Method Development: Efficient Computer Vision Based Algorithms

方法开发：基于高效计算机视觉的算法

• 批准号: 8937737
• 负责人: Ruth Nussinov
• 金额: $ 10.87万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8937737
• 关键词: Address; Adverse drug effect; Affect; Algorithms; Behavior; Benchmarking; Binding; Binding Proteins; Binding Sites; Biological; Biological Process; CCR; Case Study; Cell physiology; Cells; Characteristics; Charge; Chemical Engineering; Complement; Complex; Comprehension; Computational Biology; Computer Simulation; Computer Vision Systems; Data; Data Set; Databases; Detection; Diffusion; Dimensions; Docking; Drug Design; Environment; Escherichia coli; Genome; Goals; Growth; Inflammation; Libraries; Life; Ligands; Link; Literature; Location; Lower Organism; Malignant Neoplasms; Maps; Membrane Proteins; Methodology; Methods; Mitogen-Activated Protein Kinases; Modeling; Modeling of Cellular Pathways; Molecular; Molecular Conformation; Molecular Weight; Mono-S; Movement; Mycoplasma pneumoniae; Nature; Network-based; Organism; Pathway interactions; Phage Display; Pharmaceutical Preparations; Pharmacology; Population; Process; Protein Fragment; Proteins; Proteome; RNA; Randomized; Regulation; Resources; Robotics; Series; Side; Signal Pathway; Simulate; Site; Specific qualifier value; Speed; Structure; Systems Biology; Testing; Time; Update; Validation; Vertebral column; base; biological research; blind; comparative; computerized tools; data management; data modeling; drug discovery; experience; flexibility; functional group; globular protein; instrument; interest; knowledge base; method development; molecular assembly/self assembly; molecular dynamics; operation; prevent; programs; protein folding; protein function; protein protein interaction; protein structure; research study; success; tau Proteins; tool; web site

The cellular network and its environment govern cell and organism behavior and are fundamental to the comprehension of function, misfunction and drug discovery. Over the last few years, drugs were observed to often bind to more than one target; thus, poly-pharmacology approaches can be advantageous, complementing the "one drug - one target" strategy. Targeting drug discovery from the systems biology standpoint can help in studies of network effects of mono- and poly-pharmacology. In this mini-review, we provide an overview of the usefulness of network description and tools for mono- and poly-pharmacology, and the ways through which protein interactions can help single- and multi-target drug discovery efforts. We further describe how, when combined with experimental data, modeled structural networks which can predict which proteins interact and provide the structures of their interfaces, can model the cellular pathways, and suggest which specific pathways are likely to be affected. Such structural networks may facilitate structure-based drug design; forecast side effects of drugs; and suggest how the effects of drug binding can propagate in multi-molecular complexes and pathways. Cellular functions are performed through protein-protein interactions; therefore, identification of these interactions is crucial for understanding biological processes. Recent studies suggest that knowledge-based approaches are more useful than "blind" docking for modeling at large scales. However, a caveat of knowledge-based approaches is that they treat molecules as rigid structures. The Protein Data Bank (PDB) offers a wealth of conformations. Here, we exploited an ensemble of the conformations in predictions by a knowledge-based method, PRISM. We tested "difficult" cases in a docking-benchmark data set, where the unbound and bound protein forms are structurally different. Considering alternative conformations for each protein, the percentage of successfully predicted interactions increased from about 26 to 66%, and 57% of the interactions were successfully predicted in an "unbiased" scenario, in which data related to the bound forms were not utilized. If the appropriate conformation, or relevant template interface, is unavailable in the PDB, PRISM could not predict the interaction successfully. The pace of the growth of the PDB promises a rapid increase of ensemble conformations emphasizing the merit of such knowledge-based ensemble strategies for higher success rates in protein-protein interaction predictions on an interactome scale. We constructed the structural network of ERK interacting proteins as a case study. We constructed and simulated a "minimal proteome" model using Langevin dynamics. It contains 206 essential protein types that were compiled from the literature. For comparison, we generated six proteomes with randomized concentrations. We found that the net charges and molecular weights of the proteins in the minimal genome are not random. The net charge of a protein decreases linearly with molecular weight, with small proteins being mostly positively charged and large proteins negatively charged. The protein copy numbers in the minimal genome have the tendency to maximize the number of protein-protein interactions in the network. Negatively charged proteins that tend to have larger sizes can provide a large collision cross-section allowing them to interact with other proteins; on the other hand, the smaller positively charged proteins could have higher diffusion speed and are more likely to collide with other proteins. Proteomes with random charge/mass populations form less stable clusters than those with experimental protein copy numbers. Our study suggests that "proper" populations of negatively and positively charged proteins are important for maintaining a protein-protein interaction network in a proteome. It is interesting to note that the minimal genome model based on the charge and mass of Escherichia coli may have alarger protein-protein interaction network than that based on the lower organism Mycoplasma pneumoniae. Proteins function through their interactions, and the availability of protein interaction networks could help in understanding cellular processes. However, the known structural data are limited and the classical network node-and-edge representation, where proteins are nodes and interactions are edges, shows only which proteins interact; not how they interact. Structural networks provide this information. Protein-protein interface structures can also indicate which binding partners can interact simultaneously and which are competitive, and can help forecasting potentially harmful drug side effects. Here, we use a powerful protein-protein interactions prediction tool which is able to carry out accurate predictions on the proteome scale to construct the structural network of the extracellular signal-regulated kinases (ERK) in the mitogen-activated protein kinase (MAPK) signaling pathway. This knowledge-based method, PRISM, is motif-based, and is combined with flexible refinement and energy scoring. PRISM predicts protein interactions based on structural and evolutionary similarity to known protein interfaces. We focus on improvement of PRISM toward modeling of specific interaction of key proteins in cancer and inflammation pathways to figure out regulation in central processes in the cell.

细胞网络及其环境支配着细胞和生物体的行为，是理解功能、故障和药物发现的基础。在过去的几年里，观察到药物经常与一个以上的靶点结合；因此，多种药理学方法可能是有利的，补充了“一种药物-一种靶点”策略。从系统生物学的角度定位药物发现，有助于研究单一和多元药理的网络效应。在这篇简短的综述中，我们提供了网络描述和工具对单一和多个药理学的有用性的概述，以及蛋白质相互作用可以帮助单目标和多目标药物发现努力的方式。我们进一步描述了当与实验数据结合时，可以预测哪些蛋白质相互作用并提供其界面结构的建模结构网络如何能够对细胞通路进行建模，并建议哪些特定的通路可能受到影响。这样的结构网络结构可能有助于基于结构的药物设计；预测药物的副作用；并建议药物结合的影响如何在多分子复合体和途径中传播。细胞功能是通过蛋白质-蛋白质相互作用来实现的，因此，识别这些相互作用对于理解生物过程至关重要。最近的研究表明，在大规模建模中，基于知识的方法比“盲目”对接更有用。然而，基于知识的方法的一个警告是，它们将分子视为刚性结构。蛋白质数据库(PDB)提供了丰富的构象。在这里，我们利用基于知识的方法PRISM预测中的构象集合。我们在对接基准数据集中测试了“困难”的情况，其中非结合蛋白和结合蛋白的形式在结构上是不同的。考虑到每个蛋白质的替代构象，成功预测相互作用的百分比从大约26%增加到66%，在没有利用与结合形式相关的数据的情况下，成功预测了57%的相互作用。如果PDB中没有合适的构象或相关的模板界面，则PRISM无法成功预测相互作用。PDB的发展速度保证了集合构象的快速增加，强调了这种基于知识的集合策略在交互作用组规模上预测蛋白质-蛋白质相互作用的更高成功率的优点。我们构建了ERK相互作用蛋白的结构网络作为案例研究。我们利用朗之万动力学构建并模拟了一个“最小蛋白质组”模型。它包含206种必需蛋白质类型，这些类型是从文献中汇编而来的。为了进行比较，我们产生了六个随机浓度的蛋白质组。我们发现，最小基因组中蛋白质的净电荷和分子量不是随机的。蛋白质的净电荷随分子量呈线性减少，小的蛋白质大多带正电荷，大的蛋白质带负电荷。最小基因组中的蛋白质拷贝数具有最大化网络中蛋白质-蛋白质相互作用的数量的趋势。带负电荷的蛋白质往往尺寸较大，可以提供较大的碰撞横截面，使它们能够与其他蛋白质相互作用；另一方面，较小的带正电荷的蛋白质可能具有更快的扩散速度，更有可能与其他蛋白质碰撞。具有随机电荷/质量群体的蛋白质组形成的稳定簇比具有实验蛋白质拷贝数的蛋白质组更不稳定。我们的研究表明，“适当的”带正负电荷的蛋白质群体对于维持蛋白质组中的蛋白质-蛋白质相互作用网络是重要的。值得注意的是，基于大肠杆菌电荷和质量的最小基因组模型可能比基于低等生物肺炎支原体的最小基因组模型具有更大的蛋白质-蛋白质相互作用网络。蛋白质通过相互作用发挥作用，而蛋白质相互作用网络的可用性可以帮助理解细胞过程。然而，已知的结构数据是有限的，经典的网络节点-边表示，其中蛋白质是节点，相互作用是边，只显示了哪些蛋白质相互作用；而不是它们如何相互作用。结构性网络提供了这方面的信息。蛋白质-蛋白质界面结构还可以指示哪些结合伙伴可以同时相互作用，哪些是竞争性的，并有助于预测潜在的有害药物副作用。在这里，我们使用一个强大的蛋白质相互作用预测工具，能够在蛋白质组水平上进行准确的预测，以构建丝裂原活化蛋白激酶(MAPK)信号通路中细胞外信号调节蛋白激酶(ERK)的结构网络结构。这种基于知识的方法PRISM是基于Motif的，并与灵活的求精和能量评分相结合。PRISM基于与已知蛋白质界面的结构和进化相似性来预测蛋白质相互作用。我们专注于改进PRISM对癌症和炎症途径中关键蛋白的特定相互作用的建模，以找出细胞中央过程中的调节。