Koc University, Center for Computational Biology and Bioinformatics and College of Engineering, Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey; Basic Research Program, SAIC−Frederick, Inc., Center for Cancer Research Nanobiology Program, NCIsFrederick, Frederick, Maryland 21702; and Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel

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Principles of Protein−Protein Interactions: What are the Preferred Ways For Proteins To Interact?

Compared to the available protein sequences of different organisms, the number of revealed protein– protein interactions (PPIs) is still very limited. So many computational methods have been developed to facilitate the identification of novel PPIs. However, the methods only using the information of protein sequences are more universal than those that depend on some additional information or predictions about the proteins. In this article, a sequence-based method is proposed by combining a new feature representation using auto covariance (AC) and support vector machine (SVM). AC accounts for the interactions between residues a certain distance apart in the sequence, so this method adequately takes the neighbouring effect into account. When performed on the PPI data of yeast Saccharomyces cerevisiae, the method achieved a very promising prediction result. An independent data set of 11 474 yeast PPIs was used to evaluate this prediction model and the prediction accuracy is 88.09%. The performance of this method is superior to those of the existing sequence-based methods, so it can be a useful supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at http://www. scucic.cn/Predict_PPI/index.htm.

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Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences

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.

Contact: xwchen@ku.edu

Availability: Source code is written in Java and is available upon request from the authors.

Supplementary information: http://www.ittc.ku.edu/~xwchen/PPI/random_forest_PPI

/pdf/prediction-of-protein-protein-interactions-using-random-wimu1fqb9m.pdf

Prediction of protein--protein interactions using random decision forest framework

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.

https://escholarship.org/content/qt5v86989p/qt5v86989p.pdf?t=nbc3jd

Geometric De-noising of Protein-Protein Interaction Networks

The molecular architecture of protein–protein binding sites

With the accumulation of protein and its related data on the Internet, many domain-based computational techniques to predict protein interactions have been developed. However, most techniques still have many limitations when used in real fields. They usually suffer from low accuracy in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we propose a probabilistic framework to predict the interaction probability of proteins and develop an interaction possibility ranking method for multiple protein pairs. Using the ranking method, one can discern the protein pairs that are more likely to interact with each other in multiple protein pairs. The validity of the prediction model was evaluated using an interacting set of protein pairs in yeast and an artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in the DIP (Database of Interacting Proteins) was used as a learning set of interacting protein pairs, high sensitivity (77%) and specificity (95%) were achieved for the test groups containing common domains with the learning set of proteins within our framework. The stability of the prediction model was also evident when tested over DIP CORE, HMS-PCI and TAP data. In the validation of the ranking method, we reveal that some correlations exist between the interacting probability and the accuracy of the prediction.

PreSPI: a domain combination based prediction system for protein–protein interaction

With the recognition of the importance of computational approach for protein-protein interaction prediction, many techniques have been developed to computationally predict protein-protein interactions. However, few techniques are actually implemented and announced in service form for general users to readily access and use the techniques. In this paper, we design and implement a protein interaction prediction service system based on the domain combination based protein-protein interaction prediction technique, which is known to show superior accuracy to other conventional computational protein-protein interaction prediction methods. In the prediction accuracy test of the method, high sensitivity (77%) and specificity (95%) are achieved for test protein pairs containing common domains with learning sets of proteins in a Yeast. The stability of the method is also manifested through the testing over DIP CORE, HMS-PCI, and TAP data. The functions of the system are divided into core, subsidiary, and general service function categories. The core function category includes the functions that can be provided only by using the domain combination based protein-protein interaction prediction method. Interaction prediction for a single protein pair and visualization of interaction probability distributions are the functions in this category. The subsidiary function category includes the functions that can be derived from the core functions. Domain combination pair search with high appearance probability and construction of protein interaction network are the functions in this category. Lastly, the general service function category includes the functions that can be implemented by collecting and organizing the protein and domain data in the Internet. Performance, openness and flexibility are the major design goals and they are achieved by adopting parallel execution techniques, Web Services standards, and layered architecture respectively. In this paper, several representative user interfaces of the system are also introduced with comprehensive usage guides.

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PreSPI: design and implementation of protein-protein interaction prediction service system.

With the accumulation of protein and its related data on the Internet, many domain based computational techniques to predict protein interactions have been developed. However most of the techniques still have many limitations to be used in real fields. They usually suffer from low accuracy problem in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we reevaluate a domain combination based protein interaction prediction method and develop an interaction possibility ranking method for multiple protein pairs. Using the ranking method, one can discern which protein pair is more probable to interact with each other than other protein pairs in multiple protein pairs. In the reevaluation, we have found that the accuracy of the prediction is improved as the size of non-interacting set of protein pairs is increased. When the size of non-interacting set of protein pairs is increased to 20 times bigger than that of interacting set of protein pairs in learning sets, 84% sensitivity and 75% specificity were achieved in yeast organism. In the validation of the ranking method, we revealed that there exist some correlations between the interacting probability and the accuracy of the prediction in case of the protein pair group having the matching PIP values in the interacting or non-interacting PIP distributions.

Domain combination based protein-protein interaction possibility ranking method

In this paper, we propose adaptive QoS(Quality of Service) control mechanism and a architecture using multiagent framework to improve flexibility of a videoconference system. The proposed mechanism realizes more flexibility by changing their QoS control strategies dynamically. We implemented the mechanism, and our prototype system shows its capability of flexible problem solving against the QOS degradation, along with other possible problems within the given time limitation.

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Multiagent based adaptive QoS control mechanism in flexible videoconference system

In this paper, we propose a feedback based framework for semi-automatic composition of Web services. In the framework, whenever a service process is constructed by composing Web services, the connection patterns of Web services in the service process are analyzed and registered in terms of data and control flows. Also a method of measuring the degree of coupling between Web services is devised within the framework. Semi-automatic composition of Web services is possible in the framework because the framework can recommend which Web services are more tightly coupled with each other using the method. When one highlights a Web service in a service domain, the next and the previous connectable Web services are listed according to the degree of coupling with the Web services.

Sung-Doke Lee

Papers

PreSPI: a domain combination based prediction system for protein–protein interaction

PreSPI: design and implementation of protein-protein interaction prediction service system.

Domain combination based protein-protein interaction possibility ranking method

Multiagent based adaptive QoS control mechanism in flexible videoconference system

A feedback based framework for semi-automic composition of web services