Computational Prediction of Protein Complexes from Protein Interaction Networks. Sriganesh Srihari
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of protein complex prediction may be just one of the plethora of computational problems that have opened up since the deluge of proteomics (protein-protein interaction; PPI) data over the last several years. However, in reality this problem encompasses or directly relates to several important and open problems in the area—in particular, the fundamental problems of modeling, visualizing, and denoising of PPI networks, prediction of PPIs (novel as well as evolutionarily conserved), and protein function prediction from PPI data. Therefore, to write a comprehensive self-contained book, we had to cover even these closely related problems to some extent or at least allude to or reference them in the book. We had to do so without missing the connection between these problems and our central problem of protein complex prediction in the book.
The early tone to write the book in this manner was set by our review article in a 2015 special issue of FEBS Letters, where we covered a number of protein complex prediction methods which are based on a diverse range of topological, functional, temporal, structural, and evolutionary information. However, being only a single-volume article, the description of the methods was brief, and to compile this description in the form of a book we had to delve a lot deeper into the algorithmic underpinnings of each of the methods, highlight how each method utilized the information (topological, functional, temporal, structural, and evolutionary) on which it was based in its own unique way, and evaluate and study the applications of the methods across a diverse range of datasets and scenarios. To do this well, we had to: (i) cover in substantial detail the preliminaries such as the experimental techniques available to infer PPIs, the limitations of each of these techniques, PPI network topology, modeling, and denoising, PPI databases that are available, and how functional, temporal, structural, and evolutionary information of proteins can be integrated with PPI networks; and (ii) we had to categorize protein complex prediction methods into logical groups based on some criteria, and dedicate a separate chapter for each group to make our description comprehensive. In the book, we cover (i) in Chapter 2 and in the form of independent sections within each of the other Chapters 3, 5, 6, 7, and 8. We cover (ii) by allocating Chapters 3 and 4 for “classical” methods and their comprehensive evaluation, Chapter 5 for methods that predict certain kinds of “challenging complexes” which the classical methods do not predict well, Chapter 6 for methods that utilize temporal and structural information, Chapter 7 for methods that utilize information on evolutionary conservation, and Chapter 8 for methods that integrate other kinds of omics datasets to predict “specialized” complexes—e.g., protein complexes in diseases.
The requirement for a book exclusively dedicated to the problem of protein complex prediction from PPI networks at this point in time cannot be understated. Over the last two decades, a major focus of high-throughput experimental technologies and of computational methods to analyze the generated data has been in genomics—e.g., in the analysis of genome sequencing data. It is relatively recently that this focus has started to shift toward proteomics and computational methods to analyze proteomics data. For example, while the complete sequence of the human genome was assembled more than a decade ago, it is only over the last three years that there have been similar large-scale efforts to map the human proteome. The ProteomicsDB (http://www.proteomicsdb.org/), Human Proteome Map (http://humanproteomemap.org/), and the Human Protein Atlas projects (http://www.proteinatlas.org/) have all appeared only over the last three years. Similarly is The Cancer Proteome Atlas (TCPA) project (http://app1.bioinformatics.mdanderson.org/tcpa/_design/basic/index.html) which complements The Cancer Genome Atlas project (TCGA). This means that developing more effective solutions for fundamental problems such as protein complex prediction has become all the more important today, as we try to apply these solutions to larger and more complex datasets arising from these newer technologies and projects. In this respect, we had to write the book not just by considering protein complex prediction methods (i.e., their algorithmic details) as important in their own right but also by giving significant importance to the applications of these methods in the light of today’s complex datasets and research questions. There are several sections within each of the Chapters 6, 7, and 8 that play this dual role, e.g., a section in Chapter 7 discusses the evolutionary conservation of core cellular processes based on conservation patterns of protein complexes, and a section in Chapter 8 discusses the dysregulation of these processes in diseases based on rewiring of protein complexes between normal and disease conditions.
In the end, we hope that we have done justice to what we intend this book to be. We hope that this book provides valuable insights into protein complex prediction and inspires further research in the area especially for tackling the open challenges, as well as inspires new applications in diverse areas of biomedicine.
Acknowledgments
Although this book is primarily concerned with the problem of protein complex prediction, the book also covers several other aspects of PPI networks. We would like to therefore dedicate this book to the students—Honors, Masters, and Ph.D. students—who worked on these different aspects of PPI networks by being part of the computational biology group at the Department of Computer Science, National University of Singapore, over the years. Several of the methods covered in this book are a result of the extensive research conducted by these students. Sriganesh would like to thank Hon Wai Leong (Professor of Computer Science, National University of Singapore) under whom he conducted his Ph.D. research on protein complex prediction; Mark Ragan (Head of Division of Genomics of Development and Disease at Institute for Molecular Bioscience, The University of Queensland) under whom he conducted his postdoctoral research, a substantial portion of which was on identifying protein complexes in diseases; and Kum Kum Khanna (Senior Principal Research Fellow and Group Leader at QIMR-Berghofer Medical Research Institute) whose guidance played a significant part in his understanding of biological aspects of protein complexes. Sriganesh is grateful to Mark for passing him an original copy of a 1977 volume of Progress in Biophysics and Molecular Biology in which G. Rickey Welch makes a consistent principled argument that “multienzyme clusters” are advantageous to the cell and organism because they enable metabolites to be channeled within the clusters and protein expression to be co-regulated [Welch 1977]—a possession which Sriganesh will deeply cherish. Chern Han would like to thank his coauthors: Sriganesh for doing the heavy lifting in writing, editing, and driving this project and Limsoon Wong for guiding him through his Ph.D. journey on protein complexes. He would also like to acknowledge the support of Bin Tean Teh (Professor with Program in Cancer and Stem Cell Biology, Duke-NUS Medical School), who currently oversees his postdoctoral research. Limsoon would like to acknowledge Chern Han and Sriganesh for doing the bulk of the writing for this book, and especially thank Sriganesh for taking the overall lead on the project. When he suggested the book to Chern Han and Sriganesh, he had not imagined that he would eventually be a co-author.
We are indebted also to the Editor-in-Chief of ACM Books, Tamer Özsu, Executive Editor Diane Cerra, Production Manager Paul C. Anagnostopoulos, and the entire