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

Clicks: 383
ID: 58792
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #12 of 38 articles by views in journal of molecular graphics & modelling

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Sphingosine 1-phosphate type 1 (S1P) receptors are expressed on lymphocytes and regulate immune cells trafficking. Sphingosine 1-phosphate and its analogues cause internalization and degradation of S1P receptors, preventing the auto reactivity of immune cells in the target tissues. It has been shown that S1P 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 S1P to be used in virtual screening of chemical libraries. The developed model for the S1P receptor agonists showed appropriate power of predictivity in internal (r 0.93 and SDEC 0.18) and external (r 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 S1P receptor. To propose potential chemical entities with S1P agonistic activity, PubChem chemicals database was searched and the selected compounds were virtually tested for S1P receptor agonistic activity using the generated models, which resulted in four potential compounds with high potency and selectivity towards S1P receptor. Moreover, the affinities of the identified compounds towards S1P 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, 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 S1P receptor agonists with potential use in diseases such as multiple sclerosis.
Reference Key
alizadeh2019alignmentjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Alizadeh, Ali Akbar;Jafari, Behzad;Dastmalchi, Siavoush;
Journal journal of molecular graphics & modelling
Year 2019
DOI
S1093-3263(19)30422-X
URL
Keywords
molecular docking multiple sclerosis molecular dynamics simulation 3d-qsar sphingosine 1-phosphate

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.