Computational Analysis of S1PR1 SNPs Reveals Drug Binding Modes Relevant to Multiple Sclerosis Treatment.

Multiple sclerosis (MS) is an autoimmune disorder of the central nervous system (CNS) characterized by myelin and axonal damage with a globally rising incidence. While there is no known cure for MS, various disease-modifying treatments (DMTs) exist, including those targeting Sphingosine-1-Phosphate Receptors (S1PRs), which play important roles in immune response, CNS function, and cardiovascular regulation. This study focuses on understanding how nonsynonymous single nucleotide polymorphisms (rs1299231517, rs1323297044, rs1223284736, rs1202284551, rs1209378712, rs201200746, and rs1461490142) in the S1PR1's active site affect the binding of endogenous ligands, as well as different drugs used in MS management. Extensive molecular dynamics simulations and linear interaction energy (LIE) calculations were employed to predict binding affinities and potentially guide future personalized medicinal therapies. The empirical parameters of the LIE method were optimized using the binding free energies calculated from experimentally determined values. These optimized parameters were then applied to calculate the binding free energies of S1P to mutated S1PR1, which correlated well with experimental values, confirming their validity for assessing the impact of SNPs on S1PR1 binding affinities. The binding free energies varied from the least favorable -8.2 kcal/mol for the wild type with ozanimod to the most favorable -16.7 kcal/mol for the combination of siponimod with the receptor carrying the F205L mutation. We successfully demonstrated the differences in the binding modes, interactions, and affinities of investigated MS drugs in connection with SNPs in the S1PR1 binding site, resulting in several viable options for personalized therapies depending on the present mutations.