ENHANCED MEAN-FIELD SIMULATIONS OF ANTIBODY CDR LOOPS

抗体 CDR 环的增强平均场模拟

• 批准号: 6387206
• 负责人: CARLOS SIMMERLING
• 金额: $ 18.81万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-06-01 至 2005-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6387206
• 关键词: abzyme; chemical models; computer simulation; conformation; immunoglobulin structure; protein structure function; solvents; structural biology

The availability of accurate three-dimensional structtires for important biomolecules such as proteins and nucleic acids can greatly aid in the understanding of their fimction, as well as in the rational design of pharmaceutical compounds. However, the structural database is relatively smaii. Structures are often built using comparative modeling. but these are typically less accurate in loop regions. A new approach to predict highly accurate loop conformations in proteins will be developed, with specific application to antibody CDR loops. This mean-field method will dramatically increase sampling of alternate conformations while maintaining compatibility with state of the art simulation techniques such as molecular dynamics in explicit solvation. Individual water molecules often have structural roles, and replacement with a continuum model may result in an unacceptable loss of accuracy. This is a key advantage of the method over those currently in use. Since the method is enezgy-based it is more general than those using databases, and will improve many areas of structural modeling. The project will consist of development of an enhanced locally Enhanced Sampling approach. This will be applied to successively more difficult problems in prediction of antibody loops. Initial studies will focus on reproducing known cases of induced fit for the important H3 loop, followed by true prediction for systems in which the H3 conformation is unknown. These will provide new insights into the determinants of induced fit, the role of solvent and key structural details for several antibodies of biomedical importance. Subsequent studies will predict all 6 loops in antibody CDR regions for several catalytic antibodies. These structures will aid in understanding the factors that influence the catalytic activity of these antibodies, and how efficiency is aifected by loop flexibility, solvent molecules or deficiencies in transition state analogs against which the antibodies are raised. The method will be extended to the prediction not only of antibody conformations, but also the structures of antibody-antigen complexes. This information can be critical to the understanding of these important biomolecular interactions.

获得蛋白质、核酸等重要生物分子的精确三维结构有助于理解它们的功能，也有助于药物化合物的合理设计。然而，结构化数据库相对较小。通常使用比较模型来构建结构。但在循环区域中，这些通常不太准确。将开发一种新的方法来预测蛋白质中高精度的环构象，并具体应用于抗体CDR环。这种平均场方法将大大增加交替构象的采样，同时保持与最先进的模拟技术的兼容性，例如显式溶剂化中的分子动力学。单个水分子通常具有结构作用，用连续介质模型替换可能会导致无法接受的精度损失。与目前使用的方法相比，这是该方法的一个关键优势。由于该方法是基于能量的，因此它比使用数据库的方法更具一般性，并将改进结构建模的许多领域。该项目将包括制定一种强化的当地强化抽样方法。这将被应用于抗体环预测中更困难的问题。最初的研究将集中在重现重要的H3环的诱导匹配的已知案例，然后是对其中H3构象未知的系统的真实预测。这些将为几种具有生物医学重要性的抗体的诱导匹配的决定因素、溶剂的作用和关键结构细节提供新的见解。随后的研究将预测几种催化抗体在抗体CDR区域的所有6个循环。这些结构将有助于理解影响这些抗体催化活性的因素，以及环灵活性、溶剂分子或抗体所针对的过渡态类似物的缺陷如何影响效率。该方法不仅可以预测抗体的构象，还可以预测抗体-抗原复合体的结构。这些信息对于理解这些重要的生物分子相互作用是至关重要的。