Subtractive genomics integrated with deep learning, molecular docking, and MD simulation decipher therapeutic drug targets and their potential inhibitors against Nocardia farcinica strain IFM 10152

Abstract

Nocardia farcinica is a Gram-positive bacteria that causes opportunistic infections. Chronic lung illness, diabetes mellitus, renal disease, immunosuppressive treatment, hematological neoplasm, and transplant recipients are predisposing factors. Brain abscesses, skin or soft tissue infections, and pulmonary infections are examples of clinical manifestations of Nocardia farcinica infection. Nocardia farcinica is the rare cause of brain abscesses, making up 2% of all intracranial abscesses. In the present study, the subtractive proteomics approach was used for potential drug targets identification in the Nocardia farcinica strain IFM 10152 to evaluate the pathogen-specific and essential proteins by using various bioinformatics tools. A total eighteen specific pathways were found in Nocardia farcinica strain IFM 10152 and eight proteins were involved in these specific pathways. A total of three proteins were found as Druggable. Furthermore, 3D structure prediction for one of the drug targets putative penicillin binding protein was carried out by Alpha-Fold2 one of the deep learning approaches. Virtual screening was performed for the identified drug target. To evaluate the stability of the new hits against the drug target molecular dynamics simulation was carried out. This study provides valuable information about the discovery of new drug targets that may help to eradicate Nocardia farcinica associated infections. To the best of our knowledge, this is the first attempt to apply the subtractive proteomics approach on the complete proteomic data of Nocardia farcinica strain IFM 10152, thus providing an opportunity to predict specific therapeutic targets and their inhibitors against Nocardia farcinica.