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Virtual screening for potential inhibitors of Mcl-1 conformations sampled by normal modes, molecular dynamics, and nuclear magnetic resonance.

ABSTRACT: Myeloid cell leukemia-1 (Mcl-1) is often overexpressed in human cancer and is an important target for developing antineoplastic drugs. In this study, a data set containing 2.3 million lead-like molecules and a data set of all the US Food and Drug Administration (FDA)-approved drugs are virtually screened for potential Mcl-1 ligands using Protein Data Bank (PDB) ID 2MHS. The potential Mcl-1 ligands are evaluated and computationally docked on to three conformation ensembles generated by normal mode analysis (NMA), molecular dynamics (MD), and nuclear magnetic resonance (NMR), respectively. The evaluated potential Mcl-1 ligands are then compared with their clinical use. Remarkably, half of the top 30 potential drugs are used clinically to treat cancer, thus partially validating our virtual screen. The partial validation also favors the idea that the other half of the top 30 potential drugs could be used in the treatment of cancer. The normal mode-, MD-, and NMR-based conformation greatly expand the conformational sampling used herein for in silico identification of potential Mcl-1 inhibitors.

SUBMITTER: Glantz-Gashai Y

PROVIDER: S-EPMC5484510 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Virtual screening for potential inhibitors of Mcl-1 conformations sampled by normal modes, molecular dynamics, and nuclear magnetic resonance.

Glantz-Gashai Yitav Y Meirson Tomer T Reuveni Eli E Samson Abraham O AO

Drug design, development and therapy 20170619

Myeloid cell leukemia-1 (Mcl-1) is often overexpressed in human cancer and is an important target for developing antineoplastic drugs. In this study, a data set containing 2.3 million lead-like molecules and a data set of all the US Food and Drug Administration (FDA)-approved drugs are virtually screened for potential Mcl-1 ligands using Protein Data Bank (PDB) ID 2MHS. The potential Mcl-1 ligands are evaluated and computationally docked on to three conformation ensembles generated by normal mod ...[more]

PMID: 28684899

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Project description:Protein conformational change is an important consideration in ligand-docking screens, but it is difficult to predict. A simple way to account for protein flexibility is to soften the criterion for steric fit between ligand and receptor. A more comprehensive but more expensive method would be to sample multiple receptor conformations explicitly. Here, these two approaches are compared. A "soft" scoring function was created by attenuating the repulsive term in the Lennard-Jones potential, allowing for a closer approach between ligand and protein. The standard, "hard" Lennard-Jones potential was used for docking to multiple receptor conformations. The Available Chemicals Directory (ACD) was screened against two cavity sites in the T4 lysozyme. These sites undergo small but significant conformational changes on ligand binding, making them good systems for soft docking. The ACD was also screened against the drug target aldose reductase, which can undergo large conformational changes on ligand binding. We evaluated the ability of the scoring functions to identify known ligands from among the over 200 000 decoy molecules in the database. The soft potential was always better at identifying known ligands than the hard scoring function when only a single receptor conformation was used. Conversely, the soft function was worse at identifying known leads than the hard function when multiple receptor conformations were used. This was true even for the cavity sites and was especially true for aldose reductase. To test the multiple-conformation method predictively, we screened the ACD for molecules that preferentially docked to the expanded conformation of aldose reductase, known to bind larger ligands. Six novel molecules that ranked among the top 0.66% of hits from the multiple-conformation calculation, but ranked relatively poorly in the soft docking calculation, were tested experimentally for enzyme inhibition. Four of these six inhibited the enzyme, the best with an IC(50) of 8 microM. Although ligands can get better scores in soft docking, the same is also true for decoys. The improved ranking of such decoys can come at the expense of true ligands.

Dataset Information

Virtual screening for potential inhibitors of Mcl-1 conformations sampled by normal modes, molecular dynamics, and nuclear magnetic resonance.

Publications

Virtual screening for potential inhibitors of Mcl-1 conformations sampled by normal modes, molecular dynamics, and nuclear magnetic resonance.

Similar Datasets

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