic (possibly continuous) structural transition from a random coil to a globular conformation on reducing the temperature. The range over which such a systematic response in variation of the radius of gyration (R(g)) with the temperature (T) occurs, however, depends on the potential, i.e. ΔT(MJ) ≈ 0.013-0.020, ΔT(BT) ≈ 0.018-0.026, and ΔT(BFKV) ≈ 0.006-0.013 (in reduced unit). Unlike MJ and BT potentials, results from the BFKV potential show an anomaly where the magnitude of R(g) decreases on raising the temperature in a range ΔT(A) ≈ 0.015-0.018 before reaching its steady-state random coil configuration. Scaling of the structure factor, S(q) ∝ q(-1/ν), with the wave vector, q=2π/λ, and the wavelength, λ, reveals a systematic change in the effective dimension (D(e)∼1/ν) of the histone with all potentials (MJ, BT, BFKV): D(e)∼3 in the globular structure with D(e)∼2 for the random coil. Reproducibility of the general yet unique (monotonic) structural transition of the protein H3.1 with the temperature (in contrast to non-monotonic structural response of a similar but different protein H2AX) with three interaction sets shows that the knowledge-based contact potential is viable tool to investigate structural response of proteins. Caution should be exercise with the quantitative comparisons due to differences in transition regimes with these interactions.

Toward a molecular theory of early and late events in monomer to amyloid fibril formation

Quantitative understanding of the kinetics of fibril formation and the molecular mechanism of transition from monomers to fibrils is needed to obtain insights into the growth of amyloid fibrils and more generally self-assembly multisubunit protein complexes. Significant advances using computations of protein aggregation in a number of systems have established generic and sequence-specific aspects of the early steps in oligomer formation. Theoretical considerations, which view oligomer and fibril growth as diffusion in a complex energy landscape, and computational studies, involving minimal lattice and coarse-grained models, have revealed general principles governing the transition from monomeric protein to ordered fibrillar aggregates. Detailed atomistic calculations have explored the early stages of the protein aggregation pathway for a number of amyloidogenic proteins, most notably amyloid β- (Aβ-) protein and fragments from proteins linked to various diseases. These computational studies have provided insights into the role of sequence, role of water, and specific interatomic interactions underlying the thermodynamics and dynamics of elementary kinetic steps in the aggregation pathway. Novel methods are beginning to illustrate the structural basis for the production of Aβ-peptides through interactions with secretases in the presence of membranes. We show that a variety of theoretical approaches, ranging from scaling arguments to minimal models to atomistic simulations, are needed as a complement to experimental studies probing the principles governing protein aggregation.

Stability tests on known and misfolded structures with discrete and all atom molecular dynamics simulations

The brevity of molecular dynamics simulations often limits their utility in developing and evaluating structural models of proteins. The duration of simulations can be increased greatly using discrete molecular dynamics (DMD). However, the trade off is that coarse graining, implicit solvent, and other time-saving procedures reduce the accuracy of DMD simulations. Here we address some of these issues by comparing results of DMD and conventional all atom MD simulations on proteins of known structure and misfolded proteins. DMD simulations were performed at a range of temperatures to identify a 'physiological' temperature for DMD that mimicked molecular motions of conventional MD simulations at 310K. We also compared results obtained with a new implicit solvent model developed here based on Miyazawa-Jernigan interaction pair potential to those obtained with a previously used model based on Kyte-Doolittle hydropathy scale. We compared DMD and all atom molecular dynamics with explicit water by simulating both correctly and incorrectly folded structures, and monomeric and dimeric α β-barrel structures to analyze the ability of these procedures to distinguish between good and bad models. Deviations from the correct structures were substantially greater with DMD, as would be expected from coarse-graining and longer simulation time. Deviations were smallest for β-strands and greatest for coiled loops. Structures of the incorrectly folded models were very poorly preserved during the DMD simulations; but both methods were able to distinguish between the correct and the incorrect structures based on differences in the magnitudes of the root mean squared deviation (RMSD) from the starting conformation.

Competition between folding and aggregation in a model for protein solutions

We study the thermodynamic and kinetic consequences of the competition between single-protein folding and protein-protein aggregation using a phenomenological model, in which the proteins can be in the unfolded (U), misfolded (M) or folded (F) states. The phase diagram shows the coexistence between a phase with aggregates of misfolded proteins and a phase of isolated proteins (U or F) in solution. The spinodal at low protein concentrations shows non-monotonic behavior with temperature, with implications for the stability of solutions of folded proteins at low temperatures. We follow the dynamics upon "quenching" from the U-phase (cooling) or the F-phase (heating) to the metastable or unstable part of the phase diagram that results in aggregation. We describe how interesting consequences to the distribution of aggregate size, and growth kinetics arise from the competition between folding and aggregation.

Protein flexibility from discrete molecular dynamics simulations using quasi-physical potentials

We have applied all atoms discrete molecular dynamics (DMD) based on a quasi-physical potential to study the flexibility of an extended set of proteins for which atomistic MD simulations are available. The method uses pure physical potentials supplemented by information on secondary structure and despite its simplicity is able to reproduce with good accuracy the dynamics of proteins in solution. The method presents a clear improvement with respect to coarse-grained methods based on structure potentials and opens the possibility to explore dynamics of proteins out from the equilibrium and to trace conformational changes induced by interaction of proteins with both small and macromolecular ligands.

Elongation dynamics of amyloid fibrils: a rugged energy landscape picture

Protein amyloid fibrils are a form of linear protein aggregates that are implicated in many neurodegenerative diseases. Here, we study the dynamics of amyloid fibril elongation by performing Langevin dynamic simulations on a coarse-grained model of peptides. Our simulation results suggest that the elongation process is dominated by a series of local minimum due to frustration in monomer-fibril interactions. This rugged energy landscape picture indicates that the amount of recycling of monomers at the fibrils' ends before being fibrilized is substantially reduced in comparison to the conventional two-step elongation model. This picture, along with other predictions discussed, can be tested with current experimental techniques.

Generic coarse-grained model for protein folding and aggregation

A generic coarse-grained (CG) protein model is presented. The intermediate level of resolution (four beads per amino acid, implicit solvent) allows for accurate sampling of local conformations. It relies on simple interactions that emphasize structure, such as hydrogen bonds and hydrophobicity. Realistic alpha/beta content is achieved by including an effective nearest-neighbor dipolar interaction. Parameters are tuned to reproduce both local conformations and tertiary structures. The thermodynamics and kinetics of a three-helix bundle are studied. We check that the CG model is able to fold proteins with tertiary structures and amino acid sequences different from the one used for parameter tuning. By studying both helical and extended conformations we make sure the force field is not biased toward any particular secondary structure. The accuracy involved in folding not only the test protein but also other ones show strong evidence for amino acid cooperativity embedded in the model. Without any further adjustments or bias a realistic oligopeptide aggregation scenario is observed.

A condensation-ordering mechanism in nanoparticle-catalyzed peptide aggregation

Nanoparticles introduced in living cells are capable of strongly promoting the aggregation of peptides and proteins. We use here molecular dynamics simulations to characterise in detail the process by which nanoparticle surfaces catalyse the self-assembly of peptides into fibrillar structures. The simulation of a system of hundreds of peptides over the millisecond timescale enables us to show that the mechanism of aggregation involves a first phase in which small structurally disordered oligomers assemble onto the nanoparticle and a second phase in which they evolve into highly ordered as their size increases.

Protein misfolding and aggregation are associated with a wide variety of human disorders, which include Alzheimer's and Parkinson's diseases and late onset diabetes. It has been recently realised that the process of aggregation may be triggered by the presence of nanoparticles. We use here molecular dynamics simulations to characterise the molecular mechanism by which such nanoparticles are capable of enhancing the rate of formation of peptide aggregates. Our findings indicate that nanoparticle surfaces act as a catalyst that increases the local concentration of peptides, thus facilitating their subsequent assembly into stable fibrillar structures. The approach that we present, in addition to providing a description of the process of aggregation of peptides in the presence of nanoparticles, will enable the study of the mechanism of action of a variety of other potential aggregation-promoting agents present in living organisms, including lipid membranes and other cellular components.

A coarse-grained protein force field for folding and structure prediction

We have revisited the protein coarse-grained optimized potential for efficient structure prediction (OPEP). The training and validation sets consist of 13 and 16 protein targets. Because optimization depends on details of how the ensemble of decoys is sampled, trial conformations are generated by molecular dynamics, threading, greedy, and Monte Carlo simulations, or taken from publicly available databases. The OPEP parameters are varied by a genetic algorithm using a scoring function which requires that the native structure has the lowest energy, and the native-like structures have energy higher than the native structure but lower than the remote conformations. Overall, we find that OPEP correctly identifies 24 native or native-like states for 29 targets and has very similar capability to the all-atom discrete optimized protein energy model (DOPE), found recently to outperform five currently used energy models.

Multiscale approaches for studying energy transduction in dynein

Cytoplasmic dynein is an important motor that drives all minus-end directed movement along microtubules. Dynein is a complex motor whose processive motion is driven by ATP-hydrolysis. Dynein's run length has been measured to be several millimetres with typical velocities in the order of a few nanometres per second. Therefore, the average time between steps is a fraction of a second. When this time scale is compared with typical time scales for protein side chain and backbone movements (approximately 10(-9) s and approximately 10(-5) s, respectively), it becomes clear that a multi-timescale modelling approach is required to understand energy transduction in this protein. Here, we review recent efforts to use computational and mathematical modelling to understand various aspects of dynein's chemomechanical cycle. First, we describe a structural model of dynein's motor unit showing a heptameric organization of the motor subunits. Second, we describe our molecular dynamics simulations of the motor unit that are used to investigate the dynamics of the various motor domains. Third, we present a kinetic model of the coordination between the two dynein heads. Lastly, we investigate the various potential geometries of the dimer during its hydrolytic and stepping cycle.

Exploring the suitability of coarse-grained techniques for the representation of protein dynamics

A systematic study of two coarse-grained techniques for the description of protein dynamics is presented. The two techniques exploit either Brownian or discrete molecular dynamics algorithms applied in the context of simple C(alpha)-C(alpha) potentials, like those used in coarse-grained normal mode analysis. Coarse-grained simulations of the flexibility of protein metafolds are compared to those computed with fully atomistic molecular dynamics simulations using state-of-the-art physical potentials and explicit solvent. Both coarse-grained models efficiently capture critical features of the protein dynamics.