Such fusion events typically bring together proteins involved in the same function or process, presumably for reasons of improved efficiency and regulation Yanai et al. Finding a fusion protein within a reference genome, and assuming that selective pressure is required for such a fusion event to occur, leads to the prediction that the two component.
Individual proteins, A and B, from one genome can often be found as a single fused protein, C, in another genome.
The finding of such a fused protein suggests that protein A and B interact either physically or functionally. This is the basis for the gene fusion or Rosetta Stone method Enright et al. Of the fused pairs with known function, most were metabolic enzymes. In the work of Marcotte et al. In this manner, they identified protein pairs in E.
In the second approach, they used nonoverlapping regions of high sequence similarity rather than domains, and for E. It is interesting to note that most pairs could be identified by only one of these approaches, with only pairs identified by both methods.
Prediction accuracy was assessed by comparing annotations finding annotation keywords shared by both proteins , database searches looking for experimental evidence of the interaction within appropriate databases , and phylogenetic profiles described below.
The total accuracy was estimated to be on the order of. By filtering out promiscuous domains, for instance the SH2 domains which are known to be present in many unrelated proteins, the total number of predicted interactions in E. Protein Phylogenetic Profiles If proteins are functionally linked and thus involved as a group in a particular process, pathway, or structure, it may be expected that their evolution would also be linked; specifically, their pattern of inheritance would be identicali.
For instance, one would expect the protein components of a flagellum to be inherited together, with loss of one or more components resulting in a nonfunctional structure. This pattern of inheritance is the basis of the phylogenetic profile method, and was first used for generating profiles for all E.
With this method, a profile is created for each protein in a target genome. As depicted in Figure 8. The absence or presence of a homolog to the protein in each of the surveyed genomes is marked in the string with a zero or one, respectively.
After profiles have been generated for each protein, the proteins are clustered together according to the similarity of their profiles. Proteins having identical or nearly identical profiles Pellegrini et al. Genomes G1 to G6 are searched for the absence 0 or presence 1 of proteins P1 to P6.
Genes with identical profiles, or perhaps differing at a single position, can be linked into functionally related groups. As one example of the accuracy of this method, the profile for the ribosomal protein RL7 was studied. Four other proteins across 16 genomes were found to have identical profiles, with three of the four being known to have ribosome-associated function.
There were 27 profiles that differed by a single bit. Of these, 15 were also known to have function related to RL7. This approach was also evaluated as part of a study attempting to predict protein function on a genomic scale Marcotte et al.
Using S. A promising aspect of this method is that as the number of completely sequenced genomes increases, the number of unique profiles grows exponentially. For n complete genomes, there are 2n possible profiles, rapidly increasing the discriminative power of this approach. In addition, with the expected significant growth in the number of eukaryotic organisms sequenced, the applicability of this method will grow significantly.
A disadvantage of this method is the large number of false positives that are often generated. However, recent work by Barker and Pagel has improved on this basic approach. Coevolution and Correlation of Phylogenetic Distances The previous phylogenetic profile method is based on the idea of a coevolutionary process where the pattern of inheritance of certain sets.
Similarly, at the sequence level, coevolutionary processes have also been proposed as occurring between interacting protein pairs. While a number of methods have been developed for the comparison of phylogenetic trees, it turns out that only the simplest approach has generally been adopted, which involves the comparison not of the trees, but rather their underlying distance matrices. Specifically, the Pearson correlation coefficient between distance matrices is calculated, with high correlations indicating high degrees of similarity and hence coevolution.
This approach has since undergone additional development, with a major improvement being the subtraction of the inherent similarity between trees that arises from the fact that the members of the two protein families being compared are each drawn from the same set of branches from the tree of life tol; Pazos et al. This inherent similarity is corrected by subtracting background correlations between 16S rRNA orthologs from. A Trees or sequence alignments of two possibly interacting protein families are first generated along with the 16S ribosomal RNA sequence alignments for the same taxa.
B Distance matrices are generated from the alignments with tree-of-life distances subtracted from the distance matrices in the case of the tol-mirrortree approach and the correlation C between matrices determined, typically, using the Pearson correlation coefficient. Similarly, use of partial correlation has been suggested for such corrections Sato et al. A general schematic of the tol-mirrortree approach is shown in Figure 8. This approach has also been applied to the coevolution of protein domains Jothi et al.
Recently, Yeang and. Haussler developed a full continuoustime Markov process model describing sequence coevolution and used it to detect coevolution within and between protein domains. Probabilistic Prediction of Interaction Networks Due to the increased availability and use of high-throughput methods, there has been a rapid rise in the amount of experimental protein-protein interaction data available for the study of molecular systems.
As the quantity and quality of these data grow, methods capable of extracting useful information are becoming increasingly valuable. As a result, a. The use of protein features is based on the assumption that in order for a protein interaction to occur, at least one pair of featuresi. Features can be anything from stretches of identical charge to structural domains or motifs e. Implicit in this assumption is that features are basic units of protein functioni. As a result, knowledge of feature pairs that are known to interact in one species is potentially transferable to similar pairs found in another.
Likewise, online databases such as the Database of Interacting Proteins DIP and the Biomolecular Interaction Network Database BIND , combined with published datasets extracted from multiple sources, have greatly facilitated the development of this approach Xenarios et al. As noted, several groups have made investigations using such data types. To give a better idea of how at least one of these methods work, the approach described by Gomez et al.
The results described here are generally relevant to other methods e. A probabilistic model The method described here is a probabilistic one, and is based on the representation of a protein network as a graph, with proteins. It consists of two components, one for assigning a probability to each edge between proteins a local property , and one for generating a probability for each particular shape a global property, namely the particular arrangement of edges connecting all proteins that the network can take.
These two components can be combined, basically through multiplication of their respective probabilities, to give the final probability of any particular network. In practice, approaches such as this one use a large set of interaction data, often called a training set, in the generation of model parameters.
After training, predictions of interactions for a new set of proteins can then be made. As the first step of this approach, component domains are found for each protein in the network Fig. Next, for each proteinconnecting edge, counts are taken of every unique domain-domain interaction.
In the end, what is produced is a matrix of counts detailing how many times a domain of type X was found in an interaction with a domain of type Y. This matrix of counts can now be converted into a matrix of domain-domain probabilities through a variety of methods.
An important assumption in this model is that, in the absence of any data, an edge between any two proteins is possible. Specifically, it is supposed that the initial or prior probability of interaction between any two domains is equal to 0. As counts of particular domain-domain interactions increase, the probability of a particular interaction moves away from 0. In the conversion of each element of the count matrix into a probability, if a particular domain-domain interaction has never been observed, this assumption requires that a 0.
This is done under the assumption that if one observes a pair of domains occurring in many proteins that are thought not to interact, then the domain pair is actually predictive with regard to the absence of an interaction, thus lowering the probability below 0. Given a set of protein interactions, all individual domain-domain interactions are extracted and counted.
After training, counts are converted into probabilities of domain-domain interaction as well as proteinprotein interaction. In the second stage, network topology is incorporated to improve predictions. See text for details. Probabilities between 0. Probabilities less than 0. While not a detailed description of the first part of the model, it should be clear that it is now possible to use protein features and protein interaction data together to generate predictions.
Given a set of proteins with domains, but without knowledge of any associations, a probability can now be assigned to all possible interactions between them based on knowledge extracted from the training data. A domain-pair that is enriched within the training data will provide greater support for an interaction between a new pair of proteins sharing the domain-pair, increasing its probability above 0.
As will be discussed later, increasing the amount and quality of both interaction and domain data is an important factor in the accuracy and coverage of predictions.
By itself, the process just described can be used to predict protein-protein interactions, assigning probabilities to all possible pair-wise interactions. However, a unique aspect of this approach is the addition of information concerning the structure or topology of the network into the generation of predictions. Here, topology refers to the shape of the network be-. This distribution gives the probability P k of a protein having k edges or interactions.
When this is plotted in log-log coordinates with the number of edges on the x axis and probability on the y axis, it becomes apparent that the plot is essentially linear with a negative slope Fig. This distribution suggests that the majority of proteins will have very few connections, while a very small percentage will be very highly connected.
What is interesting is that this particular type of distributiona powerlaw distributionhas been found for a number of biological e. It also implies that networks of this type can be characterized as being scale-free i. If a subnetwork is extracted out of a much larger network, the connectivity distribution will look identical for each.
Protein interaction networks have also been shown to be scale-free, and thus share these as well as other properties Gomez et al. Alone, this topology information can be used as a guide in predictions by giving higher probabilities to those networks that look more biologically realistic, thus helping to filter erroneous predictions, especially with regard to false positives.
This is particularly important since, as is probably becoming quite evident, all methods are capable of generating.
The majority of proteins will have few interactions left end of the x axis ; however, a few will be highly connected right end. The inclusion of topology into this model is one way to reduce the noise generated by these errors and focus predictions onto those networks that are more biologically relevant. Finally, it is possible to combine the probabilities of a group of interactions with that of a network topology so that a probability for the complete network can be generated.
As a result, different hypothesized networks, consisting of both known and predicted edges, can be directly compared with more likely ones chosen for further investigation. Prediction This approach was tested by attempting to predict S.
While more interaction data were available, the amount used here was limited due to a number of factors, including the requirement that both proteins in an interaction used either for training or predictions must have at least one domain. In addition, the number of domains that can be found in these data is dependent on the cutoff threshold used. The effectiveness of this technique was assessed with the use of cross-validation, a common and extremely useful technique used for.
In cross-validation, the data set is generally broken into equally sized subsets, or folds, with all but one of these folds being used for training. Predictions are then performed on the single remaining fold. In an iterative manner, predictions are made for each fold. The accuracy of predictions described here was assessed using leave-one-out cross-validation, where all but one edge from the data are used for training, and then a prediction is made as to whether the remaining edge exists or not.
All edges are predicted in turn, and afterwards the total accuracy is assessed. The use of negative information was found to greatly improve predictions, with ROC scores improving to 0. In addition, if one of the proteins had been observed as having other interactions before i. Note that it cannot be said for sure if all false positives are actually. Summary Limitations of this approach arise primarily from the fact that at this time, not all proteins have identifiable domains that can be used as features Gomez and Rzhetsky, As a result, only a portion of available interaction data can be used for training.
Also, predictions can only be made for those proteins that have at least one domain. To bypass this issue, different types of features capable of providing better coverage are currently being investigated. In addition, while growing rapidly, interaction data are only now becoming of sufficient quantity that high-confidence predictions can be made.
As it does, however, the quality of predictions should improve rapidly. This approach is readily applicable to eukaryotic genomes and can integrate data derived from different sources into a single prediction.
A major benefit of this approach is that it provides a probability for any given interaction. A researcher can instantly identify the relative strength of predictions and then decide which are worth investigating further. In addition, since this approach is probabilistic in nature, it is quite easy to integrate additional information into the prediction.
For instance, knowledge of the localization of a protein to particular regions of a cell can help improve predictions; if it is known that two proteins are found in the same subcellular compartment, the probability of an interaction should increase, or at least stay the same.
Also, given the variable accuracy of current interaction data, a probabilistic framework provides a natural mechanism for dealing with these uncertainties. Thus, in an effort to make more accurate and comprehensive predictions, recent efforts have focused on the problem of how to combine multiple types of genomic data into a single consensus prediction.
Note that within these various data types are both direct and indirect information regarding protein interactions. As an example of indirect information, it has been shown that interacting proteins often show a high degree of coexpression.
Thus, correlations in gene-expression data can also be predictive of protein interactions. As another example, yeast two-hybrid data, while providing direct information regarding protein interactions, are known to be a noisy data type prone to large numbers of false positives Sprinzak et al. In the rest of this section, we give a brief overview of three data integration approaches that have been used in the prediction of protein interactions: the Bayes classifier Jansen et al.
One of the earliest efforts to predict protein interactions through the integration of different data types was performed by Jansen et al. Separately, four high-throughput interaction datasets consisting of yeast two-hybrid and in vivo pull-down experiments were integrated with a fully connected Bayesian network which does not assume independence between datasets.
Finally, both sets were again integrated through another Nave Bayes network. Results from this work indicate that evidence for an interaction arising from any single data source did not have sufficient weight or sufficiently high likelihood to be predictedi. On the other hand, interactions were predicted for the combined genomic features data set, with another interactions predicted from the integrated high-throughput experiments. Since then, similar studies have been carried out on the prediction of protein interactions by integrating different genomic features using Bayesian approaches.
For example, Lu et al. Rhodes et al. Scott and Barton also conducted a similar human study by integrating different evidences including orthology, functional associations, and local network topology. By using each evidence itself, the ROC ranges from 0 to 0.
However, by integrating different evidences, the most accurate predictor has a much higher ROC of 0. Non-Bayesian data integration models are also of interest and have the often desirable property of not needing any prior information or discretization of the raw genomic data.
Bock and Gough combined known protein interactions collected from different experiments using a support vector machine SVM classifier to predict protein interactions based on primary structure and associated physicochemical properties.
Gomez et al. Later, Ben-Hur and Noble proposed to predict protein-protein interactions in yeast by combing data sources including protein sequences, Gene Ontology annotations, local properties of the network, and homologous interactions using different SVM kernels.
Decision tree approaches are another widely used non-Bayesian data integration method, and the importance of different data types can be easily assessed through these methods. In the work of Zhang et al. Lin et al. They showed that, although computationally more expensive, the random forest method had better performance over the logistic regression and BN approaches. With their aid, researchers may be better able to cut away extraneous or otherwise confusing information, focusing in on the most relevant aspects of a given process.
Highly promising predictions can be followed up with direct experimentation. A number of challenges currently exist, however, not the least of which is properly assessing the accuracy of these approaches. Which method is best? It must be emphasized that determining the effectiveness of any single method is often an extremely difficult task.
Generally, large amounts of trusted, experimentally verified interaction data are not available at this time. Also, deciding whether a predicted interaction is in fact real is often impossible without further experimental work. Some of these challenges are highlighted by von Mering et al.
Using all methods, and evaluating predictions with a trusted set of yeast protein complexes as a reference, they found over 80, potential interactions.
However, only 2, were supported by more than one method. Computational methods were extremely competitive with experimental approaches. Thus, an important observation that should be made is that none of these methods are exclusive. In fact, it should be assumed that it is necessary to use multiple, complementary methods. Different approaches have different biases, and these can be used to maximize the coverage of predictions. Similarly, understanding these biases will aid in the accurate assessment of the reliability of predictions.
Thus the data integration approaches discussed earlier will take on an increasingly important role in future methodologies. These results also highlight the importance of rigorous validation on appropriate test data. If the performance of a method can be well characterized on a test data set, it is much easier to assess the confidence of predictions on novel data, as well as to compare predictions from different methods. Managing tradeoffs in performance will also be assisted.
For example, increasing the accuracy of predictions will have the effect of decreasing the coverage, with fewer total predictions being generated. Great emphasis is currently being placed on attempting to understand how biological systems regulate and control their behavior. Understanding this regulation requires a deeper appreciation for the relationships between genes, proteins, and other cellular components.
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