bioRxivBiophysicsPreprint
In Silico Targeting of Calpain-5 in Autosomal Dominant Neovascular Inflammatory Vitreoretinopathy (ADNIV) with Peptides and Natural Products
Abstract
Autosomal dominant neovascular inflammatory vitreoretinopathy (ADNIV) is a rare retinal disease caused by gain-of-function mutations in the non-classical calcium-activated cysteine protease calpain-5 (CAPN5). These mutations lower the calcium threshold for catalytic-triad alignment with downstream effects including excessive proteolysis and retinal degeneration, making CAPN5 a therapeutic target. Clinical studies showed that knockout of calpain-5 resulted in no negative side effects, supporting therapy through inhibition. We mapped the druggable pockets of CAPN5 with a 500 ns phenol cosolvent molecular dynamics (MD) simulation. Occupancy analysis resolved five pockets, against which 448,314 COCONUT natural products were screened with Uni-Dock (2,241,570 docked combinations). In parallel, BoltzGen was used to design peptide binders against multiple candidate regions, from which three were selected: the PC1-PC2 subdomain interface, the PC2 regulatory loop (PC2L1) and the catalytic region. The top three designs were co-folded with Boltz-2 at high interface confidence (ipTM 0.91-0.95). The top three peptides and four small molecules were then simulated against wild-type CAPN5 and the four canonical ADNIV variants R243L, L244P, K250N and R289W, each condition in independent triplicate, giving 105 production simulations of 100 ns. Scoring by MM-PBSA revealed favorable peptide interface energies, the most favorable being the largest of the three designs ({Delta}TOTAL -58.6 {+/-} 5.6 kcal/mol for a 23-residue peptide against wild type), while the small-molecule panel returned -8.7 to -23.8 kcal/mol. A total of 15.8 {micro}s of cosolvent, filtering, and production MD prioritizes the catalytic cleft and an adjacent groove for experimental testing and provides candidate peptide and small-molecule binders for evaluating CAPN5 inhibition in ADNIV.
- organism: human
- assay: protein structure
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- Citations
- 0
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