Unveiling protein functions through the dynamics of the interaction network

Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.     

Functional domains in tetraspanin proteins

Exciting new findings have emerged about the structure, function and biochemistry of tetraspanin proteins. Five distinct tetraspanin regions have now been delineated linking structural features to specific functions. Within the large extracellular loop of tetraspanins, there is a variable region that mediates specific interactions with other proteins, as well as a more highly conserved region that has been suggested to mediate homodimerization. Within the transmembrane region, the four tetraspanin transmembrane domains are probable sites of both intra- and inter-molecular interactions that are crucial during biosynthesis and assembly of the network of tetraspanin-linked membrane proteins known as the 'tetraspanin web'. In the intracellular juxtamembrane region, palmitoylation of cysteine residues also contributes to tetraspanin web assembly, and the C-terminal cytoplasmic tail region could provide specific functional links to cytoskeletal or signaling proteins.     

Prediction of functional class of novel bacterial proteins without the use of sequence similarity by a statistical learning method

A substantial percentage of the putative protein-encoding open reading frames (ORFs) in bacterial genomes have no homolog of known function, and their function cannot be confidently assigned on the basis of sequence similarity. Methods not based on sequence similarity are needed and being developed. One method, SVMProt (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi), predicts protein functional family irrespective of sequence similarity (Nucleic Acids Res. 2003;31:3692-3697). While it has been tested on a large number of proteins, its capability for non-homologous proteins has so far been evaluated for a relatively small number of proteins, and additional tests are needed to more fully assess SVMProt. In this work, 90 novel bacterial proteins (non-homologous to known proteins) are used to evaluate the capability of SVMProt. These proteins are such that none of their homologs are in the Swiss-Prot database, their functions not clearly described in the literature, and they themselves and their homologs are not included in the training sets of SVMProt. They represent proteins whose function cannot be confidently predicted by sequence similarity methods at present. The predicted functional class of 76.7% of each of these proteins shows various levels of consistency with the literature-described function, compared to the overall accuracy of 87% for the SVMProt functional class assignment of 34,582 proteins that have at least one homolog of known function. Our study suggests that SVMProt is capable of assigning functional class for novel bacterial proteins at a level not too much lower than that of sequence alignment methods for homologous proteins.     

Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach

The function of a protein that has no sequence homolog of known function is difficult to assign on the basis of sequence similarity. The same problem may arise for homologous proteins of different functions if one is newly discovered and the other is the only known protein of similar sequence. It is desirable to explore methods that are not based on sequence similarity. One approach is to assign functional family of a protein to provide useful hint about its function. Several groups have employed a statistical learning method, support vector machines (SVMs), for predicting protein functional family directly from sequence irrespective of sequence similarity. These studies showed that SVM prediction accuracy is at a level useful for functional family assignment. But its capability for assignment of distantly related proteins and homologous proteins of different functions has not been critically and adequately assessed. Here SVM is tested for functional family assignment of two groups of enzymes. One consists of 50 enzymes that have no homolog of known function from PSI-BLAST search of protein databases. The other contains eight pairs of homologous enzymes of different families. SVM correctly assigns 72% of the enzymes in the first group and 62% of the enzyme pairs in the second group, suggesting that it is potentially useful for facilitating functional study of novel proteins. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.     

A probabilistic framework to predict protein function from interaction data integrated with semantic knowledge

Background  

The functional characterization of newly discovered proteins has been a challenge in the post-genomic era. Protein-protein interactions provide insights into the functional analysis because the function of unknown proteins can be postulated on the basis of their interaction evidence with known proteins. The protein-protein interaction data sets have been enriched by high-throughput experimental methods. However, the functional analysis using the interaction data has a limitation in accuracy because of the presence of the false positive data experimentally generated and the interactions that are a lack of functional linkage.     

Rampant Exchange of the Structure and Function of Extramembrane Domains between Membrane and Water Soluble Proteins

Of the membrane proteins of known structure, we found that a remarkable 67% of the water soluble domains are structurally similar to water soluble proteins of known structure. Moreover, 41% of known water soluble protein structures share a domain with an already known membrane protein structure. We also found that functional residues are frequently conserved between extramembrane domains of membrane and soluble proteins that share structural similarity. These results suggest membrane and soluble proteins readily exchange domains and their attendant functionalities. The exchanges between membrane and soluble proteins are particularly frequent in eukaryotes, indicating that this is an important mechanism for increasing functional complexity. The high level of structural overlap between the two classes of proteins provides an opportunity to employ the extensive information on soluble proteins to illuminate membrane protein structure and function, for which much less is known. To this end, we employed structure guided sequence alignment to elucidate the functions of membrane proteins in the human genome. Our results bridge the gap of fold space between membrane and water soluble proteins and provide a resource for the prediction of membrane protein function. A database of predicted structural and functional relationships for proteins in the human genome is provided at sbi.postech.ac.kr/emdmp.     

Functional integrative levels in the human interactome recapitulate organ organization

Interactome networks represent sets of possible physical interactions between proteins. They lack spatio-temporal information by construction. However, the specialized functions of the differentiated cell types which are assembled into tissues or organs depend on the combinatorial arrangements of proteins and their physical interactions. Is tissue-specificity, therefore, encoded within the interactome? In order to address this question, we combined protein-protein interactions, expression data, functional annotations and interactome topology. We first identified a subnetwork formed exclusively of proteins whose interactions were observed in all tested tissues. These are mainly involved in housekeeping functions and are located at the topological center of the interactome. This ‘Largest Common Interactome Network’ represents a ‘functional interactome core’. Interestingly, two types of tissue-specific interactions are distinguished when considering function and network topology: tissue-specific interactions involved in regulatory and developmental functions are central whereas tissue-specific interactions involved in organ physiological functions are peripheral. Overall, the functional organization of the human interactome reflects several integrative levels of functions with housekeeping and regulatory tissue-specific functions at the center and physiological tissue-specific functions at the periphery. This gradient of functions recapitulates the organization of organs, from cells to organs. Given that several gradients have already been identified across interactomes, we propose that gradients may represent a general principle of protein-protein interaction network organization.     

Structural and functional characterization of gene products encoded in the human genome by homology detection

Availability of the human genome data has enabled the exploration of a huge amount of biological information encoded in it. There are extensive ongoing experimental efforts to understand the biological functions of the gene products encoded in the human genome. However, computational analysis can aid immensely in the interpretation of biological function by associating known functional/structural domains to the human proteins. In this article we have discussed the implications of such associations. The association of structural domains to human proteins could help in prioritizing the targets for structure determination in the structural genomics initiatives. The protein kinase family is one of the most frequently occurring protein domain families in the human proteome while P-loop hydrolase, which comprises many GTPases and ATPases, is a highly represented superfamily. Using the superfamily relationships between families of unknown and known structures we could increase structural information content of the human genome by about 5%. We could also make new associations of domain families to 33 human proteins that are potentially linked to genetically inherited diseases.     

The role of peptide motifs in the evolution of a protein network

Naturally occurring proteins in cellular networks often share peptide motifs. These motifs have been known to play a pivotal role in protein interactions among the components of a network. However, it remains unknown how these motifs have contributed to the evolution of the protein network. Here we addressed this issue by a synthetic biology approach. Through the motif programming method, we have constructed an artificial protein library by mixing four peptide motifs shared among the Bcl-2 family proteins that positively or negatively regulate the apoptosis networks. We found one strong pro-apoptotic protein, d29, and two proteins having moderate, but unambiguous anti-apoptotic functions, a10 and d16, from the 28 tested clones. Thus both the pro- and anti-apoptotic modulators were present in the library, demonstrating that functional proteins with opposing effects can emerge from a single pool prepared from common motifs. Motif programming studies have exhibited that the annotated function of the motifs were significantly influenced by the context that the motifs embedded. The results further revealed that reshuffling of a set of motifs realized the promiscuous state of protein, from which disparate functions could emerge. Our finding suggests that motifs contributed to the plastic evolvability of the protein network.     

Calcium-binding proteins: basic concepts and clinical implications.

Calcium ions exert their effects in part via interactions with a wide variety of intracellular calcium-binding proteins. One class of these proteins shares a common calcium-binding motif, the EF-hand. A consensus amino acid sequence for this motif has aided the identification of new members of this family of EF-hand proteins, which now has over 200 members. A few of these proteins are present in all cells, whereas the vast majority are expressed in a tissue-specific fashion. The physiological function of a few of these proteins is known to be achieved via a calcium-dependent interaction with other proteins, thereby regulating their activity. Some members, like parvalbumin, calbindin, and calretinin, proved to be useful neuronal markers for a variety of functional brain systems and their circuitries. Their major role is assumed to be buffering, transport of Ca2+, and regulation of various enzyme systems. Since cellular degeneration is accompanied by impaired Ca2+ homeostasis, a protective role for Ca(2+)-binding proteins in certain neuron populations has been postulated. Another protein family are the annexins, members of which interact with phospholipids and cellular membranes in a calcium-dependent manner. In some cases members of the annexin family were even found to interact with EF-hand proteins. Certain annexins have been suggested to be involved in anti-inflammatory response, inhibition of blood coagulation, membrane trafficking or cytoskeletal organization, but several of these functions have been questioned recently. The elucidation of the interactions and functions of the majority of these proteins remains a challenging task for the coming years.     

Respiratory metabolism: glycolysis, the TCA cycle and mitochondrial electron transport

The respiratory pathways of glycolysis, the tricarboxylic acid (TCA) cycle and the mitochondrial electron transport chain are ubiquitous throughout nature. They are essential for both energy provision in heterotrophic cells and a wide range of other physiological functions. Although the series of enzymes and proteins that participate in these pathways have long been known, their regulation and control are much less well understood. Further complexity arises due to the extensive interaction among these pathways in particular, and also between cytosolic and mitochondrial metabolism in general. These interactions include those between mitochondrial function in the photosynthetic and photorespiratory processes, amino-acid biosynthesis and the regulation of cellular redox. Recently, a wide range of molecular and biochemical strategies have been adopted to elucidate the functional significance of these interactions.     

Systematic interpretation of genetic interactions using protein networks

Genetic interaction analysis,in which two mutations have a combined effect not exhibited by either mutation alone, is a powerful and widespread tool for establishing functional linkages between genes. In the yeast Saccharomyces cerevisiae, ongoing screens have generated >4,800 such genetic interaction data. We demonstrate that by combining these data with information on protein-protein, prote in-DNA or metabolic networks, it is possible to uncover physical mechanisms behind many of the observed genetic effects. Using a probabilistic model, we found that 1,922 genetic interactions are significantly associated with either between- or within-pathway explanations encoded in the physical networks, covering approximately 40% of known genetic interactions. These models predict new functions for 343 proteins and suggest that between-pathway explanations are better than within-pathway explanations at interpreting genetic interactions identified in systematic screens. This study provides a road map for how genetic and physical interactions can be integrated to reveal pathway organization and function.     

Specificity in Computational Protein Design

A long-standing goal of computational protein design is to create proteins similar to those found in Nature. One motivation is to harness the exquisite functional capabilities of proteins for our own purposes. The extent of similarity between designed and natural proteins also reports on how faithfully our models represent the selective pressures that determine protein sequences. As the field of protein design shifts emphasis from reproducing native-like protein structure to function, it has become important that these models treat the notion of specificity in molecular interactions. Although specificity may, in some cases, be achieved by optimization of a desired protein in isolation, methods have been developed to address directly the desire for proteins that exhibit specific functions and interactions.     

Prediction of functional class of novel plant proteins by a statistical learning method

In plant genomes, the function of a substantial percentage of the putative protein-coding open reading frames (ORFs) is unknown. These ORFs have no significant sequence similarity to known proteins, which complicates the task of functional study of these proteins. Efforts are being made to explore methods that are complementary to, or may be used in combination with, sequence alignment and clustering methods. A web-based protein functional class prediction software, SVMProt, has shown some capability for predicting functional class of distantly related proteins. Here the usefulness of SVMProt for functional study of novel plant proteins is evaluated. To test SVMProt, 49 plant proteins (without a sequence homolog in the Swiss-Prot protein database, not in the SVMProt training set, and with functional indications provided in the literature) were selected from a comprehensive search of MEDLINE abstracts and Swiss-Prot databases in 1999-2004. These represent unique proteins the function of which, at present, cannot be confidently predicted by sequence alignment and clustering methods. The predicted functional class of 31 proteins was consistent, and that of four other proteins was weakly consistent, with published functions. Overall, the functional class of 71.4% of these proteins was consistent, or weakly consistent, with functional indications described in the literature. SVMProt shows a certain level of ability to provide useful hints about the functions of novel plant proteins with no similarity to known proteins.     

Semantic and layered protein function prediction from PPI networks

Background

The past few years have seen a rapid development in novel high-throughput technologies that have created large-scale data on protein-protein interactions (PPI) across human and most model species. This data is commonly represented as networks, with nodes representing proteins and edges representing the PPIs. A fundamental challenge to bioinformatics is how to interpret this wealth of data to elucidate the interaction of patterns and the biological characteristics of the proteins. One significant purpose of this interpretation is to predict unknown protein functions. Although many approaches have been proposed in recent years, the challenge still remains how to reasonably and precisely measure the functional similarities between proteins to improve the prediction effectiveness.

Results

We used a Semantic and Layered Protein Function Prediction (SLPFP) framework to more effectively predict unknown protein functions at different functional levels. The framework relies on a new protein similarity measurement and a clustering-based protein function prediction algorithm. The new protein similarity measurement incorporates the topological structure of the PPI network, as well as the protein’s semantic information in terms of known protein functions at different functional layers. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed framework in predicting unknown protein functions.

Conclusion

The proposed framework has a higher prediction accuracy compared with other similar approaches. The prediction results are stable even for a large number of proteins. Furthermore, the framework is able to predict unknown functions at different functional layers within the Munich Information Center for Protein Sequence (MIPS) hierarchical functional scheme. The experimental results demonstrated that the new protein similarity measurement reflects more reasonably and precisely relationships between proteins.     

Topological structure analysis of the protein-protein interaction network in budding yeast

Interaction detection methods have led to the discovery of thousands of interactions between proteins, and discerning relevance within large-scale data sets is important to present-day biology. Here, a spectral method derived from graph theory was introduced to uncover hidden topological structures (i.e. quasi-cliques and quasi-bipartites) of complicated protein-protein interaction networks. Our analyses suggest that these hidden topological structures consist of biologically relevant functional groups. This result motivates a new method to predict the function of uncharacterized proteins based on the classification of known proteins within topological structures. Using this spectral analysis method, 48 quasi-cliques and six quasi-bipartites were isolated from a network involving 11,855 interactions among 2617 proteins in budding yeast, and 76 uncharacterized proteins were assigned functions.     

T-cell ubiquitin ligand affects cell death through a functional interaction with apoptosis-inducing factor, a key factor of caspase-independent apoptosis

The lymphoid protein T-cell ubiquitin ligand (TULA)/suppressor of T-cell receptor signaling (Sts)-2 is associated with c-Cbl and ubiquitylated proteins and has been implicated in the regulation of signaling mediated by protein-tyrosine kinases. The results presented in this report indicate that TULA facilitates T-cell apoptosis independent of either T-cell receptor/CD3-mediated signaling or caspase activity. Mass spectrometry-based analysis of protein-protein interactions of TULA demonstrates that TULA binds to the apoptosis-inducing protein AIF, which has previously been shown to function as a key factor of caspase-independent apoptosis. Using RNA interference, we demonstrate that AIF is essential for the apoptotic effect of TULA. Analysis of the subcellular localization of TULA and AIF together with the functional analysis of TULA mutants is consistent with the idea that TULA enhances the apoptotic effect of AIF by facilitating the interactions of AIF with its apoptotic co-factors, which remain to be identified. Overall, our results shed new light on the biological functions of TULA, a recently discovered protein, describing its role as one of very few known functional interactors of AIF.     

To the 30-nm chromatin fiber and beyond

Chromatin fibers are intrinsically dynamic macromolecular complexes whose biological functions are intimately linked with their structure and interactions with chromatin-associated proteins (CAPs). Three-dimensional architectural transitions between or within the two co-existing chromatin types referred to as euchromatin and heterochromatin have been associated with activation or repression of nuclear functions. The presence of specific subsets of chromosomal proteins co-existing with the different chromatin conformations suggests a functional significance for their co-localization. The major points of emphasis of this review will assess the structure, function and recently documented exchanges amongst various members of the CAP family.     

Proteomics of boar seminal plasma - current studies and possibility of their application in biotechnology of animal reproduction

Proteomics is critical to identify the properties and functions of proteins involved in the mechanism regulating the male reproductive tract function. This approach is important in male fertility assessment and clinical diagnosis of the physiological state of individual reproductive organs. Proteomics also provides a tool to understand the interactions of seminal plasma proteins with spermatozoa, which could provide a useful model for studying ligand-cell interaction occurring at the sperm cell surface. This review covers a selection of advances in the realm of functional proteomics of boar seminal plasma proteins and is focused on some fundamental proteomic technologies. Also, this review explores key themes in proteomics and their application in animal reproductive techniques.