Suppr超能文献

分子动力学模拟表明构象选择控制伊马替尼与几种酪氨酸激酶结合偏好性。

Molecular dynamics simulations show that conformational selection governs the binding preferences of imatinib for several tyrosine kinases.

机构信息

Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France.

出版信息

J Biol Chem. 2010 Apr 30;285(18):13807-15. doi: 10.1074/jbc.M110.109660. Epub 2010 Mar 3.

Abstract

Tyrosine kinases transmit cellular signals through a complex mechanism, involving their phosphorylation and switching between inactive and active conformations. The cancer drug imatinib binds tightly to several homologous kinases, including Abl, but weakly to others, including Src. Imatinib specifically targets the inactive, so-called "DFG-out" conformation of Abl, which differs from the preferred, "DFG-in" conformation of Src in the orientation of a conserved Asp-Phe-Gly (DFG) activation loop. However, recent x-ray structures showed that Src can also adopt the DFG-out conformation and uses it to bind imatinib. The Src/Abl-binding free energy difference can thus be decomposed into two contributions. Contribution i measures the different protein-imatinib interactions when either kinase is in its DFG-out conformation. Contribution ii depends on the ability of imatinib to select or induce this conformation, i.e. on the relative stabilities of the DFG-out and DFG-in conformations of each kinase. Neither contribution has been measured experimentally. We use molecular dynamics simulations to show that contribution i is very small, 0.2 +/- 0.6 kcal/mol; imatinib interactions are very similar in the two kinases, including long range electrostatic interactions with the imatinib positive charge. Contribution ii, deduced using the experimental binding free energy difference, is much larger, 4.4 +/- 0.9 kcal/mol. Thus, conformational selection, easy in Abl, difficult in Src, underpins imatinib specificity. Contribution ii has a simple interpretation; it closely approximates the stability difference between the DFG-out and DFG-in conformations of apo-Src. Additional calculations show that conformational selection also governs the relative binding of imatinib to the kinases c-Kit and Lck. These results should help clarify the current framework for engineering kinase signaling.

摘要

酪氨酸激酶通过一种复杂的机制传递细胞信号,涉及它们的磷酸化和无活性与活性构象之间的转换。抗癌药物伊马替尼与包括 Abl 在内的几种同源激酶紧密结合,但与包括 Src 在内的其他激酶结合较弱。伊马替尼特异性靶向 Abl 的无活性,即所谓的“DFG-out”构象,与 Src 的首选,“DFG-in”构象在保守的天冬氨酸-苯丙氨酸-甘氨酸(DFG)激活环的取向上有所不同。然而,最近的 X 射线结构表明,Src 也可以采用 DFG-out 构象,并利用它来结合伊马替尼。因此,Src/Abl 结合自由能差异可以分解为两个贡献。贡献 i 测量当任一激酶处于 DFG-out 构象时激酶-伊马替尼相互作用的差异。贡献 ii 取决于伊马替尼选择或诱导这种构象的能力,即每种激酶的 DFG-out 和 DFG-in 构象的相对稳定性。这两个贡献都没有通过实验测量。我们使用分子动力学模拟表明,贡献 i 非常小,为 0.2 +/- 0.6 kcal/mol;伊马替尼在两种激酶中的相互作用非常相似,包括与伊马替尼正电荷的远程静电相互作用。使用实验结合自由能差异推断出的贡献 ii 要大得多,为 4.4 +/- 0.9 kcal/mol。因此,构象选择,在 Abl 中很容易,在 Src 中很难,这为伊马替尼的特异性提供了基础。贡献 ii 有一个简单的解释;它非常接近 apo-Src 的 DFG-out 和 DFG-in 构象之间的稳定性差异。额外的计算表明,构象选择也控制了伊马替尼与激酶 c-Kit 和 Lck 的相对结合。这些结果应该有助于澄清当前用于工程激酶信号的框架。

相似文献

2
Equally potent inhibition of c-Src and Abl by compounds that recognize inactive kinase conformations.
Cancer Res. 2009 Mar 15;69(6):2384-92. doi: 10.1158/0008-5472.CAN-08-3953. Epub 2009 Mar 10.
3
Computational analysis of the binding specificity of Gleevec to Abl, c-Kit, Lck, and c-Src tyrosine kinases.
J Am Chem Soc. 2013 Oct 2;135(39):14741-53. doi: 10.1021/ja405939x. Epub 2013 Sep 20.
5
A Src-like inactive conformation in the abl tyrosine kinase domain.
PLoS Biol. 2006 May;4(5):e144. doi: 10.1371/journal.pbio.0040144. Epub 2006 May 2.
6
Small molecule recognition of c-Src via the Imatinib-binding conformation.
Chem Biol. 2008 Oct 20;15(10):1015-22. doi: 10.1016/j.chembiol.2008.09.007.
9
Activity of dual SRC-ABL inhibitors highlights the role of BCR/ABL kinase dynamics in drug resistance.
Proc Natl Acad Sci U S A. 2006 Jun 13;103(24):9244-9. doi: 10.1073/pnas.0600001103. Epub 2006 Jun 5.
10
Computational study of Gleevec and G6G reveals molecular determinants of kinase inhibitor selectivity.
J Am Chem Soc. 2014 Oct 22;136(42):14753-62. doi: 10.1021/ja504146x. Epub 2014 Oct 7.

引用本文的文献

1
New ABL1 Kinase Domain Mutations in BCR::ABL1-Positive Acute Lymphoblastic Leukemia.
Cancer Med. 2024 Oct;13(20):e70317. doi: 10.1002/cam4.70317.
2
Molecular basis for differential recognition of an allosteric inhibitor by receptor tyrosine kinases.
Proteins. 2024 Aug;92(8):905-922. doi: 10.1002/prot.26685. Epub 2024 Mar 20.
3
Classifying protein kinase conformations with machine learning.
Protein Sci. 2024 Apr;33(4):e4918. doi: 10.1002/pro.4918.
4
TYROSINE KINASES: COMPLEX MOLECULAR SYSTEMS CHALLENGING COMPUTATIONAL METHODOLOGIES.
Eur Phys J B. 2021 Oct;94(10). doi: 10.1140/epjb/s10051-021-00207-7. Epub 2021 Oct 11.
5
KinaseMD: kinase mutations and drug response database.
Nucleic Acids Res. 2021 Jan 8;49(D1):D552-D561. doi: 10.1093/nar/gkaa945.
6
Landscape of drug-resistance mutations in kinase regulatory hotspots.
Brief Bioinform. 2021 May 20;22(3). doi: 10.1093/bib/bbaa108.
7
Cryptic pocket formation underlies allosteric modulator selectivity at muscarinic GPCRs.
Nat Commun. 2019 Jul 23;10(1):3289. doi: 10.1038/s41467-019-11062-7.
9
Why the Energy Landscape of Barnase Is Hierarchical.
Front Mol Biosci. 2018 Dec 20;5:115. doi: 10.3389/fmolb.2018.00115. eCollection 2018.
10
Dynamic Equilibrium of the Aurora A Kinase Activation Loop Revealed by Single-Molecule Spectroscopy.
Angew Chem Int Ed Engl. 2017 Sep 11;56(38):11409-11414. doi: 10.1002/anie.201704654. Epub 2017 Aug 7.

本文引用的文献

1
Calculation of Standard Binding Free Energies:  Aromatic Molecules in the T4 Lysozyme L99A Mutant.
J Chem Theory Comput. 2006 Sep;2(5):1255-73. doi: 10.1021/ct060037v.
2
All-atom empirical potential for molecular modeling and dynamics studies of proteins.
J Phys Chem B. 1998 Apr 30;102(18):3586-616. doi: 10.1021/jp973084f.
3
Conformational selection and induced fit mechanism underlie specificity in noncovalent interactions with ubiquitin.
Proc Natl Acad Sci U S A. 2009 Nov 17;106(46):19346-51. doi: 10.1073/pnas.0906966106. Epub 2009 Nov 3.
4
The role of dynamic conformational ensembles in biomolecular recognition.
Nat Chem Biol. 2009 Nov;5(11):789-96. doi: 10.1038/nchembio.232.
5
Alchemical free energy simulations for biological complexes: powerful but temperamental...
J Mol Recognit. 2010 Mar-Apr;23(2):117-27. doi: 10.1002/jmr.980.
6
Conformational selection or induced fit: a flux description of reaction mechanism.
Proc Natl Acad Sci U S A. 2009 Aug 18;106(33):13737-41. doi: 10.1073/pnas.0907195106. Epub 2009 Jul 30.
7
Class effects of tyrosine kinase inhibitors in the treatment of chronic myeloid leukemia.
Leukemia. 2009 Oct;23(10):1698-707. doi: 10.1038/leu.2009.111. Epub 2009 May 28.
8
CHARMM: the biomolecular simulation program.
J Comput Chem. 2009 Jul 30;30(10):1545-614. doi: 10.1002/jcc.21287.
9
Binding site similarity analysis for the functional classification of the protein kinase family.
J Chem Inf Model. 2009 Feb;49(2):318-29. doi: 10.1021/ci800289y.
10
Trapping moving targets with small molecules.
Science. 2009 Apr 10;324(5924):213-5. doi: 10.1126/science.1169378.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验