Reproducible, Unbiased Ligand Identification Assisted by Artificial Intelligence and Development of Ligand Reference Libraries

人工智能辅助的可重复、公正的配体鉴定和配体参考文库的开发

基本信息

批准号：
10019572
负责人：
WLADEK MINOR
金额：
$ 56.12万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-17 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10019572
关键词：
Address Advisory Committees Algorithms Artificial Intelligence Benchmarking Binding Sites Bioinformatics Biological Biology Biomedical Research Categories Cognitive Complex Cryoelectron Microscopy Crystallization Data Data Set Descriptor Development Disease Docking Drug Design Drug Targeting Effectiveness Electron Microscopy Ensure FAIR principles Generations Human Intuition Ions Libraries Ligand Binding Ligands Machine Learning Methodology Methods Modeling Molecular Molecular Conformation Molecular Structure Nucleic Acid Binding Ontology Peptides Pharmaceutical Preparations Pharmacotherapy Proteins Protocols documentation Public Health Recommendation Reproducibility Research Resolution Resources Roentgen Rays Software Tools Standardization Stratification Structural Models Structural Protein Structure System Teaching Materials Techniques Training Uncertainty Update Validation X-Ray Crystallography base computational chemistry density drug discovery electron density experience improved inhibitor/antagonist machine learning algorithm macromolecule novel online resource simulation small molecule software development stem structured data three dimensional structure tool

项目摘要

Our current understanding of the molecular mechanisms of disease and structure-based design of drugs for treatment, rely on experimentally determined 3D structures of proteins and other macromolecules complexed with small molecule ligands. Many of these structures have direct relevance to public health, especially complexes of drug targets with drugs, inhibitors, substrates, or allosteric effectors. Yet, structure-based drug discovery is severely complicated and hindered by experimental bias and the shortcomings of current methods of experimental ligand identification, which often result in misidentified, missing, or misplaced ligands. The propagation of erroneous structures combined with an increased accessibility to structural data not only thwarts reproducibility in biomedical research and drug discovery, but also diverts valuable resources down doomed research avenues. We will leverage our extensive experience validating and refining ligand binding sites to generate ligand reference libraries that will be made publically available on a new web resource dedicated to the interaction of small molecules and macromolecules. These libraries can be used in many downstream applications, such as drug design, computational chemistry, biology, and bioinformatics. We will utilize recent technological advances in machine learning in conjunction with existing tools to create a standardized protocol for density interpretation and unbiased, reproducible ligand identification. This pipeline will not only be able identify and model ligands in unassigned density fragments, but also be able to detect and correct suboptimally refined ligands in existing structures. As the proposed AI will be free from cognitive bias, it should alleviate the most severe problems in structure-based drug design. Because improperly interpreted structures can have a significant deleterious ripple effect, we will experimentally verify select biomedically important structures with dubious experimental support for critical small molecules using use X-ray crystallography or electron microscopy.

我们目前对疾病和基于结构设计的分子机制的理解用于治疗的药物，依靠实验确定的蛋白质和其他的3D结构与小分子配体复合的大分子。这些结构中的许多有直接与公共卫生，尤其是药物，抑制剂，底物，尤其是药物目标的复合物相关或变构效应子。然而，基于结构的药物发现严重复杂并受到阻碍通过实验偏差和实验配体的当前方法的缺点识别通常会导致配体误认为，缺失或错位的配体。传播错误的结构以及对结构数据的可访问性不仅增加挫败生物医学研究和药物发现中的可重复性，但也会转移有价值的资源降低了注定要使的研究途径。我们将利用我们的丰富经验来验证并完善配体绑定位点以生成将公开制作的配体参考库可在新的Web资源上提供，该资源专门用于小分子的相互作用和大分子。这些库可以用于许多下游应用中，例如药物设计，计算化学，生物学和生物信息学。我们将利用最近的机器学习方面的技术进步以及现有工具创建一个用于密度解释和无偏，可重复的配体的标准化方案鉴别。该管道不仅能够以未分配的密度识别和建模配体片段，但也能够在现有的结构。由于拟议的AI将摆脱认知偏见，因此应减轻最严重的基于结构的药物设计问题。因为不当解释的结构可以有一个明显的有害连锁反应，我们将在实验上验证选择生物医学重要的选择使用X射线使用X射线的临界小分子的可疑实验支持的结构晶体学或电子显微镜。