Thermodynamic phase diagram of amyloid-β (16-22) peptide

Inhibition of Aβ16-22 Aggregation by [TEA]+[Ms]- Follows Weakening of the Hydrophobic Core and Sequestration of Peptides in Ionic Liquid Nanodomains

We developed a coarse-grained model for the protic ionic liquid, triethylammonium mesylate ([TEA]+[Ms]-), to characterize its inhibitory effects on amyloid aggregation using the K16LVFFAE22 fragment of the amyloid-β (Aβ16-22) as a model amyloidogenic peptide. In agreement with previous experiments, coarse-grained molecular dynamics simulations showed that increasing concentrations of [TEA]+[Ms]- in aqueous media led to increasingly small Aβ16-22 aggregates with low beta-sheet contents. The cause of [TEA]+[Ms]-'s inhibition of peptide aggregation was found to be a result of two interrelated effects. At a local scale, the enrichment of interactions between [TEA]+ cations and hydrophobic phenylalanine side chains weakened the hydrophobic cores of amyloid aggregates, resulting in poorly ordered structures. At a global level, peptides tended to localize at the interfaces of IL-rich nanostructures with water. At high IL concentrations, when the IL-water interface was large or fragmented, Aβ16-22 peptides were dispersed in the simulation cell, sometimes sequestered at unaggregated monomeric states. Together, these phenomena underlie [TEA]+[Ms]-'s inhibition of amyloid aggregation. This work addresses the critical lack of knowledge on the mechanisms of protein-ionic liquid interactions and may have broader implications for industrial applications.

Pore Formation by Amyloid-like Peptides: Effects of the Nonpolar-Polar Sequence Pattern

One of the mechanisms accounting for the toxicity of amyloid peptides in diseases like Alzheimer's and Parkinson's is the formation of pores on the plasma membrane of neurons. Here, we perform unbiased all-atom simulations of the full membrane damaging pathway, which includes adsorption, aggregation, and perforation of the lipid bilayer accounting for pore-like structures. Simulations are performed using four peptides made with the same amino acids. Differences in the nonpolar-polar sequence pattern of these peptides prompt them to adsorb into the membrane with the extended conformations oriented either parallel [peptide labeled F1, Ac-(FKFE)2-NH2], perpendicular (F4, Ac-FFFFKKEE-NH2), or with an intermediate orientation (F2, Ac-FFKKFFEE-NH2, and F3, Ac-FFFKFEKE-NH2) in regard to the membrane surface. At the water-lipid interface, only F1 fully self-assembles into β-sheets, and F2 peptides partially fold into an α-helical structure. The β-sheets of F1 emerge as electrostatic interactions attract neighboring peptides to intermediate distances where nonpolar side chains can interact within the dry core of the bilayer. This complex interplay between electrostatic and nonpolar interactions is not observed for the other peptides. Although β-sheets of F1 peptides are mostly parallel to the membrane, some of their edges penetrate deep inside the bilayer, dragging water molecules with them. This precedes pore formation, which starts with the flow of two water layers through the membrane that expand into a stable cylindrical pore delimited by polar faces of β-sheets spanning both leaflets of the bilayer.

A density of states-based approach to determine temperature-dependent aggregation rates

Here, we establish an approach to determine temperature-dependent aggregation rates in terms of thermostatistical quantities, which can be obtained directly from flat-histogram and statistical temperature algorithms considering the density of states of the system. Our approach is validated through simulations of an Ising-like model with anisotropically interacting particles at temperatures close to its first-order phase transition. Quantitative comparisons between the numerically obtained forward and reverse rates to approximate analytical expressions corroborate its use as a model-independent approach.

Design of parallel 𝛽-sheet nanofibrils using Monte Carlo search, coarse-grained simulations, and experimental testing

Peptide self-assembly into amyloid fibrils provides numerous applications in drug delivery and biomedical engineering applications. We augment our previously-established computational screening technique along with experimental biophysical characterization to discover 7-mer peptides that self-assemble into "parallel β-sheets", that is, β-sheets with N-terminus-to-C-terminus 𝛽-strand vectors oriented in parallel. To accomplish the desired β-strand organization, we applied the PepAD amino acid sequence design software to the Class-1 cross-β spine defined by Sawaya et al. This molecular configuration includes two layers of parallel β-sheets stacked such that N-terminus-to-C-terminus vectors are oriented antiparallel for molecules on adjacent β-sheets. The first cohort of PepAD identified peptides were examined for their fibrillation behavior in DMD/PRIME20 simulations, and the top performing sequence was selected as a prototype for a subsequent round of sequence refinement. The two rounds of design resulted in a library of eight 7-mer peptides. In DMD/PRIME20 simulations, five of these peptides spontaneously formed fibril-like structures with a predominantly parallel 𝛽-sheet arrangement, two formed fibril-like structure with <50% in parallel 𝛽-sheet arrangement and one remained a random coil. Among the eight candidate peptides produced by PepAD and DMD/PRIME20, five were synthesized and purified. All five assembled into amyloid fibrils composed of parallel β-sheets based on Fourier transform infrared spectroscopy, circular dichroism, electron microscopy, and thioflavin-T fluorescence spectroscopy measurements.

Biological effects and mechanism of β-amyloid aggregation inhibition by penetrable recombinant human HspB5-ACD structural domain protein

Alzheimer's disease (AD) is a global medical challenge. Studies have shown that neurotoxicity caused by pathological aggregation of β-amyloid (Aβ) is an important factor leading to AD. Therefore, inhibiting the pathological aggregation of Aβ is the key to treating AD. The recombinant human HspB5-ACD structural domain protein (AHspB5) prepared by our group in the previous period has been shown to have anti-amyloid aggregation effects, but its inability to penetrate biological membranes has limited its development. In this study, we prepared a recombinant fusion protein (T-AHspB5) of TAT and AHspB5. In vitro experiments showed that T-AHspB5 inhibited the formation of Aβ1-42 protofibrils and had the ability to penetrate the blood-brain barrier; in cellular experiments, T-AHspB5 prevented Aβ1-42-induced oxidative stress damage, apoptosis, and inflammatory responses in neuronal cells, and its mechanism of action was related to microglia activation and mitochondria-dependent apoptotic pathway. In animal experiments, T-AHspB5 improved memory and cognitive dysfunction and inhibited pathological changes of AD in APP/PS1 mice. In conclusion, this paper is expected to reveal the intervention mechanism and biological effect of T-AHspB5 on pathological aggregation of Aβ1-42, provide a new pathway for the treatment of AD, and lay the foundation for the future development and application of T-AHspB5.

How ATP suppresses the fibrillation of amyloid peptides: analysis of the free-energy contributions

Recent experiments have revealed that adenosine triphosphate (ATP) suppresses the fibrillation of amyloid peptides - a process closely linked to neurodegenerative diseases such as Alzheimer's and Parkinson's. Apart from the adsorption of ATP onto amyloid peptides, the molecular understanding is still limited, leaving the underlying mechanism for the fibrillation suppression by ATP largely unclear, especially in regards to the molecular energetics. Here we provide an explanation at the molecular scale by quantifying the free energies using all-atom molecular dynamics simulations. We found that the changes of the free energies due to the addition of ATP lead to a significant equilibrium shift towards monomeric peptides in agreement with experiments. Despite ATP being a highly charged species, the decomposition of the free energies reveals that the van der Waals interactions with the peptide are decisive in determining the relative stabilization of the monomeric state. While the phosphate moiety exhibits strong electrostatic interactions, the compensation by the water solvent results in a minor, overall Coulomb contribution. Our quantitative analysis of the free energies identifies which intermolecular interactions are responsible for the suppression of the amyloid fibril formation by ATP and offers a promising method to analyze the roles of similarly complex cosolvents in aggregation processes.

Peptide Self-Assembly into Amyloid Fibrils: Unbiased All-Atom Simulations

Protein self-assembly plays an important role in biological systems, accounting for the formation of mesoscopic structures that can be highly symmetric as in the capsid of viruses or disordered as in molecular condensates or exhibit a one-dimensional fibrillar morphology as in amyloid fibrils. Deposits of the latter in tissues of individuals with degenerative diseases like Alzheimer's and Parkinson's has motivated extensive efforts to understand the sequence of molecular events accounting for their formation. These studies aim to identify on-pathway intermediates that may be the targets for therapeutic intervention. This detailed knowledge of fibril formation remains obscure, in part due to challenges with experimental analyses of these processes. However, important progress is being achieved for short amyloid peptides due to advances in our ability to perform completely unbiased all-atom simulations of the self-assembly process. This perspective discusses recent developments, their implications, and the hurdles that still need to be overcome to further advance the field.

Amino acid chiral amplification using Monte Carlo dynamic

This study investigates the stability of chiral-molecule solution phases, with a specific focus on amino acids. The model framework is based on a two-dimensional square lattice model, where individual sites may be occupied by oriented chiral molecules or structureless solvent particles. Utilizing the Glauber dynamics and statistical mechanical formalism, as previously introduced and examined by Lombardo et al., we explore the influence of temperature, amino acid concentration, enantiomeric excess, and homochiral interaction strength on nucleation mechanisms, equilibrium phase behavior, and crystal composition. Our findings offer thermodynamic insights into the chiral amplification process of amino acids, contributing to a deeper understanding of the underlying processes.

Computational Design of Peptides for Biomaterials Applications

Computer-aided molecular design and protein engineering emerge as promising and active subjects in bioengineering and biotechnological applications. On one hand, due to the advancing computing power in the past decade, modeling toolkits and force fields have been put to use for accurate multiscale modeling of biomolecules including lipid, protein, carbohydrate, and nucleic acids. On the other hand, machine learning emerges as a revolutionary data analysis tool that promises to leverage physicochemical properties and structural information obtained from modeling in order to build quantitative protein structure-function relationships. We review recent computational works that utilize state-of-the-art computational methods to engineer peptides and proteins for various emerging biomedical, antimicrobial, and antifreeze applications. We also discuss challenges and possible future directions toward developing a roadmap for efficient biomolecular design and engineering.

Zn2+ Binding Increases Parallel Structure in the Aβ(16-22) Oligomer by Disrupting Salt Bridge in Antiparallel Structure

The aggregation of monomeric amyloid β protein (Aβ) into oligomers and amyloid plaque in the brain is associated with Alzheimer's disease. The hydrophobic central core Aβ16-22 has been widely studied due to its essential role in the fibrillization of full-length Aβ peptides. Compared to the homogeneous antiparallel structure of Aβ16-22 at the late stage, the early-stage prefibrillar aggregates contain varying proportions of different β structures. In this work, we studied the appearance probabilities of various self-assembly structures of Aβ16-22 and the effects of Zn2+ on these probabilities by replica exchange molecular dynamics simulations. It was found that at room temperature, Aβ16-22 can readily form assembled β-sheet structures in pure water, where a typical antiparallel arrangement dominates (24.8% of all sampled trimer structures). The addition of Zn2+ to the Aβ16-22 solution will dramatically decrease the appearance probability of antiparallel trimer structures to 12.5% by disrupting the formation of the Lys16-Glu22 salt bridge. Meanwhile, the probabilities of hybrid antiparallel/parallel structures increase. Our simulation results not only reveal the competition between antiparallel and parallel structures in the Aβ16-22 oligomers but also show that Zn2+ can affect the oligomer structures. The results also provide insights into the role of metal ions in the self-assembly of short peptides.

Computational Design of Peptide Assemblies

With the ongoing development of peptide self-assembling materials, there is growing interest in exploring novel functional peptide sequences. From short peptides to long polypeptides, as the functionality increases, the sequence space is also expanding exponentially. Consequently, attempting to explore all functional sequences comprehensively through experience and experiments alone has become impractical. By utilizing computational methods, especially artificial intelligence enhanced molecular dynamics (MD) simulation and de novo peptide design, there has been a significant expansion in the exploration of sequence space. Through these methods, a variety of supramolecular functional materials, including fibers, two-dimensional arrays, nanocages, etc., have been designed by meticulously controlling the inter- and intramolecular interactions. In this review, we first provide a brief overview of the current main computational methods and then focus on the computational design methods for various self-assembled peptide materials. Additionally, we introduce some representative protein self-assemblies to offer guidance for the design of self-assembling peptides.

Perspective for Molecular Dynamics Simulation Studies of Amyloid-β Aggregates

The cause of Alzheimer's disease is related to aggregates such as oligomers and amyloid fibrils consisting of amyloid-β (Aβ) peptides. Molecular dynamics (MD) simulation studies have been conducted to understand the molecular mechanism of the formation and disruption of Aβ aggregates. In this Perspective, the MD simulation studies are classified into four categories, focusing on the target systems: aggregation of Aβ peptides in bulk solution, Aβ aggregation at the interface, aggregation inhibitor against Aβ peptides, and nonequilibrium MD simulation of Aβ aggregates. MD simulation studies in these categories are first reviewed. Future perspectives in each category are then presented. Finally, the overall perspective is presented on how MD simulations of Aβ aggregates can be utilized for developing Alzheimer's disease treatment.

Mapping the configurational landscape and aggregation phase behavior of the tau protein fragment PHF6

The PHF6 (Val-Gln-Ile-Val-Tyr-Lys) motif, found in all isoforms of the microtubule-associated protein tau, forms an integral part of ordered cores of amyloid fibrils formed in tauopathies and is thought to play a fundamental role in tau aggregation. Because PHF6 as an isolated hexapeptide assembles into ordered fibrils on its own, it is investigated as a minimal model for insight into the initial stages of aggregation of larger tau fragments. Even for this small peptide, however, the large length and time scales associated with fibrillization pose challenges for simulation studies of its dynamic assembly, equilibrium configurational landscape, and phase behavior. Here, we develop an accurate, bottom-up coarse-grained model of PHF6 for large-scale simulations of its aggregation, which we use to uncover molecular interactions and thermodynamic driving forces governing its assembly. The model, not trained on any explicit information about fibrillar structure, predicts coexistence of formed fibrils with monomers in solution, and we calculate a putative equilibrium phase diagram in concentration-temperature space. We also characterize the configurational and free energetic landscape of PHF6 oligomers. Importantly, we demonstrate with a model of heparin that this widely studied cofactor enhances the aggregation propensity of PHF6 by ordering monomers during nucleation and remaining associated with growing fibrils, consistent with experimentally characterized heparin-tau interactions. Overall, this effort provides detailed molecular insight into PHF6 aggregation thermodynamics and pathways and, furthermore, demonstrates the potential of modern multiscale modeling techniques to produce predictive models of amyloidogenic peptides simultaneously capturing sequence-specific effects and emergent aggregate structures.

Nucleation and Growth of Amyloid Fibrils

The formation of amyloid fibrils is a complex phenomenon that remains poorly understood at the atomic scale. Herein, we perform extended unbiased all-atom simulations in explicit solvent of a short amphipathic peptide to shed light on the three mechanisms accounting for fibril formation, namely, nucleation via primary and secondary mechanisms, and fibril growth. We find that primary nucleation takes place via the formation of an intermediate state made of two laminated β-sheets oriented perpendicular to each other. The amyloid fibril spine subsequently emerges from the rotation of these β-sheets to account for peptides that are parallel to each other and perpendicular to the main axis of the fibril. Growth of this spine, in turn, takes place via a dock-and-lock mechanism. We find that peptides dock onto the fibril tip either from bulk solution or after diffusing on the fibril surface. The latter docking pathway contributes significantly to populate the fibril tip with peptides. We also find that side chain interactions drive the motion of peptides in the lock phase during growth, enabling them to adopt the structure imposed by the fibril tip with atomic fidelity. Conversely, the docked peptide becomes trapped in a local free energy minimum when docked-conformations are sampled randomly. Our simulations also highlight the role played by nonpolar fibril surface patches in catalyzing and orienting the formation of small cross-β structures. More broadly, our simulations provide important new insights into the pathways and interactions accounting for primary and secondary nucleation as well as the growth of amyloid fibrils.

Visualization of Self-Assembly and Hydration of a β-Hairpin through Integrated Small and Wide-Angle Neutron Scattering

Fundamental understanding of the structure and assembly of nanoscale building blocks is crucial for the development of novel biomaterials with defined architectures and function. However, accessing self-consistent structural information across multiple length scales is challenging. This limits opportunities to exploit atomic scale interactions to achieve emergent macroscale properties. In this work we present an integrative small- and wide-angle neutron scattering approach coupled with computational modeling to reveal the multiscale structure of hierarchically self-assembled β hairpins in aqueous solution across 4 orders of magnitude in length scale from 0.1 Å to 300 nm. Our results demonstrate the power of this self-consistent cross-length scale approach and allows us to model both the large-scale self-assembly and small-scale hairpin hydration of the model β hairpin CLN025. Using this combination of techniques, we map the hydrophobic/hydrophilic character of this model self-assembled biomolecular surface with atomic resolution. These results have important implications for the multiscale investigation of aqueous peptides and proteins, for the prediction of ligand binding and molecular associations for drug design, and for understanding the self-assembly of peptides and proteins for functional biomaterials.

Integrative modeling of diverse protein-peptide systems using CABS-dock

The CABS model can be applied to a wide range of protein-protein and protein-peptide molecular modeling tasks, such as simulating folding pathways, predicting structures, docking, and analyzing the structural dynamics of molecular complexes. In this work, we use the CABS-dock tool in two diverse modeling tasks: 1) predicting the structures of amyloid protofilaments and 2) identifying cleavage sites in the peptide substrates of proteolytic enzymes. In the first case, simulations of the simultaneous docking of amyloidogenic peptides indicated that the CABS model can accurately predict the structures of amyloid protofilaments which have an in-register parallel architecture. Scoring based on a combination of symmetry criteria and estimated interaction energy values for bound monomers enables the identification of protofilament models that closely match their experimental structures for 5 out of 6 analyzed systems. For the second task, it has been shown that CABS-dock coarse-grained docking simulations can be used to identify the positions of cleavage sites in the peptide substrates of proteolytic enzymes. The cleavage site position was correctly identified for 12 out of 15 analyzed peptides. When combined with sequence-based methods, these docking simulations may lead to an efficient way of predicting cleavage sites in degraded proteins. The method also provides the atomic structures of enzyme-substrate complexes, which can give insights into enzyme-substrate interactions that are crucial for the design of new potent inhibitors.

Multistep molecular mechanisms of Aβ16-22 fibril formation revealed by lattice Monte Carlo simulations

As a model of self-assembly from disordered monomers to fibrils, the amyloid-β fragment Aβ16-22 was subject to past numerous experimental and computational studies. Because dynamics information between milliseconds and seconds cannot be assessed by both studies, we lack a full understanding of its oligomerization. Lattice simulations are particularly well suited to capture pathways to fibrils. In this study, we explored the aggregation of 10 Aβ16-22 peptides using 65 lattice Monte Carlo simulations, each simulation consisting of 3 × 109 steps. Based on a total of 24 and 41 simulations that converge and do not converge to the fibril state, respectively, we are able to reveal the diversity of the pathways leading to fibril structure and the conformational traps slowing down the fibril formation.

Identifying the Template for Oligomer to Fibril Conversion for Amyloid-β (1-42) Oligomers using Hamiltonian Replica Exchange Molecular Dynamics

The toxicity of amyloid-β (Aβ) oligomers has been known to be higher compared to mature fibrils. Yet the presence of plaques in Alzheimer's disease patients indicates the significance of oligomer to fibril conversion for Aβ aggregates. In this study, we investigate Aβ_13-42 oligomers having two to five peptide chains using extensive all-atom molecular dynamics simulations to identify the on- or off-pathway intermediates in fibril formation pathway. Hamiltonian replica exchange method through solute tempering (REST2) has been employed to explore the different structures attained by these aggregates. Using intra-chain and inter-chain contacts as reaction coordinates, we obtain the free energy surface for the Aβ_13-42 oligomers. Consequently, their stable conformations and structural features have been identified. The found conformations belonging to most probable structures possess both parallel and anti-parallel β-sheets, characteristic of on- and off-pathway intermediates, respectively. Further, we have measured the tendency to form fibril like interactions among the β-sheet forming residues. Our analysis finds that residues 30-36 possess higher tendency to form fibril like contacts among all the residues. While we find stronger interaction among residues 30-36, these amino acids are also found to be more shielded from water compared to others. With previous experimental studies finding these residues to be more crucial for the stability of Aβ42 oligomers, we propose that interactions within this patch could trigger seed formation that leads to conversion of on-pathway oligomers into disease relevant fibrils.

Self-Assembly of Amyloid-Beta (Aβ) Peptides from Solution to Near In Vivo Conditions

Understanding the atomistic resolution changes during the self-assembly of amyloid peptides or proteins is important to develop compounds or conditions to alter the aggregation pathways and suppress the toxicity and potentially aid in the development of drugs. However, the complexity of protein aggregation and the transient order/disorder of oligomers along the pathways to fibril are very challenging. In this Perspective, we discuss computational studies of amyloid polypeptides carried out under various conditions, including conditions closely mimicking in vivo and point out the challenges in obtaining physiologically relevant results, focusing mainly on the amyloid-beta Aβ peptides.

Sequence patterns and signatures: Computational and experimental discovery of amyloid-forming peptides

Screening amino acid sequence space via experiments to discover peptides that self-assemble into amyloid fibrils is challenging. We have developed a computational peptide assembly design (PepAD) algorithm that enables the discovery of amyloid-forming peptides. Discontinuous molecular dynamics (DMD) simulation with the PRIME20 force field combined with the FoldAmyloid tool is used to examine the fibrilization kinetics of PepAD-generated peptides. PepAD screening of ∼10,000 7-mer peptides resulted in twelve top-scoring peptides with two distinct hydration properties. Our studies revealed that eight of the twelve in silico discovered peptides spontaneously form amyloid fibrils in the DMD simulations and that all eight have at least five residues that the FoldAmyloid tool classifies as being aggregation-prone. Based on these observations, we re-examined the PepAD-generated peptides in the sequence pool returned by PepAD and extracted five sequence patterns as well as associated sequence signatures for the 7-mer amyloid-forming peptides. Experimental results from Fourier transform infrared spectroscopy (FTIR), thioflavin T (ThT) fluorescence, circular dichroism (CD), and transmission electron microscopy (TEM) indicate that all the peptides predicted to assemble in silico assemble into antiparallel β-sheet nanofibers in a concentration-dependent manner. This is the first attempt to use a computational approach to search for amyloid-forming peptides based on customized settings. Our efforts facilitate the identification of β-sheet-based self-assembling peptides, and contribute insights towards answering a fundamental scientific question: "What does it take, sequence-wise, for a peptide to self-assemble?".

Kinetic Mechanism of Amyloid-β-(16-22) Peptide Fibrillation

The kinetic mechanism of amyloid fibril formation by a peptide fragment containing seven residues of the amyloid-β protein Aβ-(16-22) was investigated. We found that the N- and C-terminal unprotected Aβ-(16-22), containing no aggregation nuclei, showed rapid fibrillation within seconds to minutes in a neutral aqueous buffer solution. The fibrillation kinetics were well described by the nucleation-elongation model, suggesting that primary nucleation was the rate-limiting step. On the basis of both experimental and theoretical analyses, the aggregated nucleus was estimated to be composed of 6-7 peptide molecules, wherein the two β-sheets were associated with their hydrophobic surfaces. Thin fibers with widths of 10-20 nm were formed, which increased their length and thickness, attaining a width of >20 nm over several tens of minutes, probably owing to the lateral association of the fibers. Electrostatic and hydrophobic interactions play important roles in aggregation. These results provide a basis for understanding the fibrillation of short peptides.

Transferable and Polarizable Coarse Grained Model for Proteins─ProMPT

The application of classical molecular dynamics (MD) simulations at atomic resolution (fine-grained level, FG), to most biomolecular processes, remains limited because of the associated computational complexity of representing all the atoms. This problem is magnified in the presence of protein-based biomolecular systems that have a very large conformational space, and MD simulations with fine-grained resolution have slow dynamics to explore this space. Current transferable coarse grained (CG) force fields in literature are either limited to only peptides with the environment encoded in an implicit form or cannot capture transitions into secondary/tertiary peptide structures from a primary sequence of amino acids. In this work, we present a transferable CG force field with an explicit representation of the environment for accurate simulations with proteins. The force field consists of a set of pseudoatoms representing different chemical groups that can be joined/associated together to create different biomolecular systems. This preserves the transferability of the force field to multiple environments and simulation conditions. We have added electronic polarization that can respond to environmental heterogeneity/fluctuations and couple it to protein's structural transitions. The nonbonded interactions are parametrized with physics-based features such as solvation and partitioning free energies determined by thermodynamic calculations and matched with experiments and/or atomistic simulations. The bonded potentials are inferred from corresponding distributions in nonredundant protein structure databases. We present validations of the CG model with simulations of well-studied aqueous protein systems with specific protein fold types─Trp-cage, Trpzip4, villin, WW-domain, and β-α-β. We also explore the applications of the force field to study aqueous aggregation of Aβ 16-22 peptides.

Multiscale Exploration of Concentration-Dependent Amyloid-β(16-21) Amyloid Nucleation

Atomic descriptions of peptide aggregation nucleation remain lacking due to the difficulty of exploring complex configurational spaces on long time scales. To elucidate this process, we develop a multiscale approach combining a metadynamics-based method with cluster statistical mechanics to derive concentration-dependent free energy surfaces of nucleation at near-atomic resolution. A kinetic transition network of nucleation is then constructed and employed to systematically explore nucleation pathways and kinetics through stochastic simulations. This approach is applied to describe Aβ_16-21 amyloid nucleation, revealing a two-step mechanism involving disordered aggregates at millimolar concentration, and an unexpected mechanism at submillimolar concentrations that exhibits kinetics reminiscent of classical nucleation but atypical pathways involving growing clusters with structured cores wrapped by disordered surface. When this atypical mechanism is operative, critical nucleus size can be reflected by the nucleation reaction order. Collectively, our approach paves the way for a more quantitative and detailed understanding of peptide aggregation nucleation.

Development of a Hybrid-Resolution Force Field for Peptide Self-Assembly Simulations: Optimizing Peptide-Peptide and Peptide-Solvent Interactions

Atomic descriptions of peptide self-assembly are crucial to an understanding of disease-related peptide aggregation and the design of peptide-assembled materials. Obtaining these descriptions through computer simulation is challenging because current force fields, which were not designed for this process and are often unable to describe correctly peptide self-assembly behavior and the sequence dependence. Here, we developed a framework using dipeptide aggregation as a model system to improve force fields for simulations of self-assembly. Aggregation-related structural properties were designed and used to guide the optimization of peptide-peptide and peptide-solvent interactions. With this framework, we developed a self-assembly force field, termed PACE-ASM, by reoptimizing a hybrid-resolution force field that was originally developed for folding simulation. With its applicability in folding simulations, the new PACE was used to simulate the self-assembly of two disease-related short peptides, Aβ_16-21 and PHF6, into β-sheet-rich cross-β amyloids. These simulations reproduced the crystal structures of Aβ_16-21 and PHF6 amyloids at near-atomic resolution and captured the difference in packing orientations between the two sequences, a task which is challenging even with all-atom force fields. Apart from cross-β amyloids, the self-assembly of emerging helix-rich cross-α amyloids by another peptide PSMα3 can also be correctly described with the new PACE, manifesting the versatility of the force field. We demonstrated that the ability of the PACE-ASM to model peptide self-assembly is based largely on its improved description of peptide-peptide and peptide-solvent interactions. This was achieved with our optimization framework that can readily identify and address the deficiency in describing these interactions.

Early aggregation mechanism of Aβ16-22 revealed by Markov state models

Aβ_16-22 is believed to have critical role in early aggregation of full length amyloids that are associated with the Alzheimer's disease and can aggregate to form amyloid fibrils. However, the early aggregation mechanism is still unsolved. Here, multiple long-term molecular dynamics simulations combining with Markov state model were used to probe the early oligomerization mechanism of Aβ_16-22 peptides. The identified dimeric form adopted either globular random-coil or extended β-strand like conformations. The observed dimers of these variants shared many overall conformational characteristics but differed in several aspects at detailed level. In all cases, the most common type of secondary structure was intermolecular antiparallel β-sheets. The inter-state transitions were very frequent ranges from few to hundred nanoseconds. More strikingly, those states which contain fraction of β secondary structure and significant amount of extended coiled structures, therefore exposed to the solvent, were majorly participated in aggregation. The assembly of low-energy dimers, in which the peptides form antiparallel β sheets, occurred by multiple pathways with the formation of an obligatory intermediates. We proposed that these states might facilitate the Aβ_16-22 aggregation through a significant component of the conformational selection mechanism, because they might increase the aggregates population by promoting the inter-chain hydrophobic and the hydrogen bond contacts. The formation of early stage antiparallel β sheet structures is critical for oligomerization, and at the same time provided a flat geometry to seed the ordered β-strand packing of the fibrils. Our findings hint at reorganization of this part of the molecule as a potentially critical step in Aβ aggregation and will insight into early oligomerization for large β amyloids.

Engineering β-Sheet Peptide Coassemblies for Biomaterial Applications

Peptide coassembly, wherein at least two different peptides interact to form multicomponent nanostructures, is an attractive approach for generating functional biomaterials. Current efforts seek to design pairs of peptides, A and B, that form nanostructures (e.g., β-sheets with ABABA-type β-strand patterning) while resisting self-assembly (e.g., AAAAA-type or BBBBB-type β-sheets). To confer coassembly behavior, most existing designs have been based on highly charged variants of known self-assembling peptides; like-charge repulsion limits self-assembly while opposite-charge attraction promotes coassembly. Recent analyses using solid-state NMR and coarse-grained simulations reveal that preconceived notions of structure and molecular organization are not always correct. This perspective highlights recent advances and key challenges to understanding and controlling peptide coassembly.

Hydroxylated single-walled carbon nanotube inhibits β2m21-31 fibrillization and disrupts pre-formed proto-fibrils

Pathological aggregation of amyloid polypeptides is associated with numerous degenerative diseases. Preventing aggregation and clearing amyloid deposits are considered as promising strategies against amyloidosis. With the capacity of crossing the blood-brain barrier and good biocompatibility, the hydroxylated single-walled carbon nanotube (SWCNT-OH) has been shown with excellent anti-amyloid properties. Here, we systematically studied the SWCNT-OH effects on the fibrillization of the β2m_21-31 peptides utilizing all-atom discrete molecular dynamics (DMD) simulation. Our results demonstrated the isolated β2m_21-31 peptides first nucleated into unstructured oligomers followed by coil-to-sheet conformational conversions in oligomers with at least six peptides. The elongation and lateral surfaces of the preformed β-sheet could catalyze the other unstructured monomers and small oligomers converted into β-sheet formations via dock-lock fibril growth and secondary nucleation processes. Eventually, the β2m_21-31 peptides would self-assemble into well-ordered cross-β structures. Regardless of isolated monomers or well-defined cross-β assemblies, the β2m_21-31 would attach on the surfaces of SWCNT-OH adopting unstructured formations indicating the SWCNT-OH not only inhibited the fibrillization of β2m_21-31 but also destroyed pre-formed proto-fibrils. Overall, our study displays a complete picture of the fibrillization mechanism of β2m_21-31 and the amyloid inhibitory mechanism of SWCNT-OH, offering new insight into the de-novo design of anti-amyloid inhibitors.

De novo design of peptides that coassemble into β sheet-based nanofibrils

Peptides’ hierarchical coassembly into nanostructures enables controllable fabrication of multicomponent biomaterials. In this work, we describe a computational and experimental approach to design pairs of charge-complementary peptides that selectively coassemble into β-sheet nanofibers when mixed together but remain unassembled when isolated separately. The key advance is a peptide coassembly design (PepCAD) algorithm that searches for pairs of coassembling peptides. Six peptide pairs are identified from a pool of ~10⁶ candidates via the PepCAD algorithm and then subjected to DMD/PRIME20 simulations to examine their co-/self-association kinetics. The five pairs that spontaneously aggregate in kinetic simulations selectively coassemble in biophysical experiments, with four forming β-sheet nanofibers and one forming a stable nonfibrillar aggregate. Solid-state NMR, which is applied to characterize the coassembling pairs, suggests that the in silico peptides exhibit a higher degree of structural order than the previously reported CATCH(+/−) peptides.

Effect of charged mutation on aggregation of a pentapeptide: Insights from molecular dynamics simulations

Aggregation of therapeutic monoclonal antibodies (mAbs) can negatively affect their chemistry, manufacturing, and control attributes and lead to undesirable immune responses in patients. Therefore, optimization of lead mAb drug candidates during discovery stages to mitigate aggregation is increasingly becoming an integral part of their developability assessments. The disruption of short sequence motifs called aggregation prone regions (APRs) found in amino acid sequences of mAb candidates can potentially mitigate their aggregation. In this work, we have performed molecular dynamics simulations to study the aggregation of an APR (VLVIY) found in λ light chains of human antibodies and its single point mutant KLVIY. Eighteen different multicopy peptide simulation systems of "VLVIY" and "KLVIY" were constructed by varying their concentrations, temperatures, termini capping, and flanking gate-keeper regions. Within 20 ns of the simulation, peptide "VLVIY" formed an aggregate of 100 peptides at ~0.1 M concentration with a 60% reduction in solvent accessible surface area (SASA). Furthermore, analysis of the SASA change, peptide cluster distribution, and water residence time demonstrated how Val ➔ Lys mutation resists aggregation and improves solubility. Presence of Lys slows down aggregation kinetics via charge-charge repulsions and by raising the kinetic barrier to formation of large oligomers. However, the effect of the Val ➔ Lys mutation is dependent on sequence and structural contexts around the APR. This mutation also alters the solvation shell around the peptide by favoring solute-solvent interactions, thereby increasing its solubility. This work has provided a detailed mechanistic explanation of how APR disruption can mitigate aggregation in biotherapeutics and improve their developability.

Energy landscapes of protein aggregation and conformation switching in intrinsically disordered proteins

The protein folding problem was apparently solved recently by the advent of a deep learning method for protein structure prediction called AlphaFold. However, this program is not able to make predictions about the protein folding pathways. Moreover, it only treats about half of the human proteome, as the remaining proteins are intrinsically disordered or contain disordered regions. By definition these proteins differ from natively folded proteins and do not adopt a properly folded structure in solution. However these intrinsically disordered proteins (IDPs) also systematically differ in amino acid composition and uniquely often become folded upon binding to an interaction partner. These factors preclude solving IDP structures by current machine-learning methods like AlphaFold, which also cannot solve the protein aggregation problem, since this meta-folding process can give rise to different aggregate sizes and structures. An alternative computational method is provided by molecular dynamics simulations that already successfully explored the energy landscapes of IDP conformational switching and protein aggregation in multiple cases. These energy landscapes are very different from those of 'simple' protein folding, where one energy funnel leads to a unique protein structure. Instead, the energy landscapes of IDP conformational switching and protein aggregation feature a number of minima for different competing low-energy structures. In this review, I discuss the characteristics of these multifunneled energy landscapes in detail, illustrated by molecular dynamics simulations that elucidated the underlying conformational transitions and aggregation processes.

Dynamic nanoassembly-based drug delivery system (DNDDS): Learning from nature

Dynamic nanoassembly-based drug delivery system (DNDDS) has evolved from being a mere curiosity to emerging as a promising strategy for high-performance diagnosis and/or therapy of various diseases. However, dynamic nano-bio interaction between DNDDS and biological systems remains poorly understood, which can be critical for precise spatiotemporal and functional control of DNDDS in vivo. To deepen the understanding for fine control over DNDDS, we aim to explore natural systems as the root of inspiration for researchers from various fields. This review highlights ingenious designs, nano-bio interactions, and controllable functionalities of state-of-the-art DNDDS under endogenous or exogenous stimuli, by learning from nature at the molecular, subcellular, and cellular levels. Furthermore, the assembly strategies and response mechanisms of tailor-made DNDDS based on the characteristics of various diseased microenvironments are intensively discussed. Finally, the current challenges and future perspectives of DNDDS are briefly commented.

Different Folding States from the Same Protein Sequence Determine Reversible vs Irreversible Amyloid Fate

The propensity to self-assemble into amyloid fibrils with a shared cross-β architecture is a generic feature of proteins. Amyloid-related diseases affect millions of people worldwide, yet they are incurable and cannot be effectively prevented, largely due to the irreversible assembly and extraordinary stability of amyloid fibrils. Recent studies suggest that labile amyloids may be possible in certain proteins containing low-complexity domains often involved in the formation of subcellular membraneless organelles. Although the fundamental understanding of this reversible amyloid folding process is completely missing, the current view is that a given protein sequence will result in either irreversible, as in most of the cases, or reversible amyloid fibrils, as in few exceptions. Here we show that two common globular proteins, human lysozyme and its homologue from hen egg white, can self-assemble into both reversible and irreversible amyloid fibrils depending on the folding path followed by the protein. In both folding states, the amyloid nature of the fibrils is demonstrated at the molecular level by its cross-β structure, yet with substantial differences on the mesoscopic polymorphism and the labile nature of the amyloid state. Structural analysis shows that reversible and irreversible amyloid fibrils possess the same full-length protein sequence but different fibril core structures and β-sheet arrangements. These results illuminate a mechanistic link between the reversible and irreversible nature of amyloids and highlight the central role of protein folding states in regulating the lability and reversibility of amyloids.

Toward the equilibrium and kinetics of amyloid peptide self-assembly

Several devastating human diseases are linked to peptide self-assembly, but our understanding their onset and progression is not settled. This is a sign of the complexity of the aggregation process, which is prevented, catalyzed, or retarded by numerous factors in body fluids and cells, varying in time and space. Biophysical studies of pure peptide solutions contribute insights into the underlying steps in the process and quantitative parameters relating to rate constants (energy barriers) and equilibrium constants (population distributions). This requires methods to quantify the concentration of at least one species in the process. Translation to an in vivo situation poses an enormous challenge, and the effects of selected components (bottom up) or entire body fluids (top down) need to be quantified.

Misfolding and Self-Assembly Dynamics of Microtubule-Binding Repeats of the Alzheimer-Related Protein Tau

Pathological aggregation of intrinsically disordered tau protein, driven by the interactions between microtubule-binding (MTB) domains, is associated with Alzheimer's disease. The MTB domain contains either three or four repeats with sequence similarities. Compared to amyloid β, many aspects of the misfolding and aggregation mechanisms of tau are largely unknown. In this study, we systematically investigated the dynamics of monomer misfolding and dimerization of each MTB repeat using atomistic discrete molecular dynamic simulations. Our results revealed that all the four repeat monomers (R1-R4) were very dynamic, featuring frequent conformational conversion and lacking stable conformations. While R1, R2, and R4 monomers occasionally adopted partially helical conformations, R3 monomers frequently formed β-sheets. In dimerization simulations, R3 displayed the strongest aggregation propensity with high β-sheet contents, while R1 was the least prone to aggregation. The R2 and R4 dimers contained both helix and β-sheet structures. The β-sheets in R4 assemblies were dominant with β-hairpin conformation. In R2 and R3 dimers, intermolecular β-sheets were mainly driven by residues around the paired helical filament (PHF) regions. Residues around the PHF6* in R2 and PHF6 in R3 had significantly higher intermolecular contacts than other regions, suggesting that these residues play a key role in the amyloid aggregation of tau. Our results on the structural ensembles and early aggregation dynamics of each tau MTB repeat will help understand the nucleation and fibrillization of tau.

Five-Site Model for Brownian Dynamics Simulations of a Molecular Walker in Three Dimensions

Motor proteins play an important role in many biological processes and have inspired the development of synthetic analogues. Molecular walkers, such as kinesin, dynein, and myosin V, fulfill a diverse set of functions including transporting cargo along tracks, pulling molecules through membranes, and deforming fibers. The complexity of molecular motors and their environment makes it difficult to model the detailed dynamics of molecular walkers over long time scales. In this work, we present a simple, three-dimensional model for a molecular walker on a bead-spring substrate. The walker is represented by five spherically symmetric particles that interact through common intermolecular potentials and can be simulated efficiently in Brownian dynamics simulations. The movement of motor protein walkers entails energy conversion through ATP hydrolysis while artificial motors typically rely on a local conversion of energy supplied through external fields. We model energy conversion through rate equations for mechanochemical states that couple positional and chemical degrees of freedom and determine the walker conformation through interaction potential parameters. We perform Brownian dynamics simulations for two scenarios: In the first, the model walker transports cargo by walking on a substrate whose ends are fixed. In the second, a tethered motor pulls a mobile substrate chain against a variable force. We measure relative displacements and determine the effects of cargo size and retarding force on the efficiency of the walker. We find that, while the efficiency of our model walker is less than for the biological system, our simulations reproduce trends observed in single-molecule experiments on kinesin. In addition, the model and simulation method presented here can be readily adapted to biological and synthetic systems with multiple walkers.

Molecular simulation study of 3,4-dihydroxyphenylalanine in the context of underwater adhesive design

Adhesives that can stick to multiple surface types in underwater and high moisture conditions are critical for various applications such as marine coatings, sealants, and medical devices. The analysis of natural underwater adhesives shows that L-3,4-dihydroxyphenylalanine (DOPA) and functional amyloid nanostructures are key components that contribute to the adhesive powers of these natural glues. The combination of DOPA and amyloid-forming peptides into DOPA-amyloid(-forming peptide) conjugates provides a new approach to design generic underwater adhesives. However, it remains unclear how the DOPA monomers may interact with amyloid-forming peptides and how these interactions may influence the adhesive ability of the conjugates. In this paper, we investigate the behavior of DOPA monomers, (glycine-DOPA)₃ chains, and a KLVFFAE and DOPA-glycine chain conjugate in aqueous environments using molecular simulations. The DOPA monomers do not aggregate significantly at concentrations lower than 1.0M. Simulations of (glycine-DOPA)₃ chains in water were done to examine the intra-molecular interactions of the chain, wherein we found that there were unlikely to be interactions detrimental to the adhesion process. After combining the alternating DOPA-glycine chain with the amyloid-forming peptide KLVFFAE into a single chain conjugate, we then simulated the conjugate in water and saw the possibility of both intra-chain folding and no chain folding in the conjugate.

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.

Structural insights into peptide self-assembly using photo-induced crosslinking experiments and discontinuous molecular dynamics

Determining the structure of the (oligomeric) intermediates that form during the self-assembly of amyloidogenic peptides is challenging because of their heterogeneous and dynamic nature. Thus, there is need for methodology to analyze the underlying molecular structure of these transient species. In this work, a combination of fluorescence quenching, photo-induced crosslinking (PIC) and molecular dynamics simulation was used to study the assembly of a synthetic amyloid-forming peptide, Aβ16-22. A PIC amino acid containing a trifluormethyldiazirine (TFMD) group-Fmoc(TFMD)Phe-was incorporated into the sequence (Aβ*16-22). Electrospray ionization ion-mobility spectrometry mass-spectrometry (ESI-IMS-MS) analysis of the PIC products confirmed that Aβ*16-22 forms assemblies with the monomers arranged as anti-parallel, in-register β-strands at all time points during the aggregation assay. The assembly process was also monitored separately using fluorescence quenching to profile the fibril assembly reaction. The molecular picture resulting from discontinuous molecule dynamics simulations showed that Aβ16-22 assembles through a single-step nucleation into a β-sheet fibril in agreement with these experimental observations. This study provides detailed structural insights into the Aβ16-22 self-assembly processes, paving the way to explore the self-assembly mechanism of larger, more complex peptides, including those whose aggregation is responsible for human disease.

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.

GPU-Accelerated Flexible Molecular Docking

Virtual screening is a key enabler of computational drug discovery and requires accurate and efficient structure-based molecular docking. In this work, we develop algorithms and software building blocks for molecular docking that can take advantage of graphics processing units (GPUs). Specifically, we focus on MedusaDock, a flexible protein-small molecule docking approach and platform. We accelerate the performance of the coarse docking phase of MedusaDock, as this step constitutes nearly 70% of total running time in typical use-cases. We perform a comprehensive evaluation of the quality and performance with single-GPU and multi-GPU acceleration using a data set of 3875 protein-ligand complexes. The algorithmic ideas, data structure design choices, and performance optimization techniques shed light on GPU acceleration of other structure-based molecular docking software tools.

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 aggregation simulations: challenges, advances and perspectives

In amyloid aggregation diseases soluble proteins coalesce into a wide array of undesirable structures, ranging through oligomers and prefibrillar assemblies to highly ordered amyloid fibrils and plaques. Explicit-solvent all-atom molecular dynamics (MD) simulations of amyloid aggregation have been performed for almost 20 years, revealing valuable information about this phenomenon. However, these simulations are challenged by three main problems. Firstly, current force fields modeling amyloid aggregation are insufficiently accurate. Secondly, the protein concentrations in MD simulations are usually orders of magnitude higher than those used in vitro or found in vivo, which has direct consequences on the aggregates that form. Finally, the third problem is the well-known time-scale limit of MD simulations. In this review I highlight recent approaches to overcome these three limitations.

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.

Amyloids and their untapped potential as hydrogelators

Amyloid fibrils are cross-β-sheet-rich fibrous aggregates. They were originally identified as disease-associated protein/peptide deposits. The cross-β motif was consequently labelled as an alien and pathogenic fold. Subsequent research revealed that the fibrillar aggregates were benign, and the cytotoxicity in the amyloid diseases was attributed to the pre-fibrillar structures. Research in the past two decades has identified the native functional amyloids in organisms ranging from bacteria to human. The amyloid-like fibrils, therefore, are not necessarily pathogenic, and the cross-β motif is very much native. This premise makes way for the amyloids to be used as biocompatible materials. Many naturally occurring amyloidogenic proteins/peptides or their fragments have been reported in the literature to form hydrogels. Hydrogels constitute one of the most interesting classes of soft materials that find application in diverse fields such as environmental, electronic, and biomedical engineering. Applications of hydrogels in medicine are particularly extensive. Among various classes of peptides that form hydrogels, the potential of amyloids is largely untapped. In this review, we have attempted to compile the literature on amyloid hydrogels and discuss their potential applications.