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. 2022 Jun 2:9:836572.
doi: 10.3389/fmolb.2022.836572. eCollection 2022.

Hierarchical Virtual Screening Based on Rocaglamide Derivatives to Discover New Potential Anti-Skin Cancer Agents

Igor V F Dos Santos  1   2 , Rosivaldo S Borges  1   3 , Guilherme M Silva  4   5 , Lúcio R de Lima  1   3 , Ruan S Bastos  1   3 , Ryan S Ramos  1   2 , Luciane B Silva  1   3 , Carlos H T P da Silva  4   5 , Cleydson B R Dos Santos  1   2   3
Affiliations

Hierarchical Virtual Screening Based on Rocaglamide Derivatives to Discover New Potential Anti-Skin Cancer Agents

Igor V F Dos Santos et al. Front Mol Biosci. .

Abstract

Skin Cancer (SC) is among the most common type of cancers worldwide. The search for SC therapeutics using molecular modeling strategies as well as considering natural plant-derived products seems to be a promising strategy. The phytochemical Rocaglamide A (Roc-A) and its derivatives rise as an interesting set of reference compounds due to their in vitro cytotoxic activity with SC cell lines. In view of this, we performed a hierarchical virtual screening study considering Roc-A and its derivatives, with the aim to find new chemical entities with potential activity against SC. For this, we selected 15 molecules (Roc-A and 14 derivatives) and initially used them in docking studies to predict their interactions with Checkpoint kinase 1 (Chk1) as a target for SC. This allowed us to compile and use them as a training set to build robust pharmacophore models, validated by Pearson's correlation (p) values and hierarchical cluster analysis (HCA), subsequentially submitted to prospective virtual screening using the Molport® database. Outputted compounds were then selected considering their similarities to Roc-A, followed by analyses of predicted toxicity and pharmacokinetic properties as well as of consensus molecular docking using three software. 10 promising compounds were selected and analyzed in terms of their properties and structural features and, also, considering their previous reports in literature. In this way, the 10 promising virtual hits found in this work may represent potential anti-SC agents and further investigations concerning their biological tests shall be conducted.

Keywords: anticancer activity; hierarchical virtual screening; pharmacophore; rocaglamide; skin cancer.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a shared affiliation, though no other collaboration, with several of the authors GS, CS at the time of the review.

Figures

FIGURE 1
FIGURE 1
Representation of 2D and 3D structures of Roc-A (1R,2R,3S,3aR,8bS)-1,8b-dihydroxy-6,8-dimethoxy-3a-(4-methoxyphenyl)-N,N-dimethyl-3-phenyl-2,3-dihydro-1H-cyclopenta [b][1]benzofuran-2-carboxamide).
FIGURE 2
FIGURE 2
General scheme summarizing the methodological steps proposed via hierarchical virtual screening in this work.
FIGURE 3
FIGURE 3
Training set consisting of fourteen Roc-A derivatives, described by Nugroho et al. (1997), Nugroho et al. (1999), used to build pharmacophore models.
FIGURE 4
FIGURE 4
2D structures of native ligands complexed with Chk1 protein structures retrieved from PDB.
FIGURE 5
FIGURE 5
Representations of overlap similarities between molecular structures of 3D3 ligand (green) and Rocaglamide-A (yellow) according to different steric/electrostatic contributions.
FIGURE 6
FIGURE 6
Qualitative characteristics of the best pharmacophore model initially generated by PharmaGist. (A) Aligned molecules. (B) Pharmacophoric features positioned over Roc-A (pivot molecule). (C) Pharmacophoric features: 3 aromatic groups (Aro) in purple, and 4 hydrogen bond acceptor groups (Acc) in yellow.
FIGURE 7
FIGURE 7
(A) HCA dendrogram built considering pharmacophoric features and TI values. (B) HCA dendrogram for compounds of training set - more similar in blue cluster and less similar in red cluster.
FIGURE 8
FIGURE 8
Structural differences between most similar compounds of training set. The red circles indicate which functional groups are exchanged. 1* corresponds to Roc-A (pivot molecule).
FIGURE 9
FIGURE 9
Docking poses obtained for Roc-A using (A) GOLD in orange, (B) FRED in purple, and (C) Dockthor in light blue, and for PubChem-135638768 (D,E,F) using same corresponding software/colors. Results obtained using the protein Chk1 (PDB ID 2CGX). Dashed lines in yellow represent hydrogen bonds and in green cation-pi. Figures were prepared using Maestro.
FIGURE 10
FIGURE 10
Representation of 2D structures of 10 promising compounds obtained by hierarchical virtual screening. PC: PubChem.
FIGURE 11
FIGURE 11
logP values predicted using different methodologies for pivotal molecule and 10 promising compounds. Pivotal molecule: Roc-A; PC: PubChem.
FIGURE 12
FIGURE 12
logS values predicted using different methodologies for pivotal molecule and 10 promising compounds. Pivotal molecule: Roc-A; PC: PubChem.
FIGURE 13
FIGURE 13
Heatmap plot comparing the experimental, commercial and promissing compounds binding affinity (ΔG) values in receptors: Chk1 (PBD ID 2CGX), elF4A1-ATP (PDB ID 5ZC9) and BRAF (PDB ID 6XFP).
See this image and copyright information in PMC

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