Literature DB >> 27722817

Combining molecular dynamics simulation and ligand-receptor contacts analysis as a new approach for pharmacophore modeling: beta-secretase 1 and check point kinase 1 as case studies.

Ma'mon M Hatmal1, Shadi Jaber2, Mutasem O Taha3.   

Abstract

Ligand-based pharmacophore modeling require relatively long lists of active compounds, while a pharmacophore based on a single ligand-receptor crystallographic structure is often promiscuous. These problems prompted us to combine molecular dynamics (MD) simulation with ligand-receptor contacts analysis as means to develop valid pharmacophore model(s). The particular ligand-receptor complex is allowed to perturb over a few nano-seconds using MD simulation. Subsequently, ligand-receptor contact points (≤2.5 Å) are identified. Ligand-receptor contacts maintained above certain threshold during molecular dynamics simulation are considered critical and used to guide pharmacophore development. We termed this method as Molecular-Dynamics Based Ligand-Receptor Contact Analysis. We implemented this new methodology to develop valid pharmacophore models for check point kinase 1 (Chk1) and beta-secretase 1 (BACE1) inhibitors as case studies. The resulting pharmacophore models were validated by receiver operating characteristic curved analysis against inhibitors obtained from CHEMBL database.

Entities:  

Keywords:  Chk1; Ligand receptor contact analysis; Molecular dynamics simulation; Pharmacophore

Mesh:

Substances:

Year:  2016        PMID: 27722817     DOI: 10.1007/s10822-016-9984-2

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  40 in total

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7.  Potent Human Telomerase Inhibitors: Molecular Dynamic Simulations, Multiple Pharmacophore-Based Virtual Screening, and Biochemical Assays.

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  3 in total

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

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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
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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

  3 in total

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