Computational design for thermostabilization of GPCRs

Petr Popov, Igor Kozlovskii, Vsevolod Katritch

    Research output: Contribution to journalReview articlepeer-review

    8 Citations (Scopus)

    Abstract

    GPCR superfamily is the largest clinically relevant family of targets in human genome; however, low thermostability and high conformational plasticity of these integral membrane proteins make them notoriously hard to handle in biochemical, biophysical, and structural experiments. Here, we describe the recent advances in computational approaches to design stabilizing mutations for GPCR that take advantage of the structural and sequence conservation properties of the receptors, and employ machine learning on accumulated mutation data for the superfamily. The fast and effective computational tools can provide a viable alternative to existing experimental mutation screening and are poised for further improvements with expansion of thermostability datasets for training the machine learning models. The rapidly growing practical applications of computational stability design streamline GPCR structure determination and may contribute to more efficient drug discovery.

    Original languageEnglish
    Pages (from-to)25-33
    Number of pages9
    JournalCurrent Opinion in Structural Biology
    Volume55
    DOIs
    Publication statusPublished - Apr 2019

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