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

ABSTRACT: 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.

SUBMITTER: Alizadeh AA

PROVIDER: S-EPMC7110456 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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

Alizadeh Ali Akbar AA Jafari Behzad B Dastmalchi Siavoush S

Journal of molecular graphics & modelling 20190929

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-Q ...[more]

PMID: 31589999

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Project description:Tyrosine threonine kinase (TTK) is the key component of the spindle assembly checkpoint (SAC) that ensures correct attachment of chromosomes to the mitotic spindle and thereby their precise segregation into daughter cells by phosphorylating specific substrate proteins. The overexpression of TTK has been associated with various human malignancies, including breast, colorectal and thyroid carcinomas. TTK has been validated as a target for drug development, and several TTK inhibitors have been discovered. In this study, ligand and structure-based alignment as well as various partial charge models were used to perform 3D-QSAR modelling on 1H-Pyrrolo[3,2-c] pyridine core containing reported inhibitors of TTK protein using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches to design better active compounds. Different statistical methods i.e., correlation coefficient of non-cross validation (r2), correlation coefficient of leave-one-out cross-validation (q2), Fisher's test (F) and bootstrapping were used to validate the developed models. Out of several charge models and alignment-based approaches, Merck Molecular Force Field (MMFF94) charges using structure-based alignment yielded highly predictive CoMFA (q2 = 0.583, Predr2 = 0.751) and CoMSIA (q2 = 0.690, Predr2 = 0.767) models. The models exhibited that electrostatic, steric, HBA, HBD, and hydrophobic fields play a key role in structure activity relationship of these compounds. Using the contour maps information of the best predictive model, new compounds were designed and docked at the TTK active site to predict their plausible binding modes. The structural stability of the TTK complexes with new compounds was confirmed using MD simulations. The simulation studies revealed that all compounds formed stable complexes. Similarly, MM/PBSA method based free energy calculations showed that these compounds bind with reasonably good affinity to the TTK protein. Overall molecular modelling results suggest that newly designed compounds can act as lead compounds for the optimization of TTK inhibitors.

Dataset Information

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

Publications

Alignment independent 3D-QSAR studies and molecular dynamics simulations for the identification of potent and selective S1P<sub>1</sub> receptor agonists.

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