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Biomolecular Recognition and Binding Mechanisms

Biomolecular Recognition and Binding Mechanisms
生物分子识别和结合机制
批准号:
7291812
负责人:
Ruth Nussinov
金额:
$0.0万
依托单位国家:
美国
项目类别:
财政年份:
--
资助国家:
美国
项目状态:
未结题
起止时间:

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中文摘要
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英文摘要
Proteins are the "working horse" of the cell. Their roles span functions as diverse as being molecular machines and signaling. They carry out catalytic reactions, transport, form the viral capsids, traverse the membranes and form regulated channels, transmit the information from the DNA to RNA, making possible the synthesis of new proteins, and they are responsible for the degradation of the unnecessary proteins and nucleic acids. They are the vehicles of the immune response and are responsible for viral entry into the cell. Given their importance, considerable effort has been centered on the prediction of protein function. A prime way to predict protein function is through identification of binding partners. If the function of at least one of the components with which the protein interacts is known, that should facilitate assigning its function(s) and the pathway(s) in which it plays a role. This holds since the vast majority of their chores in the living cell involve protein-protein interactions. Proteins never function in isolation. Hence, through the intricate network of protein-protein interactions we can map cellular pathways, their interconnectivities and their dynamic regulation. Identification of protein-protein interactions is at the heart of functional genomics. Prediction of protein-protein interactions is crucial for drug discovery. Knowledge of the pathway, its topology, length, and dynamics may provide useful information for forecasting side effects. Yet, the goal of predicting protein-protein interaction is daunting. Some associations are obligatory, whereas others are continuously forming and dissociating. In principle, from the physical standpoint, any two proteins can interact. The question is under what conditions and at which strength. The principles of protein-protein interactions are general: The non-covalent interactions of two proteins are largely the outcome of the hydrophobic effect. The hydrophobic effect drives protein-protein interactions. In addition, hydrogen bonds and electrostatic interactions play important roles. Thus, many of the interactions observed in vitro are the outcome of experimental over-expression. This complicates the functional prediction. Energetic hot spots account for a significant portion of the total binding free energy and correlate with structurally conserved interface residues. We map experimentally determined hot spots and structurally conserved residues to investigate their geometrical organization. 'Unfilled pockets' are pockets that remain unfilled after protein-protein complexation, while 'complemented pockets' are pockets that disappear upon binding, representing tightly fit regions. We find that structurally conserved residues and energetic hot spots are strongly favored to be located in complemented pockets, and disfavored in unfilled pockets. For the three available protein-protein complexes with complemented pockets where both complex-members were alanine-scanned, 62% of all hot spots (DeltaDeltaG greater than 2 kcal/mol) are within these pockets, and 60% of the residues in the complemented pockets are hot spots. 93% of all "redhot" residues (DeltaDeltaG greater than 4 kcal/mol) either protrude into or are located in complemented pockets. The occurrence of hot spots and conserved residues in complemented pockets highlights the role of local tight packing in protein associations, and rationalizes their energy contribution and conservation. Complemented pockets and their corresponding protruding residues emerge among the most important geometric features in protein-protein interactions. By screening the solvent, this organization shields backbone hydrogen bonds and charge-charge interactions. Complemented pockets often pre-exist binding. For 19 protein-protein complexes with complemented pockets whose unbound structures are also available, in 16 the pockets pre-exist in the unbound structures.
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Method Development: Efficient Computer Vision Based Algo
Protein Structure, Stability, and Amyloid Formation
Method Development: Efficient Computer Vision Based Algorithms
Biomolecular Recognition and Binding Mechanisms
国内基金
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基于Recognition-VR 虚拟现实的“家庭-社区-医院三向联动”轻度认知障碍防治模式研究
  • 批准号:
    2021JJ60094
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2021
  • 负责人:
    谢丽琴
  • 依托单位: