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Many aspects of protein motion can be comprehended with coarse-grained models. Our hypothesis is that atomic detail is not required to explain many aspects of protein behavior, and this simplification can facilitate a deeper understanding. The overall goal is to develop an understanding of how protein motions and function are contolled by structure, why protein sequences fold to a limited set of structures, and to establish the roles of tight packing and the shapes of proteins on their motions. In this project we will investigate the relationships among motions, shapes, structures, interactions and levels of cooperativity. Aim I: Modeling protein dynamics with Elastic Networks. We will use elastic network models to study how proteins restrict their motions to the motions most essential for function. Normal mode analyses will be performed to discern these important functional motions with high computational efficiency to develop molecular mechanisms. We will investigate the atomic motions in active sites of enzymes to see how the large domain motions control the atom movements. We will use elastic networks to interpret single molecule pulling experiments and predict the order in which proteins unravel. Preliminary results show that elastic network models are applicable not only to fluctuations around native conformations, but also to transient states arising when an external force is applied to deform a protein and break its native contacts. These results suggest that structure controls the global motions of proteins, even for transient states. To further verify this hypothesis we will perform more single molecule pulling simulations, and structural analyses of transient protein conformations along folding pathways. The major successes achieved with the elastic models rely upon having good representations of the packing density and protein shape, which we will investigate in Aim II. Aim II: Modeling Protein Packing and Cooperativity of Interactions. Dense packing of residues in proteins is one of their most important characteristic features. We plan to continue our studies of internal packing. The emphasis for new potentials will be on the relative orientations of amino acids in proteins. We will develop many-body contact potentials for identifying native structures among decoys in threading, and also study orientational distributions within clusters of nearby residues in proteins, using regular polyhedra such as icosahedra, or Catalan solids such as tetrakis hexahedra. Our rationale is to use various polyhedral models to comprehend protein packing and amino acid interactions for developing improved many-body potentials. A better understanding of the cooperativity of interactions within proteins is extremely important because this directly influences the ways in which proteins move and respond to forces. Both Aims are highly interconnected and will significantly advance our knowledge of protein structure, dynamics and function.
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DOI: 10.1504/ijbra.2011.040093
发表时间: 2011
期刊: International journal of bioinformatics research and applications
影响因子: --
作者: [Wu D, Smith S, Mahan H, Jernigan RL, Zhijun Wu]
通讯作者: Zhijun Wu
Fold-specific sequence scoring improves protein sequence matching.
折叠特异性序列评分改善了蛋白质序列匹配。
DOI: 10.1186/s12859-016-1198-z
发表时间: 2016-08-30
期刊: BMC bioinformatics
影响因子: 3
作者: [Leelananda SP, Kloczkowski A, Jernigan RL]
通讯作者: Jernigan RL
Models to Approximate the Motions of Protein Loops.
近似蛋白质环运动的模型。
DOI: 10.1021/ct1001413
发表时间: 2010
期刊: Journal of chemical theory and computation
影响因子: 5.5
作者: [Skliros,Aris, Jernigan,RobertL, Kloczkowski,Andrzej]
通讯作者: Kloczkowski,Andrzej
Chain dimensions and fluctuations in elastomeric networks in which the junctions alternate regularly in their functionality.
弹性体网络中的链尺寸和波动,其中连接点的功能定期交替。
DOI: 10.1063/1.3063115
发表时间: 2009
期刊: The Journal of chemical physics
影响因子: --
作者: [Skliros,Aris, Mark,JamesE, Kloczkowski,Andrzej]
通讯作者: Kloczkowski,Andrzej
51
    Novel Use of Genome Information to Understand Mutations
    • 批准号:
      10488281
    • 项目类别:
    • 资助金额:
      $46.39万
    • 财政年份:
      2021
    • 负责人:
      ROBERT L JERNIGAN
    • 依托单位:
    Novel Use of Genome Information to Understand Mutations
    • 批准号:
      10303852
    • 项目类别:
    • 资助金额:
      $48.06万
    • 财政年份:
      2021
    • 负责人:
      ROBERT L JERNIGAN
    • 依托单位:
    Novel Use of Genome Information to Understand Mutations
    • 批准号:
      10661834
    • 项目类别:
    • 资助金额:
      $46.5万
    • 财政年份:
      2021
    • 负责人:
      ROBERT L JERNIGAN
    • 依托单位:
    Modeling Ribosomal Control, Function and Assembly
    • 批准号:
      7290378
    • 项目类别:
    • 资助金额:
      $25.14万
    • 财政年份:
      2006
    • 负责人:
      ROBERT L JERNIGAN
    • 依托单位:
    海外基金