Accelerated Determination of 3D Structures of Proteins and Complexes

加速测定蛋白质和复合物的 3D 结构

• 批准号: 9059732
• 负责人: CHRIS SANDER
• 金额: $ 42.38万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-03 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9059732
• 关键词: Area; Bacteria; Biology; Collaborations; Computational Biology; Crystallography; DNA Sequence; Data Set; Development; Entropy; Genomic approach; Genomics; Head; Health; Human; Hybrids; Maps; Memorial Sloan-Kettering Cancer Center; Methods; Molecular; Molecular Computations; Pharmaceutical Preparations; Physics; Protein Family; Protein Structure Initiative; Proteins; Research; Roentgen Rays; Sequence Alignment; Site; Structure; Systems Biology; Technology; Tertiary Protein Structure; Vertebral column; Virus; Work; X-Ray Crystallography; base; biological research; experience; fungus; international center; medical schools; microorganism; new technology; programs; protein complex; protein structure; rapid growth; structural biology; structural genomics; targeted treatment; three dimensional structure

DESCRIPTION (provided by applicant): The project will provide new technology to accelerate the discovery of protein structures and assemblies from humans and other species. Knowing the 3D structures of proteins has important biomedical implications for the development of protein-based therapies and targeted therapeutic drugs, but the 3D structures of proteins of thousands of important protein types remain unsolved. The project aims to close this gap, based on two recent advances: (1) the rapid development of new DNA sequencing technologies and (2) a recent breakthrough in protein 3D structure prediction using statistical physics and bio-molecular computation. The new structure prediction method, developed by the applicant team, extracts evolutionary residue-residue couplings from multiple sequence alignments, using a maximum entropy method. The team will use the evolutionary couplings as distance constraints to predict the structure of many single domains, of multidomain proteins and of protein complexes, and to map functional sites on known and predicted structures, with potentially broad impact on diverse biological research areas. The team will also aim to aid the development of hybrid computational- experimental technologies for structure determination. For X-ray crystallography, the aim is bridge the gap between the predicted 3D structures and the basin of convergence for molecular replacement, allowing structure determination from a single native data set without the need for anomalous or derivative diffraction datasets. For NMR, the aim is to add evolutionary couplings to NMR-derived backbone and residue-residue distance information and thus reduce experimental effort and/or permit the solution of larger structures. The project is a close collaboration between the Computational Biology Program at Memorial Sloan-Kettering Cancer Center and the Department of Systems Biology at Harvard Medical School. Experimental collaborations with PSI:Biology centers and the international structural genomics effort will aim to implement a more efficient technology for the determination of biomedically relevant protein structures.

描述（由申请人提供）：该项目将提供新技术来加速发现人类和其他物种的蛋白质结构和组装。了解蛋白质的 3D 结构对于开发基于蛋白质的疗法和靶向治疗药物具有重要的生物医学意义，但数千种重要蛋白质类型的蛋白质 3D 结构仍未解决。该项目旨在基于两项最新进展来缩小这一差距：(1) 新 DNA 测序技术的快速发展，以及 (2) 最近在利用统计物理学和生物分子计算进行蛋白质 3D 结构预测方面取得的突破。申请人团队开发的新结构预测方法使用最大熵方法从多个序列比对中提取进化残基-残基耦合。该团队将使用进化耦合作为距离约束来预测许多单域、多域蛋白质和蛋白质复合物的结构，并在已知和预测的结构上绘制功能位点，这对不同的生物研究领域具有潜在的广泛影响。该团队还将致力于帮助开发用于结构测定的混合计算实验技术。对于 X 射线晶体学，目标是弥合预测的 3D 结构与分子替换收敛盆地之间的差距，从而允许从单个原始数据集确定结构，而不需要异常或衍生衍射数据集。对于 NMR，目标是将进化耦合添加到 NMR 衍生的主链和残基-残基距离信息中，从而减少实验工作和/或允许解决更大的结构。该项目是纪念斯隆-凯特琳癌症中心计算生物学项目和哈佛医学院系统生物学系之间的密切合作。与 PSI:Biology 中心和国际结构基因组学工作的实验合作旨在实施更有效的技术来确定生物医学相关的蛋白质结构。

项目成果

Sequence co-evolution gives 3D contacts and structures of protein complexes.

序列协同进化提供蛋白质复合物的 3D 接触和结构。

• 发表时间: 2014-09-25
• 影响因子: 7.7
• 作者: Hopf, Thomas A;Schärfe, Charlotta P I;Rodrigues, João P G L M;Green, Anna G;Kohlbacher, Oliver;Sander, Chris;Bonvin, Alexandre M J J;Marks, Debora S
• 通讯作者: Marks, Debora S

3D RNA and Functional Interactions from Evolutionary Couplings.

3D RNA 和进化耦合的功能相互作用。

• 发表时间: 2016-05-05
• 影响因子: 64.5
• 作者: Weinreb, Caleb;Riesselman, Adam J;Ingraham, John B;Gross, Torsten;Sander, Chris;Marks, Debora S
• 通讯作者: Marks, Debora S

FreeContact: fast and free software for protein contact prediction from residue co-evolution.

FreeContact：快速且免费的软件，用于通过残基共同进化进行蛋白质接触预测。

• 发表时间: 2014-03-26
• 影响因子: 3
• 作者: Kaján, László;Hopf, Thomas A;Kalaš, Matúš;Marks, Debora S;Rost, Burkhard
• 通讯作者: Rost, Burkhard

Amino acid coevolution reveals three-dimensional structure and functional domains of insect odorant receptors.

氨基酸共同进化揭示了昆虫气味受体的三维结构和功能域。

• 发表时间: 2015-01-13
• 影响因子: 16.6
• 作者: Hopf, Thomas A;Morinaga, Satoshi;Ihara, Sayoko;Touhara, Kazushige;Marks, Debora S;Benton, Richard
• 通讯作者: Benton, Richard

Inferring protein 3D structure from deep mutation scans.

从深度突变扫描推断蛋白质 3D 结构。

• 发表时间: 2019-07
• 影响因子: 30.8
• 作者: Rollins, Nathan J;Brock, Kelly P;Poelwijk, Frank J;Stiffler, Michael A;Gauthier, Nicholas P;Sander, Chris;Marks, Debora S
• 通讯作者: Marks, Debora S