D3SC: Mining for mechanistic information to predict protein function

D3SC：挖掘机制信息来预测蛋白质功能

• 批准号: 1905214
• 负责人: Mary Jo Ondrechen
• 金额: $ 60万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1905214&HistoricalAwards=false
• 关键词: D3SC; Mining; mechanistic; information; predict

Project Title: D3SC: Mining for Mechanistic Information to Predict Protein FunctionProteins perform a variety of essential functions in a cell, including catalyzing chemical reactions as enzymes. With this award, the Chemistry of Life Processes Program in the Chemistry Division is funding Dr. Mary Jo Ondrechen, Dr. Penny Beuning and Dr. Deniz Erdogomus at Northeastern University to develop new ways to predict the function of a protein from its three-dimensional structure. This computational problem is a major challenge in genomics - the study of DNA sequences and their protein products. Research in genomics is opening the door to tremendous current and future innovations to benefit society, in areas as diverse as food production, energy, the economy, the environment, and health. In this project, chemical properties are computed and coupled with machine learning algorithms to identify the specific biochemical roles for the active amino acids in a protein structure, which then leads to the prediction of the protein's function. These predictions of function are tested experimentally by direct biochemical assays and by ligand binding studies, for selected cases. Doctoral students and undergraduate research interns, including those from minority groups that are underrepresented in STEM fields, are being trained through this project to become highly qualified scientists in the areas of computational chemistry, informatics, machine learning, and biochemistry. These skills are vital to the regional high-tech economy of New England and to United States competitiveness in the global economy. The computational prediction of biochemical functional roles of individual amino acids in a protein structure is entirely new. The predictive power of properties obtained from computational chemistry are being enhanced by machine learning approaches, including Support Vector Machines (SVM) and Graph Convolutional Neural Networks (GCNN). Improved, experimentally tested methods for the prediction of protein function contribute significantly to the interpretation of the massive quantities of data from genome sequencing and Structural Genomics (SG) initiatives. A significant feature of this project is that it incorporates computed chemical properties of the amino acids in a protein structure into more conventional informatics methods to predict function, whereas most current methods are purely informatics-based approaches. This project is unique in that it employs computed chemical reactivity and electrostatic features on the atomic scale in the protein function prediction problem to obtain residue-specific mechanistic information. With the capability to match functional types across different structural folds, i.e. cases with neither sequence nor 3D structure similarity, the ability to assign biochemical function reliably is substantially increased for SG proteins of unknown or uncertain function. This work also leads to better understanding of how enzymes work and of how specific amino acid residues achieve their catalytic power.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.

Project Title: D3SC: Mining for Mechanistic Information to Predict Protein FunctionProteins perform a variety of essential functions in a cell, including catalyzing chemical reactions as enzymes. With this award, the Chemistry of Life Processes Program in the Chemistry Division is funding Dr. Mary Jo Ondrechen, Dr. Penny Beuning and Dr. Deniz Erdogomus at Northeastern University to develop new ways to predict the function of a protein from its three-dimensional structure. This computational problem is a major challenge in genomics - the study of DNA sequences and their protein products. Research in genomics is opening the door to tremendous current and future innovations to benefit society, in areas as diverse as food production, energy, the economy, the environment, and health. In this project, chemical properties are computed and coupled with machine learning algorithms to identify the specific biochemical roles for the active amino acids in a protein structure, which then leads to the prediction of the protein's function. These predictions of function are tested experimentally by direct biochemical assays and by ligand binding studies, for selected cases. Doctoral students and undergraduate research interns, including those from minority groups that are underrepresented in STEM fields, are being trained through this project to become highly qualified scientists in the areas of computational chemistry, informatics, machine learning, and biochemistry. These skills are vital to the regional high-tech economy of New England and to United States competitiveness in the global economy. The computational prediction of biochemical functional roles of individual amino acids in a protein structure is entirely new. The predictive power of properties obtained from computational chemistry are being enhanced by machine learning approaches, including Support Vector Machines (SVM) and Graph Convolutional Neural Networks (GCNN). Improved, experimentally tested methods for the prediction of protein function contribute significantly to the interpretation of the massive quantities of data from genome sequencing and Structural Genomics (SG) initiatives. A significant feature of this project is that it incorporates computed chemical properties of the amino acids in a protein structure into more conventional informatics methods to predict function, whereas most current methods are purely informatics-based approaches. This project is unique in that it employs computed chemical reactivity and electrostatic features on the atomic scale in the protein function prediction problem to obtain residue-specific mechanistic information. With the capability to match functional types across different structural folds, i.e. cases with neither sequence nor 3D structure similarity, the ability to assign biochemical function reliably is substantially increased for SG proteins of unknown or uncertain function. This work also leads to better understanding of how enzymes work and of how specific amino acid residues achieve their catalytic power.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.

项目成果

Stereoselective Synthesis of β-Glycinamide Ribonucleotide.

• DOI: 10.3390/molecules27082528
• 发表时间: 2022-04-14
• 期刊: MOLECULES
• 影响因子: 4.6
• 作者: Ngu, Lisa;Ray, Debarpita;Watson, Samantha S.;Beuning, Penny J.;Ondrechen, Mary Jo;O'Doherty, George A.
• 通讯作者: O'Doherty, George A.

Probing remote residues important for catalysis in Escherichia coli ornithine transcarbamoylase

• DOI: 10.1371/journal.pone.0228487
• 发表时间: 2020-02-06
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Ngu, Lisa;Winters, Jenifer N.;Beuning, Penny J.
• 通讯作者: Beuning, Penny J.

Electrostatic fingerprints of catalytically active amino acids in enzymes

• DOI: 10.1002/pro.4291
• 发表时间: 2022-05-01
• 期刊: PROTEIN SCIENCE
• 影响因子: 8
• 作者: Iyengar, Suhasini M.;Barnsley, Kelly K.;Ondrechen, Mary Jo
• 通讯作者: Ondrechen, Mary Jo

Identification and characterization of alternative sites and molecular probes for SARS-CoV-2 target proteins.

• DOI: 10.3389/fchem.2022.1017394
• 发表时间: 2022
• 期刊: Frontiers in chemistry
• 影响因子: 5.5

Hydration sphere structure of architectural molecules: polyethylene glycol and polyoxymethylene oligomers

建筑分子的水化球结构：聚乙二醇和聚甲醛低聚物

• 发表时间: 2023
• 期刊: Journal of Molecular Liquids
• 影响因子: 6
• 作者: A. M. Rozza;Danny E. P. Vanpoucke;Eva;J. Bouckaert;R. Blossey;M. Lensink;Mary Jo Ondrechen;I. Bakó;J. Oláh;Goedele Roos
• 通讯作者: Goedele Roos