Complexes of physically interacting proteins constitute fundamental functional units that drive almost all biological processes within cells. A faithful reconstruction of the entire set of protein complexes (the "complexosome") is therefore important not only to understand the composition of complexes but also the higher level functional organization within cells. Advances over the last several years, particularly through the use of high-throughput proteomics techniques, have made it possible to map substantial fractions of protein interactions (the "interactomes") from model organisms including Arabidopsis thaliana (a flowering plant), Caenorhabditis elegans (a nematode), Drosophila melanogaster (fruit fly), and Saccharomyces cerevisiae (budding yeast). These interaction datasets have enabled systematic inquiry into the identification and study of protein complexes from organisms. Computational methods have played a significant role in this context, by contributing accurate, efficient, and exhaustive ways to analyze the enormous amounts of data. These methods have helped to compensate for some of the limitations in experimental datasets including the presence of biological and technical noise and the relative paucity of credible interactions.
In this book, we systematically walk through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). We present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios including the absence of many true interactions and the presence of false-positive interactions (noise) in PPI networks. Based on this evaluation, we highlight challenges faced by the methods, for instance in identifying sparse, sub-, or small complexes and in discerning overlapping complexes, and reveal how a combination of strategies is necessary to accurately reconstruct the entire complexosome.
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978-1-970001-54-9 (9781970001549)
Schweitzer Klassifikation
Sriganesh Srihari is a Senior Research Fellow with the Institute for Molecular Bioscience at The University of Queensland, Australia. He has a background in computer science (having received a Ph.D. in 2012 from National University of Singapore) and has worked extensively on graph (network) and combinatorial algorithms and in applying these to large omics datasets in biomedicine. He has devised systems-biology models to integrate "multiomics" datasets spanning genomics, RNAseq, and proteomics (protein-protein interaction) with clinical profiles to decipher molecular-clinical associations and identify new therapeutic targets in cancers. He has published in leading journals in the field including Bioinformatics, BMC Systems Biology, Biology Direct, Molecular Biosystems, and Nucleic Acids Research. He has closely collaborated with experimental biologists and has contributed to joint publications in Oncogene (Nature Publishing), Trends in Pharmacological Sciences (Cell Press), and Molecular Oncology. His postdoctoral work on cancer network models was highlighted in International Innovation (Healthcare issue, 2014), a Research Media periodical. His recent computational approach MutExSL (Biology Direct, 2015), co-authored with Limsoon Wong, for predicting synthetic-lethal targets by mining mutually exclusive genetic alterations in cancers was presented at the San Antonio Breast Cancer Symposium 2015 (San Antonio, Texas, USA), for which he won an American Association for Cancer Research (AACR)-Susan G.Komen for the Cure(R) Scholar-in-training Award. He serves on the Editorial Board for the cancer bioinformatics theme of Scientific Reports, and is a Guest Editor for Methods. Srihari has recently moved to the South Australian Health and Medical Research Institute, Australia, as a Senior Research Scientist. He is also an Adjunct Senior Lecturer with the School of Computer Science, Engineering, and Mathematics at Flinders University, Australia.
Table of Contents: Preface / 1. Introduction to Protein Complex Prediction / 2. Constructing Reliable Protein-Protein Interaction (PPI) Networks / 3. Computational Methods for Protein Complex Prediction from PPI Networks / 4. Evaluating Protein Complex Prediction Methods / 5. Open Challenges in Protein Complex Prediction / 6. Identifying Dynamic Protein Complexes / 7. Identifying Evolutionarily Conserved Protein Complexes / 8. Protein Complex Prediction in the Era of Systems Biology / 9. Conclusion / References / Authors' Biographies
