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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.
期刊论文(13)
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会议论文
DOI: 10.1093/bioinformatics/btu458
发表时间: 2014-09-01
期刊: Bioinformatics (Oxford, England)
影响因子: --
作者: [Michel M, Hayat S, Skwark MJ, Sander C, Marks DS, Elofsson A]
通讯作者: Elofsson A
DOI: 10.1016/j.cell.2016.03.030
发表时间: 2016-05-05
期刊: CELL
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.
Inferring protein 3D structure from deep mutation scans.
从深度突变扫描推断蛋白质 3D 结构。
DOI: 10.1038/s41588-019-0432-9
发表时间: 2019
期刊: Nature genetics
影响因子: 30.8
作者: [Rollins,NathanJ, Brock,KellyP, Poelwijk,FrankJ, Stiffler,MichaelA, Gauthier,NicholasP, Sander,Chris, Marks,DeboraS]
通讯作者: Marks,DeboraS
DOI: 10.1186/1471-2105-15-85
发表时间: 2014-03-26
期刊: BMC bioinformatics
影响因子: 3
作者: [Kaján L, Hopf TA, Kalaš M, Marks DS, Rost B]
通讯作者: Rost B
11
    Accelerated Determination of 3D Structures of Proteins and Complexes
    Accelerated Determination of 3D Structures of Proteins and Complexes
    Pathway Commons: A Public Library of Biological Pathways
    Pathway Commons: A Public Library of Biological Pathways
    国内基金
    海外基金
    Segmented Filamentous Bacteria激活宿主免疫系统抑制其拮抗菌 Enterobacteriaceae维持菌群平衡及其机制研究
    • 批准号:
      81971557
    • 项目类别:
      面上项目
    • 资助金额:
      65.0万元
    • 批准年份:
      2019
    • 负责人:
      毛开睿
    • 依托单位:
    电缆细菌(Cable bacteria)对水体沉积物有机污染的响应与调控机制