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.

细胞网络及其环境控制细胞和生物体的行为，是对功能，功能错误和药物发现的理解至关重要的。在过去的几年中，观察到药物经常与多个靶标结合。因此，多种药理学方法可以是有利的，可以补充“一种药物 - 一种目标”策略。从系统生物学的角度靶向药物发现可以帮助研究单一和多药理学的网络效应。在这次迷你审查中，我们概述了网络描述和多种药理学工具的实用性，以及蛋白质相互作用可以帮助单一和多目标药物发现工作的方式。我们进一步描述了如何与实验数据结合使用建模结构网络，这些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，可以对细胞途径进行建模，并建议哪些特定途径可能受到影响。这种结构网络可能会促进基于结构的药物设计；预测药物的副作用；并提出药物结合的作用如何在多分子复合物和途径中传播。细胞功能是通过蛋白质 - 蛋白质相互作用进行的。因此，对这些相互作用的识别对于理解生物学过程至关重要。最近的研究表明，基于知识的方法比在大规模建模的“盲”对接更有用。但是，基于知识的方法的警告是，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，我们通过基于知识的方法Prism利用了预测中的构象合奏。我们在对接基准数据集中测试了“困难”病例，其中未结合的蛋白质形式在结构上不同。考虑到每种蛋白质的替代构象，成功预测相互作用的百分比从约26％增加到66％，而57％的相互作用在“公正”的情况下成功地预测了，其中未利用与界限相关的数据。如果在PDB中无法使用适当的构象或相关的模板接口，则Prism无法成功预测相互作用。 PDB增长的速度有望迅速增加集合构象，强调了这种基于知识的合奏策略的优点，以在相互作用范围内进行蛋白质 - 蛋白质相互作用预测的较高成功率。我们构建了ERK相互作用蛋白的结构网络作为案例研究。我们使用Langevin Dynamics构建并模拟了“最小蛋白质组”模型。它包含从文献中汇编的206种必需蛋白质类型。为了进行比较，我们产生了六个具有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。蛋白质的净电荷随着分子量的线性降低，小蛋白主要带正电荷，大蛋白质带负电。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。带负电荷的蛋白质往往具有较大尺寸的蛋白质可以提供较大的碰撞横截面，从而使其与其他蛋白质相互作用。另一方面，较小的带电蛋白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。与具有实验蛋白质拷贝数的蛋白质组相比，随机电荷/质量种群的蛋白质组织的稳定簇要稳定。我们的研究表明，负电荷蛋白的“适当”种群对于维持蛋白质组中的蛋白质 - 蛋白质相互作用网络很重要。有趣的是，基于大肠杆菌的电荷和质量的最小基因组模型可能具有Alarger蛋白 - 蛋白质相互作用网络，而不是基于下生物体支原体肺炎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以及蛋白质相互作用网络的可用性起作用，可以帮助理解细胞过程。但是，已知的结构数据受到限制，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，仅显示哪些蛋白质相互作用。不是它们的互动方式。结构网络提供此信息。蛋白质 - 蛋白质界面结构还可以指出哪些结合伴侣可以同时相互作用且具有竞争力，并且可以帮助预测潜在的有害药物副作用。在这里，我们使用强大的蛋白质 - 蛋白质相互作用预测工具，该工具能够在蛋白质组尺度上进行准确的预测，以在有丝分裂原激活的蛋白激酶（MAPK）信号通路中构建细胞外信号调节激酶（ERK）的结构网络。这种基于知识的方法，PRISM，基于图案，并与灵活的精致和能量评分结合使用。棱镜预测基于与已知蛋白质界面的结构和进化相似性的蛋白质相互作用。我们专注于改善棱镜对癌症和炎症途径的特定相互作用的建模，以找出细胞中心过程中的调节。