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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual

Alzbeta Tuerkova1, Brandon J Bongers2, Ulf Norinder3,4

  • 1Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, University of Vienna, Althanstraße 14, A-1090 Vienna, Austria.

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Summary
This summary is machine-generated.

This study used machine learning and docking to find new inhibitors for hepatic organic anion transporting polypeptides (OATPs), which are key in drug interactions and liver toxicity. Researchers identified potent new compounds for OATP1B1, OATP1B3, and OATP2B1, advancing transporter research.

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Area of Science:

  • Pharmacology and Drug Discovery
  • Computational Chemistry
  • Biochemistry

Background:

  • Hepatic organic anion transporting polypeptides (OATPs) are crucial for drug and chemical transport, and their off-target activity can cause liver toxicity and drug-drug interactions.
  • Understanding OATP structure-function relationships is vital for predicting and mitigating adverse effects.
  • Virtual screening combined with structure-based modeling offers a powerful strategy for identifying novel transporter modulators.

Purpose of the Study:

  • To identify novel chemical compounds that inhibit hepatic OATPs (OATP1B1, OATP1B3, OATP2B1) using a consensus virtual screening approach.
  • To elucidate structure-function relationships and binding determinants for ligands interacting with these transporters.
  • To discover tool compounds for further research into OATP-mediated transport and its clinical implications.

Main Methods:

  • A consensus virtual screening strategy integrating proteochemometric, conformal prediction, and XGBoost machine learning models for hepatic OATPs.
  • Molecular docking of preselected compounds against established structural models of OATP1B1, OATP1B3, and OATP2B1.
  • In vitro experimental validation using percentage inhibition assays to determine the potency of identified inhibitors.

Main Results:

  • High hit rates were observed for OATP1B1 (36%), OATP1B3 (32%), and particularly OATP2B1 (66%) using the virtual screening approach.
  • In vitro assays identified potent inhibitors: six for OATP2B1 (IC50: 0.04–6 μM), three for OATP1B1 (IC50: 2.69–10 μM), and five for OATP1B3 (IC50: 1.53–10 μM).
  • Two novel OATP2B1 inhibitors (C7 and H5) demonstrated high affinity (IC50: 40 nM and 390 nM), comparable to known inhibitors.

Conclusions:

  • The integrated virtual screening approach successfully identified novel, potent inhibitors for key hepatic OATPs.
  • The discovered compounds serve as valuable tools for further investigation of OATP function, selectivity, and structure-activity relationships.
  • Structural analysis of binding sites and poses provides molecular insights into transporter-ligand interactions and selectivity.