ArticleTurkish journal of biology = Turk biyoloji dergisi2024
A novel algorithm for the virtual screening of extensive small molecule libraries against ERCC1/XPF protein-protein interaction for the identification of resistance-bypassing potential anticancer molecules.
Article in Turkish journal of biology = Turk biyoloji dergisi, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Prioritizing Candidate YB‑1 Cold Shock Domain Ligands via 5D-ElectroShape and Boltz Generative Cofolding.ACS omega · 2026Article
- Identification of novel VPS4 inhibitors using multi-tiered structure based virtual screening.Molecular diversity · 2026Article
- Beyond Docking: A Multitier Computational Pipeline for USP7 Inhibitor Optimization.ChemMedChem · 2025Article
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Abstract
Background and aim: Cancer cell's innate chemotherapeutic resistance continues to be an obstacle in molecular oncology. This theory is firmly tied to the cancer cells' integral DNA repair mechanisms continuously neutralizing the effects of chemotherapy. Amidst these mechanisms, the nuclear excision repair pathway is crucial in renovating DNA lesions prompted by agents like Cisplatin. The ERCC1/XPF complex stands center-stage as a structure-specific endonuclease in this repair pathway. Targeting the ERCC1/XPF dimerization brings forth a strategy to augment chemotherapy by eschewing the resistance mechanism integral to cancer cells. This study tracks and identifies small anticancer molecules, with ERCC1/XPF inhibiting potential, within extensive small-molecule compound libraries. Materials and methods: A novel hybrid virtual screening algorithm, conjoining ligand- and target-based approaches, was developed. All-atom molecular dynamics (MD) simulations were then run on the obtained hit molecules to reveal their structural and dynamic contributions within the binding site. MD simulations were followed by MM/GBSA calculations to qualify the change in binding free energies of the protein/ligand complexes throughout MD simulations. Results: Conducted analyses highlight new potential inhibitors AN-487/40936989 from the SPECS SC library, K219-1359, and K786-1161 from the ChemDiv Representative Set library as showing better predicted activity than previously discovered ERCC1/XPF inhibitor, CHEMBL3617209. Conclusion: The algorithm implemented in this study expands our comprehension of chemotherapeutic resistance and how to overcome it through identifying ERCC1/XPF inhibitors with the aim of enhancing chemotherapeutic impact giving hope for ameliorated cancer treatment outcomes.
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