Discovery of novel PARP-1 inhibitors using tandem in silico studies: integrated docking, e-pharmacophore, deep learning based de novo and molecular dynamics simulation approach.
Aayushi BhatnagarVirendra NathNeeraj KumarVipin KumarPublished in: Journal of biomolecular structure & dynamics (2023)
Cancer accounts for the majority of deaths worldwide, and the increasing incidence of breast cancer is a matter of grave concern. Poly (ADP-ribose) polymerase-1 (PARP-1) has emerged as an attractive target for the treatment of breast cancer as it has an important role in DNA repair. The focus of the study was to identify novel PARP-1 inhibitors using a blend of tandem structure-based screening (Docking and e-pharmacophore-based screening) and artificial intelligence (deep learning)-based de novo approaches. The scrutiny of compounds having good binding characteristics for PARP-1 was carried out using a tandem mode of screening along with parameters such as binding energy and ADME analysis. The efforts afforded compound Vab1 (PubChem ID 129142036), which was chosen as a seed for obtaining novel compounds through a trained artificial intelligence (AI)-based model. Resultant compounds were assessed for PARP-1 inhibition; binding affinity prediction and interaction pattern analysis were carried out using the extra precision (XP) mode of docking. Two best hits, Vab1-b and Vab1-g, exhibiting good dock scores and suitable interactions, were subjected to 100 nanoseconds (ns) of molecular dynamics simulation in the active site of PARP-1 and compared with the reference Protein-Ligand Complex. The stable nature of PARP-1 upon binding to these compounds was revealed through MD simulation.Communicated by Ramaswamy H. Sarma.
Keyphrases
- dna repair
- artificial intelligence
- molecular dynamics simulations
- molecular docking
- deep learning
- dna damage
- molecular dynamics
- machine learning
- big data
- dna damage response
- convolutional neural network
- protein protein
- binding protein
- dna binding
- oxidative stress
- zika virus
- risk factors
- resistance training
- small molecule
- squamous cell carcinoma
- single cell
- combination therapy
- mass spectrometry
- amino acid
- squamous cell
- dengue virus
- body composition