Modeling Transition Metal Ion Binding to Proteins

模拟过渡金属离子与蛋白质的结合

• 批准号: 10387132
• 负责人: Hasan Metin Aktulga
• 金额: $ 20.99万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10387132
• 关键词: Address; Atomic Force Microscopy; Binding; Biological; Biological Process; Biology; Biophysics; Communities; Complex; Computer Models; Development; Dissociation; Drug Interactions; Goals; Health; Homeostasis; Infection; Ions; Ligands; Metal Binding Site; Metal Ion Binding; Metalloproteins; Metals; Methodology; Modeling; Molecular; Nutritional Immunity; Play; Proteins; Role; Site; Structural Models; Structure; Thermodynamics; Transition Elements; aqueous; base; insight; metalloenzyme; next generation; pathogen; simulation; single molecule; success

Project Summary/Abstract Transition metal (TM) ions play myriad roles in biology and are present in >30% of structures in the PDB yet the accurate computational modeling of these ions is less evolved than for the “organic” framework of proteins. Hence, the simulation of metalloprotein structure, function and dynamics is lagging behind related studies on proteins that do not contain structural or functional TM ions. To address this issue a Balkanized approach has been taken over the last several decades where multiple groups have built models validated based on varying criteria with many being unavailable or only available in specific packages further making it difficult to focus best practices. Because of this gap in the modeling of TM ions important biological problems associated with TM ion homeostasis, metal center assembly, TM/drug interactions, dynamics of ligand association and product dissociation in metalloenzymes, attacking pathogens using nutritional immunity via TM sequestration near infection sites, etc. are a significant challenge to address. Through the development of robust computational models of TMs these problems, amongst others, focused on metal ion biology will become as addressable as is currently possible for biological molecules lacking TM ions. Our over-arching goal is to develop validated classical force field models that are readily available that can routinely and accurately model the structure and thermodynamics of TMs bound to proteins and in aqueous solution in order to address critical biological questions involving metal ions. Our long-term goal is to incorporate a range of extant TM modeling approaches into AMBER and to then validate and disseminate the various methodologies for our own use and for that of the community. Moreover, in this proposal we will apply our validated models to simulate TM binding at model and at protein metal binding sites, explore aspects of TM ion homeostasis (TMIH) and provide molecular-level insights into Atomic Force Microscopy (AFM) single- molecule studies on metalloproteins. The fundamental overarching biophysical question we are addressing is: what is required to accurately and routinely model TMs and what are the molecular-level details of metal ion complexation in proteins. Building on our success with developing class-leading bonded metal ion force fields we will create next generation models (with a strong focus on nonbonded representations) that can be exploited in understanding the critical role of TMs in biological processes.

项目总结/摘要 过渡金属（TM）离子在生物学中起着无数的作用，并且在PDB中存在于>30%的结构中 这些离子的精确计算模型比蛋白质的“有机”框架的精确计算模型发展得更少。 因此，对金属蛋白结构、功能和动力学的模拟相对滞后， 不含结构或功能性TM离子的蛋白质。为了解决这个问题， 在过去的几十年里，多个小组已经建立了基于不同的 许多标准不可用或仅在特定包中可用，这进一步使得难以集中注意力 最佳实践由于TM离子建模中的这一差距，与TM离子相关的重要生物学问题变得更加复杂。 TM离子稳态，金属中心组装，TM/药物相互作用，配体缔合动力学和产物 金属酶的解离，通过TM隔离利用营养免疫攻击病原体， 感染部位等是需要解决的重大挑战。通过开发强大的计算 这些问题，除其他外，集中在金属离子生物学将成为可解决的， 是目前可能的生物分子缺乏TM离子。 我们的首要目标是开发经过验证的经典力场模型，这些模型可以随时获得， 常规和准确地模拟与蛋白质结合的TM的结构和热力学， 解决方案，以解决涉及金属离子的关键生物学问题。我们的长期目标是 将一系列现有的TM建模方法纳入AMBER，然后验证和传播 各种方法供我们自己和社区使用。此外，在本提案中，我们将适用 我们验证的模型，以模拟TM结合在模型和蛋白质金属结合位点，探索方面的TM 离子稳态（TMIH），并提供分子水平的见解，原子力显微镜（AFM）单， 金属蛋白的分子研究。我们正在解决的基本的首要生物物理问题是： 精确和常规地建模TM需要什么？金属离子的分子水平细节是什么？ 蛋白质中的络合作用。在我们成功的基础上，开发一流的键合金属离子力场 我们将创建下一代模型（重点关注非绑定表示）， 在理解TM在生物过程中的关键作用中被利用。