PhD Scientific Days 2023

Budapest, 22-23 June 2023

Molecular Sciences - Posters L

Substrate selectivity of Sulfotransferase Isoenzymes, results based on Molecular Dynamics and Virtual Screening

Dániel Tóth1, Bálint Dudas2, David Perahia3, Erika Balog4, Maria A. Miteva5
1 Department of Biophysics and Radiation Biology, Semmelweis University, Hungary
2 Inserm U1268 MCTR, CiTCoM UMR 8038 CNRS - Université Paris Cité, France
3 Laboratoire de biologie et pharmacologie appliquee, Ecole Normale Superieure Paris-Saclay, France
4 Department of Biophysics and Radiation Biology, Semmelweis University, Hungary
5 Inserm U1268 MCTR, CiTCoM UMR 8038 CNRS - Université Paris Cité, France

Text of the abstract

Sulfotransferase enzymes (SULTs) are a family of cytosolic globular proteins in the chain of metabolism. By catalysing a sulfate transfer from their co-factor, 3′-Phosphoadenosine 5′-Phosphosulfate (PAPS), they eliminate a large variety of small molecules like drugs, hormones and neurotransmitters. Even though the tertiary structure across the family is very similar, their substrates vary considerably in size and complexity. The aim of our project is to better understand the reasons of selectivity between the different SULT isoenzymes, by comparing the broad targeting hepatic detoxifier SULT1A1, and the ileum located, dopamine selective SULT1A3.
Based on our previous results and Molecular Dynamics (MD) and Molecular Dynamics with excited Normal Modes (MDeNM), an extended conformational space of the PAPS-bound SULT1A1 was explored. Further developments of our method utilising ensemble docking with categorised ligands, a method known as Virtual Screening was achieved. Moreover, we have broadened our scope to use the same approach for the SULT1A3.
Based on our new results, we identified the key differences, that are responsible for changing the protein dynamics and binding mechanisms, by opening the binding pocket to an unfavourable conformation for the most common ligands of 1A1, thus acting as efficient selectors. These results can be helpful in the future to develop an algorithm for machine learning, that could differentiate and even predict new substrates of the different isoforms, thus helping in the development of ADME-Tox profiling of novel drug candidates and xenobiotics.