Antibody binding increases the flexibility of the prion protein

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.

Prion therapeutics: Lessons from the past

Prion diseases are a group of incurable zoonotic neurodegenerative diseases (NDDs) in humans and other animals caused by the prion proteins. The abnormal folding and aggregation of the soluble cellular prion proteins (PrPC) into scrapie isoform (PrPSc) in the Central nervous system (CNS) resulted in brain damage and other neurological symptoms. Different therapeutic approaches, including stalling PrPC to PrPSc conversion, increasing PrPSc removal, and PrPC stabilization, for which a spectrum of compounds, ranging from organic compounds to antibodies, have been explored. Additionally, a non-PrP targeted drug strategy using serpin inhibitors has been discussed. Despite numerous scaffolds being screened for anti-prion activity in vitro, only a few were effective in vivo and unfortunately, almost none of them proved effective in the clinical studies, most likely due to toxicity and lack of permeability. Recently, encouraging results from a prion-protein monoclonal antibody, PRN100, were presented in the first human trial on CJD patients, which gives a hope for better future for the discovery of other new molecules to treat prion diseases. In this comprehensive review, we have re-visited the history and discussed various classes of anti-prion agents, their structure, mode of action, and toxicity. Understanding pathogenesis would be vital for developing future treatments for prion diseases. Based on the outcomes of existing therapies, new anti-prion agents could be identified/synthesized/designed with reduced toxicity and increased bioavailability, which could probably be effective in treating prion diseases.