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Interaction Profile-Based Protein Classification of Death Domain

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2004
Abstract: 

Background: The increasing number of protein sequences and 3D structure obtained fromgenomic initiatives is leading many of us to focus on proteomics, and to dedicate our experimentaland computational efforts on the creation and analysis of information derived from 3D structure.In particular, the high-throughput generation of protein-protein interaction data from a feworganisms makes such an approach very important towards understanding the molecularrecognition that make-up the entire protein-protein interaction network. Since the generation ofsequences, and experimental protein-protein interactions increases faster than the 3D structuredetermination of protein complexes, there is tremendous interest in developing in silico methodsthat generate such structure for prediction and classification purposes. In this study we focused onclassifying protein family members based on their protein-protein interaction distinctiveness.Structure-based classification of protein-protein interfaces has been described initially by Ponstinglet al. [1] and more recently by Valdar et al. [2] and Mintseris et al. [3], from complex structures thathave been solved experimentally. However, little has been done on protein classification based onthe prediction of protein-protein complexes obtained from homology modeling and dockingsimulation.Results: We have developed an in silico classification system entitled HODOCO (Homologymodeling, Docking and Classification Oracle), in which protein Residue Potential InteractionProfiles (RPIPS) are used to summarize protein-protein interaction characteristics. This systemapplied to a dataset of 64 proteins of the death domain superfamily was used to classify eachmember into its proper subfamily. Two classification methods were attempted, heuristic andsupport vector machine learning. Both methods were tested with a 5-fold cross-validation. Theheuristic approach yielded a 61% average accuracy, while the machine learning approach yielded an89% average accuracy.Conclusion: We have confirmed the reliability and potential value of classifying proteins via theirpredicted interactions. Our results are in the same range of accuracy as other studies that classifyprotein-protein interactions from 3D complex structure obtained experimentally. While ourclassification scheme does not take directly into account sequence information our results are inagreement with functional and sequence based classification of death domain family members.

Document type: 
Article

A High-Accuracy Nonintrusive Networking Testbed for Wireless Sensor Networks

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2010
Abstract: 

It becomes increasingly important to obtain the accurate and spontaneous runtime network behavior for further studies onwireless sensor networks. However, the existing testbeds cannot appropriately match such requirements. A High-accuracyNonintrusive Networking Testbed (HINT) is proposed. In HINT, the interconnected chip-level signals are passively captured withauxiliary test boards and the captured data are transferred in additional networks to test server. The test server of HINT collects allthe test data and depicts the full network behavior. HINT supports networking test, protocol verification, performance evaluationand so on. The experiments show that HINT transparently gathers accurate runtime data and does not disturb the spontaneousbehavior of sensor networks. HINT is also extendible to different hardware platforms of sensor nodes. Consequently, HINT isan upstanding testbed solution for the future fine-grained and experimental studies on the resource-constrained wireless sensornetworks.

Document type: 
Article

Relationship between Insertion/Deletion (Indel) Frequency of Proteins and Essentiality

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2007
Abstract: 

Background: In a previous study, we demonstrated that some essential proteins from pathogenicorganisms contained sizable insertions/deletions (indels) when aligned to human proteins of highsequence similarity. Such indels may provide sufficient spatial differences between the pathogenicprotein and human proteins to allow for selective targeting. In one example, an indel difference wastargeted via large scale in-silico screening. This resulted in selective antibodies and smallcompounds which were capable of binding to the deletion-bearing essential pathogen proteinwithout any cross-reactivity to the highly similar human protein. The objective of the current studywas to investigate whether indels were found more frequently in essential than non-essentialproteins.Results: We have investigated three species, Bacillus subtilis, Escherichia coli, and Saccharomycescerevisiae, for which high-quality protein essentiality data is available. Using these data, wedemonstrated with t-test calculations that the mean indel frequencies in essential proteins weregreater than that of non-essential proteins in the three proteomes. The abundance of indels in bothtypes of proteins was also shown to be accurately modeled by the Weibull distribution. However,Receiver Operator Characteristic (ROC) curves showed that indel frequencies alone could not beused as a marker to accurately discriminate between essential and non-essential proteins in thethree proteomes. Finally, we analyzed the protein interaction data available for S. cerevisiae andobserved that indel-bearing proteins were involved in more interactions and had greaterbetweenness values within Protein Interaction Networks (PINs).Conclusion: Overall, our findings demonstrated that indels were not randomly distributed acrossthe studied proteomes and were likely to occur more often in essential proteins and those thatwere highly connected, indicating a possible role of sequence insertions and deletions in theregulation and modification of protein-protein interactions. Such observations will provide newinsights into indel-based drug design using bioinformatics and cheminformatics tools.

Document type: 
Article

The Optimizability-Fidelty Trade-Off in Image Analysis

Author: 
Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2011-09-01
Abstract: 

Image analysis problems (e.g. segmentation and registration) are typically formulated in an optimization framework (energy-minimization). In this work, I present two important recurring questions: What to optimize and how to optimize and expose the resulting tradeoffs between the fidelity (of the domain and range) of the objective function and its optimizability.

Document type: 
Video