Alignment independent 3D-QSAR studies and molecular dynamics simulations for the identification of potent and selective S1P1 receptor agonists.

Ali Akbar Alizadeh, Behzad Jafari, Siavoush Dastmalchi

Journal of molecular graphics & modelling 2020 Jan

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Sphingosine 1-phosphate type 1 (S1P1) receptors are expressed on lymphocytes and regulate immune cells trafficking. Sphingosine 1-phosphate and its analogues cause internalization and degradation of S1P1 receptors, preventing the auto reactivity of immune cells in the target tissues. It has been shown that S1P1 receptor agonists such as fingolimod can be suitable candidates for treatment of autoimmune diseases. The current study aimed to generate GRIND-based 3D-QSAR predictive models for agonistic activities of 2-imino-thiazolidin-4-one derivatives on S1P1 to be used in virtual screening of chemical libraries. The developed model for the S1P1 receptor agonists showed appropriate power of predictivity in internal (r2acc 0.93 and SDEC 0.18) and external (r2 0.75 and MAE (95% data), 0.28) validations. The generated model revealed the importance of variables DRY-N1 and DRY-O in the potency and selectivity of these compounds towards S1P1 receptor. To propose potential chemical entities with S1P1 agonistic activity, PubChem chemicals database was searched and the selected compounds were virtually tested for S1P1 receptor agonistic activity using the generated models, which resulted in four potential compounds with high potency and selectivity towards S1P1 receptor. Moreover, the affinities of the identified compounds towards S1P1 receptor were evaluated using molecular dynamics simulations. The results indicated that the binding energies of the compounds were in the range of -39.31 to -46.18 and -3.20 to -9.75 kcal mol-1, calculated by MM-GBSA and MM-PBSA algorithms, respectively. The findings in the current work may be useful for the identification of potent and selective S1P1 receptor agonists with potential use in diseases such as multiple sclerosis. Copyright © 2019 Elsevier Inc. All rights reserved.

Citation

Ali Akbar Alizadeh, Behzad Jafari, Siavoush Dastmalchi. Alignment independent 3D-QSAR studies and molecular dynamics simulations for the identification of potent and selective S1P1 receptor agonists. Journal of molecular graphics & modelling. 2020 Jan;94:107459