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

Aug 4, 2026

Existing therapies for neurodegenerative diseases address symptoms rather than preventing onset, often hindered by limited selectivity, stability, and bioavailability. Cyclic peptide therapeutics offer improved in vivo stability and antibody-like binding affinity, yet de novo design remains difficult due to missing druggable pockets, incomplete conformational sampling, unknown binding sites, and large combinatorial sequence space. This article reviews computational advances and proposes integrated experimental–simulation–machine-learning strategies to design cyclic peptide inhibitors that curb toxic amyloid propagation and support template generation of programmable (bio)materials.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Therapeutic challenges for neurodegenerative diseases

Cyclic peptide therapeutics and design bottlenecks

Integrated strategies: experiments, simulations, and machine learning

Computational review focus and proposed approaches

UvA-DARE (Digital Academic Repository) Unlocking novel therapies cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning de Raffele, D. ; Ilie, I. M. DOI 10.1039/d3cc04630c Publication date 2024 Document Version Final published version Published in Chemical Communications License CC BY Link to publication Citation for published version (APA): de Raffele, D. , & Ilie, I. M. (2024) . Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning. Chemical Communications, 60(6), 632-645 . [https://doi.org/10.1039/d3cc04630c](https://doi.org/10.1039/d3cc04630c) General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) . Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl)) Download date:18 Oct 2024 Open Access Article . Pu on 07 Decemberblished 2023. Downloaded on 4/ 1 1/2024 11:50:08 AM . ChemComm | | | | | --- | --- | --- | | | HIGHLIGHT View Article Online | | | | View Journal | View Issue | | Cite this: Chem. Commun., 2024, 60, 632 Received 18th September 2023, Accepted 6th December 2023 DOI: 10.1039/d3cc04630c[rsc.li/chemcomm](rsc.li/chemcomm) Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning Daria de Raﬀeleab and Ioana M. Ilie *ab 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. Wereview 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 experi

FAQs

Why are current neurodegenerative therapies often insufficient?

They mainly treat symptoms and do not prevent disease onset. Common agents also face issues with selectivity, stability, and bioavailability, leading to poor clinical performance.

What makes cyclic peptides attractive for targeting amyloidogenic processes?

They can provide long in vivo stability while retaining robust, antibody-like binding affinity, making them promising peptide-based therapeutics.

What challenges complicate de novo cyclic peptide design?

Design is hindered by lack of long-lived druggable pockets, incomplete conformational distributions of target and binder, unknown binding sites, methodological limitations, and constraints related to failed trials, time, and cost, alongside a vast sequence search space.

Aug 4, 2026

Upload by Emma Wilson