Protein-protein interactions (PPIs) play a central role in all biological processes. Akin to the complete sequencing of genomes, complete descriptions of interactomes is a fundamental step towards a deeper understanding of biological processes, and has a vast potential to impact systems biology, genomics, molecular biology and therapeutics. Although high-throughput biochemical approaches for discovering PPIs have proven very successful, the current experimental coverage of the interactome remains inadequate and would benefit from computational tools. The broad, long term goal of this proposal is to harness the information provided by structure-based computational approaches as a potentially high-quality, high-coverage data source for large-scale integrative approaches to interactome construction. Specifically, this project aims to: 1) develop new structure-based prediction methods that can be applied on a genome scale, and 2) integrate these predictions with other functional genomic information to predict PPIs at a genome scale. This project will also generate testable hypotheses for experimental investigations. A key product of the proposed research is the LTHREADER program, a localized threading program that will simultaneously align query sequence-pairs to templates of protein-protein interfaces. By exploiting information contained in the protein complex interfaces, it may significantly improve upon the state-of-the-art in coverage and prediction quality. Some of the core computational aspects are the development of algorithms for threading query sequence-pairs to templates (using linear programming), learning statistical potentials (SVMs), and combining multiple protein interface scores for PPI prediction (boosting). The output from such structure-based approaches will be combined with other functional genomic data in the Struct2Net framework for predicting PPIs (using random forests). A final product of the proposed research will be a comprehensive database of genome-wide PPI predictions derived from purely structure-based as well as integrative approaches. The database will also include extracellular ligand-receptor interactions. The prediction of PPIs will enable better elucidation of extracellular and intracellular signaling networks, which has direct medical implications in terms of drug target identification. For example, a promising public-health application of this research is the rational design of therapeutics which inhibit or interfere with the binding of extracellular ligands to receptors. All the produced computational algorithms, software, and databases will be made publicly available for further studies.Relevance Proteins interact with each other to communicate within and between cells, forming networks (the Interactome) that play fundamental roles in all biomedical processes including the maintenance of cellular integrity, metabolism, transcription/translation, and cell-cell communication. Understanding these interaction networks on a large scale will empower both rational, targeted drug design and more intelligent disease management. In this project, we develop computational methods for structure-based prediction of protein-protein interactions, and integrate these predictions with available high- throughput genomic data to predict the Interactomes of entire species'genomes.
| Cho, Hyunghoon; Berger, Bonnie; Peng, Jian (2018) Generalizable visualization of mega-scale single-cell data. Res Comput Mol Biol 10812:251-253 |
| Liu, Yang; Palmedo, Perry; Ye, Qing et al. (2018) Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks. Cell Syst 6:65-74.e3 |
| Ordovas-Montanes, Jose; Dwyer, Daniel F; Nyquist, Sarah K et al. (2018) Allergic inflammatory memory in human respiratory epithelial progenitor cells. Nature 560:649-654 |
| Bepler, Tristan; Morin, Andrew; Noble, Alex J et al. (2018) Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Res Comput Mol Biol 10812:245-247 |
| Hie, Brian; Cho, Hyunghoon; Berger, Bonnie (2018) Realizing private and practical pharmacological collaboration. Science 362:347-350 |
| Cho, Hyunghoon; Berger, Bonnie; Peng, Jian (2018) Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks. Cell Syst 7:185-191.e4 |
| Orenstein, Yaron; Ohler, Uwe; Berger, Bonnie (2018) Finding RNA structure in the unstructured RBPome. BMC Genomics 19:154 |
| Khurana, Vikram; Peng, Jian; Chung, Chee Yeun et al. (2017) Genome-Scale Networks Link Neurodegenerative Disease Genes to ?-Synuclein through Specific Molecular Pathways. Cell Syst 4:157-170.e14 |
| Orenstein, Yaron; Kim, Ryan; Fordyce, Polly et al. (2017) Joker de Bruijn: Sequence Libraries to Cover All k-mers Using Joker Characters. Res Comput Mol Biol 10229:389-390 |
| Orenstein, Yaron; Puccinelli, Robert; Kim, Ryan et al. (2017) Optimized Sequence Library Design for Efficient In Vitro Interaction Mapping. Cell Syst 5:230-236.e5 |
Showing the most recent 10 out of 50 publications
