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      Discovery of CRBN as a target of thalidomide: a breakthrough for progress in the development of protein degraders

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          Abstract

          Thalidomide and its derivatives are the only protein degraders currently used in clinical practice. This tutorial review provides an overview of the mechanism of action of thalidomide-based degraders and their future perspectives.

          Abstract

          Progress in strategies aimed at breaking down therapeutic target proteins has led to a paradigm shift in drug discovery. Thalidomide and its derivatives are the only protein degraders currently used in clinical practice. Our understanding of the molecular mechanism of action of thalidomide and its derivatives has advanced dramatically since the identification of cereblon (CRBN) as their direct target. The binding of thalidomide derivatives to CRBN, a substrate recognition receptor for Cullin 4 RING E3 ubiquitin ligase (CRL4), induces the recruitment of non-native substrates to CRL4 CRBN and their subsequent degradation. This discovery was a breakthrough in the current rapid development of protein-degrading agents because clarification of the mechanism of action of thalidomide derivatives has demonstrated the clinical value of these compounds. This review provides an overview of the mechanism of action of thalidomide and its derivatives and describes perspectives for protein degraders.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            Protacs: chimeric molecules that target proteins to the Skp1-Cullin-F box complex for ubiquitination and degradation.

            The intracellular levels of many proteins are regulated by ubiquitin-dependent proteolysis. One of the best-characterized enzymes that catalyzes the attachment of ubiquitin to proteins is a ubiquitin ligase complex, Skp1-Cullin-F box complex containing Hrt1 (SCF). We sought to artificially target a protein to the SCF complex for ubiquitination and degradation. To this end, we tested methionine aminopeptidase-2 (MetAP-2), which covalently binds the angiogenesis inhibitor ovalicin. A chimeric compound, protein-targeting chimeric molecule 1 (Protac-1), was synthesized to recruit MetAP-2 to SCF. One domain of Protac-1 contains the I kappa B alpha phosphopeptide that is recognized by the F-box protein beta-TRCP, whereas the other domain is composed of ovalicin. We show that MetAP-2 can be tethered to SCF(beta-TRCP), ubiquitinated, and degraded in a Protac-1-dependent manner. In the future, this approach may be useful for conditional inactivation of proteins, and for targeting disease-causing proteins for destruction.
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              p63 is essential for regenerative proliferation in limb, craniofacial and epithelial development.

              The p63 gene, a homologue of the tumour-suppressor p53, is highly expressed in the basal or progenitor layers of many epithelial tissues. Here we report that mice homozygous for a disrupted p63 gene have major defects in their limb, craniofacial and epithelial development. p63 is expressed in the ectodermal surfaces of the limb buds, branchial arches and epidermal appendages, which are all sites of reciprocal signalling that direct morphogenetic patterning of the underlying mesoderm. The limb truncations are due to a failure to maintain the apical ectodermal ridge, a stratified epithelium, essential for limb development. The embryonic epidermis of p63-/- mice undergoes an unusual process of non-regenerative differentiation, culminating in a striking absence of all squamous epithelia and their derivatives, including mammary, lacrymal and salivary glands. Taken together, our results indicate that p63 is critical for maintaining the progenitor-cell populations that are necessary to sustain epithelial development and morphogenesis.
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                Author and article information

                Contributors
                Journal
                CSRVBR
                Chemical Society Reviews
                Chem. Soc. Rev.
                Royal Society of Chemistry (RSC)
                0306-0012
                1460-4744
                August 01 2022
                2022
                : 51
                : 15
                : 6234-6250
                Affiliations
                [1 ]School of Life Science and Technology, Tokyo Institute of Technology, Yokohama 226-8501, Japan
                [2 ]Institute of Medical Science, Tokyo Medical University, Shinjuku, Tokyo 160-8402, Japan
                [3 ]Center for Future Medical Research, Tokyo Medical University, Shinjuku, Tokyo 160-8402, Japan
                Article
                10.1039/D2CS00116K
                35796627
                a95e8451-ba16-4ddb-b45d-0a50a0dbecf0
                © 2022

                http://creativecommons.org/licenses/by-nc/3.0/

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