Cooperativity among Short Amyloid Stretches in Long Amyloidogenic Sequences

Amyloid fibrillar aggregates of polypeptides are associated with many neurodegenerative diseases. Short peptide segments in protein sequences may trigger aggregation. Identifying these stretches and examining their behavior in longer protein segments is critical for understanding these diseases and obtaining potential therapies. In this study, we combined machine learning and structure-based energy evaluation to examine and predict amyloidogenic segments. Our feature selection method discovered that windows consisting of long amino acid segments of ∼30 residues, instead of the commonly used short hexapeptides, provided the highest accuracy. Weighted contributions of an amino acid at each position in a 27 residue window revealed three cooperative regions of short stretch, resemble the β-strand-turn-β-strand motif in A-βpeptide amyloid and β-solenoid structure of HET-s(218–289) prion (C). Using an in-house energy evaluation algorithm, the interaction energy between two short stretches in long segment is computed and incorporated as an additional feature. The algorithm successfully predicted and classified amyloid segments with an overall accuracy of 75%. Our study revealed that genome-wide amyloid segments are not only dependent on short high propensity stretches, but also on nearby residues

Computational approaches to understanding protein aggregation in neurodegeneration

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

Understanding and Modulating Amyloid Aggregation

Amyloids are the products of protein misfolding into fibril-like structures, which take place in human tissues and organs, including the human brain, heart, liver, and pancreas. As the deposition of amyloid fibrils usually leads to the dysfunction or death of cells, the formation of amyloid is associated with more than 20 human diseases, such as Alzheimer’s, Parkinson’s, and type 2 diabetes. Understanding how soluble peptides to form amyloid fibrils and how to modulate those amyloid aggregation are central to the design of rational therapeutic strategies against those diseases. 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, the LLPS and amyloid fibrillization of a coarse-grained peptide, capable of capturing fundamental properties of amyloid aggregation over a wide range of concentrations was investigated in Discrete Molecular Dynamics (DMD) simulations. I also studied the different biomolecules and nanoparticles interactions with amyloid proteins. This study provides a unified picture of amyloid aggregation for a wide range of concentrations within the framework of LLPS and the potential mitigation of amyloid aggregations, which may help us better understand the etiology of amyloid diseases and disease therapist for human diseases

Unlocking novel therapies:cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning

Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties

Structure-Property Relationship of Amyloidogenic Prion Nanofibrils

The structure and its property for the prion nanofibrils, which exhibit self-assembled steric zipper, amyloid fibrils, are described in this chapter. There is the belief of origin for the infectiousness of the prion can be its molecular structure. It is due to the amyloid toxicity, which is related to its beta sheet rich molecular structure and self-aggregated long fibrils. There is evidence that the difference between PrPc and PrPsc is transitioned beta sheet from alpha helix to self-assemble and then to the amyloidogenic fibrils. Therefore, the scope of this chapter is the amyloidogenic structural characteristics of prion fibrils and its relationship to the property. The molecular structural characteristics can be changed by properties such as affinity, toxicity, infectivity, and so on, so this is a key factor to understand the origin of prion disease and develop the therapeutic strategy. One of the main properties of amyloid fibrils that we want to describe here is mechanical property such as dynamic property and material property for prion nanofibrils. This chapter can shed light on understanding the infectious characteristics of prion and the relationship of its molecular structures

Computational prediction of protein aggregation : advances in proteomics, conformation-specific algorithms and biotechnological applications

Protein aggregation is a widespread phenomenon that stems from the establishment of non-native intermolecular contacts resulting in protein precipitation. Despite its deleterious impact on fitness, protein aggregation is a generic property of polypeptide chains, indissociable from protein structure and function. Protein aggregation is behind the onset of neurodegenerative disorders and one of the serious obstacles in the production of protein-based therapeutics. The development of computational tools opened a new avenue to rationalize this phenomenon, enabling prediction of the aggregation propensity of individual proteins as well as proteome-wide analysis. These studies spotted aggregation as a major force driving protein evolution. Actual algorithms work on both protein sequences and structures, some of them accounting also for conformational fluctuations around the native state and the protein microenvironment. This toolbox allows to delineate conformation-specific routines to assist in the identification of aggregation-prone regions and to guide the optimization of more soluble and stable biotherapeutics. Here we review how the advent of predictive tools has change the way we think and address protein aggregation

Computational methods to predict protein aggregation

Altres ajuts: Acord transformatiu CRUE-CSICIn most cases, protein aggregation stems from the establishment of non-native intermolecular contacts. The formation of insoluble protein aggregates is associated with many human diseases and is a major bottleneck for the industrial production of protein-based therapeutics. Strikingly, fibrillar aggregates are naturally exploited for structural scaffolding or to generate molecular switches and can be artificially engineered to build up multi-functional nanomaterials. Thus, there is a high interest in rationalizing and forecasting protein aggregation. Here, we review the available computational toolbox to predict protein aggregation propensities, identify sequential or structural aggregation-prone regions, evaluate the impact of mutations on aggregation or recognize prion-like domains. We discuss the strengths and limitations of these algorithms and how they can evolve in the next future

Alzheimer’s Disease as a Membrane Disorder: Spatial Cross-Talk Among Beta-Amyloid Peptides, Nicotinic Acetylcholine Receptors and Lipid Rafts

Biological membranes show lateral and transverse asymmetric lipid distribution. Cholesterol (Chol) localizes in both hemilayers, but in the external one it is mostly condensed in lipid-ordered microdomains (raft domains), together with saturated phosphatidyl lipids and sphingolipids (including sphingomyelin and glycosphingolipids). Membrane asymmetries induce special membrane biophysical properties and behave as signals for several physiological and/or pathological processes. Alzheimer’s disease (AD) is associated with a perturbation in different membrane properties. Amyloid-β (Aβ) plaques and neurofibrillary tangles of tau protein together with neuroinflammation and neurodegeneration are the most characteristic cellular changes observed in this disease. The extracellular presence of Aβ peptides forming senile plaques, together with soluble oligomeric species of Aβ, are considered the major cause of the synaptic dysfunction of AD. The association between Aβ peptide and membrane lipids has been extensively studied. It has been postulated that Chol content and Chol distribution condition Aβ production and posterior accumulation in membranes and, hence, cell dysfunction. Several lines of evidence suggest that Aβ partitions in the cell membrane accumulate mostly in raft domains, the site where the cleavage of the precursor AβPP by β- and γ- secretase is also thought to occur. The main consequence of the pathogenesis of AD is the disruption of the cholinergic pathways in the cerebral cortex and in the basal forebrain. In parallel, the nicotinic acetylcholine receptor has been extensively linked to membrane properties. Since its transmembrane domain exhibits extensive contacts with the surrounding lipids, the acetylcholine receptor function is conditioned by its lipid microenvironment. The nicotinic acetylcholine receptor is present in high-density clusters in the cell membrane where it localizes mainly in lipid-ordered domains. Perturbations of sphingomyelin or cholesterol composition alter acetylcholine receptor location. Therefore, Aβ processing, Aβ partitioning, and acetylcholine receptor location and function can be manipulated by changes in membrane lipid biophysics. Understanding these mechanisms should provide insights into new therapeutic strategies for prevention and/or treatment of AD. Here, we discuss the implications of lipid-protein interactions at the cell membrane level in AD.Fil: Fabiani, Camila. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Bioquímicas de Bahía Blanca. Universidad Nacional del Sur. Instituto de Investigaciones Bioquímicas de Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia; ArgentinaFil: Antollini, Silvia Susana. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Bioquímicas de Bahía Blanca. Universidad Nacional del Sur. Instituto de Investigaciones Bioquímicas de Bahía Blanca; Argentin