<i>In silico</i> identification of angiotensin-1 converting enzyme inhibitors using text mining and virtual screening

Şahin, Kader

doi:10.1080/07391102.2020.1827038

Cited by 4 publications

(1 citation statement)

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“…MetaDrug/ MetaCore ADME parameters filter drug candidates based on their physicochemical and pharmacokinetic properties before they reach the preclinical phase, blood-brain penetration, lipophilicity, human serum protein binding, affinity to human serum albumin, and water solubility. MetaDrug/ MetaCore toxicity QSAR models 5 provide 26 independent toxicity filters that further investigate pharmacokinetic profiles ( Sahin, 2022 ). Details of the model building parameters are provided in the supplementary tables .…”

Section: Methodsmentioning

confidence: 99%

A Novel Algorithm for the Virtual Screening of Extensive Small Molecule Libraries Against ERCC1/XPF Protein-Protein Interaction for the Identification of Therapeutic Resistance-Bypassing Small Anticancer Molecules

Ghazy,

Oktay,

Durdagi

2024

Turkish Journal of Biology

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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 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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Section: Methodsmentioning

confidence: 99%