Employing an integrated computational simulation strategy to identify high-affinity ligands for TDP-43 amyloid
Yunqing Gao1, Zhenghao Sun1, Qiumei Wei1
1Henan Key Laboratory of Brain Targeted Bio-nanomedicine, Henan-Macquarie University Joint Centre for Biomedical Innovation, School of Life Sciences, Henan University, Kaifeng 475004, China.
Abstract:
Developing high-affinity ligands targeting TDP-43 amyloid species is a potential therapeutic approach for amyotrophic lateral sclerosis (ALS). Here, we propose an integrated computational simulation strategy, which integrates multiple virtual screening methods, molecular dynamics simulations and binding free energy evaluations. Using this strategy, we successfully identified TDPL1, a high-affinity ligand for TDP-43 amyloid proteins. In vitro affinity assays confirmed the computational predictions. Based on the MD simulation results, we further investigated the binding mode between TDPL1 and TDP-43 amyloid proteins. Additionally, steered molecular dynamics simulations were employed to assess the impact of TDPL1 on the stability of β-sheet interactions within the TDP-43 amyloid structure. Our data demonstrate that TDPL1 not only binds effectively to TDP-43 amyloid proteins but also possesses the potential to disrupt the stability of amyloid aggregates. These findings provide a molecular foundation for the future development of diagnostic agents or targeted therapeutics for ALS and related diseases.
More Related Videos
05:08Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
Published on: July 8, 2025
06:50Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
