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实验和模拟揭示了靶标结合如何调节钙调蛋白钙结合特性的残基细节。

Experiment and Simulation Reveal Residue Details for How Target Binding Tunes Calmodulin's Calcium-Binding Properties.

机构信息

Department of Physics, University of Washington, Seattle, Washington 98105, United States.

Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, Texas 77030, United States.

出版信息

J Phys Chem B. 2023 Apr 6;127(13):2900-2908. doi: 10.1021/acs.jpcb.2c08734. Epub 2023 Mar 28.

DOI:10.1021/acs.jpcb.2c08734
PMID:36977372
Abstract

We aim to elucidate the molecular mechanism of the reciprocal relation of calmodulin's (CaM) target binding and its affinity for calcium ions (Ca), which is central to decoding CaM-dependent Ca signaling in a cell. We employed stopped-flow experiments and coarse-grained molecular simulations that learn the coordination chemistry of Ca in CaM from first-principle calculations. The associative memories as part of the coarse-grained force fields built on known protein structures further influence CaM's selection of its polymorphic target peptides in the simulations. We modeled the peptides from the Ca/CaM-binding domain of Ca/CaM-dependent kinase II (CaMKII), CaMKIIp (293-310) and selected distinctive mutations at the N-terminus. Our stopped-flow experiments have shown that the CaM's affinity for Ca in the bound complex of Ca/CaM/CaMKIIp decreased significantly when Ca/CaM bound to the mutant peptide (296-AAA-298) compared to that bound to the wild-type peptide (296-RRK-298). The coarse-grained molecular simulations revealed that the 296-AAA-298 mutant peptide destabilized the structures of Ca-binding loops at the C-domain of CaM (c-CaM) due to both loss of electrostatic interactions and differences in polymorphic structures. We have leveraged a powerful coarse-grained approach to advance a residue-level understanding of the reciprocal relation in CaM, that could not be possibly achieved by other computational approaches.

摘要

我们旨在阐明钙调蛋白(CaM)靶标结合与其对钙离子(Ca)亲和力之间相互关系的分子机制,这对于解码细胞中 CaM 依赖的 Ca 信号至关重要。我们采用了停流实验和粗粒分子模拟,从第一性原理计算中学习 CaM 中 Ca 的配位化学。作为基于已知蛋白质结构构建的粗粒力场的联想记忆进一步影响 CaM 在模拟中对其多态靶肽的选择。我们模拟了来自钙/钙调蛋白依赖性激酶 II(CaMKII)的钙/钙调蛋白结合域的肽,CaMKIIp(293-310),并在 N 端选择了独特的突变。我们的停流实验表明,与与野生型肽(296-RRK-298)结合相比,当 Ca/CaM 与突变肽(296-AAA-298)结合时,CaM 与结合复合物中 Ca 的亲和力显着降低。粗粒分子模拟表明,由于静电相互作用的丧失和多态结构的差异,296-AAA-298 突变肽使 CaM 的 C 域(c-CaM)中的 Ca 结合环结构不稳定。我们利用强大的粗粒方法推进了对 CaM 中相互关系的残基水平理解,这是其他计算方法不可能实现的。

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引用本文的文献

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Chemistry-informed Machine Learning Explains Calcium-binding Proteins' Fuzzy Shape for Communicating Changes in the Atomic States of Calcium Ions.化学信息机器学习解释钙结合蛋白的模糊形状以传递钙离子原子状态的变化。
ArXiv. 2024 Jul 24:arXiv:2407.17017v1.