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

From experimental studies to computational approaches: recent trends in designing novel therapeutics for amyloidogenesis

Amyloidosis is a condition marked by misfolded proteins that build up in tissues and eventually destroy organs. It has been connected to a number of fatal illnesses, including non-neuropathic and neurodegenerative conditions, which in turn have a significant influence on the worldwide health sector. The inability to identify the underlying etiology of amyloidosis has hampered efforts to find a treatment for the condition. Despite the identification of a multitude of putative pathogenic variables that may operate independently or in combination, the molecular mechanisms responsible for the development and progression of the disease remain unclear. A thorough investigation into protein aggregation and the impacts of toxic aggregated species will help to clarify the cytotoxicity of aggregation-mediated cellular apoptosis and lay the groundwork for future studies aimed at creating effective treatments and medications. This review article provides a thorough summary of the combination of various experimental and computational approaches to modulate amyloid aggregation. Further, an overview of the latest developments of novel therapeutic agents is given, along with a discussion of the possible obstacles and viewpoints on this developing field. We believe that the information provided by this review will help scientists create innovative treatment strategies that affect the way proteins aggregate.

Cooperative β-sheet coassembly controls intermolecular orientation of amphiphilic peptide-polydiacetylene conjugates

In this work, we elucidated the structural organization of stimuli-responsive peptide-polydiacetylene (PDA) conjugates that can self-assemble as 1D nanostructures under neutral aqueous conditions. The amino acid sequences bear positively or negatively charged domains at the periphery of the peptide segments to promote solubility in water while also driving assembly of the individual and combined components into β-sheets. The photopolymerization of PDA, as well as the sensitivity of the resulting optical properties of the polymeric material to external stimuli, highly depends on the structural organization of the assembly of amphiphilic peptide-diacetylene units into 1D-nanostructures. Solid-state NMR measurements on 13C-labeled and 15N-labeled samples show that positively charged and negatively charged peptide amphiphiles are each capable of self-assembly, but self-assembly favors antiparallel β-sheet structure. When positively and negatively charged peptide amphiphiles interact in stoichiometric solutions, cooperative coassembly dominates over self-assembly, resulting in the desired parallel β-sheet structure with a concomitant increase in structural order. These results reveal that rational placement of oppositely charged residues can control β-strand organization in a peptide amphiphile coassembly, which would have implications on the adaptive properties of stimuli-responsive biomaterials such as the peptide-PDAs studied here.

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.

Tips and Tricks in the Modeling of Supramolecular Peptide Assemblies

Supramolecular peptide assemblies (SPAs) hold promise as materials for nanotechnology and biomedicine. Although their investigation often entails adapting experimental techniques from their protein counterparts, SPAs are fundamentally distinct from proteins, posing unique challenges for their study. Computational methods have emerged as indispensable tools for gaining deeper insights into SPA structures at the molecular level, surpassing the limitations of experimental techniques, and as screening tools to reduce the experimental search space. However, computational studies have grappled with issues stemming from the absence of standardized procedures and relevant crystal structures. Fundamental disparities between SPAs and protein simulations, such as the absence of experimentally validated initial structures and the importance of the simulation size, number of molecules, and concentration, have compounded these challenges. Understanding the roles of various parameters and the capabilities of different models and simulation setups remains an ongoing endeavor. In this review, we aim to provide readers with guidance on the parameters to consider when conducting SPA simulations, elucidating their potential impact on outcomes and validity.

Parallel β-Sheet Structure and Structural Heterogeneity Detected within Q11 Self-Assembling Peptide Nanofibers

Q11 peptide nanofibers are used as a biomaterial for applications such as antigen presentation and tissue engineering, yet detailed knowledge of molecular-level structure has not been reported. The Q11 peptide sequence was designed using heuristics-based patterning of hydrophobic and polar amino acids with oppositely charged amino acids placed at opposite ends of the sequence to promote antiparallel β-sheet formation. In this work, we employed solid-state nuclear magnetic resonance spectroscopy (NMR) to evaluate whether the molecular organization within Q11 self-assembled peptide nanofibers is consistent with the expectations of the peptide designers. We discovered that Q11 forms a distribution of molecular structures. NMR data from two-dimensional (2D) 13C-13C dipolar-assisted rotational resonance indicate that the K3 and E9 residues between Q11 β-strands are spatially proximate (within ∼0.6 nm). Frequency-selective rotational echo double resonance (fsREDOR) on K3 Nζ and E9 Cδ-labeled sites showed that approximately 9% of the sites are close enough for salt bridge formation to occur. Surprisingly, dipolar recoupling measurements revealed that Q11 peptides do not assemble into antiparallel β-sheets as expected, and structural analysis using Fourier-transform infrared spectroscopy and 2D NMR alone can be misleading. 13C PITHIRDS-CT dipolar recoupling measurements showed that the most abundant structure consists of parallel β-sheets, in contrast to the expected antiparallel β-sheet structure. Structural heterogeneity was detected from 15N{13C} REDOR measurements, with approximately 22% of β-strands having antiparallel nearest neighbors. We cannot propose a complete structural model of Q11 nanofibers because of the complexity involved when examining structurally heterogeneous samples using NMR. Altogether, our results show that while heuristics-based patterning is effective in promoting β-sheet formation, designing a peptide sequence to form a targeted β-strand arrangement remains challenging.

Structural Consequences of Introducing Bioactive Domains to Designer β-Sheet Peptide Self-Assemblies

We applied solid- and solution-state nuclear magnetic resonance spectroscopy to examine the structure of multidomain peptides composed of self-assembling β-sheet domains linked to bioactive domains. Bioactive domains can be selected to stimulate specific biological responses (e.g., via receptor binding), while the β-sheets provide the desirable nanoscale properties. Although previous work has established the efficacy of multidomain peptides, molecular-level characterization is lacking. The bioactive domains are intended to remain solvent-accessible without being incorporated into the β-sheet structure. We tested for three possible anticipated molecular-level consequences of introducing bioactive domains to β-sheet-forming peptides: (1) the bioactive domain has no effect on the self-assembling peptide structure; (2) the bioactive domain is incorporated into the β-sheet nanofiber; and (3) the bioactive domain interferes with self-assembly such that nanofibers are not formed. The peptides involved in this study incorporated self-assembling domains based on the (SL)6 motif and bioactive domains including a VEGF-A mimic (QK), an IGF-mimic (IGF-1c), and a de novo SARS-CoV-2 binding peptide (SBP3). We observed all three of the anticipated outcomes from our examination of peptides, illustrating the unintended structural effects that could adversely affect the desired biofunctionality and biomaterial properties of the resulting peptide hydrogel. This work is the first attempt to evaluate the structural effects of incorporating bioactive domains into a set of peptides unified by a similar self-assembling peptide domain. These structural insights reveal unmet challenges in the design of highly tunable bioactive self-assembling peptide hydrogels.

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.

Structural studies of catalytic peptides using molecular dynamics simulations

Many self-assembling peptides can form amyloid like structures with different sizes and morphologies. Driven by non-covalent interactions, their aggregation can occur through distinct pathways. Additionally, they can bind metal ions to create enzyme like active sites that allow them to catalyze diverse reactions. Due to the non-crystalline nature of amyloids, it is quite challenging to elucidate their structures using experimental spectroscopic techniques. In this aspect, molecular dynamics (MD) simulations provide a useful tool to derive structures of these macromolecules in solution. They can be further validated by comparing with experimentally measured structural parameters. However, these simulations require a multi-step process starting from the selection of the initial structure to the analysis of MD trajectories. There are multiple force fields, parametrization protocols, equilibration processes, software and analysis tools available for this process. Therefore, it is complicated for non-experts to select the most relevant tools and perform these simulations effectively. In this chapter, a systematic methodology that covers all major aspects of modeling of catalytic peptides is provided in a user-friendly manner. It will be helpful for researchers in this critical area of research.