PROTEIN-PROTEIN INTERACTIONS –COMPUTATIONAL AND EXPERIMENTAL TOOLS doc

PROTEIN-PROTEIN INTERACTIONS – COMPUTATIONAL AND EXPERIMENTAL TOOLS Edited by Weibo Cai and Hao Hong Protein-Protein Interactions – Computational and Experimental Tools Edited by Weibo Cai and Hao Hong Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2012 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. As for readers, this license allows users to download, copy and build upon published chapters even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. Notice Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Marina Jozipovic Technical Editor Teodora Smiljanic Cover Designer InTech Design Team First published March, 2012 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechopen.com Protein-Protein Interactions – Computational and Experimental Tools, Edited by Weibo Cai and Hao Hong p. cm. ISBN 978-953-51-0397-4 Contents Preface IX Part 1 Computational Approaches 1 Chapter 1 Computational Methods for Prediction of Protein-Protein Interaction Sites 3 Aleksey Porollo and Jaroslaw Meller Chapter 2 Advances in Human-Protein Interaction - Interactive and Immersive Molecular Simulations 27 Nicolas Férey, Alex Tek, Benoist Laurent, Marc Piuzzi, Zhihan Lu, Marc Baaden, Olivier Delalande, Matthieu Chavent, Christine Martin, Lorenzo Piccinali, Brian Katz, Patrick Bourdot and Ludovic Autin Chapter 3 Protein Interactome and Its Application to Protein Function Prediction 65 Woojin Jung, Hyun-Hwan Jeong, and KiYoung Lee Chapter 4 Integrative Approach for Detection of Functional Modules from Protein-Protein Interaction Networks 97 Zelmina Lubovac-Pilav Chapter 5 Mining Protein Interaction Groups 113 Lusheng Wang Chapter 6 Prediction of Combinatorial Protein-Protein Interaction from Expression Data Based on Conditional Probability 131 Takatoshi Fujiki, Etsuko Inoue, Takuya Yoshihiro and Masaru Nakagawa Chapter 7 Inferring Protein-Protein Interactions (PPIs) Based on Computational Methods 147 Shuichi Hirose VI Contents Chapter 8 Slow Protein Conformational Change, Allostery and Network Dynamics 169 Fan Bai, Zhanghan Wu, Jianshi Jin, Phillip Hochendoner and Jianhua Xing Chapter 9 Prediction of Protein Interaction Sites Using Mimotope Analysis 189 Jian Huang, Beibei Ru and Ping Dai Chapter 10 Structural Bioinformatics of Proteins: Predicting the Tertiary and Quaternary Structure of Proteins from Sequence 207 J. Planas-Iglesias, J. Bonet, M.A. Marín-López, E. Feliu, A. Gursoy and B. Oliva Chapter 11 Computational Approaches to Predict Protein Interaction 231 Darby Tien-Hao Chang Chapter 12 G-Protein Coupled Receptors: Experimental and Computational Approaches 247 Amirhossein Sakhteman, Hamid Nadri and Alireza Moradi Chapter 13 Computational Approaches to Elucidating Transient Protein-Protein Interactions, Predicting Receptor-Ligand Pairings 259 Ernesto Iacucci, Samuel Xavier de Souza and Yves Moreau Chapter 14 Finding Protein Complexes via Fuzzy Learning Vector Quantization Algorithm 273 Hamid Ravaee, Ali Masoudi-Nejad and Ali Moeini Part 2 Experimental Approaches 285 Chapter 15 In Vivo Imaging of Protein-Protein Interactions 287 Hao Hong, Shreya Goel and Weibo Cai Chapter 16 NMR Investigations on Ruggedness of Native State Energy Landscape in Folded Proteins 305 Poluri Maruthi Krishna Mohan Chapter 17 Conformational and Disorder to Order Transitions in Proteins: Structure / Function Correlation in Apolipoproteins 331 José Campos-Terán, Paola Mendoza-Espinosa, Rolando Castillo and Jaime Mas-Oliva Chapter 18 Protein-Protein Interactions in Salt Solutions 359 Jifeng Zhang Contents VII Part 3 Others Chapter 19 Computational Tools and Databases for the Study and Characterization of Protein Interactions 379 Jose Ramon Blas, Joan Segura and Narcis Fernandez-Fuentes Chapter 20 Protein-Protein Interaction Networks: Structures, Evolution, and Application to Drug Design 405 Takeshi Hase and Yoshihito Niimura Chapter 21 A Survey on Evolutionary Analysis in PPI Networks 427 Pavol Jancura and Elena Marchiori Chapter 22 Scalable, Integrative Analysis and Visualization of Protein Interactions 457 David Otasek, Chiara Pastrello and Igor Jurisica Preface Proteins are indispensable players in virtually all biological events. The functions of proteins are coordinated through intricate regulatory networks of transient protein- protein interactions (PPIs). To predict and/or study PPIs, a wide variety of techniques have been developed over the last several decades. Many in vitro and in vivo assays have been implemented to explore the mechanism of these ubiquitous interactions. However, despite significant advances in these experimental approaches, many limitations exist such as false-positives/false-negatives, difficulty in obtaining crystal structures of proteins, challenges in the detection of transient PPI, among others. To overcome these limitations, many computational approaches have been developed which are becoming increasingly widely used to facilitate the investigation of PPIs. To provide a centralized resource for scientists who are either new to or working in the area of PPIs, we have organized this book. An international ensemble of experts in the field were invited to contribute a total of 22 chapters, which have been broadly categorized into Computational Approaches, Experimental Approaches, and Others. The section of “Computational Approaches” contains 14 chapters. In the first chapter, Dr. Porollo and Dr. Meller gave an excellent review of the computational methods for the prediction of protein interaction sites, which were mainly focused on structure- based approaches. Next, an international team of experts from France, United Kingdom, and USA summarized the recent advances that are related to interactive molecular simulation approaches. Simulation design, software architectures, and applications in protein-protein docking were all discussed in exquisite detail. The following chapter, written by Jung et al. from the Republic of Korea, reviewed the PPI data available through public databases. Both non-network-based and network-based approaches were discussed, along with computational prediction methods of protein subcellular localization by exploiting the PPI data. Dr. Lubovac-Pilav from Sweden focused on defining the similarity between protein interactions based on an integrated score. The SWEMODE (Semantic WEights for MODule Elucidation) algorithm was discussed in detail in this chapter. Next, Dr. Wang from Hong Kong, China introduced the use of quasi-bicliques for finding interacting protein group pairs and proposed approximation and heuristic algorithms for finding large quasi-bicliques in PPI networks. In the following chapter, Fujiki et al. from Japan focused on the interactions among three proteins. The X Preface combinatorial effect level, which emerges only when those three proteins gather, was derived and estimated in a fully statistical manner. Dr. Hirose provided an excellent review on PPI prediction by computational techniques. The concepts and applications of several methods for inferring PPIs were covered, along with the databases and prediction methods that deal with protein flexibility, as well as the possibility of inferring PPIs from protein dynamics. Prof. Xing and co-workers presented a unified mathematical formalism describing both conformational change and chemical reactions of proteins. The implications of slow conformational changes in protein allostery and network dynamics were also discussed in this chapter. Next, Prof. Huang and colleagues reviewed the methods for prediction of PPI sites using mimotope analysis. The current status, as well as the challenges and future directions of the field, were summarized. Prof. Oliva from Spain covered the strategies for modeling the interaction between two proteins from sequence data and reviewed the existing techniques to model large cellular protein complexes. In the next chapter, Dr. Chang focused on the concept of co-occurrence pattern and implementation details of methods in PPI prediction based on this concept. Sakhteman et al. from Iran gave an overview on the biochemistry details of G-Protein coupled receptors (GPCRs) and provided information on homology modeling and molecular dynamic simulation methods for studying interactions involving GRPRs. Next, Dr. Iacucci and Dr. Moreau from Belgium evaluated the application of least square support vector machines (LS-SVM) to receptor-ligand interaction prediction and discussed various other methods to study PPIs, most of which relying on the phylogenetic profile analysis of candidate interactors. In the last chapter of this section, Ravaee et al. from Iran introduced the fuzzy learning vector quantization (FLVQ) as a high tolerant method for clustering PPI network to find protein complexes, which is less vulnerable to false-negative and false-positive interactions in PPI data than other techniques. Although computational simulation is a powerful tool for studying PPIs, novel experimental approaches for investigating PPIs that can overcome the limitations of existing techniques are continuously been developed. Such techniques represent a vibrant area of research on PPIs. In the section of “Experimental Approaches”, the current state-of-the-art experimental strategies to study PPIs are presented in four chapters. Molecular imaging, an extremely powerful tool to study molecular events in living subjects, can provide invaluable information and insight in elucidating the process of various PPIs. In the first chapter of this section, we summarized the current status of in vivo imaging of PPIs with various techniques, including fluorescence, bioluminescence, and positron emission tomography imaging. Next, Dr. Mohan illustrated the theoretical aspects of non-linear behavior of amide proton chemical shifts. In this chapter, he demonstrated the residue level nuclear magnetic resonance [...]... Russell, 2004; Ritchie, 2008) Limitations of experimental techniques and attempts to circumvent the problem by focusing directly on protein interactions create an opportunity for computational approaches to complement and facilitate experimental efforts in that regard In particular, 4 Protein-Protein Interactions – Computational and Experimental Tools statistical and machine learning-based approaches are... Eddy, S R., Sonnhammer, E L., and Bateman, A (2008) The Pfam protein families database Nucleic Acids Res 36, D281-288 24 Protein-Protein Interactions – Computational and Experimental Tools Fiorucci, S., and Zacharias, M (2010) Prediction of protein-protein interaction sites using electrostatic desolvation profiles Biophys J 98, 1921-1930 Fletcher, S., and Hamilton, A D (2007) Protein-protein interaction... protein interactions and other functional Computational Methods for Prediction of Protein-Protein Interaction Sites 21 annotations, see e.g., (Kniazeff et al., 2002; Shenoy et al., 2006) and (He et al., 2003; Lietha et al., 2007), for ET and ConSurf, respectively 7 Discussion and conclusions Protein-protein interactions are essential for enzymatic functions, signal transduction, cell cycle regulation and. .. mean or weighted average over a sequence window 14 Protein-Protein Interactions – Computational and Experimental Tools The structure-based methods, on the other hand, also utilize features derived from a 3D protein structure, such as solvent accessibility and secondary structure states, local topology (e.g., protrusions and cavities), hydrophobic and polar surface patches, temperature or Bfactors (for... However, experimental identification and validation of a protein interface remains a challenging task, both in terms of labor and cost Therefore, efforts to map and characterize protein interactions can considerably benefit from computational biology and structural bioinformatics In particular, methods that integrate sequence and structure information achieved accuracies that are useful in selecting and. .. other proteins can help direct and streamline mutagenesis and other experimental studies, and to facilitate efforts to map entire interactomes It can also reduce the levels of false positives (by assessing compatibility between predicted interfaces), and false negatives (by helping identify novel interactions) observed for experimental approaches that are used to map protein interactions Another promising... Gilliland, G., Bhat, T N., Weissig, H., Shindyalov, I N., and Bourne, P E (2000) The Protein Data Bank Nucleic Acids Res 28, 235-242 Bock, J R., and Gough, D A (2001) Predicting protein protein interactions from primary structure Bioinformatics 17, 455-460 Bordner, A J., and Abagyan, R (2005) Statistical analysis and prediction of protein-protein interfaces Proteins 60, 353-366 Bradford, J R., and Westhead,... methods used it for their own and comparative evaluation.(de Vries and Bonvin, 2011; de Vries et al., 2006; Fiorucci and Zacharias, 2010; Guharoy and Chakrabarti, 2010; Li et al., 2008; Liu and Zhou, 2009; Qin and Zhou, 2007; Zhou and Qin, 2007) However, a thorough analysis of this benchmark set led us to conclusion that it is not suitable for evaluation of the methods predicting protein-protein interaction... role of solvent in protein-protein and proteinDNA recognition Structure 7, R277-279 Jones, S., and Thornton, J M (1995) Protein-protein interactions: a review of protein dimer structures Prog Biophys Mol Biol 63, 31-65 Jones, S., and Thornton, J M (1997) Analysis of protein-protein interaction sites using surface patches J Mol Biol 272, 121-132 Kim, W K., Henschel, A., Winter, C., and Schroeder, M (2006)... available (PDBID_ChainID: 3dpa_A and 2tld_I) These cases may be challenging to prediction methods that rely on high resolution data with all atoms resolved The last benchmark set to be used in this work is the control set of the SPPIDER method.(Porollo and Meller, 2007) It was compiled based on the protein complexes 12 Protein-Protein Interactions – Computational and Experimental Tools deposited in PDB after . PROTEIN-PROTEIN INTERACTIONS – COMPUTATIONAL AND EXPERIMENTAL TOOLS Edited by Weibo Cai and Hao Hong Protein-Protein Interactions – Computational and Experimental. copies can be obtained from orders@intechopen.com Protein-Protein Interactions – Computational and Experimental Tools, Edited by Weibo Cai and Hao Hong p. cm. ISBN 978-953-51-0397-4 . Mendoza-Espinosa, Rolando Castillo and Jaime Mas-Oliva Chapter 18 Protein-Protein Interactions in Salt Solutions 359 Jifeng Zhang Contents VII Part 3 Others Chapter 19 Computational Tools and Databases