Exploring the early steps of amyloid peptide aggregation by computers

Effect of caffeine on the aggregation of amyloid-β-A 3D RISM study

Alzheimer's disease is a detrimental neurological disorder caused by the formation of amyloid fibrils due to the aggregation of amyloid-β peptide. The primary therapeutic approaches for treating Alzheimer's disease are targeted to prevent this amyloid fibril formation using potential inhibitor molecules. The discovery of such inhibitor molecules poses a formidable challenge to the design of anti-amyloid drugs. This study investigates the effect of caffeine on dimer formation of the full-length amyloid-β using a combined approach of all-atom, explicit water molecular dynamics simulations and the three-dimensional reference interaction site model theory. The change in the hydration free energy of amyloid-β dimer, with and without the inhibitor molecules, is calculated with respect to the monomeric amyloid-β, where the hydration free energy is decomposed into energetic and entropic components, respectively. Dimerization is accompanied by a positive change in the partial molar volume. Dimer formation is spontaneous, which implies a decrease in the hydration free energy. However, a reverse trend is observed for the dimer with inhibitor molecules. It is observed that the negatively charged residues primarily contribute for the formation of the amyloid-β dimer. A residue-wise decomposition reveals that hydration/dehydration of the side-chain atoms of the charged amino acid residues primarily contribute to dimerization.

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.

Dynamics of Amyloid Formation from Simplified Representation to Atomistic Simulations

Amyloid fibril formation is an intrinsic property of short peptides, non-disease proteins, and proteins associated with neurodegenerative diseases. Aggregates of the Aβ and tau proteins, the α-synuclein protein, and the prion protein are observed in the brain of Alzheimer's, Parkinson's, and prion disease patients, respectively. Due to the transient short-range and long-range interactions of all species and their high aggregation propensities, the conformational ensemble of these devastating proteins, the exception being for the monomeric prion protein, remains elusive by standard structural biology methods in bulk solution and in lipid membranes. To overcome these limitations, an increasing number of simulations using different sampling methods and protein models have been performed. In this chapter, we first review our main contributions to the field of amyloid protein simulations aimed at understanding the early aggregation steps of short linear amyloid peptides, the conformational ensemble of the Aβ40/42 dimers in bulk solution, and the stability of Aβ aggregates in lipid membrane models. Then we focus on our studies on the interactions of amyloid peptides/inhibitors to prevent aggregation, and long amyloid sequences, including new results on a monomeric tau construct.

Defining the Neuropathological Aggresome across in Silico, in Vitro, and ex Vivo Experiments

The loss of proteostasis over the life course is associated with a wide range of debilitating degenerative diseases and is a central hallmark of human aging. When left unchecked, proteins that are intrinsically disordered can pathologically aggregate into highly ordered fibrils, plaques, and tangles (termed amyloids), which are associated with countless disorders such as Alzheimer's disease, Parkinson's disease, type II diabetes, cancer, and even certain viral infections. However, despite significant advances in protein folding and solution biophysics techniques, determining the molecular cause of these conditions in humans has remained elusive. This has been due, in part, to recent discoveries showing that soluble protein oligomers, not insoluble fibrils or plaques, drive the majority of pathological processes. This has subsequently led researchers to focus instead on heterogeneous and often promiscuous protein oligomers. Unfortunately, significant gaps remain in how to prepare, model, experimentally corroborate, and extract amyloid oligomers relevant to human disease in a systematic manner. This Review will report on each of these techniques and their successes and shortcomings in an attempt to standardize comparisons between protein oligomers across disciplines, especially in the context of neurodegeneration. By standardizing multiple techniques and identifying their common overlap, a clearer picture of the soluble neuropathological aggresome can be constructed and used as a baseline for studying human disease and aging.

The Effect of (-)-Epigallocatechin-3-Gallate on the Amyloid-β Secondary Structure

Amyloid-β (Aβ) is a macromolecular structure of great interest because its misfolding and aggregation, along with changes in the secondary structure, have been correlated with its toxicity in various neurodegenerative diseases. Small drug-like molecules can modulate the amyloid secondary structure and therefore have raised significant interest in applications to active and passive therapies targeting amyloids. In this study, we investigate the interactions of epigallocatechin-3-gallate (EGCG), found in green tea, with Aβ polypeptides, using a combination of in vitro immuno-infrared sensor measurements, docking, molecular dynamics simulations, and ab initio calculations. We find that the interactions of EGCG are dominated by only a few residues in the fibrils, including hydrophobic π-π interactions with aromatic rings of side chains and hydrophilic interactions with the backbone of Aβ, as confirmed by extended (1-μs-long) molecular dynamics simulations. Immuno-infrared sensor data are consistent with degradation of Aβ fibril induced by EGCG and inhibition of Aβ fibril and oligomer formation, as manifested by the recovery of the amide-I band of monomeric Aβ, which is red-shifted by 26 cm^-1 when compared to the amide-I band of the fibrillar form. The shift is rationalized by computations of the infrared spectra of Aβ42 model structures, suggesting that the conformational change involves interchain hydrogen bonds in the amyloid fibrils that are broken upon binding of EGCG.

Molecular Model for the Self-Assembly of the Cyclic Lipodepsipeptide Pseudodesmin A

Self-assembly of peptides into supramolecular structures represents an active field of research with potential applications ranging from material science to medicine. Their study typically involves the application of a large toolbox of spectroscopic and imaging techniques. However, quite often, the structural aspects remain underexposed. Besides, molecular modeling of the self-assembly process is usually difficult to handle, since a vast conformational space has to be sampled. Here, we have used an approach that combines short molecular dynamics simulations for peptide dimerization and NMR restraints to build a model of the supramolecular structure from the dimeric units. Experimental NMR data notably provide crucial information about the conformation of the monomeric units, the supramolecular assembly dimensions, and the orientation of the individual peptides within the assembly. This in silico/in vitro mixed approach enables us to define accurate atomistic models of supramolecular structures of the bacterial cyclic lipodepsipeptide pseudodesmin A.

Polyphenols Modulate Alzheimer's Amyloid Beta Aggregation in a Structure-Dependent Manner

Some polyphenols, which are common natural compounds in fruits, vegetables, seeds, and oils, have been considered as potent inhibitors of amyloid beta (Aβ) aggregation, one critical pathogenic event in Alzheimer’s disease (AD). However, the mechanisms by which polyphenols affect aggregation are not fully understood. In this study, we aimed to investigate the effect of two classes of polyphenols (flavonoids and stilbenes) on the self-assembly of Aβ_42, in particular, how this relates to structure. We found that the flavonoids gallocatechin gallate (GCG) and theaflavin (TF) could completely inhibit Aβ aggregation, while two stilbenes, resveratrol and its glucoside derivative piceid, could also suppress Aβ aggregation, but to a much lesser extent. Intriguingly, resveratrol accelerated the formation of Aβ fibrils before its decreasing effect on fibrillation was detected. Atomic force microscopy (AFM) images showed a huge mass of long and thin Aβ fibrils formed in the presence of resveratrol. Although the morphology was the same in the presence of piceid, the fibrils were sparse in the presence of picead. In the presence of flavonoids, Aβ morphology was unchanged from prior to incubation (0 h), in agreement with amyloid beta kinetics analysis using thioflavin-T fluorescence assay. The electrochemical data showed a higher ability of GCG and TF to interact with Aβ than resveratrol and piceid, which could be attributed to the presence of more aromatic rings and hydroxyl groups. In addition, the two flavonoids exhibited a similar propensity for Aβ aggregation, despite having some differences in their structure. However, in the case of stilbenes, the addition of a glucoside at C-7 slightly decreased anti-Aβ aggregation property compared to resveratrol. These findings contribute to a better understanding of the essential structural features of polyphenols required for inhibiting Aβ aggregation, and the possible mechanisms for modulating aggregation.

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.

d-Amino Acid Pseudopeptides as Potential Amyloid-Beta Aggregation Inhibitors

A causative factor for neurotoxicity associated with Alzheimer's disease is the aggregation of the amyloid-β (Aβ) peptide into soluble oligomers. Two all d-amino acid pseudo-peptides, SGB1 and SGD1, were designed to stop the aggregation. Molecular dynamics (MD) simulations have been carried out to study the interaction of the pseudo-peptides with both Aβ13⁻23 (the core recognition site of Aβ) and full-length Aβ1⁻42. Umbrella sampling MD calculations have been used to estimate the free energy of binding, ∆G, of these peptides to Aβ13⁻23. The highest ∆Gbinding is found for SGB1. Each of the pseudo-peptides was also docked to Aβ1⁻42 and subjected up to seven microseconds of all atom molecular dynamics simulations. The resulting structures lend insight into how the dynamics of Aβ1⁻42 are altered by complexation with the pseudo-peptides and confirmed that SGB1 may be a better candidate for developing into a drug to prevent Alzheimer's disease.

Thermodynamic properties of amyloid fibrils in equilibrium

In this manuscript we use a two-dimensional coarse-grained model to study how amyloid fibrils grow towards an equilibrium state where they coexist with proteins dissolved in a solution. Free-energies to dissociate proteins from fibrils are estimated from the residual concentration of dissolved proteins. Consistent with experiments, the concentration of proteins in solution affects the growth rate of fibrils but not their equilibrium state. Also, studies of the temperature dependence of the equilibrium state can be used to estimate thermodynamic quantities, e.g., heat capacity and entropy.

A Protocol for the Design of Protein and Peptide Nanostructure Self-Assemblies Exploiting Synthetic Amino Acids

In recent years there has been increasing interest in nanostructure design based on the self-assembly properties of proteins and polymers. Nanodesign requires the ability to predictably manipulate the properties of the self-assembly of autonomous building blocks, which can fold or aggregate into preferred conformational states. The design includes functional synthetic materials and biological macromolecules. Autonomous biological building blocks with available 3D structures provide an extremely rich and useful resource. Structural databases contain large libraries of protein molecules and their building blocks with a range of sizes, shapes, surfaces, and chemical properties. The introduction of engineered synthetic residues or short peptides into these building blocks can greatly expand the available chemical space and enhance the desired properties. Herein, we summarize a protocol for designing nanostructures consisting of self-assembling building blocks, based on our recent works. We focus on the principles of nanostructure design with naturally occurring proteins and synthetic amino acids, as well as hybrid materials made of amyloids and synthetic polymers.

Water ordering controls the dynamic equilibrium of micelle-fibre formation in self-assembly of peptide amphiphiles

Understanding the role of water in governing the kinetics of the self-assembly processes of amphiphilic peptides remains elusive. Here, we use a multistage atomistic-coarse-grained approach, complemented by circular dichroism/infrared spectroscopy and dynamic light scattering experiments to highlight the dual nature of water in driving the self-assembly of peptide amphiphiles (PAs). We show computationally that water cage formation and breakage near the hydrophobic groups control the fusion dynamics and aggregation of PAs in the micellar stage. Simulations also suggest that enhanced structural ordering of vicinal water near the hydrophilic amino acids shifts the equilibrium towards the fibre phase and stimulates structure and order during the PA assembly into nanofibres. Experiments validate our simulation findings; the measured infrared O–H bond stretching frequency is reminiscent of an ice-like bond which suggests that the solvated water becomes increasingly ordered with time in the assembled peptide network, thus shedding light on the role of water in a self-assembly process.

The role of water in the kinetics of the self-assembly process of amphiphilic peptides still remains unknown. Sankaranarayanan et al. have shown through computational study that water has a dual nature when dictating the mechanism and dynamics of self-assembly of peptide amphiphiles.

Advances in the Simulation of Protein Aggregation at the Atomistic Scale

Protein aggregation into highly structured amyloid fibrils is associated with various diseases including Alzheimer's disease, Parkinson's disease, and type II diabetes. Amyloids can also have normal biological functions and, in the future, could be used as the basis for novel nanoscale materials. However, a full understanding of the physicochemical forces that drive protein aggregation is still lacking. Such understanding is crucial for the development of drugs that can effectively inhibit aberrant amyloid aggregation and for the directed design of functional amyloids. Atomistic simulations can help understand protein aggregation. In particular, atomistic simulations can be used to study the initial formation of toxic oligomers which are hard to characterize experimentally and to understand the difference in aggregation behavior between different amyloidogenic peptides. Here, we review the latest atomistic simulations of protein aggregation, concentrating on amyloidogenic protein fragments, and provide an outlook for the future in this field.

Exploring the role of hydration and confinement in the aggregation of amyloidogenic peptides Aβ(16-22) and Sup35(7-13) in AOT reverse micelles

Knowledge of how intermolecular interactions of amyloid-forming proteins cause protein aggregation and how those interactions are affected by sequence and solution conditions is essential to our understanding of the onset of many degenerative diseases. Of particular interest is the aggregation of the amyloid-β (Aβ) peptide, linked to Alzheimer's disease, and the aggregation of the Sup35 yeast prion peptide, which resembles the mammalian prion protein linked to spongiform encephalopathies. To facilitate the study of these important peptides, experimentalists have identified small peptide congeners of the full-length proteins that exhibit amyloidogenic behavior, including the KLVFFAE sub-sequence, Aβ16-22, and the GNNQQNY subsequence, Sup357-13. In this study, molecular dynamics simulations were used to examine these peptide fragments encapsulated in reverse micelles (RMs) in order to identify the fundamental principles that govern how sequence and solution environment influence peptide aggregation. Aβ16-22 and Sup357-13 are observed to organize into anti-parallel and parallel β-sheet arrangements. Confinement in the sodium bis(2-ethylhexyl) sulfosuccinate (AOT) reverse micelles is shown to stabilize extended peptide conformations and enhance peptide aggregation. Substantial fluctuations in the reverse micelle shape are observed, in agreement with earlier studies. Shape fluctuations are found to facilitate peptide solvation through interactions between the peptide and AOT surfactant, including direct interaction between non-polar peptide residues and the aliphatic surfactant tails. Computed amide I IR spectra are compared with experimental spectra and found to reflect changes in the peptide structures induced by confinement in the RM environment. Furthermore, examination of the rotational anisotropy decay of water in the RM demonstrates that the water dynamics are sensitive to the presence of peptide as well as the peptide sequence. Overall, our results demonstrate that the RM is a complex confining environment where substantial direct interaction between the surfactant and peptides plays an important role in determining the resulting ensemble of peptide conformations. By extension the results suggest that similarly complex sequence-dependent interactions may determine conformational ensembles of amyloid-forming peptides in a cellular environment.

On the relationship between low-frequency normal modes and the large-scale conformational changes of proteins

Normal mode analysis is a computational technique that allows to study the dynamics of biological macromolecules. It was first applied to small protein cases, more than thirty years ago. The interest in this technique then raised when it was realized that it can provide insights about the large-scale conformational changes a protein can experience, for instance upon ligand binding. As it was also realized that studying highly simplified protein models can provide similar insights, meaning that this kind of analysis can be both quick and simple to handle, several applications were proposed, in the context of various structural biology techniques. This review focuses on these applications, as well as on how the functional relevance of the lowest-frequency modes of proteins was established.

Chirality-assisted ring-like aggregation of aβ(1-40) at liquid-solid interfaces: a stereoselective two-step assembly process

Molecular chirality is introduced at liquid-solid interfaces. A ring-like aggregation of amyloid Aβ(1-40) on N-isobutyryl-L-cysteine (L-NIBC)-modified gold substrate occurs at low Aβ(1-40) concentration, while D-NIBC modification only results in rod-like aggregation. Utilizing atomic force microscope controlled tip-enhanced Raman scattering, we directly observe the secondary structure information for Aβ(1-40) assembly in situ at the nanoscale. D- or L-NIBC on the surface can guide parallel or nonparallel alignment of β-hairpins through a two-step process based on electrostatic-interaction-enhanced adsorption and subsequent stereoselective recognition. Possible electrostatic interaction sites (R5 and K16) and a chiral recognition site (H14) of Aβ(1-40) are proposed, which may provide insight into the understanding of this effect.

Stability of amyloid oligomers

Molecular simulations are now commonly used to complement experimental techniques in investigating amyloids and their role in human diseases. In this chapter, we will summarize techniques and approaches often used in amyloid simulations and will present recent success stories. Our examples will be focused on lessons learned from molecular dynamics simulations in aqueous environments that start from preformed aggregates. These studies explore the limitations that arise from the choice of force field, the role of mutations in the growth of amyloid aggregates, segmental polymorphism, and the importance of cross-seeding. Furthermore, they give evidence for potential toxicity mechanisms. We finally discuss the role of molecular simulations in the search for aggregation inhibitors.

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.

Understanding amyloid fibril nucleation and aβ oligomer/drug interactions from computer simulations

Evolution has fine-tuned proteins to accomplish a variety of tasks. Yet, with aging, some proteins assemble into harmful amyloid aggregates associated with neurodegenerative diseases, such as Alzheimer's disease (AD), which presents a complex and costly challenge to our society. Thus, far, drug after drug has failed to slow the progression of AD, characterized by the self-assembly of the 39-43 amino acid β-amyloid (Aβ) protein into extracellular senile plaques that form a cross-β structure. While there is experimental evidence that the Aβ small oligomers are the primary toxic species, standard tools of biology have failed to provide structures of these transient, inhomogeneous assemblies. Despite extensive experimental studies, researchers have not successfully characterized the nucleus ensemble, the starting point for rapid fibril formation. Similarly scientists do not have atomic data to show how the compounds that reduce both fibril formation and toxicity in cells bind to Aβ42 oligomers. In this context, computer simulations are important tools for gaining insights into the self-assembly of amyloid peptides and the molecular mechanism of inhibitors. This Account reviews what analytical models and simulations at different time and length scales tell us about the dynamics, kinetics, and thermodynamics of amyloid fibril formation and, notably, the nucleation process. Though coarse-grained and mesoscopic protein models approximate atomistic details by averaging out unimportant degrees of freedom, they provide generic features of amyloid formation and insights into mechanistic details of the self-assembly process. The thermodynamics and kinetics vary from linear peptides adopting straight β-strands in fibrils to longer peptides adopting in parallel U shaped conformations in fibrils. In addition, these properties change with the balance between electrostatic and hydrophobic interactions and the intrinsic disorder of the system. However, simulations suggest that the critical nucleus size might be on the order of 20 chains under physiological conditions. The transition state might be characterized by a simultaneous change from mixed antiparallel/parallel β-strands with random side-chain packing to the final antiparallel or parallel states with the steric zipper packing of the side chains. Second, we review our current computer-based knowledge of the 3D structures of inhibitors with Aβ42 monomer and oligomers, a prerequisite for developing new drugs against AD. Recent extensive all-atom simulations of Aβ42 dimers with known inhibitors such as the green tea compound epigallocatechin-3-gallate and 1,4-naphthoquinon-2-yl-l-tryptophan provide a spectrum of initial Aβ42/inhibitor structures useful for screening and drug design. We conclude by discussing future directions that may offer opportunities to fully understand nucleation and further AD drug development.

Combination of theoretical and experimental approaches for the design and study of fibril-forming peptides

Self-assembling peptides that can form supramolecular structures such as fibrils, ribbons, and nanotubes are of particular interest to modern bionanotechnology and materials science. Their ability to form biocompatible nanostructures under mild conditions through non-covalent interactions offers a big biofabrication advantage. Structural motifs extracted from natural proteins are an important source of inspiration for the rational design of such peptides. Examples include designer self-assembling peptides that correspond to natural coiled-coil motifs, amyloid-forming proteins, and natural fibrous proteins. In this chapter, we focus on the exploitation of structural information from beta-structured natural fibers. We review a case study of short peptides that correspond to sequences from the adenovirus fiber shaft. We describe both theoretical methods for the study of their self-assembly potential and basic experimental protocols for the assessment of fibril-forming assembly.

Coarse-grained models for protein folding and aggregation

Coarse-grained models for protein folding and aggregation are used to explore large dimension scales and timescales that are inaccessible to all-atom models in explicit aqueous solution. Combined with enhanced configuration search methods, these simplified models with various levels of granularity offer the possibility to determine equilibrium structures, compare folding kinetics and thermodynamics with experiments for single proteins and understand the dynamic assembly of amyloid proteins leading to neurodegenerative diseases. I shall describe recent progress in developing such models, and discuss their potentials and limitations in probing the folding and misfolding of proteins with computer simulations.

Kinetics of amyloid aggregation: a study of the GNNQQNY prion sequence

The small amyloid-forming GNNQQNY fragment of the prion sequence has been the subject of extensive experimental and numerical studies over the last few years. Using unbiased molecular dynamics with the OPEP coarse-grained potential, we focus here on the onset of aggregation in a 20-mer system. With a total of 16.9 of simulations at 280 K and 300 K, we show that the GNNQQNY aggregation follows the classical nucleation theory (CNT) in that the number of monomers in the aggregate is a very reliable descriptor of aggregation. We find that the critical nucleus size in this finite-size system is between 4 and 5 monomers at 280 K and 5 and 6 at 300 K, in overall agreement with experiment. The kinetics of growth cannot be fully accounted for by the CNT, however. For example, we observe considerable rearrangements after the nucleus is formed, as the system attempts to optimize its organization. We also clearly identify two large families of structures that are selected at the onset of aggregation demonstrating the presence of well-defined polymorphism, a signature of amyloid growth, already in the 20-mer aggregate.

Protein aggregation plays an important pathological role in numerous neurodegenerative diseases such as Alzheimer's, Parkinson's, Creutzfeldt-Jakob, the Prion disease and diabetes mellitus. In most cases, misfolded proteins are involved and aggregate irreversibly to form highly ordered insoluble macrostructures, called amyloid fibrils, which deposit in the brain. Studies have revealed that all proteins are capable of forming amyloid fibrils that all share common structural features and therefore aggregation mechanisms. The toxicity of amyloid aggregates is however not attributed to the fibrils themselves but rather to smaller more disordered aggregates, oligomers, forming parallel to or prior to fibrils. Understanding the assembly process of these amyloid oligomers is key to understanding their toxicity mechanism in order to devise a possible treatment strategy targeting these toxic aggregates. Our approach here is to computationally study the aggregation dynamics of a 20-mer of an amyloid peptide GNNQQNY from a prion protein. Our findings suggest that the assembly is a spontaneous process that can be described as a complex nucleation and growth mechanism and which can lead to two classes of morphologies for the aggregates, one of which resembles a protofibril-like structure. Such numerical studies are crucial to understanding the details of fast biological processes and complement well experimental studies.

Coarse-grain Protein Models

Coarse-graining is a powerful approach for modeling biomolecules that, over the last few decades, has been extensively applied to proteins. Coarse-grain models offer access to large systems and to slow processes without becoming computationally unmanageable. In addition, they are very versatile, enabling both the protein representation and the energy function to be adapted to the biological problem in hand. This review concentrates on modeling soluble proteins and their assemblies. It presents an overview of the coarse-grain representations, of the associated interaction potentials, and of the optimization procedures used to define them. It then shows how coarse-grain models have been used to understand processes involving proteins, from their initial folding to their functional properties, their binary interactions, and the assembly of large complexes.

Virtual Screening for Dipeptide Aggregation: Toward Predictive Tools for Peptide Self-Assembly

Several short peptide sequences are known to self-assemble into supramolecular nanostructures with interesting properties. In this study, coarse-grained molecular dynamics is employed to rapidly screen all 400 dipeptide combinations and predict their ability to aggregate as a potential precursor to their self-assembly. The simulation protocol and scoring method proposed allows a rapid determination of whether a given peptide sequence is likely to aggregate (an indicator for the ability to self-assemble) under aqueous conditions. Systems that show strong aggregation tendencies in the initial screening are selected for longer simulations, which result in good agreement with the known self-assembly or aggregation of dipeptides reported in the literature. Our extended simulations of the diphenylalanine system show that the coarse-grain model is able to reproduce salient features of nanoscale systems and provide insight into the self-assembly process for this system.

Unfolding of the amyloid β-peptide central helix: mechanistic insights from molecular dynamics simulations

Alzheimer's disease (AD) pathogenesis is associated with formation of amyloid fibrils caused by polymerization of the amyloid β-peptide (Aβ), which is a process that requires unfolding of the native helical structure of Aβ. According to recent experimental studies, stabilization of the Aβ central helix is effective in preventing Aβ polymerization into toxic assemblies. To uncover the fundamental mechanism of unfolding of the Aβ central helix, we performed molecular dynamics simulations for wild-type (WT), V18A/F19A/F20A mutant (MA), and V18L/F19L/F20L mutant (ML) models of the Aβ central helix. It was quantitatively demonstrated that the stability of the α-helical conformation of both MA and ML is higher than that of WT, indicating that the α-helical propensity of the three nonpolar residues (18, 19, and 20) is the main factor for the stability of the whole Aβ central helix and that their hydrophobicity plays a secondary role. WT was found to completely unfold by a three-step mechanism: 1) loss of α-helical backbone hydrogen bonds, 2) strong interactions between nonpolar sidechains, and 3) strong interactions between polar sidechains. WT did not completely unfold in cases when any of the three steps was omitted. MA and ML did not completely unfold mainly due to the lack of the first step. This suggests that disturbances in any of the three steps would be effective in inhibiting the unfolding of the Aβ central helix. Our findings would pave the way for design of new drugs to prevent or retard AD.

A fast method for large-scale de novo peptide and miniprotein structure prediction

Although peptides have many biological and biomedical implications, an accurate method predicting their equilibrium structural ensembles from amino acid sequences and suitable for large-scale experiments is still missing. We introduce a new approach-PEP-FOLD-to the de novo prediction of peptides and miniproteins. It first predicts, in the terms of a Hidden Markov Model-derived structural alphabet, a limited number of local conformations at each position of the structure. It then performs their assembly using a greedy procedure driven by a coarse-grained energy score. On a benchmark of 52 peptides with 9-23 amino acids, PEP-FOLD generates lowest-energy conformations within 2.8 and 2.3 A Calpha root-mean-square deviation from the full nuclear magnetic resonance structures (NMR) and the NMR rigid cores, respectively, outperforming previous approaches. For 13 miniproteins with 27-49 amino acids, PEP-FOLD reaches an accuracy of 3.6 and 4.6 A Calpha root-mean-square deviation for the most-native and lowest-energy conformations, using the nonflexible regions identified by NMR. PEP-FOLD simulations are fast-a few minutes only-opening therefore, the door to in silico large-scale rational design of new bioactive peptides and miniproteins.

Polymorphism of Alzheimer's Abeta17-42 (p3) oligomers: the importance of the turn location and its conformation

Abeta(17-42) (so-called p3) amyloid is detected in vivo in the brains of individuals with Alzheimer's disease or Down's syndrome. We investigated the polymorphism of Abeta(17-42) oligomers based on experimental data from steady-state NMR measurements, electron microscopy, two-dimensional hydrogen exchange, and mutational studies, using all-atom molecular-dynamics simulation with explicit solvent. We assessed the structural stability and the populations. Our results suggest that conformational differences in the U-turn of Abeta(17-42) lead to polymorphism in beta-sheet registration and retention of an ordered beta-strand organization at the termini. Further, although the parallel Abeta(17-42) oligomer organization is the most stable of the conformers investigated here, different antiparallel Abeta(17-42) organizations are also stable and compete with the parallel architectures, presenting a polymorphic population. In this study we propose that 1), the U-turn conformation is the primary factor leading to polymorphism in the assembly of Abeta(17-42) oligomers, and is also coupled to oligomer growth; and 2), both parallel Abeta(17-42) oligomers and an assembly of Abeta(17-42) oligomers that includes both parallel and antiparallel organizations contribute to amyloid fibril formation. Finally, since a U-turn motif generally appears in amyloids formed by full proteins or long fragments, and since to date these have been shown to exist only in parallel architectures, our results apply to a broad range of oligomers and fibrils.

An artificial intelligence approach for modeling molecular self-assembly: agent-based simulations of rigid molecules

Agent-based simulations are rule-based models traditionally used for the simulations of complex systems. In this paper, an algorithm based on the concept of agent-based simulations is developed to predict the lowest energy packing of a set of identical rigid molecules. The agents are identified with rigid portions of the system under investigation, and they evolve following a set of rules designed to drive the system toward the lowest energy minimum. The algorithm is compared with a conventional Metropolis Monte Carlo algorithm, and it is applied on a large set of representative models of molecules. For all the systems studied, the agent-based method consistently finds a significantly lower energy minima than the Monte Carlo algorithm because the system evolution includes elements of adaptation (new configurations induce new types of moves) and learning (past successful choices are repeated).

Structures and thermodynamics of Alzheimer's amyloid-beta Abeta(16-35) monomer and dimer by replica exchange molecular dynamics simulations: implication for full-length Abeta fibrillation

Many proteins display a strand-loop-strand motif in their amyloid fibrillar states. For instance, the amyloid beta-protein, Abeta1-40, associated with Alzheimer's disease, displays a loop at positions 22-28 in its amyloid fibril state. It has been suggested that this loop could appear early in the aggregation process, but quantitative information regarding its presence in small oligomers remains scant. Because residues 1-15 are disordered in Abeta1-42 fibrils and Abeta10-35 forms fibrils in vitro, we select the peptide Abeta16-35, centered on residues 22-28 and determine the structures and thermodynamics of the monomer and dimer using coarse-grained implicit solvent replica exchange molecular dynamics simulations. Our simulations totalling 5 mus for the monomer and 12 micros for the dimer show no sign of strong secondary structure signals in both instances and the significant impact of dimerization on the global structure of Abeta16-35. They reveal however that the loop 22-28 acts as a quasi-independent unit in both species. The loop structure ensemble we report in Abeta16-35 monomer and dimer has high similarity to the loop formed by the Abeta21-30 peptide in solution and, to a lesser extent, to the loop found in Abeta1-40 fibrils. We discuss the implications of our findings on the assembly of full-length Abeta.

Self-assembly of phenylalanine oligopeptides: insights from experiments and simulations

Studies of peptide-based nanostructures provide general insights into biomolecular self-assembly and can lead material engineering toward technological applications. The diphenylalanine peptide (FF) self-assembles into discrete, hollow, well ordered nanotubes, and its derivatives form nanoassemblies of various morphologies. Here we demonstrate for the first time, to our knowledge, the formation of planar nanostructures with beta-sheet content by the triphenylalanine peptide (FFF). We characterize these structures using various microscopy and spectroscopy techniques. We also obtain insights into the interactions and structural properties of the FF and FFF nanostructures by 0.4-micros, implicit-solvent, replica-exchange, molecular-dynamics simulations of aqueous FF and FFF solutions. In the simulations the peptides form aggregates, which often contain open or ring-like peptide networks, as well as elementary and network-containing structures with beta-sheet characteristics. The networks are stabilized by polar and nonpolar interactions, and by the surrounding aggregate. In particular, the charged termini of neighbor peptides are involved in hydrogen-bonding interactions and their aromatic side chains form "T-shaped" contacts, as in three-dimensional FF crystals. These interactions may assist the FF and FFF self-assembly at the early stage, and may also stabilize the mature nanostructures. The FFF peptides have higher network propensities and increased aggregate stabilities with respect to FF, which can be interpreted energetically.

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.

Targeting the early steps of Abeta16-22 protofibril disassembly by N-methylated inhibitors: a numerical study

Aggregation of the Abeta1-40/Abeta1-42 peptides is a key factor in Alzheimer's disease. Though the inhibitory effect of N-methylated Abeta16-22 (mAbeta16-22) peptides is well characterized in vitro, there is little information on how they disassemble full-length Abeta fibrils or block fibril formation. Here, we report coarse-grained implicit solvent molecular dynamics (MD) and replica exchange molecular dynamics (REMD) simulations on Abeta16-22 and mAbeta16-22 monomers, and then a preformed six-chain Abeta16-22 bilayer with either four copies of Abeta16-22 or four copies of mAbeta16-22. Our simulations show that the effect of N-methylation on mAbeta16-22 monomer is to reduce the density of compact forms. While 100 ns MD trajectories do not reveal any significant differences between the two ten-chain systems, the REMD simulations totaling 1 micros help understand the first steps of Abeta16-22 protofibril disassembly by N-methylated inhibitors. Notably, we find that mAbeta16-22 preferentially interacts with Abeta16-22 by blocking both beta-sheet extension and lateral association of layers, but also by intercalation of the inhibitors allowing sequestration of Abeta16-22 peptides. This third binding mode is particularly appealing for blocking Abeta fibrillogenesis.

Molecular simulations of peptides: a useful tool for the development of new drugs and for the study of molecular recognition

The study of the molecular recognition and self-organization properties of peptides has emerged in recent years as a very active and diverse field of research, ranging from biomedicine to biotechnology and even to material sciences. In the case of biomedicine, peptides can be used as ligands of biological receptors to gain insights into the structural, dynamical, and chemical determinants underlying the formation of complexes and identify new effectors of biological processes of interest. In the case of biotechnology and material science, short sequences have been used to understand the sequence determinants of the formation of ordered supra-molecular structures of nanoscale dimensions. In this work, we will describe our research activities in these two areas of modern chemical biology. In the first part, we will describe the development of a new, specific, potent, and selective anticancer peptide and its use to obtain the information needed to identify a non-peptidic small molecular lead to be used as an inhibitor of cancer growth. In the second part, we will describe the introduction of a new method for the description of the self-organization process at the basis of the growth of ordered supra-molecular structures held together by weak, non-covalent, yet specific interactions.

What can we learn from highly connected beta-rich structures for structural interface design?

Most hubs' binding sites are able to transiently interact with numerous proteins. We focus on beta-rich hubs with the goal of inferring features toward design. Since they are able to interact with many partners and association of beta-conformations may lead to amyloid fibrils, we ask whether there is some property that distinguishes them from low-connectivity beta-rich proteins, which may be more interaction specific. Identification of such features should be useful as they can be incorporated in interface design while avoiding polymerization into fibrils. We classify the proteins in the yeast interaction map according to the types of their secondary structures. The small number of the obtained beta-rich protein structures in the Protein Data Bank likely reflects their low occurrence in the proteome. Analysis of the obtained structures indicates that highly connected beta-rich proteins tend to have clusters of conserved residues in their cores, unlike beta-rich structures with low connectivity, suggesting that the highly packed conserved cores are important to the stability of proteins, which have residue composition and sequence prone to beta-structure and amyloid formation. The enhanced stability may hinder partial unfolding, which, depending on the conditions, is more likely to lead to polymerization of these sequences.