Dataset Information

Dasatinib-SIK2 Binding Elucidated by Homology Modeling, Molecular Docking, and Dynamics Simulations.

ABSTRACT: Salt-inducible kinases (SIKs) are calcium/calmodulin-dependent protein kinase (CAMK)-like (CAMKL) family members implicated in insulin signal transduction, metabolic regulation, inflammatory response, and other processes. Here, we focused on SIK2, which is a target of the Food and Drug Administration (FDA)-approved pan inhibitor N-(2-chloro-6-methylphenyl)-2-(6-(4-(2-hydroxyethyl)piperazin-1-yl)-2-methylpyrimidin-4-ylamino)thiazole-5-carboxamide (dasatinib), and constructed four representative SIK2 structures by homology modeling. We investigated the interactions between dasatinib and SIK2 via molecular docking, molecular dynamics simulation, and binding free energy calculation and found that dasatinib showed strong binding affinity for SIK2. Binding free energy calculations suggested that the modification of various dasatinib regions may provide useful information for drug design and to guide the discovery of novel dasatinib-based SIK2 inhibitors.

SUBMITTER: Shi M

PROVIDER: S-EPMC8153941 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Dasatinib-SIK2 Binding Elucidated by Homology Modeling, Molecular Docking, and Dynamics Simulations.

Shi Mingsong M Wang Lun L Li Penghui P Liu Jiang J Chen Lijuan L Xu Dingguo D

ACS omega 20210415 16

Salt-inducible kinases (SIKs) are calcium/calmodulin-dependent protein kinase (CAMK)-like (CAMKL) family members implicated in insulin signal transduction, metabolic regulation, inflammatory response, and other processes. Here, we focused on SIK2, which is a target of the Food and Drug Administration (FDA)-approved pan inhibitor <i>N</i>-(2-chloro-6-methylphenyl)-2-(6-(4-(2-hydroxyethyl)piperazin-1-yl)-2-methylpyrimidin-4-ylamino)thiazole-5-carboxamide (dasatinib), and constructed four represent ...[more]

PMID: 34056256

Similar Datasets

Project description:We have modeled the structure of human lymphotactin (hLpnt), by homology modeling and molecular dynamics simulations. This chemokine is unique in having a single disulfide bond and a long C-terminal tail. Because other structural classes of chemokines have two pairs of Cys residues, compared to one in Lpnt, and because it has been shown that both disulfide bonds are required for stability and function, the question arises how the Lpnt maintains its structural integrity. The initial structure of hLpnt was constructed by homology modeling. The first 63 residues in the monomer of hLpnt were modeled using the structure of the human CC chemokine, RANTES, whose sequence appeared most similar. The structure of the long C-terminal tail, missing in RANTES, was taken from the human muscle fatty-acid binding protein. In a Protein Data Bank search, this protein was found to contain a sequence that was most homologous to the long tail. Consequently, the modeled hLpnt C-terminal tail consisted of both alpha-helical and beta-motifs. The complete model of the hLpnt monomer consisted of two alpha-helices located above the five-stranded beta-sheet. Molecular dynamics simulations of the solvated initial model have indicated that the stability of the predicted fold is related to the geometry of Pro78. The five-stranded beta-sheet appeared to be preserved only when Pro78 was modeled in the cis conformation. Simulations were also performed both for the C-terminal truncated forms of the hLpnt that contained one or two (CC chemokine-like) disulfide bonds, and for the chicken Lpnt (cLpnt). Our MD simulations indicated that the turn region (T30-G34) in hLpnt is important for the interactions with the receptor, and that the long C-terminal region stabilizes both the turn (T30-G34) and the five-stranded beta-sheet. The major conclusion from our theoretical studies is that the lack of one disulfide bond and the extension of the C-terminus in hLptn are mutually complementary. It is very likely that removal of two Cys residues sufficiently destabilizes the structure of a chemokine molecule, particularly the core beta-sheet, to abolish its biological function. However, this situation is rectified by the long C-terminal segment. The role of this long region is most likely to stabilize the first beta-turn region and alpha-helix H1, explaining how this chemokine can function with a single disulfide bond.

Project description:The intricate complex system of the differentiation 47 (CD47) and the signal-regulatory protein alpha (SIRPα) cluster is a crucial target for cancer immunotherapy. Although the conformational state of the CD47-SIRPα complex has been revealed through crystallographic studies, further characterization is needed to fully understand the binding mechanism and to identify the hot spot residues involved. In this study, molecular dynamics (MD) simulations were carried out for the complexes of CD47 with two SIRPα variants (SIRPαv1, SIRPαv2) and the commercially available anti-CD47 monoclonal antibody (B6H12.2). The calculated binding free energy of CD47-B6H12.2 is lower than that of CD47-SIRPαv1 and CD47-SIRPαv2 in all the three simulations, indicating that CD47-B6H12.2 has a higher binding affinity than the other two complexes. Moreover, the dynamical cross-correlation matrix reveals that the CD47 protein shows more correlated motions when it binds to B6H12.2. Significant effects were observed in the energy and structural analyses of the residues (Glu35, Tyr37, Leu101, Thr102, Arg103) in the C strand and FG region of CD47 when it binds to the SIRPα variants. The critical residues (Leu30, Val33, Gln52, Lys53, Thr67, Arg69, Arg95, and Lys96) were identified in SIRPαv1 and SIRPαv2, which surround the distinctive groove regions formed by the B2C, C'D, DE, and FG loops. Moreover, the crucial groove structures of the SIRPα variants shape into obvious druggable sites. The C'D loops on the binding interfaces undergo notable dynamical changes throughout the simulation. For B6H12.2, the residues Tyr32LC, His92LC, Arg96LC, Tyr32HC, Thr52HC, Ser53HC, Ala101HC, and Gly102HC in its initial half of the light and heavy chains exhibit obvious energetic and structural impacts upon binding with CD47. The elucidation of the binding mechanism of SIRPαv1, SIRPαv2, and B6H12.2 with CD47 could provide novel perspectives for the development of inhibitors targeting CD47-SIRPα.

Dataset Information

Dasatinib-SIK2 Binding Elucidated by Homology Modeling, Molecular Docking, and Dynamics Simulations.

Publications

Dasatinib-SIK2 Binding Elucidated by Homology Modeling, Molecular Docking, and Dynamics Simulations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets