Protein-Protein Docking: From Interaction to Interactome

The protein-protein docking problem is one of the focal points of activity in computational biophysics and structural biology. The three-dimensional structure of a protein-protein complex, generally, is more difficult to determine experimentally than the structure of an individual protein. Adequate computational techniques to model protein interactions are important because of the growing number of known protein structures, particularly in the context of structural genomics. Docking offers tools for fundamental studies of protein interactions and provides a structural basis for drug design. Protein-protein docking is the prediction of the structure of the complex, given the structures of the individual proteins. In the heart of the docking methodology is the notion of steric and physicochemical complementarity at the protein-protein interface. Originally, mostly high-resolution, experimentally determined (primarily by x-ray crystallography) protein structures were considered for docking. However, more recently, the focus has been shifting toward lower-resolution modeled structures. Docking approaches have to deal with the conformational changes between unbound and bound structures, as well as the inaccuracies of the interacting modeled structures, often in a high-throughput mode needed for modeling of large networks of protein interactions. The growing number of docking developers is engaged in the community-wide assessments of predictive methodologies. The development of more powerful and adequate docking approaches is facilitated by rapidly expanding information and data resources, growing computational capabilities, and a deeper understanding of the fundamental principles of protein interactions.     

Protein-Protein Docking Benchmark 2.0: an update

We present a new version of the Protein-Protein Docking Benchmark, reconstructed from the bottom up to include more complexes, particularly focusing on more unbound-unbound test cases. SCOP (Structural Classification of Proteins) was used to assess redundancy between the complexes in this version. The new benchmark consists of 72 unbound-unbound cases, with 52 rigid-body cases, 13 medium-difficulty cases, and 7 high-difficulty cases with substantial conformational change. In addition, we retained 12 antibody-antigen test cases with the antibody structure in the bound form. The new benchmark provides a platform for evaluating the progress of docking methods on a wide variety of targets. The new version of the benchmark is available to the public at http://zlab.bu.edu/benchmark2.     

蛋白质相互作用数据库及其应用

对蛋白质相互作用及其网络的了解不仅有助于深入理解生命活动的本质和疾病发生的机制,而且可以为药物研发提供靶点.目前,通过高通量筛选、计算方法预测和文献挖掘等方法,获得了大批量的蛋白质相互作用数据,并由此构建了很多内容丰富并日益更新的蛋白质相互作用数据库.本文首先简要阐述了大规模蛋白质相互作用数据产生的3种方法,然后重点介绍了几个人类相关的蛋白质相互作用公共数据库,包括HPRD、BIND、 IntAct、MINT、 DIP 和MIPS,并概述了蛋白质相互作用数据库的整合情况以及这些数据库在蛋白质相互作用网络构建上的应用.     

蛋白质相互作用的研究方法

蛋白质相互作用的研究是现代分子生物学研究领域一个很重要的方面。该介绍了经典的和最近新建立的研究蛋白质相互作用的方法,并比较了各种方法的优缺点和所适用的领域。     

Protein-Protein Docking with Dynamic Residue Protonation States

Protein-protein interactions depend on a host of environmental factors. Local pH conditions influence the interactions through the protonation states of the ionizable residues that can change upon binding. In this work, we present a pH-sensitive docking approach, pHDock, that can sample side-chain protonation states of five ionizable residues (Asp, Glu, His, Tyr, Lys) on-the-fly during the docking simulation. pHDock produces successful local docking funnels in approximately half (79/161) the protein complexes, including 19 cases where standard RosettaDock fails. pHDock also performs better than the two control cases comprising docking at pH 7.0 or using fixed, predetermined protonation states. On average, the top-ranked pHDock structures have lower interface RMSDs and recover more native interface residue-residue contacts and hydrogen bonds compared to RosettaDock. Addition of backbone flexibility using a computationally-generated conformational ensemble further improves native contact and hydrogen bond recovery in the top-ranked structures. Although pHDock is designed to improve docking, it also successfully predicts a large pH-dependent binding affinity change in the Fc–FcRn complex, suggesting that it can be exploited to improve affinity predictions. The approaches in the study contribute to the goal of structural simulations of whole-cell protein-protein interactions including all the environmental factors, and they can be further expanded for pH-sensitive protein design.     

Exploring Angular Distance in Protein-Protein Docking Algorithms

We present a two-stage hybrid-resolution approach for rigid-body protein-protein docking. The first stage is carried out at low-resolution (15°) angular sampling. In the second stage, we sample promising regions from the first stage at a higher resolution of 6°. The hybrid-resolution approach produces the same results as a 6° uniform sampling docking run, but uses only 17% of the computational time. We also show that the angular distance can be used successfully in clustering and pruning algorithms, as well as the characterization of energy funnels. Traditionally the root-mean-square-distance is used in these algorithms, but the evaluation is computationally expensive as it depends on both the rotational and translational parameters of the docking solutions. In contrast, the angular distances only depend on the rotational parameters, which are generally fixed for all docking runs. Hence the angular distances can be pre-computed, and do not add computational time to the post-processing of rigid-body docking results.     

细胞蛋白质相互作用的结构基础

随着人类基因组计划的进行 ,大量基因被发现和定位 ,基因的功能问题将成为今后研究的热点。大多数基因的最终产物是相应的蛋白质 ,因此要认识基因的功能 ,必然要研究基因所表达的蛋白质。蛋白质的功能往往体现在与其他蛋白质及 /或核酸的相互作用之中。细胞各种重要的生理过程 ,包括信号的转导 ,细胞对外界环境及内环境变化的反应等 ,都是以蛋白质间相互作用为纽带 ,并形成网络。所以 ,近年来 ,蛋白质间相互作用的研究逐渐得到重视。蛋白质分子的结构域有很多种 ,但是现在明确作为为介导蛋白质 蛋白质间相互作用的结构域并不多 ,这里取已明…     

How to Use Not-Always-Reliable Binding Site Information in Protein-Protein Docking Prediction

In many protein-protein docking algorithms, binding site information is used to help predicting the protein complex structures. Using correct and accurate binding site information can increase protein-protein docking success rate significantly. On the other hand, using wrong binding sites information should lead to a failed prediction, or, at least decrease the success rate. Recently, various successful theoretical methods have been proposed to predict the binding sites of proteins. However, the predicted binding site information is not always reliable, sometimes wrong binding site information could be given. Hence there is a high risk to use the predicted binding site information in current docking algorithms. In this paper, a softly restricting method (SRM) is developed to solve this problem. By utilizing predicted binding site information in a proper way, the SRM algorithm is sensitive to the correct binding site information but insensitive to wrong information, which decreases the risk of using predicted binding site information. This SRM is tested on benchmark 3.0 using purely predicted binding site information. The result shows that when the predicted information is correct, SRM increases the success rate significantly; however, even if the predicted information is completely wrong, SRM only decreases success rate slightly, which indicates that the SRM is suitable for utilizing predicted binding site information.     

Protein-Protein Docking with F2Dock 2.0 and GB-Rerank

Motivation

Computational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F² Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introducing improvements in the shape-complementarity and electrostatics affinity functions, a new knowledge-based interface propensity term with FFT formulation, a set of novel knowledge-based filters and finally a solvation energy (GBSA) based reranking technique. Our algorithms are based on highly efficient data structures including the dynamic packing grids and octrees which significantly speed up the computations and also provide guaranteed bounds on approximation error.

Results

The improved affinity functions show superior performance compared to their traditional counterparts in finding correct docking poses at higher ranks. We found that the new filters and the GBSA based reranking individually and in combination significantly improve the accuracy of docking predictions with only minor increase in computation time. We compared F² Dock 2.0 with ZDock 3.0.2 and found improvements over it, specifically among 176 complexes in ZLab Benchmark 4.0, F² Dock 2.0 finds a near-native solution as the top prediction for 22 complexes; where ZDock 3.0.2 does so for 13 complexes. F² Dock 2.0 finds a near-native solution within the top 1000 predictions for 106 complexes as opposed to 104 complexes for ZDock 3.0.2. However, there are 17 and 15 complexes where F² Dock 2.0 finds a solution but ZDock 3.0.2 does not and vice versa; which indicates that the two docking protocols can also complement each other.

Availability

The docking protocol has been implemented as a server with a graphical client (TexMol) which allows the user to manage multiple docking jobs, and visualize the docked poses and interfaces. Both the server and client are available for download. Server: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dock.shtml. Client: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dockclient.shtml.     

Performance and Its Limits in Rigid Body Protein-Protein Docking

1. Download : Download high-res image (363KB)
2. Download : Download full-size image

     

PPIM: A Protein-Protein Interaction Database for Maize

Maize (Zea mays) is one of the most important crops worldwide. To understand the biological processes underlying various traits of the crop (e.g. yield and response to stress), a detailed protein-protein interaction (PPI) network is highly demanded. Unfortunately, there are very few such PPIs available in the literature. Therefore, in this work, we present the Protein-Protein Interaction Database for Maize (PPIM), which covers 2,762,560 interactions among 14,000 proteins. The PPIM contains not only accurately predicted PPIs but also those molecular interactions collected from the literature. The database is freely available at http://comp-sysbio.org/ppim with a user-friendly powerful interface. We believe that the PPIM resource can help biologists better understand the maize crop.Maize (Zea mays) is one of the most important crops in the world. Understanding the molecular mechanisms underlying various traits of maize (e.g. response to drought and salt) is important to improve the quality and yield of the crop. Although the maize genome sequence has unraveled the gene components of the crop, most traits involve complex interactions among molecules. Some protein-protein interactions (PPIs) have been experimentally determined in maize. For example, the CENTRORADIALIS8 protein was found to interact with the floral activator DLF1 protein with yeast two-hybrid assays (Danilevskaya et al., 2008), and barren stalk1 was found to interact with barren inflorescence2 with pull-down assays (Skirpan et al., 2008). Unfortunately, unlike other model organisms, there are very few molecular interactions available for maize. Therefore, a comprehensive maize interactome map is highly demanded.Recently, with more information about maize available, it has become practical to investigate the interactions between maize molecules. For example, with accumulating gene expression data, a gene coexpression network has been built to identify gene modules that play important roles in conditions of interest. With this idea, Downs et al. (2013) constructed a gene coexpression network based on gene expression data from 50 maize tissues and identified some gene modules that are important for development. By comparing the maize and rice (Oryza sativa) coexpression networks, Ficklin and Feltus (2011) identified some conserved gene modules between the two species, indicating their essential roles in crops. With protein abundance and phosphorylation data in different maize tissues across seven developmental stages, Walley et al. (2013) built a protein coexpression network to present kinase-substrate relationships. The metabolic network MaizeCyc (Monaco et al., 2013), containing enzyme catalysts, proteins, and other metabolites, has also been constructed. Focusing on maize kernel development, the expression quantitative trait loci have been investigated with RNA sequencing data (Fu et al., 2013), and the gene regulations underlying endosperm cell differentiation have been identified (Zhan et al., 2015).Despite the above efforts to identify possible interactions between molecules, no comprehensive interactome is available for maize. Most current approaches construct gene coexpression networks; however, these only describe the associations between genes and cannot tell which genes have real interactions. Under these circumstances, we present a comprehensive Protein-Protein Interaction Database for Maize (PPIM), which provides both our predicted physical and functional interactions as well as molecular interactions collected from the literature and public databases. To our knowledge, the PPIM is the most comprehensive database for maize to date. The user-friendly powerful interface accompanying the database can help biologists better explore the database.     

Geometric De-noising of Protein-Protein Interaction Networks

Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein-protein interaction (PPI) detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.     

Computational Identification of Protein-Protein Interactions in Rice Based on the Predicted Rice Interactome Network

Plant protein-protein interaction networks have not been identified by large-scale experiments. In order to better understand the protein interactions in rice, the Predicted Rice Interactome Network (PRIN; http://bis.zju.edu.cn/ prin/) presented 76,585 predicted interactions involving 5,049 rice proteins. After mapping genomic features of rice (GO annotation, subcellular localization prediction, and gene expression), we found that a well-annotated and biologically significant network is rich enough to capture many significant functional linkages within higher-order biological systems, such as pathways and biological processes. Furthermore, we took MADS-box do- main-containing proteins and circadian rhythm signaling pathways as examples to demonstrate that functional protein complexes and biological pathways could be effectively expanded in our predicted network. The expanded molecular network in PRIN has considerably improved the capability of these analyses to integrate existing knowledge and provide novel insights into the function and coordination of genes and gene networks.     

A Web Interface for Easy Flexible Protein-Protein Docking with ATTRACT

Protein-protein docking programs can give valuable insights into the structure of protein complexes in the absence of an experimental complex structure. Web interfaces can facilitate the use of docking programs by structural biologists. Here, we present an easy web interface for protein-protein docking with the ATTRACT program. While aimed at nonexpert users, the web interface still covers a considerable range of docking applications. The web interface supports systematic rigid-body protein docking with the ATTRACT coarse-grained force field, as well as various kinds of protein flexibility. The execution of a docking protocol takes up to a few hours on a standard desktop computer.     

Analysis of Cynandione A’s Anti-Ischemic Stroke Effects from Pathways and Protein-Protein Interactome

Ischemic stroke is the third leading cause of death in the world. Our previous study found that cynandione A (CYNA), the main component from the root of Cynanchum bungei, exhibits anti-ischemic stroke activity. In this work, we investigated the therapeutic mechanisms of CYNA to ischemic stroke at protein network level. First, PC12 cells and cerebellar granule neurons were prepared to validate the effects of CYNA against glutamate injury. Our experiments suggested that CYNA could dose-dependently mitigate glutamate-induced neurons neurotoxicity and inhibit glutamate-induced upregulation of KHSRP and HMGB1, further confirming the neuroprotective effects of CYNA in vivo. Then, on the pathway sub-networks, which present biological processes that can be impacted directly or in periphery nodes by drugs via their targets, we found that CYNA regulates 11 pathways associated with the biological process of thrombotic or embolic occlusion of a cerebral artery. Meanwhile, by defining a network-based anti-ischemic stroke effect score, we showed that CYNA has a significantly higher effect score than random counterparts, which suggests a synergistic effect of CYNA to ischemic stroke. This study may shed new lights on the study of network based pharmacology.     

Pushing Structural Information into the Yeast Interactome by High-Throughput Protein Docking Experiments

The last several years have seen the consolidation of high-throughput proteomics initiatives to identify and characterize protein interactions and macromolecular complexes in model organisms. In particular, more that 10,000 high-confidence protein-protein interactions have been described between the roughly 6,000 proteins encoded in the budding yeast genome (Saccharomyces cerevisiae). However, unfortunately, high-resolution three-dimensional structures are only available for less than one hundred of these interacting pairs. Here, we expand this structural information on yeast protein interactions by running the first-ever high-throughput docking experiment with some of the best state-of-the-art methodologies, according to our benchmarks. To increase the coverage of the interaction space, we also explore the possibility of using homology models of varying quality in the docking experiments, instead of experimental structures, and assess how it would affect the global performance of the methods. In total, we have applied the docking procedure to 217 experimental structures and 1,023 homology models, providing putative structural models for over 3,000 protein-protein interactions in the yeast interactome. Finally, we analyze in detail the structural models obtained for the interaction between SAM1-anthranilate synthase complex and the MET30-RNA polymerase III to illustrate how our predictions can be straightforwardly used by the scientific community. The results of our experiment will be integrated into the general 3D-Repertoire pipeline, a European initiative to solve the structures of as many as possible protein complexes in yeast at the best possible resolution. All docking results are available at http://gatealoy.pcb.ub.es/HT_docking/.     

蛋白质相互作用研究方法及其应用

过去10年来,蛋白质组学得到迅速发展,蛋白质间的相互作用作为蛋白质组学的重要内容,更是成为国内外竞相研究的重点,研究方法的快速发展为蛋白质间相互作用的研究奠定了坚实基础。着重就经典的噬菌体展示、酵母双杂交以及新近发展起来的串联亲和纯化、荧光共振能量转移技术和表面等离子共振等蛋白质相互作用研究方法的原理及应用作一综述并展望其发展前景。     

侧链能量在蛋白质对接优化中的应用

一般的蛋白质对接程序能够提供大量的待选构象,但其中仅含有少量的正确构象。现在对接的主要工作在于如何从这些大量构象中挑出正确构象。我们先前的研究工作证明蛋白质界面比非界面表面具有更高的能量。在这里,我们使用由chen等人提出的一个用于检验、设计对接程序的蛋白质复合物标准库中的非抗原-抗体复合物,将侧链能量运用到对接中,并比较了侧链能量和残基配对倾向性、残基组成倾向性、残基保守性在对接中的表现。单独使用这四项的正确构象的平均百排分位排序分别为：38．6±19．6、26．3±20．8、22．7±16．6和37．8±26．1,但是对于个别蛋白,侧链能量的表现要优于其它的三个参数。我们将四个参数综合起来考虑,发展了一个新的打分函数,平均百排分位排序为22．2±7．8,并且提高了筛选效率。     

Protein-Protein Interaction Domains of Bacillus subtilis DivIVA

DivIVA proteins are curvature-sensitive membrane binding proteins that recruit other proteins to the poles and the division septum. They consist of a conserved N-terminal lipid binding domain fused to a less conserved C-terminal domain. DivIVA homologues interact with different proteins involved in cell division, chromosome segregation, genetic competence, or cell wall synthesis. It is unknown how DivIVA interacts with these proteins, and we used the interaction of Bacillus subtilis DivIVA with MinJ and RacA to investigate this. MinJ is a transmembrane protein controlling division site selection, and the DNA-binding protein RacA is crucial for chromosome segregation during sporulation. Initial bacterial two-hybrid experiments revealed that the C terminus of DivIVA appears to be important for recruiting both proteins. However, the interpretation of these results is limited since it appeared that C-terminal truncations also interfere with DivIVA oligomerization. Therefore, a chimera approach was followed, making use of the fact that Listeria monocytogenes DivIVA shows normal polar localization but is not biologically active when expressed in B. subtilis. Complementation experiments with different chimeras of B. subtilis and L. monocytogenes DivIVA suggest that MinJ and RacA bind to separate DivIVA domains. Fluorescence microscopy of green fluorescent protein-tagged RacA and MinJ corroborated this conclusion and suggests that MinJ recruitment operates via the N-terminal lipid binding domain, whereas RacA interacts with the C-terminal domain. We speculate that this difference is related to the cellular compartments in which MinJ and RacA are active: the cell membrane and the cytoplasm, respectively.     

Adding Protein Context to the Human Protein-Protein Interaction Network to Reveal Meaningful Interactions

Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer''s disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways.