Increasing Confidence of Protein Interactomes

Participants: Jin Chen, Hon Nian Chua, Wynne Hsu, Mong Li Lee, Haiquan Li, Jinyan Li, Guimei Liu, See-Kiong Ng, Wing-Kin Sung, Chris Tan, Limsoon Wong

Background

Progress in high-throughput experimental techniques in the past decade has resulted in a rapid accumulation of protein-protein interaction (PPI) data. However, recent surveys reveal that interaction data obtained by the popular high-throughput assays such as yeast-two-hybrid experiments may contain as much as 50% false positives and false negatives. As a result, further carefully-focused small-scale experiments are often needed to complement the large-scale methods to validate the detected interactions. However, the vast interactomes require much more scalable and inexpensive approaches.

Thus it would be useful if the list of protein-protein interactions detected by such high-throughput assays could be prioritized in some way. Advances in computational techniques for assessing the reliability of protein-protein interactions detected by such high-throughput methods are explored in this project, especially those rely only on topological information of the protein interaction network derived from such high-throughput experiments.

Objectives

In this project, we have the following goals:

Identify properties that characterize true-positive and false-positive PPIs, such as properties that are abstract mathematical characteristics of networks of reliable PPIs and those that are explicit motifs associated with true-positive PPIs.
Develop efficient and effective methods for assessing the reliability of PPIs reported in high-throughput assays.
Develop efficient and effective methods for identifying false-negative PPIs in high-throughput assays.

At the end of the project, we expect to have developed a robust and powerful system to postprocessing results of high-throughput PPI assays, yielding a more reliable protein interactome.

Selected Publications

Jin Chen, Wynne Hsu, Mong Li Lee, See-Kong Ng. Systematic Assessment of High-Throughput Experimental Data for Reliable Protein Interactions using Network Topology. Proceedings of 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pages 368--372, Florida, 15-17 November 2004.
Jin Chen, Wynne Hsu, Mong Li Lee, and See-Kiong Ng. Discovering Reliable Protein Interactions from High-Throughput Experimental Data Using Network Topology. Artificial Intelligence in Medicine, 35:37-47, 2005. PDF
Jin Chen, Wynne Hsu, Mong Li Lee, See-Kiong Ng. Increasing Confidence of Protein Interactomes Using Network Topological Metrics. Bioinformatics, 22:1998--2004, 2006. PDF
Soon-Heng Tan, Willy Hugo, Wing-Kin Sung, See-Kiong Ng. A Correlated Motif Approach for Finding Short Linear Motifs from Protein Interaction Networks. BMC Bioinformatics, 7:502, 2006. PDF
Jin Chen, Wynne Hsu, Mong Li Lee, See-Kiong Ng. NeMoFinder: Dissecting Genome-Wide Protein-Protein Interactions with Repeated and Unique Network Motifs. Proceedings of 12th ACM SIGKDD Interactional Conference on Knowledge Discovery and Data Mining (KDD), pages 106--115, Philadelphia, August 2006.
Jin Chen, Hon Nian Chua, Wynne Hsu, Mong-Li Lee, See-Kiong Ng, Rintaro Saito, Wing-Kin Sung, Limsoon Wong. Increasing Confidence of Protein-Protein Interactomes. Proceedings of 17th International Conference on Genome Informatics (GIW), pages 284--297, Yokohama, Japan, 18-20 December 2006. (invited keynote paper) PDF, FSWeight V2.0 Software
Jin Chen, Wynne Hsu, Mong Li Lee, See-Kiong Ng. Labeling Network Motifs in Protein Interactomes for Protein Function Prediction. Proceedings of 23rd International Conference on Data Engineering (ICDE), pages 546--555, Istanbul, Turkey, April 2007.
Haiquan Li, Jinyan Li, Soon-Heng Tan, See-Kiong Ng. Discovery of Binding Motif Pairs from Protein Complex Structural Data and Protein Interaction Sequence Data. Proceedings of 9th Pacific Symposium for Biocomputing (PSB), pages 312-323, Hawaii, 6-10 January 2004.
Haiquan Li, Jinyan Li. Discovery of Stable and Significant Binding Motif Pairs from PDB Complexes and Protein Interaction Datasets. Bioinformatics, 21:314-324, 2005. PDF
Haiquan Li, Jinyan Li, Limsoon Wong. Discovering Motif Pairs at Interaction Sites from Protein Sequences on a Proteome-Wide Scale. Bioinformatics, 22(8):989--996, 2006. PDF
Jinyan Li, Haiquan Li. Using Fixed Point Theorems to Model the Binding in Protein-Protein Interactions. IEEE Transactions on Knowledge and Data Engineering, 17:1079-1087, 2005.
Soon-Heng Tan, Wing-Kin Sung, See-Kiong Ng. An Automated Approach for Protein Motif Discovery Using Interaction-Driven Motif Mining. Proceedings of 2nd International Conference on Computer Science and Its Applications (ICCSA), pages 224-232, San Diego, 28-30 June 2004.
Soon-Heng Tan, Wing-Kin Sung, See-Kiong Ng. Discovering Novel Interacting Motif Pairs from Large Protein-Protein Interaction Datasets. Proceedings of 4th IEEE Symposium of Bioinformatics and Bioengineering (BIBE), pages 568-575, 19-21 May 2004.

Dissertations

Jin Chen. Systematic Assessment of Protein Interaction Data Using Graph Topology Approaches. PhD thesis, Dept of Computer Science, National University of Singapore, Singapore, 2007.
Haiquan Li. Efficient Discovery of Binding Motif Pairs from Protein-Protein Interactions. PhD thesis, Dept of Computer Science, National University of Singapore, Singapore, 2006.

Selected Presentations

Haiquan Li, Jinyan Li, Limsoon Wong. Binding Motif Pairs from Interacting Protein Groups. Invited talk at Workshop on Data Analysis and Data Mining in Proteomics, Institute for Mathematical Sciences, NUS, Singapore, 12 May 2005.
See-Kiong Ng. Unraveling the Common Denominators in Protein-Protein Interaction Networks. Invited keynote at International Symposium on Computational Biology and Bioinformatics (ISBB06), Bhubaneshwar, India, 15-17 December 2006.
See-Kiong Ng. Unraveling Multi-Domain Dependencies in Protein-Protein Interactions. Invited keynote at 1st International Conference on Computational Systems Biology (ICCSB-2006), Shanghai, 20-23 July 2006.
See-Kiong Ng. Uncovering the Biological Building Blocks for Protein Interaction Networks. Invited plenary at 8th National Symposium on Biology, Malaysia, 5-7 December 2006.
See-Kiong Ng. Computational Purification of Protein Interactomes Using Network Topological Metrics. Invited talk at BIOINFO2005 AASBi Conference, Busan, Korea, 23 September 2005.
See-Kiong Ng. Detecting False Positives and False Negatives in Protein Interactome using Network Topology. Invited talk at NTU BIRC Workshop on Computational Analysis of Proteomics Data, Nanyang Technological University, Singapore, 16 June 2005.
See-Kiong Ng. Detecting False Positives and False Negatives in Protein Interactome using Network Topology. Invited talk at IMS Workshop on Data Analysis and Data Mining in Proteomics, Institute for Mathematical Sciences, NUS, Singapore, 12 May 2005.
Limsoon Wong. Assessing Reliability of Protein-Protein Interaction Experiments. Invited talk at Lilly Systems Biology Symposium, BioPolis, Singapore, 4 February 2004. PPT
Limsoon Wong. Assessing Reliability of Protein-Protein Interaction Experiments. Invited talk at 3rd International Conference on Bioinformatics, Auckland, New Zealand, 4-8 September 2004.
Limsoon Wong. Assessing Reliability of Protein-Protein Interaction Experiments. Invited talk at 5th HUGO Pacific Meeting and 6th Asia-Pacific Meeting on Human Genetics, BioPolis, Singapore, 17-20 November 2004.
Limsoon Wong. Assessing Reliability of Protein-Protein Interaction Experiments. Invited keynote at 3rd Korea-Singapore Joint Workshop on Bioinformatics and Natural Language Processing, Muju Resort, 20-22 February 2005.
Limsoon Wong. Assessing Reliability of Protein-Protein Interaction Experiments. Invited talk at Changchun International Bioinformatics Workshop, Changchun, Jilin, China, 5-7 July 2005. PPT
Limsoon Wong. Increasing Confidence of Protein-Protein Inteactomes. Keynote talk at 17th International Conference on Genome Informatics, Yokohama, Japan, 18-20 December 2006. PPT

Acknowledgements

This project is supported in part by a A*STAR AGS scholarship (Chua: 8/03 - 7/07), and the I²R-SOC Joint Lab on Knowledge Discovery from Clinical Data (7/03 - 6/07).

Last updated: 2/2/09, Limsoon Wong.