Elements: The ThYme database and identifying representative amino acid sequences that originate thioester-active enzyme families

元素：ThYme 数据库并鉴定产生硫酯活性酶家族的代表性氨基酸序列

• 批准号: 2001385
• 负责人: David Cantu
• 金额: $ 60万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2001385&HistoricalAwards=false
• 关键词: Elements; ThYme; database; identifying; representative

The ThYme database includes most enzymes involved in the formation of fatty acids and polyketides. These are ultimately converted into valuable products. Such products include cosmetics, detergents, insecticides, fungicides, antibiotics, and other medicinal compounds. The updated ThYme database will provide vital support to research related to these products. Metabolic engineers, plant biologists, natural products and medicinal chemists will all benefit from improved access to enzyme structure and function. High school students from underrepresented populations will be recruited and trained in various aspects of coding. They will then have the opportunity to work with graduate students and postdocs to contribute to the improvement of the platform. This should ultimately strengthen the STEM workforce in Nevada, and nationally.The ThYme database contains most known sequences and structures of enzymes that act on thioesters, classified by sequence similarity into families. The advantage of classifying enzymes by sequence similarity is that one can infer that all enzymes in a family will have very similar structures and nearly identical catalytic residues and mechanisms. The goal of this project is to launch a new and updated ThYme database by identifying the current families of thioester active enzymes, developing a new approach to identify representative sequences, improving the database management scheme, and modernizing the online user interface. We will develop an efficient method, using submodular functions, to select the representative sequence(s) of an enzyme family among sequences experimentally verified to have a particular enzymatic activity. The database will be disseminated by a website, where every enzyme family will have its own webpage including relevant knowledge and an open query field where users will be able to search by organism, sequence accession code, function, name, sequence, or crystal structure. With input from the user community, the new website will have features to make the content more interactive and allow automated data querying. The user community will be engaged and supported with a forum page to pose questions and begin discussions to which both developers and users can contribute, as well as with tutorials of useful website functionalities.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the ivision of Chemical, Bioengineering, Environmental and Transport Systems within the NSF Directorate of Engineering.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.

ThYme数据库包括参与脂肪酸和聚酮化合物形成的大多数酶。最终转化为有价值的产品。这些产品包括化妆品、洗涤剂、杀虫剂、杀真菌剂、抗生素和其他药用化合物。更新后的ThYme数据库将为与这些产品相关的研究提供重要支持。代谢工程师，植物生物学家，天然产物和药物化学家都将受益于酶结构和功能的改善。将征聘代表性不足的高中生，并对他们进行编码各方面的培训。然后，他们将有机会与研究生和博士后合作，为平台的改进做出贡献。这将最终加强内华达州和全国的STEM劳动力。ThYme数据库包含作用于硫酯的酶的大多数已知序列和结构，按序列相似性分类为家族。通过序列相似性对酶进行分类的优点是，可以推断出一个家族中的所有酶都具有非常相似的结构和几乎相同的催化残基和机制。该项目的目标是通过识别硫酯活性酶的当前家族，开发识别代表性序列的新方法，改进数据库管理方案，并使在线用户界面现代化，来启动新的和更新的ThYme数据库。我们将开发一种有效的方法，使用子模块功能，以选择一个酶家族的代表性序列（S）中的序列实验验证具有特定的酶活性。该数据库将通过一个网站传播，其中每个酶家族都有自己的网页，包括相关知识和一个开放的查询字段，用户将能够通过生物体、序列登录代码、功能、名称、序列或晶体结构进行搜索。根据用户社区的意见，新网站将具有使内容更具互动性和允许自动数据查询的功能。用户社区将参与并支持一个论坛页面，提出问题并开始讨论，开发人员和用户都可以为此做出贡献，以及有用的网站功能教程。NSF高级网络基础设施办公室的这一奖项由化学，生物工程，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。

项目成果

Thioesterase enzyme families: Functions, structures, and mechanisms

• DOI: 10.1002/pro.4263
• 发表时间: 2022-01-04
• 期刊: PROTEIN SCIENCE
• 影响因子: 8
• 作者: Caswell, Benjamin T.;de Carvalho, Caio C.;Cantu, David C.
• 通讯作者: Cantu, David C.

DWEN: A novel method for accurate estimation of cell type compositions from bulk data samples

DWEN：一种从大量数据样本中准确估计细胞类型组成的新方法

• DOI: 10.1109/kse56063.2022.9953757
• 发表时间: 2022
• 期刊: 2022 14th International Conference on Knowledge and Systems Engineering (KSE
• 作者: Tran, Duc;Nguyen, Ha;Nguyen, Hung;Nguyen, Tin
• 通讯作者: Nguyen, Tin