Self-assembly of β-sheet forming peptides into chiral fibrillar aggregates

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

sRAGE-binding and antimicrobial bioactivities of soy and pea protein after heating and in vitro infant digestion

During infant formula production, proteins are always heated, potentially affecting their digestibility and the bioactivities of resulting peptides. Although plant proteins are a promising dairy alternative for infant formula, they remain understudied, necessitating further investigations. Therefore, this research aimed to fill this gap by assessing the impact of different heating modes on soy protein (SP) and pea protein (PP), focusing on glycation levels, peptide formation during in vitro infant digestion, and immune protection potential (sRAGE-binding and antimicrobial activities) of the resulting peptides. Consequently, dry heating led to increased glycation and glycated peptide production, particularly with higher glycation in PP than SP. Moreover, PP exhibited an overall stronger sRAGE-binding capacity than SP, regardless of heating and digestion conditions. Regarding antimicrobial activity, both SP and PP-derived peptides displayed reduced effectiveness against Enterobacter cloacae after dry heating. Additionally, Staphylococcus epidermidis was differently inhibited, where PP-derived peptides showed inherent inhibition. The primary determinant of sRAGE-binding and antimicrobial potential in digestion-derived peptides was the protein source. Subsequent bioinformatics analysis predicted 519 and 133 potential antimicrobial peptides in SP and PP, respectively. This study emphasises the importance of protein source for infant formula to ensure infant health.

Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation

The time scales of long-time atomistic molecular dynamics simulations are typically reported in microseconds, while the time scales for experiments studying the kinetics of amyloid fibril formation are typically reported in minutes or hours. This time scale deficit of roughly 9 orders of magnitude presents a major challenge in the design of computer simulation methods for studying protein aggregation events. Coarse-grained molecular simulations offer a computationally tractable path forward for exploring the molecular mechanism driving the formation of these structures, which are implicated in diseases such as Alzheimer's, Parkinson's, and type-II diabetes. Network Hamiltonian models of aggregation are centered around a Hamiltonian function that returns the total energy of a system of aggregating proteins, given the graph structure of the system as an input. In the graph, or network, representation of the system, each protein molecule is represented as a node, and noncovalent bonds between proteins are represented as edges. The parameter, i.e., a set of coefficients that determine the degree to which each topological degree of freedom is favored or disfavored, must be determined for each network Hamiltonian model, and is a well-known technical challenge. The methodology is first demonstrated by beginning with an initial set of randomly parametrized models of low fibril fraction (<5% fibrillar), and evolving to subsequent generations of models, ultimately leading to high fibril fraction models (>70% fibrillar). The methodology is also demonstrated by applying it to optimizing previously published network Hamiltonian models for the 5 key amyloid fibril topologies that have been reported in the Protein Data Bank (PDB). The models generated by the AI produced fibril fractions that surpass previously published fibril fractions in 3 of 5 cases, including the most naturally abundant amyloid fibril topology, the 1,2 2-ribbon, which features a steric zipper. The authors also aim to encourage more widespread use of the network Hamiltonian methodology for fitting a wide variety of self-assembling systems by releasing a free open-source implementation of the genetic algorithm introduced here.

Comparative Performance of Computer Simulation Models of Intrinsically Disordered Proteins at Different Levels of Coarse-Graining

Coarse-graining is commonly used to decrease the computational cost of simulations. However, coarse-grained models are also considered to have lower transferability, with lower accuracy for systems outside the original scope of parametrization. Here, we benchmark a bead-necklace model and a modified Martini 2 model, both coarse-grained models, for a set of intrinsically disordered proteins, with the different models having different degrees of coarse-graining. The SOP-IDP model has earlier been used for this set of proteins; thus, those results are included in this study to compare how models with different levels of coarse-graining compare. The sometimes naive expectation of the least coarse-grained model performing best does not hold true for the experimental pool of proteins used here. Instead, it showed the least good agreement, indicating that one should not necessarily trust the otherwise intuitive notion of a more advanced model inherently being better in model choice.

Atomistic Pictures of Self-Assembled Helical Peptide Nanofibers

Spontaneous self-assembly of peptides has been at the forefront of supramolecular chemistry and materials science research over the last two decades. Despite the wealth of information on the morphology of the assembled objects, atomic resolution details of molecular arrangements inside them are largely unknown. In this paper, we investigated non-covalent assemblies of zwitterionic l-phenylalanine tripeptides in water using all-atom explicit-solvent molecular dynamics computer simulations. Our studies produced atomistic pictures of spontaneously assembled nanofibers composed of hundreds of peptide molecules. The dimensions of the nanofibers varied from 10 to 18 nm, with irregular helical twists along the long axes. Previously published experimental data, acquired under similar conditions, provided direct validation of the fibrous morphology and indirect support for the non-trivial helicity observed in our simulations. Quantitative analyses of peptide-water and peptide-peptide interactions revealed heterogeneous local environments of molecules across the nanometer length scales. The combination of electrostatic, hydrogen bonding, van der Waals, and hydrophobic interactions, adopted by a single molecule, was dependent on its relative position inside the fiber. Despite the presence of three hydrophobic phenyl groups, very few molecules were found to be completely shielded from the surrounding water, indicating a subtle role of the hydrophobic effect. Limited conformational flexibility of the tripeptide, along with bare electrostatic interactions, appeared to play a crucial role in the emergence of fibrous morphology of the nanostructures. Our analyses led us to formulate plausible qualitative explanations of the assembly behavior in terms of thermodynamic driving forces and kinetic considerations. We established a clear relationship between details of chemical interactions operating within few molecules and characteristics of the self-assembled states at much longer length scales.

pH Induced Switch in the Conformational Ensemble of Intrinsically Disordered Protein Prothymosin-α and Its Implications for Amyloid Fibril Formation

Aggregation of intrinsically disordered proteins (IDPs) can lead to neurodegenerative diseases. Although there is experimental evidence that acidic pH promotes IDP monomer compaction leading to aggregation, the general mechanism is unclear. We studied the pH effect on the conformational ensemble of prothymosin-α (proTα), which is involved in multiple essential functions, and probed its role in aggregation using computer simulations. We show that compaction in the proTα dimension at low pH is due to the protein's collapse in the intermediate region (E41-D80) rich in glutamic acid residues, enhancing its β-sheet content. We observed by performing dimer simulations that the conformations with high β-sheet content could act as aggregation-prone (N*) states and nucleate the aggregation process. The simulations initiated using N* states form dimers within a microsecond time scale, whereas the non-N* states do not form dimers within this time scale. This study contributes to understanding the general principles of pH-induced IDP aggregation.

Computational models for studying physical instabilities in high concentration biotherapeutic formulations

Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.

Hierarchical Self-Assembly Pathways of Peptoid Helices and Sheets

Peptoids (N-substituted glycines) are a class of tailorable synthetic peptidomic polymers. Amphiphilic diblock peptoids have been engineered to assemble 2D crystalline lattices with applications in catalysis and molecular separations. Assembly is induced in an organic solvent/water mixture by evaporating the organic phase, but the assembly pathways remain uncharacterized. We conduct all-atom molecular dynamics simulations of Nbrpe6Nc6 as a prototypical amphiphilic diblock peptoid comprising an NH₂-capped block of six hydrophobic N-((4-bromophenyl)ethyl)glycine residues conjugated to a polar NH₃(CH₂)₅CO tail. We identify a thermodynamically controlled assembly mechanism by which monomers assemble into disordered aggregates that self-order into 1D chiral helical rods then 2D achiral crystalline sheets. We support our computational predictions with experimental observations of 1D rods using small-angle X-ray scattering, circular dichroism, and atomic force microscopy and 2D crystalline sheets using X-ray diffraction and atomic force microscopy. This work establishes a new understanding of hierarchical peptoid assembly and principles for the design of peptoid-based nanomaterials.

Computational Models for the Study of Protein Aggregation

Protein aggregation has been studied by many groups around the world for many years because it can be the cause of a number of neurodegenerative diseases that have no effective treatment. Obtaining the structure of related fibrils and toxic oligomers, as well as describing the pathways and main factors that govern the self-organization process, is of paramount importance, but it is also very difficult. To solve this problem, experimental and computational methods are often combined to get the most out of each method. The effectiveness of the computational approach largely depends on the construction of a reasonable molecular model. Here we discussed different versions of the four most popular all-atom force fields AMBER, CHARMM, GROMOS, and OPLS, which have been developed for folded and intrinsically disordered proteins, or both. Continuous and discrete coarse-grained models, which were mainly used to study the kinetics of aggregation, are also summarized.

Diverse Aggregation Kinetics Predicted by a Coarse-Grained Peptide Model

Protein and peptide aggregation is a ubiquitous phenomenon with implications in medicine, pharmaceutical industry, and materials science. An important issue in peptide aggregation is the molecular mechanism of aggregate nucleation and growth. In many experimental studies, sigmoidal kinetics curves show a clear lag phase ascribed to nucleation; however, experimental studies also show downhill kinetics curves, where the monomers decay continuously and no lag phase can be seen. In this work, we study peptide aggregation kinetics using a coarse-grained implicit solvent model introduced in our previous work. Our simulations explore the hypothesis that the interplay between interchain attraction and intrachain bending stiffness controls the aggregation kinetics and transient aggregate morphologies. Indeed, our model reproduces the aggregation modes seen in experiment: no observed aggregation, nucleated aggregation, and rapid downhill aggregation. We find that the interaction strength is the primary parameter determining the aggregation mode, whereas the stiffness is a secondary parameter modulating the transient morphologies and aggregation rates: more attractive and stiff chains aggregate more rapidly and the transient morphologies are more ordered. We also explore the effects of the initial monomer concentration and the chain length. As the concentration decreases, the aggregation mode shifts from downhill to nucleated and no-aggregation. This concentration effect is in line with an experimental observation that the transition between downhill and nucleated kinetics is concentration-dependent. We find that longer peptides can aggregate at conditions where short peptides do not aggregate at all. It supports an experimental observation that the elongation of a homopeptide, e.g., polyglutamine, can increase the aggregation propensity.

Multiscale Models for Fibril Formation: Rare Events Methods, Microkinetic Models, and Population Balances

Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the potential of mean force for association and thereby muddle the docking and locking steps. Here, we developed a simple microkinetic model with separate locking and docking steps and with the appropriate concentration dependences for each step. We constructed a simple model comprised of chiral dumbbells that retains qualitative aspects of fibril formation. We used rare events methods to predict separate docking and locking rate constants for the model. The rate constants were embedded in the microkinetic model, with the microkinetic model embedded in a population balance model for “bottom-up” multiscale fibril growth rate predictions. These were compared to “top-down” results using simulation data with the same model and multiscale framework to obtain maximum likelihood estimates of the separate lock and dock rate constants. We used the same procedures to extract separate docking and locking rate constants from experimental fibril growth data. Our multiscale strategy, embedding rate theories, and kinetic models in conservation laws should help to extract docking and locking rate constants from experimental data or long molecular simulations with correct units and without compromising the molecular description.

Hydrogen Bond Surrogate-Constrained Dynamic Antiparallel β-Sheets

Antiparallel β-sheets are important secondary structures within proteins that equilibrate with random-coil states; however, little is known about the exact dynamics of this process. Here, the first dynamic β-sheet models that mimic this equilibrium have been designed by using an H-bond surrogate that introduces constraint and torque into a tertiary amide bond. 2D NMR data sufficiently reveal the structure, kinetics, and thermodynamics of the folding process, thereby leading the way to similar analysis in isolated biologically relevant β-sheets.

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

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.

Amyloid-Like Peptide Aggregates

Self-assembly of proteins and peptides into the amyloid fold is a widespread phenomenon in the natural world. The structural hallmark of self-assembly into amyloid fibrillar assemblies is the cross-beta motif, which conveys distinct morphological and mechanical properties. The amyloid fibril formation has contrasting results depending on the organism, in the sense that it can bestow an organism with the advantages of mechanical strength and improved functionality or, on the contrary, could give rise to pathological states. In this chapter we review the existing information on amyloid-like peptide aggregates, which could either be derived from protein sequences, but also could be rationally or de novo designed in order to self-assemble into amyloid fibrils under physiological conditions. Moreover, the development of self-assembled fibrillar biomaterials that are tailored for the desired properties towards applications in biomedical or environmental areas is extensively analyzed. We also review computational studies predicting the amyloid propensity of the natural amino acid sequences and the structure of amyloids, as well as designing novel functional amyloid materials.

The Early Phase of β2-Microglobulin Aggregation: Perspectives From Molecular Simulations

Protein β2-microglobulin is the causing agent of two amyloidosis, dialysis related amyloidosis (DRA), affecting the bones and cartilages of individuals with chronic renal failure undergoing long-term hemodialysis, and a systemic amyloidosis, found in one French family, which impairs visceral organs. The protein’s small size and its biomedical significance attracted the attention of theoretical scientists, and there are now several studies addressing its aggregation mechanism in the context of molecular simulations. Here, we review the early phase of β2-microglobulin aggregation, by focusing on the identification and structural characterization of monomers with the ability to trigger aggregation, and initial small oligomers (dimers, tetramers, hexamers etc.) formed in the so-called nucleation phase. We focus our analysis on results from molecular simulations and integrate our views with those coming from in vitro experiments to provide a broader perspective of this interesting field of research. We also outline directions for future computer simulation studies.

Chiral structure fluctuations predicted by a coarse-grained model of peptide aggregation

This work reports on the chiral structure fluctuations of peptide clusters at the early stages of aggregation in a coarse-grained peptide model. Our model reproduces a variety of aggregate structures, from disordered to crystal-like, that are observed experimentally. Unexpectedly, our molecular dynamics simulations showed that the small peptide cluster undergoes chiral structure fluctuations although the underlying implicit solvent model does not assume the chirality of peptides. The chiral fluctuations are quantified through a cluster twist parameter. A simple model is presented where the twist parameter undergoes a stochastic diffusion on a 1D potential surface. The shape of the potential surface changes with the cluster size. The model shows semi-quantitative agreement with the simulations. We hypothesize that the chiral fluctuations at the early stages of peptide aggregation can contribute to the selection of the final fibril structures.

Theoretical and computational advances in protein misfolding

Misfolded proteins escape the cellular quality control mechanism and fail to fold properly or remain correctly folded leading to a loss in their functional specificity. Thus misfolding of proteins cause a large number of very different diseases ranging from errors in metabolism to various types of complex neurodegenerative diseases. A theoretical and computational perspective of protein misfolding is presented with a special emphasis on its salient features, mechanism and consequences. These insights quantitatively analyze different determinants of misfolding, that may be applied to design disease specific molecular targets.

Chiral Plasmonic Nanostructures Enabled by Bottom-Up Approaches

We present a comprehensive review of recent developments in the field of chiral plasmonics. Significant advances have been made recently in understanding the working principles of chiral plasmonic structures. With advances in micro- and nanofabrication techniques, a variety of chiral plasmonic nanostructures have been experimentally realized; these tailored chiroptical properties vastly outperform those of their molecular counterparts. We focus on chiral plasmonic nanostructures created using bottom-up approaches, which not only allow for rational design and fabrication but most intriguingly in many cases also enable dynamic manipulation and tuning of chiroptical responses. We first discuss plasmon-induced chirality, resulting from the interaction of chiral molecules with plasmonic excitations. Subsequently, we discuss intrinsically chiral colloids, which give rise to optical chirality owing to their chiral shapes. Finally, we discuss plasmonic chirality, achieved by arranging achiral plasmonic particles into handed configurations on static or active templates. Chiral plasmonic nanostructures are very promising candidates for real-life applications owing to their significantly larger optical chirality than natural molecules. In addition, chiral plasmonic nanostructures offer engineerable and dynamic chiroptical responses, which are formidable to achieve in molecular systems. We thus anticipate that the field of chiral plasmonics will attract further widespread attention in applications ranging from enantioselective analysis to chiral sensing, structural determination, and in situ ultrasensitive detection of multiple disease biomarkers, as well as optical monitoring of transmembrane transport and intracellular metabolism.

Aggregation of amyloid peptides into fibrils driven by nanoparticles and their curvature effect

Fibrillation of amyloid peptides induces human diseases such as Alzheimer's disease, which has become a huge challenge. Some nanoparticles (NPs) could enhance peptide fibrillation by decreasing the lag time, yet how the size and shape of NPs affect amyloid fibrillation as well as the underlying mechanism remains unclear. Here, we investigated amyloid fibrillation on the surface of spherical NPs and cylindrical nanorods (NRs) of different sizes using coarse-grained Monte Carlo simulations. We focused on the curvature effect of NPs/NRs on the adsorption and fibrillation of peptide chains due to the size/shape difference. As the size of the NPs/NRs increases, the number of assembled peptide chains shows a non-monotonic tendency, and there is an optimal size for the highest adsorption. In most cases, the NRs could adsorb more peptides than the NPs of the same diameter due to the lower curvature. The mechanism beneath these observations was elucidated from a thermodynamic point of view. Our findings could provide a physical basis for the adsorption and fibrillation of amyloid peptides on NPs, and guide the design of future curvature-dependent NP-based amyloid treatment.

Formation of α-helical and β-sheet structures in membrane-bound human IAPP monomer and the resulting membrane deformation

Upon adsorption on membrane, human IAPP monomer takes conformational changes from coils to α-helices and β-sheets. The helices inserted and β on surface cause different types of membrane deformation, implying two distinct aggregation mechanisms.

On the Applicability of Force Fields To Study the Aggregation of Amyloidogenic Peptides Using Molecular Dynamics Simulations

Molecular dynamics simulations play an essential role in understanding biomolecular processes such as protein aggregation at temporal and spatial resolutions which are not attainable by experimental methods. For a correct modeling of protein aggregation, force fields must accurately represent molecular interactions. Here, we study the effect of five different force fields on the oligomer formation of Alzheimer's Aβ_16-22 peptide and two of its mutants: Aβ_16-22(F19V,F20V), which does not form fibrils, and Aβ_16-22(F19L) which forms fibrils faster than the wild type. We observe that while oligomer formation kinetics depends strongly on the force field, structural properties, such as the most relevant protein-protein contacts, are similar between them. The oligomer formation kinetics obtained with different force fields differ more from each other than the kinetics between aggregating and nonaggregating peptides simulated with a single force field. We discuss the difficulties in comparing atomistic simulations of amyloid oligomer formation with experimental observables.

Disorder at the Tips of a Disease-Relevant Aβ42 Amyloid Fibril: A Molecular Dynamics Study

We present a simulation study of the early events of peptide dissociation from a fibril of the Alzheimer's Aβ42 peptide. The fibril consists of layers of two adjacent Aβ42 peptides each folded in an S-shaped structure which has been determined by solid state NMR spectroscopy of a monomorphic disease-relevant species. Multiple molecular dynamics runs (16 at 310 K and 15 at 370 K) were carried out starting from an 18-peptide protofibril for a cumulative sampling of about 15 μs. The simulations show structural stability of the fibrillar core and an overall increase in the twist to about 3 degrees. The N-terminal segment 1-14 is disordered in all peptides. At both ends of the fibril, the central segment 21-29, which includes part of the β₂ strand, dissociates in some of the simulations. The β₁ and β₃ strands, residues 15-20 and 35-41, respectively, are structurally stable. The transient binding of the N-terminal stretch to the β₃ strand of the adjacent peptide at the tip is likely to contribute to the arrest phase of the stop-and-go mechanism.

Sequence dependent aggregation of peptides and fibril formation

Deciphering the links between amino acid sequence and amyloid fibril formation is key for understanding protein misfolding diseases. Here we use Monte Carlo simulations to study the aggregation of short peptides in a coarse-grained model with hydrophobic-polar (HP) amino acid sequences and correlated side chain orientations for hydrophobic contacts. A significant heterogeneity is observed in the aggregate structures and in the thermodynamics of aggregation for systems of different HP sequences and different numbers of peptides. Fibril-like ordered aggregates are found for several sequences that contain the common HPH pattern, while other sequences may form helix bundles or disordered aggregates. A wide variation of the aggregation transition temperatures among sequences, even among those of the same hydrophobic fraction, indicates that not all sequences undergo aggregation at a presumable physiological temperature. The transition is found to be the most cooperative for sequences forming fibril-like structures. For a fibril-prone sequence, it is shown that fibril formation follows the nucleation and growth mechanism. Interestingly, a binary mixture of peptides of an aggregation-prone and a non-aggregation-prone sequence shows the association and conversion of the latter to the fibrillar structure. Our study highlights the role of a sequence in selecting fibril-like aggregates and also the impact of a structural template on fibril formation by peptides of unrelated sequences.

Dynamics of proteins aggregation. II. Dynamic scaling in confined media

In this paper, the second in a series devoted to molecular modeling of protein aggregation, a mesoscale model of proteins together with extensive discontinuous molecular dynamics simulation is used to study the phenomenon in a confined medium. The medium, as a model of a crowded cellular environment, is represented by a spherical cavity, as well as cylindrical tubes with two aspect ratios. The aggregation process leads to the formation of β sheets and eventually fibrils, whose deposition on biological tissues is believed to be a major factor contributing to many neuro-degenerative diseases, such as Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis diseases. Several important properties of the aggregation process, including dynamic evolution of the total number of the aggregates, the mean aggregate size, and the number of peptides that contribute to the formation of the β sheets, have been computed. We show, similar to the unconfined media studied in Paper I [S. Zheng et al., J. Chem. Phys. 145, 134306 (2016)], that the computed properties follow dynamic scaling, characterized by power laws. The existence of such dynamic scaling in unconfined media was recently confirmed by experiments. The exponents that characterize the power-law dependence on time of the properties of the aggregation process in spherical cavities are shown to agree with those in unbounded fluids at the same protein density, while the exponents for aggregation in the cylindrical tubes exhibit sensitivity to the geometry of the system. The effects of the number of amino acids in the protein, as well as the size of the confined media, have also been studied. Similarities and differences between aggregation in confined and unconfined media are described, including the possibility of no fibril formation, if confinement is severe.

Guiding principles for peptide nanotechnology through directed discovery

Life's diverse molecular functions are largely based on only a small number of highly conserved building blocks - the twenty canonical amino acids. These building blocks are chemically simple, but when they are organized in three-dimensional structures of tremendous complexity, new properties emerge. This review explores recent efforts in the directed discovery of functional nanoscale systems and materials based on these same amino acids, but that are not guided by copying or editing biological systems. The review summarises insights obtained using three complementary approaches of searching the sequence space to explore sequence-structure relationships for assembly, reactivity and complexation, namely: (i) strategic editing of short peptide sequences; (ii) computational approaches to predicting and comparing assembly behaviours; (iii) dynamic peptide libraries that explore the free energy landscape. These approaches give rise to guiding principles on controlling order/disorder, complexation and reactivity by peptide sequence design.

Polymeric peptide pigments with sequence-encoded properties

Melanins are a family of heterogeneous polymeric pigments that provide ultraviolet (UV) light protection, structural support, coloration, and free radical scavenging. Formed by oxidative oligomerization of catecholic small molecules, the physical properties of melanins are influenced by covalent and noncovalent disorder. We report the use of tyrosine-containing tripeptides as tunable precursors for polymeric pigments. In these structures, phenols are presented in a (supra-)molecular context dictated by the positions of the amino acids in the peptide sequence. Oxidative polymerization can be tuned in a sequence-dependent manner, resulting in peptide sequence-encoded properties such as UV absorbance, morphology, coloration, and electrochemical properties over a considerable range. Short peptides have low barriers to application and can be easily scaled, suggesting near-term applications in cosmetics and biomedicine.

All-Atom Molecular Dynamics Simulations of Peptide Amphiphile Assemblies That Spontaneously Form Twisted and Helical Ribbon Structures

Self-assembly of peptide amphiphiles (PAs) has been an active research area as the assemblies can be programmed into variously shaped nanostructures. Although cylindrical micelles are common structures, gold-binding peptide conjugates can self-assemble into chiral nanofibers with single or double helices. When gold nanoparticles bind to the helices, the resulting chiral nanoparticle assemblies have a collective plasmonic circular dichroism signal that can serve as nanoscale circular polarizers or chiroptical sensors. A better atomic-level understanding of the factors which lead to helical PA assemblies is therefore of significant importance. In this study we show that all-atom molecular dynamics simulations can describe the spontaneous structural transformation from a planar assembly of PAs to a twisted assembly or to a helical ribbon. The twist angle and the helical diameter calculated from the simulations closely match the experimental results, with the oxidation of a single Met residue in each PA leading to a change from bilayer to monolayer assemblies with significantly different ribbon properties. A secondary structure analysis shows how a combination of β-sheet formation near the hydrophobic core of the micelle and PPII structures from proline-rich C-terminus regions favors helix formation. The simulations presented here demonstrate the capability of predicting self-assembly in chiral structures, protocols that can easily be applied to the assembly of other amphiphilic molecules.

Dynamics of proteins aggregation. I. Universal scaling in unbounded media

It is well understood that in some cases proteins do not fold correctly and, depending on their environment, even properly-folded proteins change their conformation spontaneously, taking on a misfolded state that leads to protein aggregation and formation of large aggregates. An important factor that contributes to the aggregation is the interactions between the misfolded proteins. Depending on the aggregation environment, the aggregates may take on various shapes forming larger structures, such as protein plaques that are often toxic. Their deposition in tissues is a major contributing factor to many neuro-degenerative diseases, such as Alzheimer's, Parkinson's, amyotrophic lateral sclerosis, and prion. This paper represents the first part in a series devoted to molecular simulation of protein aggregation. We use the PRIME, a meso-scale model of proteins, together with extensive discontinuous molecular dynamics simulation to study the aggregation process in an unbounded fluid system, as the first step toward MD simulation of the same phenomenon in crowded cellular environments. Various properties of the aggregates have been computed, including dynamic evolution of aggregate-size distribution, mean aggregate size, number of peptides that contribute to the formation of β sheets, number of various types of hydrogen bonds formed in the system, radius of gyration of the aggregates, and the aggregates' diffusivity. We show that many of such quantities follow dynamic scaling, similar to those for aggregation of colloidal clusters. In particular, at long times the mean aggregate size S(t) grows with time as, S(t) ∼ t^z, where z is the dynamic exponent. To our knowledge, this is the first time that the qualitative similarity between aggregation of proteins and colloidal aggregates has been pointed out.

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

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

Protein Aggregation and Molecular Crowding: Perspectives From Multiscale Simulations

Cells are extremely crowded environments, thus the use of diluted salted aqueous solutions containing a single protein is too simplistic to mimic the real situation. Macromolecular crowding might affect protein structure, folding, shape, conformational stability, binding of small molecules, enzymatic activity, interactions with cognate biomolecules, and pathological aggregation. The latter phenomenon typically leads to the formation of amyloid fibrils that are linked to several lethal neurodegenerative diseases, but that can also play a functional role in certain organisms. The majority of molecular simulations performed before the last few years were conducted in diluted solutions and were restricted both in the timescales and in the system dimensions by the available computational resources. In recent years, several computational solutions were developed to get close to physiological conditions. In this review we summarize the main computational techniques used to tackle the issue of protein aggregation both in a diluted and in a crowded environment.

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.

Preformed template fluctuations promote fibril formation: insights from lattice and all-atom models

Fibril formation resulting from protein misfolding and aggregation is a hallmark of several neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Despite the fact that the fibril formation process is very slow and thus poses a significant challenge for theoretical and experimental studies, a number of alternative pictures of molecular mechanisms of amyloid fibril formation have been recently proposed. What seems to be common for the majority of the proposed models is that fibril elongation involves the formation of pre-nucleus seeds prior to the creation of a critical nucleus. Once the size of the pre-nucleus seed reaches the critical nucleus size, its thermal fluctuations are expected to be small and the resulting nucleus provides a template for sequential (one-by-one) accommodation of added monomers. The effect of template fluctuations on fibril formation rates has not been explored either experimentally or theoretically so far. In this paper, we make the first attempt at solving this problem by two sets of simulations. To mimic small template fluctuations, in one set, monomers of the preformed template are kept fixed, while in the other set they are allowed to fluctuate. The kinetics of addition of a new peptide onto the template is explored using all-atom simulations with explicit water and the GROMOS96 43a1 force field and simple lattice models. Our result demonstrates that preformed template fluctuations can modulate protein aggregation rates and pathways. The association of a nascent monomer with the template obeys the kinetics partitioning mechanism where the intermediate state occurs in a fraction of routes to the protofibril. It was shown that template immobility greatly increases the time of incorporating a new peptide into the preformed template compared to the fluctuating template case. This observation has also been confirmed by simulation using lattice models and may be invoked to understand the role of template fluctuations in slowing down fibril elongation in vivo.

Simulations of Protein Aggregation: Insights from Atomistic and Coarse-Grained Models

This Perspective highlights recent computational approaches to protein aggregation, from coarse-grained models to atomistic simulations, using the islet amyloid polypeptide (IAPP) as a case study. We review salient open questions where simulations can make an impact, discuss the successes and challenges met by simulations, and explore new directions.

Thermodynamics versus Kinetics Dichotomy in the Linear Self-Assembly of Mixed Nanoblocks

We report classical and replica exchange molecular dynamics simulations that establish the mechanisms underpinning the growth kinetics of a binary mix of nanorings that form striped nanotubes via self-assembly. A step-growth coalescence model captures the growth process of the nanotubes, which suggests that high aspect ratio nanostructures can grow by obeying the universal laws of self-similar coarsening, contrary to systems that grow through nucleation and elongation. Notably, striped patterns do not depend on specific growth mechanisms, but are governed by tempering conditions that control the likelihood of depropagation and fragmentation.

A coarse-grained model for peptide aggregation on a membrane surface

The aggregation of peptides on a lipid bilayer is studied using coarse-grained molecular dynamics in implicit solvent. Peptides bind to and self-assemble on the membrane surface into β-rich fibrillar aggregates, even under conditions where only disordered oligomers form in bulk solution. Relative to a solid surface, the membrane surface facilitates peptide mobility and a more complex network of morphology transitions as aggregation proceeds. Additionally, final aggregate structures realized on the membrane surface are distinct from those observed on a comparable solid surface. The aggregated fibrils alter the local structure and material properties of the lipid bilayer in their immediate vicinity but have only a modest effect on the overall bending rigidity of the bilayer.

Dynamics of an intrinsically disordered protein reveal metastable conformations that potentially seed aggregation

Amyloid fibril deposits of the intrinsically disordered hIAPP peptide are found in 95% of type II diabetes patients, and the aggregation of this peptide is suggested to induce apoptotic cell-death in insulin-producing β-cells. Understanding the structure and dynamics of the hIAPP monomer in solution is thus important for understanding the nucleation of aggregation and the formation of oligomers. In this study, we identify the metastable conformational states of the hIAPP monomer and the dynamics of transitioning between them using Markov state models constructed from extensive molecular dynamics simulations. We show that the overall structure of the hIAPP peptide is random coil-like and lacks a dominant folded structure. Despite this fact, our model reveals a large number of reasonably well-populated metastable conformational states (or local free energy minima) having populations of a few percent or less. The time scales for transitioning between these states range from several microseconds to milliseconds. In contrast to folded proteins, there is no kinetic hub. More strikingly, a few states contain significant amounts of β-hairpin secondary structure and extended hydrophobic surfaces that are exposed to the solvent. We propose that these states may facilitate the nucleation of hIAPP aggregation through a significant component of the conformational selection mechanism, because they may increase their populations upon aggregation by promoting hydrophobic interactions and at the same time provide a flat geometry to seed the ordered β-strand packing of the fibrils.