Method Development: Efficient Computer Vision Based Algorithms

在蛋白质组规模上在结构水平上预测蛋白质-蛋白质相互作用是重要的，因为它允许预测蛋白质功能，有助于药物发现，并向全基因组结构系统生物学迈进。我们提供了一个协议（称为PRISM，蛋白质相互作用的结构匹配）的大规模预测蛋白质-蛋白质相互作用和蛋白质复合物结构的组装。该方法由两个组成部分，刚体结构的目标蛋白质的比较，已知的模板蛋白质-蛋白质界面和灵活的细化使用对接能量函数。PRISM的基本原理遵循我们的观察，即全球不同的蛋白质结构可以通过相似的结构基序相互作用。PRISM预测结合残基通过使用结构相似性和进化保守的推定结合残基热点。最终，PRISM可以帮助构建细胞通路和功能，蛋白质组规模的注释。PRISM在Python中实现，并在UNIX环境中运行。该程序接受蛋白质数据库格式的蛋白质结构，折叠和结合之间的相似性使我们认识到自然界中蛋白质-蛋白质界面基序的数量是有限的，相互作用的蛋白质对可以重复使用类似的界面结构，即使它们的全局折叠完全不同。因此，已知的蛋白质-蛋白质界面结构可用于在蛋白质组规模上模拟两种靶蛋白之间的复合物，即使它们的整体结构不同。这一强大的概念与灵活的改进和全球能源评估工具相结合。该方法的准确性高度依赖于模板数据集中接口架构的结构多样性。在这里，我们验证了这种基于知识的组合方法的对接基准，并表明它有效地发现高质量的模型，基准复合物和它们的结合区域，即使在没有模板接口具有序列相似性的目标。与经典对接相比，它在计算上更快;随着靶蛋白数量的增加，差异变得更加显著。此外，它能够区分粘合剂和非粘合剂。这些功能允许执行大规模网络建模。一个独立的目标集（p53分子相互作用图谱中的蛋白质）的结果表明，目前的方法可以用来预测一个给定的蛋白质对是否相互作用。总体而言，虽然受到模板集多样性的限制，但这种方法有效地产生了高质量的蛋白质-蛋白质复合物模型。我们预计，随着越来越多的已知接口架构，这种类型的知识为基础的方法将越来越多地使用广泛的蛋白质组学community.Networks越来越多地用于研究药物的影响，在系统水平。从算法的角度来看，药物可以攻击蛋白质-蛋白质相互作用网络的节点或边缘。在这项工作中，我们提出了一种新的网络策略，接口攻击，基于蛋白质-蛋白质接口。类似的界面结构可以发生在不相关的蛋白质之间。因此，原则上，与一种药物结合的药物有一定的概率结合其他药物。接口攻击策略同时从网络中删除由类似接口图案组成的所有交互。这种策略受到网络药理学的启发，并允许推断潜在的脱靶。我们引入了一个蛋白质界面和相互作用网络模型（P2 IN），它是蛋白质-蛋白质界面结构和蛋白质相互作用网络的集成。这种基于界面的网络组织阐明了哪些蛋白质对具有结构相似的界面，以及哪些蛋白质可能竞争结合相同的表面区域。我们构建了p53信号网络的P2 IN，并进行了网络鲁棒性分析。我们发现：（1）击中频繁接口（一组分布在网络周围的边缘）可能是破坏性的，因为消除高度蛋白质（枢纽节点），（2）频繁接口并不总是拓扑关键元素在网络中，和（3）接口攻击可能会揭示功能的变化，在系统中比单个蛋白质的攻击。在脱靶检测案例研究中，我们发现阻断CDK 6与CDKN 2D之间界面的药物也可能影响CDK 4与CDKN 2D之间的相互作用，构象选择成为大分子相互作用的主题。数据证实它是蛋白质-蛋白质、蛋白质-DNA、蛋白质-RNA和蛋白质-小分子药物识别中的普遍机制。这就提出了一个问题，即这种基本的生物分子结合机制是否可以用于改善药物对接和发现。实际上，这种情况已经发生了好几年，而且越来越多。从本质上讲，它主张使用的不是一个单一的构象，而是一个合奏。构象选择的范式认为，由于系综是异质的，在它内部将有其构象与配体相匹配的状态。即使这种状态的群体很低，因为它有利于结合配体，它将与配体结合，随后群体向这种构象异构体转移。在这里，我们建议首先使用Prism（一种有效的基于基序的蛋白质-蛋白质相互作用建模策略）建模细胞中的所有蛋白质相互作用，然后进行集成生成。这样的策略可能对信号蛋白特别有用，信号蛋白是药物发现中的主要靶标，并且通过共享的结合位点结合多个伴侣，每个伴侣具有一些次要或主要构象变化。

Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components, rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue hot spots. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures.The similarity between folding and binding led us to posit the concept that the number of protein-protein interface motifs in nature is limited, and interacting protein pairs can use similar interface architectures repeatedly, even if their global folds completely vary. Thus, known protein-protein interface architectures can be used to model the complexes between two target proteins on the proteome scale, even if their global structures differ. This powerful concept is combined with a flexible refinement and global energy assessment tool. The accuracy of the method is highly dependent on the structural diversity of the interface architectures in the template dataset. Here, we validate this knowledge-based combinatorial method on the Docking Benchmark and show that it efficiently finds high-quality models for benchmark complexes and their binding regions even in the absence of template interfaces having sequence similarity to the targets. Compared to classical docking, it is computationally faster; as the number of target proteins increases, the difference becomes more dramatic. Further, it is able to distinguish binders from nonbinders. These features allow performing large-scale network modeling. The results on an independent target set (proteins in the p53 molecular interaction map) show that current method can be used to predict whether a given protein pair interacts. Overall, while constrained by the diversity of the template set, this approach efficiently produces high-quality models of protein-protein complexes. We expect that with the growing number of known interface architectures, this type of knowledge-based methods will be increasingly used by the broad proteomics community.Networks are increasingly used to study the impact of drugs at the systems level. From the algorithmic standpoint, a drug can attack nodes or edges of a protein-protein interaction network. In this work, we propose a new network strategy, The Interface Attack, based on protein-protein interfaces. Similar interface architectures can occur between unrelated proteins. Consequently, in principle, a drug that binds to one has a certain probability of binding others. The interface attack strategy simultaneously removes from the network all interactions that consist of similar interface motifs. This strategy is inspired by network pharmacology and allows inferring potential off-targets. We introduce a network model which we call Protein Interface and Interaction Network (P2IN), which is the integration of protein-protein interface structures and protein interaction networks. This interface-based network organization clarifies which protein pairs have structurally similar interfaces, and which proteins may compete to bind the same surface region. We built the P2IN of p53 signaling network and performed network robustness analysis. We show that (1) hitting frequent interfaces (a set of edges distributed around the network) might be as destructive as eleminating high degree proteins (hub nodes), (2) frequent interfaces are not always topologically critical elements in the network, and (3) interface attack may reveal functional changes in the system better than attack of single proteins. In the off-target detection case study, we found that drugs blocking the interface between CDK6 and CDKN2D may also affect the interaction between CDK4 and CDKN2D.Conformational selection emerges as a theme in macromolecular interactions. Data validate it as a prevailing mechanism in protein-protein, protein-DNA, protein-RNA, and protein-small molecule drug recognition. This raises the question of whether this fundamental biomolecular binding mechanism can be used to improve drug docking and discovery. Actually, in practice this has already been taking place for some years in increasing numbers. Essentially, it argues for using not a single conformer, but an ensemble. The paradigm of conformational selection holds that because the ensemble is heterogeneous, within it there will be states whose conformation matches that of the ligand. Even if the population of this state is low, since it is favorable for binding the ligand, it will bind to it with a subsequent population shift toward this conformer. Here we suggest expanding it by first modeling all protein interactions in the cell by using Prism, an efficient motif-based protein-protein interaction modeling strategy, followed by ensemble generation. Such a strategy could be particularly useful for signaling proteins, which are major targets in drug discovery and bind multiple partners through a shared binding site, each with some-minor or major-conformational change.