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Why does the A? peptide of Alzheimer share structural similarity with antimicrobial peptides?

ABSTRACT: The A? peptides causally associated with Alzheimer disease have been seen as seemingly purposeless species produced by intramembrane cleavage under both physiological and pathological conditions. However, it has been increasingly suggested that they could instead constitute an ancient, highly conserved effector component of our innate immune system, dedicated to protecting the brain against microbial attacks. In this antimicrobial protection hypothesis, A? aggregation would switch from an abnormal stochastic event to a dysregulated innate immune response. In this perspective, we approach the problem from a different and complementary perspective by comparing the structure and sequence of A?(1-42) with those of bona fide antimicrobial peptides. We demonstrate that A?(1-42) bears convincing structural similarities with both viral fusion domains and antimicrobial peptides, as well as sequence similarities with a specific family of bacterial bacteriocins. We suggest a model of the mechanism by which A? peptides could elicit the immune response against microbes.

SUBMITTER: Pastore A

PROVIDER: S-EPMC7081199 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Why does the Aβ peptide of Alzheimer share structural similarity with antimicrobial peptides?

Pastore Annalisa A Raimondi Francesco F Rajendran Lawrence L Temussi Piero Andrea PA

Communications biology 20200319 1

The Aβ peptides causally associated with Alzheimer disease have been seen as seemingly purposeless species produced by intramembrane cleavage under both physiological and pathological conditions. However, it has been increasingly suggested that they could instead constitute an ancient, highly conserved effector component of our innate immune system, dedicated to protecting the brain against microbial attacks. In this antimicrobial protection hypothesis, Aβ aggregation would switch from an abnorm ...[more]

PMID: 32193491

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Project description:Antimicrobial peptides (AMPs) are a class of host-defense molecules that neutralize a broad range of pathogens. Their membrane-permeabilizing behavior has been commonly attributed to the formation of pores; however, with the continuing discovery of AMPs, many are uncharacterized and their exact mechanism remains unknown. Using atomic force microscopy, we previously characterized the disruption of model membranes by protegrin-1 (PG-1), a cationic AMP from pig leukocytes. When incubated with zwitterionic membranes of dimyristoylphosphocholine, PG-1 first induced edge instability at low concentrations, then porous defects at intermediate concentrations, and finally worm-like micelle structures at high concentrations. These rich structural changes suggested that pore formation constitutes only an intermediate state along the route of PG-1's membrane disruption process. The formation of these structures could be best understood by using a mesophase framework of a binary mixture of lipids and peptides, where PG-1 acts as a line-active agent in lowering interfacial bilayer tensions. We have proposed that rather than being static pore formers, AMPs share a common ability to lower interfacial tensions that promote membrane transformations. In a study of 13 different AMPs, we found that peptide line-active behavior was not driven by the overall charge, and instead was correlated with their adoption of imperfect secondary structures. These peptide structures commonly positioned charged residues near the membrane interface to promote deformation favorable for their incorporation into the membrane. Uniquely, the data showed that barrel-stave-forming peptides such as alamethicin are not line-active, and that the seemingly disparate models of toroidal pores and carpet activity are actually related. We speculate that this interplay between peptide structure and the distribution of polar residues in relation to the membrane governs AMP line activity in general and represents a novel, to our knowledge, avenue for the rational design of new drugs.

Project description:Antimicrobial peptides attract considerable interest as novel agents to combat infections. Their long-time potency across bacteria, viruses and fungi as part of diverse innate immune systems offers a solution to overcome the rising concerns from antibiotic resistance. With the rapid increase of antimicrobial peptides reported in the databases, peptide selection becomes a challenge. We propose similarity analyses to describe key properties that distinguish between active and non-active peptide sequences building upon the physicochemical properties of antimicrobial peptides. We used an iterative supervised machine learning approach to classify active peptides from inactive peptides with low false discovery rates in a relatively short computational search time. By generating explicit boundaries, our method defines new categories of active and inactive peptides based on their physicochemical properties. Consequently, it describes physicochemical characteristics of similarity among active peptides and the physicochemical boundaries between active and inactive peptides in a single process. To build the similarity boundaries, we used the rough set theory approach; to our knowledge, this is the first time that this approach has been used to classify peptides. The modified rough set theory method limits the number of values describing a boundary to a user-defined limit. Our method is optimized for specificity over selectivity. Noting that false positives increase activity assays while false negatives only increase computational search time, our method provided a low false discovery rate. Published datasets were used to compare our rough set theory method to other published classification methods and based on this comparison, we achieved high selectivity and comparable sensitivity to currently available methods. We developed rule sets that define physicochemical boundaries which allow us to directly classify the active sequences from inactive peptides. Existing classification methods are either sequence-order insensitive or length-dependent, whereas our method generates the rule sets that combine order-sensitive descriptors with length-independent descriptors. The method provides comparable or improved performance to currently available methods. Discovering the boundaries of physicochemical properties may lead to a new understanding of peptide similarity.

Dataset Information

Why does the A? peptide of Alzheimer share structural similarity with antimicrobial peptides?

Publications

Why does the Aβ peptide of Alzheimer share structural similarity with antimicrobial peptides?

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