Functional evaluation of domain-domain interactions and human protein interaction networks

MOTIVATION: Large amounts of protein and domain interaction data are being produced by experimental high-throughput techniques and computational approaches. To gain insight into the value of the provided data, we used our new similarity measure based on the Gene Ontology (GO) to evaluate the molecular functions and biological processes of interacting proteins or domains. The applied measure particularly addresses the frequent annotation of proteins or domains with multiple GO terms. RESULTS: Using our similarity measure, we compare predicted domain-domain and human protein-protein interactions with experimentally derived interactions. The results show that our similarity measure is of significant benefit in quality assessment and confidence ranking of domain and protein networks. We also derive useful confidence score thresholds for dividing domain interaction predictions into subsets of low and high confidence. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Structural and Functional Analysis of Multi-Interface Domains

A multi-interface domain is a domain that can shape multiple and distinctive binding sites to contact with many other domains, forming a hub in domain-domain interaction networks. The functions played by the multiple interfaces are usually different, but there is no strict bijection between the functions and interfaces as some subsets of the interfaces play the same function. This work applies graph theory and algorithms to discover fingerprints for the multiple interfaces of a domain and to establish associations between the interfaces and functions, based on a huge set of multi-interface proteins from PDB. We found that about 40% of proteins have the multi-interface property, however the involved multi-interface domains account for only a tiny fraction (1.8%) of the total number of domains. The interfaces of these domains are distinguishable in terms of their fingerprints, indicating the functional specificity of the multiple interfaces in a domain. Furthermore, we observed that both cooperative and distinctive structural patterns, which will be useful for protein engineering, exist in the multiple interfaces of a domain.     

Inferring protein-protein interactions through high-throughput interaction data from diverse organisms

MOTIVATION: Identifying protein-protein interactions is critical for understanding cellular processes. Because protein domains represent binding modules and are responsible for the interactions between proteins, computational approaches have been proposed to predict protein interactions at the domain level. The fact that protein domains are likely evolutionarily conserved allows us to pool information from data across multiple organisms for the inference of domain-domain and protein-protein interaction probabilities. RESULTS: We use a likelihood approach to estimating domain-domain interaction probabilities by integrating large-scale protein interaction data from three organisms, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. The estimated domain-domain interaction probabilities are then used to predict protein-protein interactions in S.cerevisiae. Based on a thorough comparison of sensitivity and specificity, Gene Ontology term enrichment and gene expression profiles, we have demonstrated that it may be far more informative to predict protein-protein interactions from diverse organisms than from a single organism. AVAILABILITY: The program for computing the protein-protein interaction probabilities and supplementary material are available at http://bioinformatics.med.yale.edu/interaction.     

PSIbase: a database of Protein Structural Interactome map (PSIMAP)

Protein Structural Interactome map (PSIMAP) is a global interaction map that describes domain-domain and protein-protein interaction information for known Protein Data Bank structures. It calculates the Euclidean distance to determine interactions between possible pairs of structural domains in proteins. PSIbase is a database and file server for protein structural interaction information calculated by the PSIMAP algorithm. PSIbase also provides an easy-to-use protein domain assignment module, interaction navigation and visual tools. Users can retrieve possible interaction partners of their proteins of interests if a significant homology assignment is made with their query sequences. AVAILABILITY: http://psimap.org and http://psibase.kaist.ac.kr/     

Prediction of protein-protein interactions using random decision forest framework

MOTIVATION: Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Domains are the building blocks of proteins; therefore, proteins are assumed to interact as a result of their interacting domains. Many domain-based models for protein interaction prediction have been developed, and preliminary results have demonstrated their feasibility. Most of the existing domain-based methods, however, consider only single-domain pairs (one domain from one protein) and assume independence between domain-domain interactions. RESULTS: In this paper, we introduce a domain-based random forest of decision trees to infer protein interactions. Our proposed method is capable of exploring all possible domain interactions and making predictions based on all the protein domains. Experimental results on Saccharomyces cerevisiae dataset demonstrate that our approach can predict protein-protein interactions with higher sensitivity (79.78%) and specificity (64.38%) compared with that of the maximum likelihood approach. Furthermore, our model can be used to infer interactions not only for single-domain pairs but also for multiple domain pairs.     

Co-evolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein-protein interactions

Recent advances in functional genomics have helped generate large-scale high-throughput protein interaction data. Such networks, though extremely valuable towards molecular level understanding of cells, do not provide any direct information about the regions (domains) in the proteins that mediate the interaction. Here, we performed co-evolutionary analysis of domains in interacting proteins in order to understand the degree of co-evolution of interacting and non-interacting domains. Using a combination of sequence and structural analysis, we analyzed protein-protein interactions in F1-ATPase, Sec23p/Sec24p, DNA-directed RNA polymerase and nuclear pore complexes, and found that interacting domain pair(s) for a given interaction exhibits higher level of co-evolution than the non-interacting domain pairs. Motivated by this finding, we developed a computational method to test the generality of the observed trend, and to predict large-scale domain-domain interactions. Given a protein-protein interaction, the proposed method predicts the domain pair(s) that is most likely to mediate the protein interaction. We applied this method on the yeast interactome to predict domain-domain interactions, and used known domain-domain interactions found in PDB crystal structures to validate our predictions. Our results show that the prediction accuracy of the proposed method is statistically significant. Comparison of our prediction results with those from two other methods reveals that only a fraction of predictions are shared by all the three methods, indicating that the proposed method can detect known interactions missed by other methods. We believe that the proposed method can be used with other methods to help identify previously unrecognized domain-domain interactions on a genome scale, and could potentially help reduce the search space for identifying interaction sites.     

Chemical shift assignments of the C-terminal Eps15 homology domain-3 EH domain

The C-terminal Eps15 homology (EH) domain 3 (EHD3) belongs to a eukaryotic family of endocytic regulatory proteins and is involved in the recycling of various receptors from the early endosome to the endocytic recycling compartment or in retrograde transport from the endosomes to the Golgi. EH domains are highly conserved in the EHD family and function as protein-protein interaction units that bind to Asn-Pro-Phe (NPF) motif-containing proteins. The EH domain of EHD1 was the first C-terminal EH domain from the EHD family to be solved by NMR. The differences observed between this domain and proteins with N-terminal EH domains helped describe a mechanism for the differential binding of NPF-containing proteins. Here, structural studies were expanded to include the EHD3 EH domain. While the EHD1 and EHD3 EH domains are highly homologous, they have different protein partners. A comparison of these structures will help determine the selectivity in protein binding between the EHD family members and lead to a better understanding of their unique roles in endocytic regulation.     

Biological units and their effect upon the properties and prediction of protein-protein interactions

Structural data as collated in the Protein Data Bank (PDB) have been widely applied in the study and prediction of protein-protein interactions. However, since the basic PDB Entries contain only the contents of the asymmetric unit rather than the biological unit, some key interactions may be missed by analysing only the PDB Entry. A total of 69,054 SCOP (Structural Classification of Proteins) domains were examined systematically to identify the number of additional novel interacting domain pairs and interfaces found by considering the biological unit as stored in the PQS (Protein Quaternary Structure) database. The PQS data adds 25,965 interacting domain pairs to those seen in the PDB Entries to give a total of 61,783 redundant interacting domain pairs. Redundancy filtering at the level of the SCOP family shows PQS to increase the number of novel interacting domain-family pairs by 302 (13.3%) from 2277, but only 16/302 (1.4%) of the interacting domain pairs have the two domains in different SCOP families. This suggests the biological units add little to the elucidation of novel biological interaction networks. However, when the orientation of the domain pairs is considered, the PQS data increases the number of novel domain-domain interfaces observed by 1455 (34.5%) to give 5677 non-redundant domain-domain interfaces. In all, 162/1455 novel domain-domain interfaces are between domains from different families, an increase of 8.9% over the PDB Entries. Overall, the PQS biological units provide a rich source of novel domain-domain interfaces that are not seen in the studied PDB Entries, and so PQS domain-domain interaction data should be exploited wherever possible in the analysis and prediction of protein-protein interactions.     

Statistical analysis of domains in interacting protein pairs

MOTIVATION: Several methods have recently been developed to analyse large-scale sets of physical interactions between proteins in terms of physical contacts between the constituent domains, often with a view to predicting new pairwise interactions. Our aim is to combine genomic interaction data, in which domain-domain contacts are not explicitly reported, with the domain-level structure of individual proteins, in order to learn about the structure of interacting protein pairs. Our approach is driven by the need to assess the evidence for physical contacts between domains in a statistically rigorous way. RESULTS: We develop a statistical approach that assigns p-values to pairs of domain superfamilies, measuring the strength of evidence within a set of protein interactions that domains from these superfamilies form contacts. A set of p-values is calculated for SCOP superfamily pairs, based on a pooled data set of interactions from yeast. These p-values can be used to predict which domains come into contact in an interacting protein pair. This predictive scheme is tested against protein complexes in the Protein Quaternary Structure (PQS) database, and is used to predict domain-domain contacts within 705 interacting protein pairs taken from our pooled data set.     

Length constraints of multi-domain proteins in metazoans

The increasing number of annotated genome sequences in public databases has made it possible to study the length distributions and domain composition of proteins at unprecedented resolution. To identify factors that influence protein length in metazoans, we performed an analysis of all domain-annotated proteins from a total of 49 animal species from Ensembl (v.56) or EnsemblMetazoa (v.3). Our results indicate that protein length constraints are not fixed as a linear function of domain count and can vary based on domain content. The presence of repeating domains was associated with relaxation of the constraints that govern protein length. Conversely, for proteins with unique domains, length constraints were generally maintained with increased domain counts. It is clear that mean (and median) protein length and domain composition vary significantly between metazoans and other kingdoms; however, the connections between function, domain content, and length are unclear. We incorporated Gene Ontology (GO) annotation to identify biological processes, cellular components, or molecular functions that favor the incorporation of multi-domain proteins. Using this approach, we identified multiple GO terms that favor the incorporation of multi-domain proteins; interestingly, several of the GO terms with elevated domain counts were not restricted to a single gene family. The findings presented here represent an important step in resolving the complex relationship between protein length, function, and domain content. The comparison of the data presented in this work to data from other kingdoms is likely to reveal additional differences in the regulation of protein length.     

Small-Angle X-Ray Scattering Reveals Compact Domain-Domain Interactions in the N-Terminal Region of Filamin C

Filamins are multi-domain, actin cross-linking, and scaffolding proteins. In addition to the actin cross-linking function, filamins have a role in mechanosensor signaling. The mechanosensor function is mediated by domain-domain interaction in the C-terminal region of filamins. Recently, we have shown that there is a three-domain interaction module in the N-terminal region of filamins, where the neighboring domains stabilize the structure of the middle domain and thereby regulate its interaction with ligands. In this study, we have used small-angle X-ray scattering as a tool to screen for potential domain-domain interactions in the N-terminal region. We found evidence of four domain-domain interactions with varying flexibility. These results confirm our previous study showing that domains 3, 4, and 5 exist as a compact three domain module. In addition, we report interactions between domains 11–12 and 14–15, which are thus new candidate sites for mechanical regulation.     

Protein domain annotation with integration of heterogeneous information sources

Domains are structural and functional units of proteins and play an important role in functional genomics. Theoretically, the functions of a protein can be directly inferred if the biological functions of its component domains are determined. Despite the important role that domains play, only a small number of domains have been annotated so far, and few works have been performed to predict the functions of domains. Hence, it is necessary to develop automatic methods for predicting domain functions based on various available data. In this article, two new methods, that is, the threshold-based classification method and the support vector machines method, are proposed for protein domain function prediction by integrating heterogeneous information sources, including protein-domain mapping features, domain-domain interactions, and domain coexisting features. We show that the integration of heterogeneous information sources improves not only prediction accuracy but also annotation reliability when compared with the methods using only individual information sources.     

J domain independent functions of J proteins

Heat shock proteins of 40 kDa (Hsp40s), also called J proteins, are obligate partners of Hsp70s. Via their highly conserved and functionally critical J domain, J proteins interact and modulate the activity of their Hsp70 partners. Mutations in the critical residues in the J domain often result in the null phenotype for the J protein in question. However, as more J proteins have been characterized, it is becoming increasingly clear that a significant number of J proteins do not “completely” rely on their J domains to carry out their cellular functions, as previously thought. In some cases, regions outside the highly conserved J domain have become more important making the J domain dispensable for some, if not for all functions of a J protein. This has profound effects on the evolution of such J proteins. Here we present selected examples of J proteins that perform J domain independent functions and discuss this in the context of evolution of J proteins with dispensable J domains and J-like proteins in eukaryotes.     

Structural basis for interaction of DivIVA/GpsB proteins with their ligands

DivIVA proteins and their GpsB homologues are late cell division proteins found in Gram‐positive bacteria. DivIVA/GpsB proteins associate with the inner leaflet of the cytosolic membrane and act as scaffolds for other proteins required for cell growth and division. DivIVA/GpsB proteins comprise an N‐terminal lipid‐binding domain for membrane association fused to C‐terminal domains supporting oligomerization. Despite sharing the same domain organization, DivIVA and GpsB serve different cellular functions: DivIVA plays diverse roles in division site selection, chromosome segregation and controlling peptidoglycan homeostasis, whereas GpsB contributes to the spatiotemporal control of penicillin‐binding protein activity. The crystal structures of the lipid‐binding domains of DivIVA from Bacillus subtilis and GpsB from several species share a fold unique to this group of proteins, whereas the C‐terminal domains of DivIVA and GpsB are radically different. A number of pivotal features identified from the crystal structures explain the functional differences between the proteins. Herein we discuss these structural and functional relationships and recent advances in our understanding of how DivIVA/GpsB proteins bind and recruit their interaction partners, knowledge that might be useful for future structure‐based DivIVA/GpsB inhibitor design.     

Domain-domain interactions underlying herpesvirus-human protein-protein interaction networks

Protein-domains play an important role in mediating protein-protein interactions. Furthermore, the same domain-pairs mediate different interactions in different contexts and in various organisms, and therefore domain-pairs are considered as the building blocks of interactome networks. Here we extend these principles to the host-virus interface and find the domain-pairs that potentially mediate human-herpesvirus interactions. Notably, we find that the same domain-pairs used by other organisms for mediating their interactions underlie statistically significant fractions of human-virus protein inter-interaction networks. Our analysis shows that viral domains tend to interact with human domains that are hubs in the human domain-domain interaction network. This may enable the virus to easily interfere with a variety of mechanisms and processes involving various and different human proteins carrying the relevant hub domain. Comparative genomics analysis provides hints at a molecular mechanism by which the virus acquired some of its interacting domains from its human host.     

Identification of Semaphorin 5A Interacting Protein by Applying Apriori Knowledge and Peptide Complementarity Related to Protein Evolution and Structure

In the post-genomic era, various computational methods that predict proteinprotein interactions at the genome level are available; however, each method has its own advantages and disadvantages, resulting in false predictions. Here we developed a unique integrated approach to identify interacting partner（s） of Semaphorin 5A （SEMA5A）, beginning with seven proteins sharing similar ligand interacting residues as putative binding partners. The methods include Dwyer and Root- Bernstein/Dillon theories of protein evolution, hydropathic complementarity of protein structure, pattern of protein functions among molecules, information on domain-domain interactions, co-expression of genes and protein evolution. Among the set of seven proteins selected as putative SEMA5A interacting partners, we found the functions of Plexin B3 and Neuropilin-2 to be associated with SEMA5A. We modeled the semaphorin domain structure of Plexin B3 and found that it shares similarity with SEMA5A. Moreover, a virtual expression database search and RT-PCR analysis showed co-expression of SEMA5A and Plexin B3 and these proteins were found to have co-evolved. In addition, we confirmed the interaction of SEMA5A with Plexin B3 in co-immunoprecipitation studies. Overall, these studies demonstrate that an integrated method of prediction can be used at the genome level for discovering many unknown protein binding partners with known ligand binding domains.     

GRID: a novel Grb-2-related adapter protein that interacts with the activated T cell costimulatory receptor CD28

Adapter proteins such as Grb2 play a central role in the formation of signaling complexes through their association with multiple protein binding partners. These interactions are mediated by specialized domains such as the well-characterized Src homology SH2 and SH3 motifs. Using yeast three-hybrid technology, we have identified a novel adapter protein, expressed predominantly in T lymphocytes, that associates with the activated form of the costimulatory receptor, CD28. The protein is a member of the Grb2 family of adapter proteins and contains an SH3-SH2-SH3 domain structure. A unique glutamine/proline-rich domain (insert domain) of unknown function is situated between the SH2 and N-terminal SH3 domains. We term this protein GRID for Grb2-related protein with insert domain. GRID coimmunoprecipitates with CD28 from Jurkat cell lysates following activation of CD28. Using mutants of CD28 and GRID, we demonstrate that interaction between the proteins is dependent on phosphorylation of CD28 at tyrosine 173 and integrity of the GRID SH2 domain, although there are also subsidiary stabilizing contacts between the PXXP motifs of CD28 and the GRID C-terminal SH3 domain. In addition to CD28, GRID interacts with a number of other T cell signaling proteins, including SLP-76 (SH2 domain-containing leukocyte protein of 76 kDa), p62dok, and RACK-1 (receptor for activated protein kinase C-1). These findings suggest that GRID functions as an adapter protein in the CD28-mediated costimulatory pathway in T cells.     

Biochemical and computational analysis of LNX1 interacting proteins

PDZ (Post-synaptic density, 95 kDa, Discs large, Zona Occludens-1) domains are protein interaction domains that bind to the carboxy-terminal amino acids of binding partners, heterodimerize with other PDZ domains, and also bind phosphoinositides. PDZ domain containing proteins are frequently involved in the assembly of multi-protein complexes and clustering of transmembrane proteins. LNX1 (Ligand of Numb, protein X 1) is a RING (Really Interesting New Gene) domain-containing E3 ubiquitin ligase that also includes four PDZ domains suggesting it functions as a scaffold for a multi-protein complex. Here we use a human protein array to identify direct LNX1 PDZ domain binding partners. Screening of 8,000 human proteins with isolated PDZ domains identified 53 potential LNX1 binding partners. We combined this set with LNX1 interacting proteins identified by other methods to assemble a list of 220 LNX1 interacting proteins. Bioinformatic analysis of this protein list was used to select interactions of interest for future studies. Using this approach we identify and confirm six novel LNX1 binding partners: KCNA4, PAK6, PLEKHG5, PKC-alpha1, TYK2 and PBK, and suggest that LNX1 functions as a signalling scaffold.     

结构域相互作用数据库的产生、发展与应用

结构域是进化上的保守序列单元,是蛋白质的结构和功能的标准组件．典型的两个蛋白质间的相互作用涉及特殊结构域间的结合,而且识别相互作用结构域对于在结构域水平上彻底理解蛋白质的功能与进化、构建蛋白质相互作用网络、分析生物学通路等十分重要．目前,依赖于对实验数据的进一步挖掘和对各种不同输入数据的计算预测,已识别出了一些相互作用/功能连锁结构域对,并由此构建了内容丰富、日益更新的结构域相互作用数据库．综述了产生结构域相互作用的8种计算预测方法．介绍了5个结构域相互作用公共数据库3DID、iPfam、InterDom、DIMA和DOMINE的有关信息和最新动态．实例概述了结构域相互作用在蛋白质相互作用计算预测、可信度评估,蛋白质结构域注释,以及在生物学通路分析中的应用．     

Modular organization of SARS coronavirus nucleocapsid protein

The SARS-CoV nucleocapsid (N) protein is a major antigen in severe acute respiratory syndrome. It binds to the viral RNA genome and forms the ribonucleoprotein core. The SARS-CoV N protein has also been suggested to be involved in other important functions in the viral life cycle. Here we show that the N protein consists of two non-interacting structural domains, the N-terminal RNA-binding domain (RBD) (residues 45–181) and the C-terminal dimerization domain (residues 248–365) (DD), surrounded by flexible linkers. The C-terminal domain exists exclusively as a dimer in solution. The flexible linkers are intrinsically disordered and represent potential interaction sites with other protein and protein-RNA partners. Bioinformatics reveal that other coronavirus N proteins could share the same modular organization. This study provides information on the domain structure partition of SARS-CoV N protein and insights into the differing roles of structured and disordered regions in coronavirus nucleocapsid proteins. CK Chang and SC Sue contributed equally to this project.