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: 10262089
• 负责人: Ruth Nussinov
• 金额: $ 11.83万
• 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/10262089
• 关键词: Adverse drug effect; Affect; Algorithms; Bacteria; Behavior; Benchmarking; Binding; Binding Proteins; Binding Sites; Biological; Biological Process; Case Study; Cell physiology; Cells; Cellular Structures; Characteristics; Charge; Chemical Engineering; Communities; Complement; Complex; Comprehension; Computational Biology; Computer Vision Systems; Crystallization; Data; Data Set; Detection; Development; Diffusion; Docking; Drug Design; Drug Interactions; Drug Targeting; Environment; Escherichia coli; Extracellular Signal Regulated Kinases; Genome; Goals; Growth; Helicobacter pylori; Inflammation; Integral Membrane Protein; Libraries; Ligands; Literature; Location; Lower Organism; Malignant Neoplasms; Methodology; Methods; Microbe; Mitogen-Activated Protein Kinases; Modeling; Modeling of Cellular Pathways; Modernization; Molecular; Molecular Conformation; Molecular Weight; Movement; Mycoplasma pneumoniae; Nature; Network-based; Oncoproteins; Organism; Pathway interactions; Phage Display; Pharmaceutical Preparations; Pharmacology; Population; Process; Protein Fragment; Proteins; Proteome; Protozoa; Randomized; Regulation; Resources; Robotics; Series; Side; Signal Pathway; Site; Specific qualifier value; Speed; Structural Models; Structure; Surface; Systems Biology; Testing; Time; Tissues; Validation; Vertebral column; Viral; Virus; base; biological research; blind; comparative; computerized tools; data management; data warehouse; drug discovery; experimental study; flexibility; functional group; fungus; globular protein; host-microbe interactions; in silico; instrument; interest; knowledge base; machine learning algorithm; method development; microbial; microbiota; mimicry; molecular assembly/self assembly; molecular dynamics; operation; pathogen; programs; protein folding; protein function; protein protein interaction; protein structure; protein structure prediction; prototype; structured data; success; three dimensional structure; tool; user-friendly; web server

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 a larger 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. We have already developed the prototype method and applied it to Helicobacter pylori and most recently, viral oncoproteins.

细胞网络及其环境控制细胞和生物体的行为，是理解功能，功能失调和药物发现的基础。在过去几年中，观察到药物经常与一个以上的靶标结合;因此，多药理学方法可能是有利的，补充了“一种药物-一个靶标”的策略。从系统生物学的角度进行靶向药物发现有助于研究单药和多药药理学的网络效应。在这篇小型综述中，我们概述了网络描述和工具对单药理学和多药理学的有用性，以及蛋白质相互作用有助于单靶点和多靶点药物发现的方法。我们进一步描述了如何结合实验数据，建模的结构网络，可以预测哪些蛋白质相互作用，并提供其接口的结构，可以模拟细胞通路，并建议哪些特定的途径可能会受到影响。这种结构网络可以促进基于结构的药物设计;预测药物的副作用;并建议药物结合的效果如何在多分子复合物和途径中传播。细胞功能通过蛋白质-蛋白质相互作用来执行;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，基于知识的方法是更有用的比“盲目”对接建模在大尺度上。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，我们利用了一个合奏的构象预测的知识为基础的方法，棱镜。我们在对接基准数据集中测试了“困难”的情况，其中未结合和结合的蛋白质形式在结构上是不同的。考虑到每种蛋白质的替代构象，成功预测的相互作用的百分比从约26%增加到66%，并且57%的相互作用在“无偏”情况下被成功预测，其中未利用与结合形式相关的数据。如果PDB中没有合适的构象或相关的模板界面，PRISM无法成功预测相互作用。PDB的增长速度的承诺，集成构象的快速增加，强调这种基于知识的集成策略的优点，在蛋白质-蛋白质相互作用预测的相互作用组规模的更高的成功率。我们构建了ERK相互作用蛋白的结构网络作为案例研究。我们构建并模拟了一个“最小蛋白质组”模型使用朗之万动力学。它包含从文献中编译的206种必需蛋白质类型。为了比较，我们生成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线性下降，小蛋白质大多带正电荷，大蛋白质带负电荷。最小基因组中的蛋白质拷贝数具有使网络中蛋白质-蛋白质相互作用的数量最大化的趋势。带负电荷的蛋白质往往具有较大的尺寸，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高的扩散速度，并且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比具有实验蛋白质拷贝数的蛋白质组形成更不稳定的簇。我们的研究表明，“适当的”负电荷和正电荷的蛋白质的人口是重要的，以维持蛋白质组中的蛋白质-蛋白质相互作用网络。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模型具有更大的蛋白质-蛋白质相互作用网络。蛋白质通过它们的相互作用发挥作用，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，经典的网络节点和边缘表示，其中蛋白质是节点，相互作用是边缘，只显示哪些蛋白质相互作用，而不是它们如何相互作用。结构网络提供了这些信息。蛋白质-蛋白质界面结构还可以指示哪些结合伴侣可以同时相互作用，哪些是竞争性的，并且可以帮助预测潜在的有害药物副作用。在这里，我们使用一个功能强大的蛋白质-蛋白质相互作用预测工具，能够进行准确的预测蛋白质组规模构建的结构网络的细胞外信号调节激酶（ERK）的丝裂原活化蛋白激酶（MAPK）信号通路。这种基于知识的方法，PRISM，是基于图案，并结合灵活的细化和能量评分。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，以模拟癌症和炎症途径中关键蛋白质的特异性相互作用，以确定细胞中中央过程的调控。我们已经开发了原型方法，并将其应用于幽门螺杆菌和最近的病毒癌蛋白。