Discovery of dual inhibitors of KPC-3 and KPC-15 of Klebsiella pneumoniae - a ligand and structure based virtual screening study
Sharma, S.; Kumar-M, P.; Yadav, R.; Gupta, N.
Show abstract
BackgroundThe development of carbapenem resistance against Klebsiella pneumoniae is a situation of grave concern and requires urgent attention. Among the KPC produced by K.pneumoniae, KPC-3, and KPC-15, play a significant role in the development of resistance to carbapenem. Materials and methodsThe binding sites of KPC-3 and KPC-15 were predicted by the COACH server. Drug-like ligands from ZINC were then screened by ligand-based drug screening (LBVS) by keeping Relebactam as a template. The top 50,000 selected ligands were then screened by structure-based virtual screening using idock. For keeping an account of the dual inhibitors stability in complex with KPC-3 and KPC-15, MDS were carried out for each complex. ResultsBased on consensus weighted ranks, the top 3 ligands with the dual inhibitory property are ZINC76060350 (consensus weighted rank - 1.5), ZINC05528590 (2), ZINC72290395 (3.5). All the top 3 dual inhibitors have a reasonable probability of passing through the blood-brain barrier. The RDKit and Morgan fingerprint scores between Relebactam and the top three ligands were 0.24, 0.22, 0.23, and 0.26, 0.19, 0.25, respectively (showing only 20% similarity). The MD simulation result revealed good binding stability of ligand ZINC05528590 with both KPC-3 and KPC-15, whereas ligand ZINC76060350 showed good binding stability to KPC-3. ConclusionThe ligand ZINC05528590 could be taken forward to develop a new drug against a multi-resistant- Klebsiella pneumoniae infection. At the same time, ZINC76060350 can be considered to develop a new drug against KPC-15 resistant Klebsiella pneumoniae.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Molecular docking, simulation and binding free energy analysis of small molecules as PfHT1 inhibitors 98%
- Identification of Natural Antiviral Drug Candidates Against Tilapia Lake Virus: Computational Drug Design Approaches 97%
- Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of Rheumatoid Arthritis: Virtual screening to comprehensive in silico investigation 97%
Similar papers in this journal
- A program to automate the discovery of drugs for West Nile and Dengue virus -- programmatic screening of over a billion compounds on PubChem, generation of drug leads and automated In Silico modelling 97%
- Pharmacophore modeling, 2D-QSAR, Molecular Docking and ADME studies for the discovery of inhibitors of PBP2a in MRSA 97%
- De novo drug designing coupled with brute force screening and structure guided lead optimization gives highly specific inhibitor of METTL3: a potential cure for Acute Myeloid Leukaemia 97%
Similar papers in this journal
- Mechanistic insights into the Japanese Encephalitis Virus RNA dependent RNA polymerase protein inhibition by bioflavonoids from Azadirachta indica 96%
- Machine learning prediction of antiviral-HPV protein interactions for anti-HPV pharmacotherapy 95%
- Discovery of Z1362873773: A Novel Fascin Inhibitor from a Large Chemical Library for Colorectal Cancer 95%
Similar papers in this journal
- Mitoxantrone dihydrochloride, an FDA approved drug, binds with SARS-CoV-2 NSP1 C-terminal 95%
- Whole Genome Sequencing for Revealing the Point Mutations of SARS-CoV-2 Genome in Bangladeshi Isolates and their Structural Effects on Viral Proteins 94%
- Refining physico-chemical rules for herbicides using an antimalarial library 92%
Similar papers in this journal
- Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-based Classifiers 97%
- Molecular Glue-Design-Evaluator (MOLDE): An Advanced Method for In-Silico Molecular Glue Design 96%
- Support Vector Machine based prediction models for drug repurposing and designing novel drugs for colorectal cancer 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.