CDK1 structures reveal conserved and unique features of the essential cell cycle CDK

Brown, Nicholas R.; Korolchuk, S.; Martin, Mathew P.; Stanley, Will A.; Moukhametzianov, R.; Noble, M.E.M.; Endicott, Jane

doi:10.1038/ncomms7769

Cited by 153 publications

(193 citation statements)

References 69 publications

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“…4A, Right). These highly coevolving interfaces suggest that CDK1 may interact with two CKS1 at the two different binding sites (residues are shown in SI Appendix, Table S1), which is consistent with a previous structural study (39). Remarkably, the interface interaction score for site 2 of CDK1 to site 1 of CKS1 is much stronger than the one for site 1 of CDK1 to site 2 of CKS1.…”

Section: Identifying the Druggable Interface For Mdm2 Homodimer And Tsupporting

confidence: 77%

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Elucidating the druggable interface of protein−protein interactions using fragment docking and coevolutionary analysis

Bai

Morcos

Cheng

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Protein−protein interactions play a central role in cellular function. Improving the understanding of complex formation has many practical applications, including the rational design of new therapeutic agents and the mechanisms governing signal transduction networks. The generally large, flat, and relatively featureless binding sites of protein complexes pose many challenges for drug design. Fragment docking and direct coupling analysis are used in an integrated computational method to estimate druggable protein−protein interfaces. (i) This method explores the binding of fragment-sized molecular probes on the protein surface using a molecular dockingbased screen. (ii) The energetically favorable binding sites of the probes, called hot spots, are spatially clustered to map out candidate binding sites on the protein surface. (iii) A coevolution-based interface interaction score is used to discriminate between different candidate binding sites, yielding potential interfacial targets for therapeutic drug design. This approach is validated for important, well-studied disease-related proteins with known pharmaceutical targets, and also identifies targets that have yet to be studied. Moreover, therapeutic agents are proposed by chemically connecting the fragments that are strongly bound to the hot spots.protein−protein interface | druggable surface | hot spots | direct coupling analysis | drug design P rotein−protein interactions (PPIs) mediate a wide range of important cellular functions, including signal transduction and enzymatic processes. The regulation of these interactions has drawn intense focus in physiology and pathology, where PPI interfaces have emerged as a new class of molecular targets for pharmacological intervention (1, 2). The design of drugs to target PPIs, however, faces numerous challenges (3). Although over 41,000 unique human protein interactions have been experimentally discovered and reported on Human Protein Reference Database (4), only ∼2,500 nonredundant multiprotein complexes have experimentally determined structures available in the Protein Data Bank (5). Therefore, the current experimental methods cannot accommodate the high demand for structural details of these interactions. Even when the structural complexes are known, targeting PPIs with small molecules poses a significant challenge. Many protein interfaces have large, featureless surfaces that lack obvious small-molecule binding pockets, making it difficult to design drugs with the ability to modulate (inhibit or stabilize) the PPIs with the necessary selectivity and potency (6).In the past two decades, two major classes of computational methods for protein−protein interface prediction have emerged (7): (i) data-driven methods (8-13) and (ii) molecular docking methods (14). Data-driven methods include homology modeling (8, 9), machine learning (10), and coevolution-based statistical models (11-13). These approaches make predictions using homologous data as templates or by extracting interaction patterns from data using statistical mo...

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Section: Identifying the Druggable Interface For Mdm2 Homodimer And Tsupporting

confidence: 77%

“…Recently, the CDK1-CKS1 complex was crystallized with four CDK1-CKS1 heterodimers in the asymmetric unit (39). This structure revealed that the C lobe of the CDK1 interacts with two CKS1 proteins at their N lobe.…”

Section: Identifying the Druggable Interface For Mdm2 Homodimer And Tmentioning

confidence: 99%

Elucidating the druggable interface of protein−protein interactions using fragment docking and coevolutionary analysis

Bai

Morcos

Cheng

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…In mammalian, cell cycle is controlled by a subfamily of cyclin-dependent kinases (CDKs), dysregulation of which induce unscheduled proliferation [45]. CDK1 is a member of the CDK family, besides, it is the only essential cell cycle CDK in human cells [46]. As a result, CDK1 might involve in the development of ATC through the pathway of cell cycle.…”

Section: Discussionmentioning

confidence: 99%

Genes and pathways identified in thyroid carcinoma based on bioinformatics analysis

Yu¹,

Mai²,

Cui³

et al. 2016

neo

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The objective of this study was to investigate the key genes and pathways associated with thyroid carcinoma. Based on the microarray data of GSE27155, we identified the differentially expressed genes (DEGs) between four types of thyroid carcinoma samples (papillary carcinoma (PTC), oncocytic carcinoma (OTC), follicular carcinoma (FTC) and anaplastic carcinoma (ATC)) and normal controls. With the obtained DEGs, we performed gene functional interaction (FI) network analysis. Then we conducted Venn diagram analysis to identify the intersection and specific DEGs of the four types of thyroid carcinomas. The intersections DEGs were performed by functional enrichment and transcription factor (TF) prediction analyses. These specific DEGs were performed by pathway enrichment analysis. There were respectively 323, 318, 118 and 1005 DEGs identified in PTC, OTC, FTC and ATC. Twelve sub-network modules were extracted based on gene FI network analysis and eight thyroid carcinoma-associated DEGs were involved in the network, such as TIMP1. Based on the Venn diagram analysis, 27 common DEGs were identified, such as HMGB3 which was regulated by TF of NKX3-1. There were 149 PTC-specific DEGs (like CLDN1), 160 OTC-specific DEGs, 94 FTC-specific DEGs (like PPARG), and 789 ATC-specific DEGs (like CDK1). They were enriched in some pathways, such as Cell cycle, Citrate cycle, and Oxidative phosphorylation. TIMP1, HMGB3, CLDN1, CDK1 and PPARG as well as pathways of Cell cycle, Citrate cycle, and Oxidative phosphorylation may play important roles in the progression of thyroid carcinoma.

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“…CDK1 is the founding member of the CDK family (33), and it is the only CDK to partner cyclin B, which starts to accumulate in S phase (34). CDK1-cyclin B1 complexes are also thought to regulate several events during G 2 /M transition (10,35). Silence of cyclin B1 inhibits proliferation by arresting cells in G 2 /M phase (36).…”

Section: Discussionmentioning

confidence: 99%

Thromboxane A2 Receptor Inhibition Suppresses Multiple Myeloma Cell Proliferation by Inducing p38/c-Jun N-terminal Kinase (JNK) Mitogen-activated Protein Kinase (MAPK)-mediated G2/M Progression Delay and Cell Apoptosis

Liu

Tao

Liu

et al. 2016

Journal of Biological Chemistry

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Multiple myeloma (MM) is a plasma cell malignancy without effective therapeutics. Thromboxane A 2 (TxA 2 )/TxA 2 receptor (T prostanoid receptor (TP)) modulates the progression of some carcinomas; however, its effects on MM cell proliferation remain unclear. In this study, we evaluated cyclooxygenase (COX) enzymes and downstream prostaglandin profiles in human myeloma cell lines RPMI-8226 and U-266 and analyzed the effects of COX-1/-2 inhibitors SC-560 and NS-398 on MM cell proliferation. Our observations implicate COX-2 as being involved in modulating cell proliferation. We further incubated MM cells with prostaglandin receptor antagonists or agonists and found that only the TP antagonist, SQ29548, suppressed MM cell proliferation. TP silencing and the TP agonist, U46619, further confirmed this finding. Moreover, SQ29548 and TP silencing promoted MM cell G 2 /M phase delay accompanied by reducing cyclin B1/cyclin-dependent kinase-1 (CDK1) mRNA and protein expression. Notably, cyclin B1 overexpression rescued MM cells from G 2 /M arrest. We also found that the TP agonist activated JNK and p38 MAPK phosphorylation, and inhibitors of JNK and p38 MAPK depressed U46619-induced proliferation and cyclin B1/CDK1 protein expression. In addition, SQ29548 and TP silencing led to the MM cell apoptotic rate increasing with improving caspase 3 activity. The knockdown of caspase 3 reversed the apoptotic rate. Taken together, our results suggest that TxA 2 /TP promotes MM cell proliferation by reducing cell delay at G 2 /M phase via elevating p38 MAPK/JNK-mediated cyclin B1/CDK1 expression and hindering cell apoptosis. The TP inhibitor has potential as a novel agent to target kinase cascades for MM therapy. Multiple myeloma (MM)3 is a B-cell malignancy characterized by the clonal proliferation of neoplastic plasma cells in the bone marrow (BM). It accounts for 12% of all malignant hematological neoplasms and is the second most frequent hematological malignancy (1). Recently, despite the improved therapeutic effect of newly approved chemotherapeutics, including thalidomide, lenalidomide, and bortezomib, and stem cell transplantation, MM remains incurable, with only a marginal increase in the 5-year relative survival rate and a high incidence of relapse (2, 3). Thus, identification of novel therapeutic targets for MM is urgently needed.Growing evidence demonstrates that thromboxane A 2 (TxA 2 )-related molecules are actively involved in tumor progression (4, 5). TxA 2 is one of the five primary prostaglandins (PGs) generated from arachidonic acid (AA) through the cyclooxygenases (COX-1 and COX-2) and TxA 2 synthase pathways and exerts its biological activities through the TxA 2 receptor (T prostanoid receptor (TP)) located on the cell surface. TP belongs to the G-protein-coupled receptor superfamily and is expressed as two isoforms in humans, named TP ␣ and TP ␤ (6). Notably, TxA 2 stimulates proliferation of several types of cells, including oligodendrocytes (7), smooth muscle cells (8), and lung cancer cells (9). For example, ...

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CDK1 structures reveal conserved and unique features of the essential cell cycle CDK

Cited by 153 publications

References 69 publications

Elucidating the druggable interface of protein−protein interactions using fragment docking and coevolutionary analysis

Elucidating the druggable interface of protein−protein interactions using fragment docking and coevolutionary analysis

Genes and pathways identified in thyroid carcinoma based on bioinformatics analysis

Thromboxane A2 Receptor Inhibition Suppresses Multiple Myeloma Cell Proliferation by Inducing p38/c-Jun N-terminal Kinase (JNK) Mitogen-activated Protein Kinase (MAPK)-mediated G2/M Progression Delay and Cell Apoptosis

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