Rational Design of TDP-43 Derived α-Helical Peptide Inhibitors: an In-Silico Strategy to Prevent TDP-43 Aggregation in Neurodegenerative Disorders

• Published in: bioRxiv
• Main Authors: Salaikumaran, Muthu Raj, Gopal, Pallavi P
• Format: Journal Article Paper
• Language: English
• Published: United States Cold Spring Harbor Laboratory 31.10.2023
• Edition: 1.1
• Subjects: Neuroscience; aggregation; neurodegenerative disease; Helical Propensity Peptide; TDP-43
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/2023.10.26.564235

TDP-43, an essential RNA/DNA-binding protein, is central to the pathology of neurodegenerative diseases such as Amyotrophic Lateral Sclerosis and Frontotemporal Dementia. Pathological mislocalization and aggregation of TDP-43 disrupts RNA splicing, mRNA stability, and mRNA transport, thereby impairing neuronal function and survival. The formation of amyloid-like TDP-43 filaments is largely facilitated by the destabilization of an α-helical segment within the disordered C-terminal region. In this study, we hypothesized that preventing the destabilization of the α-helical domain could potentially halt the growth of these pathological filaments. To explore this, we utilized a range of techniques to design and evaluate peptide-based therapeutics. Various pathological TDP-43 amyloid-like filament crystal structures were selected for their potential to inhibit the binding of additional TDP-43 monomers to the growing filaments. Our computational approaches included biophysical and secondary structure property prediction, molecular docking, 3D structure prediction, and molecular dynamics simulations. Through these techniques, we were able to assess the structure, stability, and binding affinity of these peptides in relation to pathological TDP-43 filaments. The results of our analyses identified a selection of promising peptides, which displayed a stable α-helical structure, exhibited an increased number of intramolecular hydrogen bonds within the helical domain, and demonstrated high binding affinities for pathological TDP-43 amyloid-like filaments. Additionally, molecular dynamics simulations provided further support for the stability of these peptides, as they exhibited lower root mean square deviations in their helical propensity over 100ns. These findings establish α-helical propensity peptides as potential lead molecules for the development of novel therapeutics against TDP-43 aggregation. This structure-based computational approach for rational design of peptide inhibitors opens a new direction in the search for effective interventions for ALS, FTD, and other related neurodegenerative diseases. The peptides identified as the most promising candidates in this study are currently subject to further testing and validation through both in vitro and in vivo experiments.