CAREER: Chemical Theory for the Protein Crystal Folding Problem

爱荷华州大学的Michael Schnieders教授获得了化学系化学理论、模型和计算方法项目的奖项支持，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、药理学和工程学等领域发挥着重要作用。有机分子晶体的一个日常实例是药物片剂，其通常被配制以优化诸如保质期（即热稳定性）和溶解度（即在摄入时溶解）的性质。有机晶体的一个可能不太受重视的作用是它们通过X射线晶体学实验在理解生物分子（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，其包装（即三维排列）由称为“蛋白质折叠”的过程描述。蛋白质折叠背后的驱动力是疏水效应，这也是通常观察到的油和水分离趋势的原因。Schnieder博士小组的工作重点是将有助于蛋白质折叠的所有力严格纳入有效的算法中，用于肽和蛋白质晶体结构（多晶型物）的计算预测。该方法结合了先进的分子相互作用模型，通常用于预测小分子晶体多晶型物与复杂的分子动力学采样算法来描述蛋白质折叠。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，该战略加强了基于仿真的工程科学（SBE S）的培训并进一步多样化。该项目的教育计划包括：推广到代表性不足的高中学生，以帮助使计算科学公平的项目和创建一个现代计算生物化学课程，以培训（下）毕业生在应用SBE S方法计算（生物）化学的基本问题。 第三个目标是继续传播开放源码Force Field X软件（http：ffx.biochem.uiowa.edu）。SBE S和高性能计算（HPC）的领导地位对美国的全球竞争力至关重要。基于物理的蛋白质折叠通过分子动力学（MD）固有地占温度，压力，溶剂环境和熵的贡献，如疏水效应。另一方面，几乎所有当前的晶体结构预测（CSP）方法执行势能表面的系统或随机搜索，而不是自由能表面，随后在有限的情况下近似包含熵考虑。该项目的前提是，一个普遍适用的解决“蛋白质晶体折叠问题”的方法需要有效地包括温度，压力和溶剂环境（疏水效应，pH值等）。在多形体发现模拟中。由于结晶的缓慢成核动力学，普通无偏MD对于CSP是无效的。为了克服这一点，正在开发一种新的算法家族，以帮助打开聚合物晶体性质预测的大门。第一个目标集中在两个新的炼金术热力学路径，这不需要结晶状态的先验知识，并显着加速相变1）真空和结晶状态（即升华/沉积）和2）溶剂化和结晶状态（即溶解度）之间。这两种途径都有效地包括温度和压力的影响，而后一种途径还包括溶剂环境的影响。第二个目标集中在第一个恒定pH MD（CpHMD）算法的可极化力场（如AMOEBA），以考虑质子化的变化作为聚合物（如蛋白质或核酸）与许多可滴定的残基折叠和/或经历结晶相变。除了这个项目对蛋白质晶体的关注之外，采样算法和CpHMD理论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改进（即X射线和中子晶体学，CryoEM，NMR等）。该项目的教育计划包括：1）推广到代表性不足的高中学生，以促进计算科学公平的项目，2）创建一个现代计算生物化学课程，以培训（下）毕业生在应用SBE S方法计算（生物）化学中的基本问题，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，并已被认为值得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。

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.