Literature DB >> 28722471

Simulated annealing molecular dynamics and ligand-receptor contacts analysis for pharmacophore modeling.

Ma'mon M Hatmal1, Mutasem O Taha2.   

Abstract

AIM: Ligand-based pharmacophore modeling requires long list of inhibitors, while pharmacophores based on single ligand-receptor crystallographic structure can be too restricted or promiscuous.
METHODOLOGY: This prompted us to combine simulated annealing molecular dynamics (SAMD) with ligand-receptor contacts analysis as means to construct pharmacophore model(s) from single ligand-receptor complex. Ligand-receptor contacts that survive numerous heating-cooling SAMD cycles are considered critical and are used to guide pharmacophore development.
RESULTS: This methodology was implemented to develop pharmacophores for acetylcholinesterase and protein kinase C-θ. The resulting models were validated by receiver-operating characteristic analysis and in vitro bioassay. Assay identified four new protein kinase C-θ inhibitors among captured hits, two of which exhibited nanomolar potencies.
CONCLUSION: The results illustrate the ability of the new method to extract valid pharmacophores from single ligand-protein complex.

Entities:  

Keywords:  PKC-θ; acetycholineesterase; enzyme bioassay; ligand–receptor contact analysis; pharmacophore; simulated annealing molecular dynamics

Mesh:

Substances:

Year:  2017        PMID: 28722471     DOI: 10.4155/fmc-2017-0061

Source DB:  PubMed          Journal:  Future Med Chem        ISSN: 1756-8919            Impact factor:   3.808


  4 in total

1.  Exploiting activity cliffs for building pharmacophore models and comparison with other pharmacophore generation methods: sphingosine kinase 1 as case study.

Authors:  Lubabah A Mousa; Ma'mon M Hatmal; Mutasem Taha
Journal:  J Comput Aided Mol Des       Date:  2022-01-21       Impact factor: 3.686

2.  Docking-generated multiple ligand poses for bootstrapping bioactivity classifying Machine Learning: Repurposing covalent inhibitors for COVID-19-related TMPRSS2 as case study.

Authors:  Ma'mon M Hatmal; Omar Abuyaman; Mutasem Taha
Journal:  Comput Struct Biotechnol J       Date:  2021-08-19       Impact factor: 7.271

3.  Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors via pharmacophore exploration combined with flexible docking-based ligand/receptor contact fingerprints and machine learning.

Authors:  Mai Fayiz Al-Tawil; Safa Daoud; Ma'mon M Hatmal; Mutasem Omar Taha
Journal:  RSC Adv       Date:  2022-04-05       Impact factor: 3.361

Review 4.  Structure-Based Virtual Screening: From Classical to Artificial Intelligence.

Authors:  Eduardo Habib Bechelane Maia; Letícia Cristina Assis; Tiago Alves de Oliveira; Alisson Marques da Silva; Alex Gutterres Taranto
Journal:  Front Chem       Date:  2020-04-28       Impact factor: 5.221

  4 in total

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