ENHANCED MEAN-FIELD SIMULATIONS OF ANTIBODY CDR LOOPS

抗体 CDR 环的增强平均场模拟

• 批准号: 6748192
• 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 2005-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6748192
• 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.

对于重要的生物分子（例如蛋白质和核酸）的精确三维结构电路的可用性可以极大地帮助理解它们的脂肪以及药物化合物的合理设计。但是，结构数据库相对较高。结构通常是使用比较建模构建的。但是这些通常在循环区域中准确。将开发一种新的预测蛋白质中高度准确的环构象的方法，并在抗体CDR环上进行了特定的应用。这种平均场方法将大大增加替代构象的采样，同时保持与最先进的模拟技术（例如显式溶剂化中的分子动力学）的兼容性。单个水分子通常具有结构作用，而用连续模型替换可能会导致准确性丧失。这是该方法的关键优势，而不是当前使用的方法。由于该方法基于Enezgy，它比使用数据库的方法更一般，并且将改善许多结构建模领域。该项目将包括增强本地增强的采样方法的开发。这将应用于抗体环预测中更困难的问题。最初的研究将着重于重现重要的H3环的已知诱导拟合情况，然后对H3构象未知的系统进行真实预测。这些将为诱导拟合的决定因素，溶剂和关键结构细节的作用提供新的见解，以实现生物医学重要性的几种抗体。随后的研究将预测几种催化抗体的抗体CDR区域中的所有6个环。这些结构将有助于理解影响这些抗体的催化活性的因素，以及在过渡态类似物中，环状柔韧性，溶剂分子或缺乏效率如何提高抗体的效率。该方法将不仅扩展到抗体构象的预测，还将扩展到抗体 - 抗原复合物的结构。这些信息对于理解这些重要的生物分子相互作用至关重要。