Proteomic approaches for generating comprehensive protein interaction maps |
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| Affiliation: | 1. Dualsystems Biotech, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland;2. Institute of Veterinary Biochemistry and Molecular Biology, University of Zurich-Irchel, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland;1. Key Laboratory of Mechanism and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin 300072, China;2. School of Engineering and Technology, China University of Geosciences, Beijing 100084, China;3. State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China;1. Winogradsky Institute of Microbiology, Research Centre of Biotechnology, Russian Academy of Sciences, Moscow, Russia;2. Department of Biotechnology, Section of Environmental Biotechnology, TU Delft, The Netherlands;3. IAMC-CNR, Spianata S.Raineri 86, 98122 Messina, Italy;4. NIOZ Royal Netherlands Institute for Sea Research, Department of Marine Microbiology and Biogeochemistry, and Utrecht University, PO Box 59, 1790 AB Den Burg, Texel, The Netherlands;5. Faculty of Geosciences, Department of Earth Sciences, Utrecht University, Utrecht, The Netherlands;1. Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA;2. Department of Medicine, Division of Cardiovascular Medicine, Cardiovascular Institute and Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA;3. Biomedical Informatics Training Program and Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA;4. Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA;5. Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA;6. Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA;7. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA;8. New York Genome Center, New York, NY 10013, USA;9. Department of Computer Science, Columbia University, New York, NY 10027, USA;10. Department of Pediatrics and Adolescent Medicine, Division of Pediatric Endocrinology, Ulm University, Ulm 89075, Germany;11. Department of Medicine, Division of Cardiology and Cardiovascular Institute, Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA;12. Department of Pathology and Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA |
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| Abstract: | The availability of complete genome sequences of numerous model organisms has initiated the development of new approaches in biological research to complement conventional biochemistry and genetics. In this context, high-throughput methods for detecting protein interactions, such as mass spectrometry and yeast two-hybrid assays, have produced vast amounts of data that can be exploited to infer protein function and regulation. In this review, we explore different genome-wide protein interaction studies and comment on their extrapolation towards understanding protein functions. It is likely that improvements of these approaches, together with more sophisticated databases and the invention of novel technologies, will help to decipher the complex interactions among proteins and to integrate interacting proteins into existing and novel cellular pathways. |
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