Predicting energetically favorable residue interactions between LL-37 and familial alzheimer's disease mutants of Aβ42 using molecular dynamics simulations

Amyloid beta (Aβ) peptides self-aggregate into plaques, which is a hallmark of Alzheimer’s disease (AD) and is thought to cause the deterioration of neurons. Mutations at the E22 of the wildtype Aβ42 are likewise seen in patients with genetic and early-onset familial AD. It was previously shown that...

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Main Author: Miranda, Martin Carlos Y.
Format: text
Language:English
Published: Animo Repository 2024
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Online Access:https://animorepository.dlsu.edu.ph/etdb_chem/48
https://animorepository.dlsu.edu.ph/context/etdb_chem/article/1056/viewcontent/2024_Miranda_Predicting_energetically_favorable_residue_interactions_between_L.pdf
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Institution: De La Salle University
Language: English
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Summary:Amyloid beta (Aβ) peptides self-aggregate into plaques, which is a hallmark of Alzheimer’s disease (AD) and is thought to cause the deterioration of neurons. Mutations at the E22 of the wildtype Aβ42 are likewise seen in patients with genetic and early-onset familial AD. It was previously shown that the natural human antimicrobial peptide LL-37 binds to Aβ42 monomers, preventing the aggregation of Aβ42 plaques. However, specific residue interactions between the two peptides have not yet been studied extensively. In this study, molecular dynamics simulations were conducted on three two-peptide systems consisting of the LL-37 peptide and one of three Aβ42 peptides – wildtype, Italian mutant (E22K), and Dutch mutant (E22Q). Dihedral principal component analysis and root- mean-square deviation-based clustering were performed to obtain eight energetically favorable bound complexes for each system. These were then simulated for 100 ns each in triplicates. Contact and molecular mechanics generalized Born surface area (MMGBSA) analyses were conducted on the trajectories to determine the extent of side- chain interactions between the two peptides. The MMGBSA analysis revealed that LL-37 significantly favored binding with both mutants more than the wildtype Aβ42. In the decomposition analysis, the wildtype system prefers core-to-core contact, while the E22K system prefers terminal-to-terminal contact. The E22Q systems show a combination of the two modes. In wild type Aβ42 systems, significant energy stability was provided by the electrostatic interactions between E22 and D23 of Aβ42 and K18 and K25 of LL-37. These were not present in the E22K systems, as the C-terminal of the Aβ42 and the N- terminal of LL-37 compensated for the stability of the complex. Finally, in the E22Q systems, both D23 and the N-terminal region of Aβ42 induced strong electrostatic attractions with charged residues of LL-37. Moreover, the number of hydrophobic interactions increased in the mutant systems compared to the wild type systems. Alanine scanning also revealed that the 22nd and 23rd positions were significant in complex formation for the wildtype systems, but not for the E22K and E22Q systems. These results predict that LL-37 is also a potential binding partner for E22K and E22Q mutants of Aβ42, with the terminal regions of the Aβ42 peptides being a more favorable target site.