Characterization of Botulinum Toxin Subtypes - Computational Modeling

肉毒杆菌毒素亚型的表征 - 计算模型

• 批准号: 7632145
• 负责人: Rodney Balhorn
• 金额: $ 24.46万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7632145
• 关键词: Acids; Algorithms; Amino Acid Sequence; Antibodies; Blast Cell; Bontoxilysin; Botulinum Toxin Type A; Botulinum Toxins; Collection; Computer Simulation; Databases; Development; Epitopes; Exhibits; Facility Construction Funding Category; GLS2 gene; Homology Modeling; Maps; Modeling; Mutation; Peptide Sequence Determination; Peptides; Protein Analysis; Proteins; Structure; Toxin; Variant; Vertebral column; botulinum toxin type B; holotoxins; insertion/deletion mutation; programs; protein aminoacid sequence

This project will employ homology modeling and protein analysis algorithms developed at LLNL (the Protein Pipeline) to predict and analyze the structures of each botulinum neurotoxin (BoNT) subtype using the sequences of the proteins determined in Project 1. Sequence-structure alignments will be generated using a variety of algorithms, and the backbone of the model will be created using our AS2TS modeling package. The known crystal structures of the BoNT/A and BoNT/B holotoxins and domains will be used as templates. The construction of the terminal regions and loops, ammo acid insertions and deletions, and template-model structure comparisons will be performed using the LGA program. The collection of predicted subtype structures will be compared with the known toxin crystal structures to identify those subtypes that are most likely to exhibit functional differences, and this information will be used to identify which BoNT variants should be crystallized and carried forward for structure determination in Project 3. Each subtype toxin protein sequence will also be blasted against a database containing all known protein sequences using our Protein Pipeline to identify those peptide sequences (signatures) that are not found in any other protein and are unique to the subtype. The signatures and mutations will be mapped onto the predicted structures and examined to identify those subtype-specific peptide signatures that are sufficiently accessible to be used as epitopes for the development of BoNT subtype-specific antibodies by other key collaborators.

该项目将采用 LLNL（LLNL）开发的同源建模和蛋白质分析算法 蛋白质管道）使用预测和分析每种肉毒杆菌神经毒素 (BoNT) 亚型的结构 项目 1 中确定的蛋白质序列。序列-结构比对将使用 各种算法，模型的主干将使用我们的 AS2TS 建模包创建。 BoNT/A 和 BoNT/B 全毒素和结构域的已知晶体结构将用作模板。 末端区域和环的构建、氨基酸插入和缺失以及模板模型 结构比较将使用LGA 程序进行。预测亚型的集合 结构将与已知的毒素晶体结构进行比较，以确定最常见的亚型 可能表现出功能差异，该信息将用于识别哪些 BoNT 变体 应在项目3中结晶并进行结构测定。各亚型毒素 蛋白质序列也将使用我们的方法对包含所有已知蛋白质序列的数据库进行爆破 蛋白质管道可识别任何其他蛋白质中未发现的肽序列（特征） 是该子类型所特有的。签名和突变将被映射到预测的结构上 检查以确定那些足够容易用作的亚型特异性肽特征 其他关键合作者用于开发 BoNT 亚型特异性抗体的表位。