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Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors.
Aniceto, Natália; Marques, Vanda; Amaral, Joana D; Serra, Patrícia A; Moreira, Rui; Rodrigues, Cecília M P; Guedes, Rita C.
  • Aniceto N; Department of Pharmaceutical Sciences and Medicines and Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, 1649-003 Lisboa, Portugal.
  • Marques V; Department of Pharmaceutical Sciences and Medicines and Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, 1649-003 Lisboa, Portugal.
  • Amaral JD; Department of Pharmaceutical Sciences and Medicines and Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, 1649-003 Lisboa, Portugal.
  • Serra PA; Department of Pharmaceutical Sciences and Medicines and Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, 1649-003 Lisboa, Portugal.
  • Moreira R; Department of Pharmaceutical Sciences and Medicines and Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, 1649-003 Lisboa, Portugal.
  • Rodrigues CMP; Department of Pharmaceutical Sciences and Medicines and Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, 1649-003 Lisboa, Portugal.
  • Guedes RC; Department of Pharmaceutical Sciences and Medicines and Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, 1649-003 Lisboa, Portugal.
Molecules ; 27(15)2022 Jul 23.
Article in English | MEDLINE | ID: covidwho-1994110
ABSTRACT
Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20-25% inhibition of RIPK1's kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Necroptosis Type of study: Prognostic study Language: English Journal subject: Biology Year: 2022 Document Type: Article Affiliation country: Molecules27154718

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Necroptosis Type of study: Prognostic study Language: English Journal subject: Biology Year: 2022 Document Type: Article Affiliation country: Molecules27154718