Computational and experimental screening identifies 6-chloro-2-hydroxy-1-naphthaldehyde derivatives as disruptors of the HEG1–KRIT1 protein–protein interaction
The HEG1–KRIT1 protein–protein interaction is a central regulatory interface in cerebral cavernous malformation signaling and represents a potential target for small-molecule modulation. In this study, a focused library of 1,121 compounds containing a 2-hydroxy-1-naphthaldehyde scaffold was screened to identify candidate HEG1–KRIT1 disruptors. Compounds were prioritized through drug-likeness filtering, PAINS/reactivity assessment, diversity and purchasability evaluation, AutoDock Vina docking, short molecular dynamics refinement, and MM/PBSA rescoring. Three candidates, HN1, HN2, and HN3, were selected for 1000 ns molecular dynamics simulations, free-energy analyses, in silico ADMET profiling, and experimental evaluation using a bead-based flow-cytometry HEG1–KRIT1 competition assay. Docking and simulation analyses suggested that all three candidates could occupy the HEG1-recognition region of the KRIT1 FERM domain and maintain ligand–protein contacts during the simulations. Among the tested compounds, HN1 yielded a modest positive ΔΔG PPI estimate of + 2.66 kcal/mol, whereas HKi2, HN2, and HN3 yielded negative estimates; these values were interpreted descriptively as approximate indicators of ligand-dependent changes in the HEG1–KRIT1 interface. Experimental testing showed concentration-dependent inhibition of EGFP–KRIT1 FERM binding to HEG1-coated beads. Under the applied assay conditions, HN1 produced the numerically lowest IC₅₀ estimate at 3.51 ± 0.14 µM, compared with 3.90 ± 0.13 µM for HKi2, while HN2 and HN3 produced IC₅₀ estimates of 24.12 ± 1.59 and 31.63 ± 1.99 µM, respectively. These numerical differences do not establish superiority or equivalence among the compounds. Collectively, the computational and experimental findings support HN1 as a preliminary hit that warrants further target-engagement, mechanistic, and cellular validation.
Authors
- Badriah Saleh Alammari (ORCID: https://orcid.org/0009-0005-5614-0218)
- Fahad M. Alshabrmi (ORCID: https://orcid.org/0000-0001-5827-6040)
- Emadeldin M. Kamel (ORCID: https://orcid.org/0000-0002-1279-9564)
- Faris F. Aba Alkhayl (ORCID: https://orcid.org/0000-0002-5247-8933)
- Haifa Essa Alfassam
- Raghad A. Alotaibi
- May Bin-Jumah
- Al Mokhtar Lamsabhi
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Beni-Suef University (EG)
- Qassim University (SA)
- Imam Mohammad ibn Saud Islamic University (SA)
- Universidad Autónoma de Madrid (ES)
Publication Details
- Journal
- Scientific Reports
- Published
- 2026-11-06
- DOI
- https://doi.org/10.1038/s41598-026-76453-5
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministerio de Ciencia, Innovación y Universidades
- Ministerio de Ciencia e Innovación
- Deanship of Scientific Research, Princess Nourah Bint Abdulrahman University