PMID- 33034255
OWN - NLM
STAT- MEDLINE
DCOM- 20220315
LR  - 20220315
IS  - 1538-0254 (Electronic)
IS  - 0739-1102 (Linking)
VI  - 40
IP  - 4
DP  - 2022 Mar
TI  - Ligand-based pharmacophore modeling of TNF-alpha to design novel inhibitors using 
      virtual screening and molecular dynamics.
PG  - 1702-1718
LID - 10.1080/07391102.2020.1831962 [doi]
AB  - Tumor necrosis factor-alpha (TNF-alpha) is one of the promising targets for treating 
      inflammatory (Crohn disease, psoriasis, psoriatic arthritis, rheumatoid 
      arthritis) and various other diseases. Commercially available TNF-alpha inhibitors 
      are associated with several risks and limitations. In the present study, we have 
      identified small TNF-alpha inhibitors using in silico approaches, namely 
      pharmacophore modeling, virtual screening, molecular docking, molecular dynamics 
      simulation and free binding energy calculations. The study yielded better and 
      potent hits that bind to TNF-alpha with significant affinity. The best pharmacophore 
      model generated using LigandScout has an efficient hit rate and Area Under the 
      operating Curve. High throughput virtual screening of SPECS database molecules 
      against crystal structure of TNF-alpha protein, coupled with physicochemical 
      filtration, PAINS test. Virtual hit compounds used for molecular docking enabled 
      the identification of 20 compounds with better binding energies when compared 
      with previously known TNF-alpha inhibitors. MD simulation analysis on 20 virtual 
      identified hits showed that ligand binding with TNF-alpha protein is stable and 
      protein-ligand conformation remains unchanged. Further, 16 compounds passed ADMET 
      analysis suggesting these identified hit compounds are suitable for designing a 
      future class of potent TNF-alpha inhibitors.Communicated by Ramaswamy H. Sarma.
FAU - Jade, Dhananjay D
AU  - Jade DD
AD  - Translational Bioinformatics Group, International Centre for Genetic Engineering 
      and Biotechnology, New Delhi, India.
FAU - Pandey, Rajan
AU  - Pandey R
AD  - Translational Bioinformatics Group, International Centre for Genetic Engineering 
      and Biotechnology, New Delhi, India.
FAU - Kumar, Rakesh
AU  - Kumar R
AD  - Translational Bioinformatics Group, International Centre for Genetic Engineering 
      and Biotechnology, New Delhi, India.
FAU - Gupta, Dinesh
AU  - Gupta D
AD  - Translational Bioinformatics Group, International Centre for Genetic Engineering 
      and Biotechnology, New Delhi, India.
LA  - eng
PT  - Journal Article
DEP - 20201009
PL  - England
TA  - J Biomol Struct Dyn
JT  - Journal of biomolecular structure & dynamics
JID - 8404176
RN  - 0 (Ligands)
RN  - 0 (Tumor Necrosis Factor-alpha)
SB  - IM
MH  - Ligands
MH  - Molecular Docking Simulation
MH  - *Molecular Dynamics Simulation
MH  - Protein Conformation
MH  - Quantitative Structure-Activity Relationship
MH  - *Tumor Necrosis Factor-alpha/metabolism
OTO - NOTNLM
OT  - Pharmacophore
OT  - SPECS database
OT  - drug discovery
OT  - inhibitors
OT  - molecular docking
OT  - molecular simulation
OT  - tumor necrosis factor
OT  - virtual screening
EDAT- 2020/10/10 06:00
MHDA- 2022/03/16 06:00
CRDT- 2020/10/09 08:36
PHST- 2020/10/10 06:00 [pubmed]
PHST- 2022/03/16 06:00 [medline]
PHST- 2020/10/09 08:36 [entrez]
AID - 10.1080/07391102.2020.1831962 [doi]
PST - ppublish
SO  - J Biomol Struct Dyn. 2022 Mar;40(4):1702-1718. doi: 
      10.1080/07391102.2020.1831962. Epub 2020 Oct 9.