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

• Published in: ACS chemical neuroscience Vol. 15; no. 6; pp. 1096 - 1109
• Main Authors: Salaikumaran, Muthu Raj, Gopal, Pallavi P.
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 20.03.2024
• Subjects: Amyotrophic Lateral Sclerosis - metabolism; DNA-Binding Proteins - metabolism; Humans; Molecular Docking Simulation; Peptides - pharmacology; Protein Conformation, alpha-Helical; helical propensity peptide; aggregation; neurodegenerative disease; TDP-43
• ISSN: 1948-7193; 1948-7193
• DOI: 10.1021/acschemneuro.3c00659

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 disrupt 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 in silico techniques to design and evaluate peptide-based therapeutics that bind to pathological TDP-43 amyloid-like filament crystal structures and resist β sheet conversion. Our computational approaches, including biophysical and secondary structure property prediction, molecular docking, 3D structure prediction, and molecular dynamics simulations, were used to assess the structure, stability, and binding affinity of these peptides in relation to pathological TDP-43 filaments. The results of our in silico 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. Molecular dynamics simulations provided further support for the structural and thermodynamic stability of these peptides, as they exhibited lower root-mean-square deviation and more favorable free energy landscapes over 300 ns. 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 the 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.