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Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study

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A Correction to this article was published on 19 October 2023

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Abstract

STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.

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Data are available upon request from the corresponding author.

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Acknowledgements

The authors thank the Deanship of Academic Research at the University of Jordan for funding this project. The authors would also like to thank Dr. Walhan al Shaer, Fadwa Daoud, and Suha Wehaibi, from Cell Therapy Center for their technical assistance in biology testing experiments.

Funding

This project was funded the Deanship of Scientific Research at the University of Jordan, Amman, Jordan.

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, Jordan

    Mutasem Omar Taha

  2. Department of Medical Laboratory Sciences, Faculty of Applied Health Sciences, The Hashemite University, P.O. Box 330127, Zarqa, 13133, Jordan

    Nour Jamal Jaradat & Mamon Hatmal

  3. Cell Therapy Center, the University of Jordan, Amman, 11942, Jordan

    Dana Alqudah

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  1. Nour Jamal Jaradat
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  2. Mamon Hatmal
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NJJ: Investigation, Formal analysis, Review & Editing. MH: Investigation, Review & Editing. DA.: Formal analysis, Review & Editing. MOT: Conceptualization, Methodology, Supervision, Investigation, Resources, Writing, Review & Editing.

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Correspondence to Mutasem Omar Taha.

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Jaradat, N.J., Hatmal, M., Alqudah, D. et al. Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study. J Comput Aided Mol Des 37, 659–678 (2023). https://doi.org/10.1007/s10822-023-00528-y

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