CAREER: Chemical Theory for the Protein Crystal Folding Problem

职业：蛋白质晶体折叠问题的化学理论

• 批准号: 1751688
• 负责人: Michael Schnieders
• 金额: $ 64.95万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1751688&HistoricalAwards=false
• 关键词: CAREER; Chemical; Theory; Protein; Crystal

Professor Michael Schnieders of the University of Iowa is supported by an award from the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry to develop new theoretical approaches to predict crystal structures. Organic molecular crystals play an important role in a range of fields including chemistry, biochemistry, materials science, pharmacology, and engineering. One everyday example of organic molecular crystals are pharmaceutical tablets, which are typically formulated to optimize properties such as shelf-life (i.e. thermal stability) and solubility (i.e. dissolution upon ingestion). A perhaps less appreciated role of organic crystals has been their pivotal impact in understanding the structure and function of biomolecules (i.e. proteins) via X-ray crystallography experiments. Whereas drug molecules typically consist of only a few dozen atoms, proteins generally consist of thousands of atoms whose packing (i.e. 3-dimensional arrangement) is described by a process called "protein folding". A driving force behind the folding of proteins is the hydrophobic effect, which is also responsible for the commonly observed tendency of oil and water to separate. The work in Dr. Schnieder's group focuses on the rigorous incorporation of all forces that contribute to protein folding into efficient algorithms for the computational prediction of peptide and protein crystal structures (polymorphs). The approach combines advanced models of molecular interactions commonly used to predict small molecule crystal polymorphs with sophisticated molecular dynamics sampling algorithms needed to describe protein folding. The impact of this project is to expand the boundaries of the crystal structure prediction (CSP) field beyond small organic molecules (i.e. dozens of atoms) to include peptides and proteins (i.e. hundreds or thousands of atoms). Dr. Schnieder's research is fully integrated with a three-pronged strategy for educational outreach that strengthens and further diversifies training in Simulation Based Engineering & Science (SBE&S). The project's educational plan includes: outreach to underrepresented high school students to help make computational science fair projects and creation of a modern Computational Biochemistry course to train (under)graduates in applying SBE&S methods to fundamental problems in computational (bio)chemistry. The third aim is the continued dissemination of open source Force Field X software (http://ffx.biochem.uiowa.edu). Leadership in SBE&S and high-performance computing (HPC) is of critical importance to the global competitiveness of the United States. Physics-based protein folding via molecular dynamics (MD) inherently accounts for temperature, pressure, solvent environment and entropic contributions such as the hydrophobic effect. On the other hand, nearly all current crystal structure predication (CSP) approaches perform either a systematic or stochastic search of a potential energy surface, rather than a free energy surface, followed in limited cases by approximate inclusion of entropic considerations. The premise of this project is that a generally applicable solution to the "protein crystal folding problem" requires efficient inclusion of temperature, pressure and solvent environment (hydrophobic effect, pH, etc.) during polymorph discovery simulations. Due to the slow nucleation kinetics of crystallization, ordinary unbiased MD is not efficient for CSP. To overcome this, a novel family of algorithms are being developed to help open the door to polymer crystal property prediction. The first objective focuses on two novel alchemical thermodynamic paths, which do not require a priori knowledge of the crystalline state and that dramatically accelerate phase transitions 1) between vacuum and crystalline states (i.e. sublimation/deposition) and 2) between solvated and crystalline states (i.e. solubility). Both paths efficiently include the influence of temperature and pressure, while the latter path additionally includes the influence of the solvent environment. The second objective focuses on the first constant pH MD (CpHMD) algorithms for a polarizable force field (e.g. AMOEBA) to account for protonation changes as a polymer (e.g. a protein or nucleic acid) with numerous titratable residues folds and/or undergoes a crystalline phase transition. Beyond the focus of this project on protein crystals, the sampling algorithms and CpHMD theories are broadly applicable to a range of simulation applications, including protein-ligand binding, molecular design and refinement of structural models against experiment (i.e. X-ray and neutron crystallography, CryoEM, NMR, etc). The project's educational plan includes: 1) outreach to underrepresented high school students to facilitate computational science fair projects, 2) creation of a modern Computational Biochemistry course to train (under)graduates in applying SBE&S methods to fundamental problems in computational (bio)chemistry, and 3) continued dissemination of the open source Force Field X software (http://ffx.biochem.uiowa.edu).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

爱荷华大学的迈克尔·施尼德斯教授获得化学系化学理论、模型和计算方法项目奖项的支持，致力于开发预测晶体结构的新理论方法。 有机分子晶体在化学、生物化学、材料科学、药理学和工程学等一系列领域发挥着重要作用。有机分子晶体的一个日常例子是药片，其通常经过配制以优化保质期（即热稳定性）和溶解度（即摄入后溶解）等特性。有机晶体的一个可能不太被重视的作用是它们在通过 X 射线晶体学实验了解生物分子（即蛋白质）的结构和功能方面的关键影响。药物分子通常仅由几十个原子组成，而蛋白质通常由数千个原子组成，其堆积（即 3 维排列）通过称为“蛋白质折叠”的过程来描述。蛋白质折叠背后的驱动力是疏水效应，这也是常见的油水分离趋势的原因。 Schnieder 博士小组的工作重点是将所有有助于蛋白质折叠的力严格纳入有效的算法中，以计算预测肽和蛋白质晶体结构（多晶型物）。该方法将通常用于预测小分子晶体多晶型物的先进分子相互作用模型与描述蛋白质折叠所需的复杂分子动力学采样算法相结合。该项目的影响是将晶体结构预测（CSP）领域的边界扩展到小有机分子（即数十个原子）之外，以包括肽和蛋白质（即数百或数千个原子）。 Schnieder 博士的研究与三管齐下的教育推广战略完全结合起来，该战略加强了基于仿真的工程与科学 (SBE&S) 的培训并使其进一步多样化。该项目的教育计划包括：向代表性不足的高中生推广，帮助制定计算科学公平项目，并创建现代计算生物化学课程，以培训（本科）毕业生将 SBE&S 方法应用于计算（生物）化学的基本问题。 第三个目标是持续传播开源 Force Field X 软件 (http://ffx.biochem.uiowa.edu)。 SBE&S 和高性能计算 (HPC) 领域的领先地位对于美国的全球竞争力至关重要。通过分子动力学 (MD) 进行的基于物理的蛋白质折叠本质上解释了温度、压力、溶剂环境和疏水效应等熵的贡献。另一方面，几乎所有当前的晶体结构预测（CSP）方法都对势能面而不是自由能面进行系统或随机搜索，然后在有限的情况下近似包含熵考虑因素。该项目的前提是，“蛋白质晶体折叠问题”的普遍适用解决方案需要在多晶型发现模拟过程中有效包含温度、压力和溶剂环境（疏水效应、pH 等）。由于结晶成核动力学缓慢，普通的无偏 MD 对于 CSP 来说效率不高。为了克服这个问题，我们正在开发一系列新颖的算法，以帮助打开聚合物晶体特性预测的大门。第一个目标侧重于两种新颖的炼金热力学路径，它们不需要晶态的先验知识，并且显着加速 1) 真空和晶态（即升华/沉积）之间的相变以及 2）溶剂化态和晶态（即溶解度）之间的相变。两条路径都有效地包括温度和压力的影响，而后一条路径还包括溶剂环境的影响。第二个目标侧重于可极化力场（例如 AMOEBA）的第一个恒定 pH MD (CpHMD) 算法，以解释具有大量可滴定残基的聚合物（例如蛋白质或核酸）折叠和/或经历结晶相变时的质子化变化。除了该项目对蛋白质晶体的关注之外，采样算法和 CpHMD 理论还广泛适用于一系列模拟应用，包括蛋白质-配体结合、分子设计和针对实验的结构模型的细化（即 X 射线和中子晶体学、CryoEM、NMR 等）。该项目的教育计划包括：1) 向代表性不足的高中生推广，以促进计算科学公平项目，2) 创建现代计算生物化学课程，培训（本科）毕业生将 SBE&S 方法应用于计算（生物）化学的基本问题，以及 3）继续传播开源 Force Field X 软件 (http://ffx.biochem.uiowa.edu)。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Implicit Solvents for the Polarizable Atomic Multipole AMOEBA Force Field.

• DOI: 10.1021/acs.jctc.0c01286
• 发表时间: 2021-04-13
• 期刊: Journal of chemical theory and computation
• 影响因子: 5.5
• 作者: Corrigan RA;Qi G;Thiel AC;Lynn JR;Walker BD;Casavant TL;Lagardere L;Piquemal JP;Ponder JW;Ren P;Schnieders MJ
• 通讯作者: Schnieders MJ

Crystal Polymorph Search in the NPT Ensemble via a Deposition/Sublimation Alchemical Path

• DOI: 10.1021/acs.cgd.3c01358
• 发表时间: 2024-03-09
• 期刊: CRYSTAL GROWTH & DESIGN
• 影响因子: 3.8
• 作者: Nessler，Aaron J.;Okada，Okimasa;Schnieders，Michael J.
• 通讯作者: Schnieders，Michael J.

A generalized Kirkwood implicit solvent for the polarizable AMOEBA protein model.

用于极化 AMOEBA 蛋白质模型的广义柯克伍德隐式溶剂。

• DOI: 10.1063/5.0158914
• 发表时间: 2023
• 期刊: The Journal of chemical physics
• 作者: Corrigan,RaeA;Thiel,AndrewC;Lynn,JackR;Casavant,ThomasL;Ren,Pengyu;Ponder,JayW;Schnieders,MichaelJ
• 通讯作者: Schnieders,MichaelJ

Scalable Indirect Free Energy Method Applied to Divalent Cation-Metalloprotein Binding.

• DOI: 10.1021/acs.jctc.9b00147
• 发表时间: 2019-06
• 期刊: Journal of chemical theory and computation
• 影响因子: 5.5
• 作者: Jacob M. Litman;Andrew C Thiel;M. Schnieders

Characterization of a TP53 Somatic Variant of Unknown Function From an Ovarian Cancer Patient Using Organoid Culture and Computational Modeling.

• DOI: 10.1097/grf.0000000000000516
• 发表时间: 2020-03
• 期刊: Clinical obstetrics and gynecology
• 影响因子: 1.5
• 作者: Bi J;Thiel KW;Litman JM;Zhang Y;Devor EJ;Newtson AM;Schnieders MJ;Gonzalez Bosquet J;Leslie KK
• 通讯作者: Leslie KK