Structures in Systems Biology Pedro Beltrao1, Christina Kiel2 and Luis Serrano2
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Structures in systems biology Pedro Beltrao1, Christina Kiel2 and Luis Serrano2 Oil and water do not normally mix, and apparently Understanding the properties of a cell by abstracting the structural biology and systems biology look like two functionally relevant behaviors from the underlying cel- different universes. It can be argued that structural lular components is the very objective of systems analysis. biology could play a very important role in systems biology. To study a cell, we should be able to work with proteins as Although at the final stage of understanding a signal dots that are produced and degraded, that diffuse or move transduction pathway, a cell, an organ or a living system, with active transport, and that interact and/or change structures could be obviated, we need them to be able properties in a defined space. However, to reach this to reach that stage. Structures of macromolecules, level of abstraction of protein function, we must first especially molecular machines, could provide quantitative have a detailed understanding of protein structure and parameters, help to elucidate functional networks or dynamics. It is uncontroversial to state that without enable rational designed perturbation experiments for reverse structural biology this is not achievable. engineering. The role of structural biology in systems biology should be to provide enough understanding so that Depending on the specific biological question at hand, macromolecules can be translated into dots or even into different structural details and biophysical properties of equations devoid of atoms. protein complexes should be explored to provide signifi- Addresses cant insight. For example, when part of a signal trans- 1 European Molecular Biology Laboratory (EMBL), duction cascade is analyzed, accurate kinetic constants will Meyerhofstrasse 1, Heidelberg D69115, Germany be crucial to model a system correctly. As we will discuss 2 Centre de Regulacio Genomica (CRG), Dr Aiguader 88, 08003 Barcelona, Spain below, protein complex structures could be used to predict these kinetic constants in silico. In cases in which under- Corresponding author: Serrano, Luis ([email protected]) standing the spatial cellular distribution of larger protein complexes is the aim, the affinity or approximate kinetic constants might be enough. In such circumstances, qual- Current Opinion in Structural Biology 2007, 17:378–384 itative experimental binding information, as from pull- This review comes from a themed issue on down assays, can be combined with structural information Sequences and topology from electron microscopy and fluorescence imaging. Edited by William R Pearson and Anna Tramontano Available online 15th June 2007 In the following, we will discuss how structural genomics is being explored and the role it should increasingly play 0959-440X/$ – see front matter to reduce molecules to their key functional properties # 2007 Elsevier Ltd. All rights reserved. (Figure 1). DOI 10.1016/j.sbi.2007.05.005 Prediction of protein interactions using structural information Introduction Understanding a biological system requires knowledge of There are probably as many definitions of systems the network of interactions in space and time. In other biology as research institutes in the world. However, words, to understand who is interacting with whom, how a large number of scientists will probably agree that these interactions affect the properties of the individual systems biology implies the quantitative understanding components, what are the properties of the complexes of a system, rather than of the individual components, formed, and how these interactions change in space and allowing testable predictions to be made [1]. As such, time. Determining protein–protein interactions has there- systems biology requires acquisition of data, parameter fore become one of the favorites of large-scale projects quantification, bioinformatics analysis and mathemat- ranging from pull-down assays [2,3,4] to full yeast two- ical modeling. Normally, systems biology is associated hybrid analysis [5–8]. Although much progress has been withgenome-widestudies,havinglittletodowith achieved in this area [9], we are still far from having 100% structural biology. If one would ask a systems biologist coverage and accuracy [10]. Also, despite the progress in how they view proteins, protein complexes and so on, a structural genomics projects and the existence of specific large number of them would describe them as dots, large-scale consortia aiming to determine the structures of devoid of three-dimensional information, with some macromolecular complexes (i.e. http://www.3drepertoire. associated biophysical properties. In many cases, not org/), we are far from having a full atomic description of even the surrounding cellular spatial information would all cellular complexes. The number of possible compl- be detailed. exes, the transient nature of many of them and inherent Current Opinion in Structural Biology 2007, 17:378–384 www.sciencedirect.com Structures in systems biology Beltrao, Kiel and Serrano 379 Figure 1 Summary of the main concepts discussed in this review. Structural information can be used in many ways to help us retrieve the characteristic functional properties of cellular components. Here, we detail recent advances in the use of protein structures to predict protein interactions, protein function and quantitative binding parameters, curate large-scale protein interaction studies and understand the impact of coding variability. experimental difficulties make this goal difficult to system to the level of making successful predictions. achieve. Thus, in recent years, efforts have been made For this purpose, quantitative parameters (approximate in using available structural information to predict and or detailed depending on the problem [13]) are required. model the structures of interacting proteins (recently Currently, there are no high-throughput experimental reviewed in [11]). Although the prediction of protein– approaches to obtain these values. Thus, the possibility protein interactions using structural information is far of predicting thermodynamic and kinetic properties of from perfect, it is becoming a useful tool that enables not protein complexes, based on X-ray complex structures or only a Boolean assignment (yes or no) to a particular homology models, could be one of the major contri- putative interaction, but also the production of structural butions of structural biology to systems biology [14]. models, sometimes at very high resolution [12]. Particu- Affinities and kinetic constants are important for model- lar problems that remain to be solved are the modeling of ing cellular signal transduction pathways, as is done in loop conformations, backbone moves and docking. SmartCell (http://smartcell.embl.de)[15], whereby diffu- These problems are minimized if many structures are sion and cellular localization is taken into account. Suc- available of complexes involving members of the same cessful predictions of binding affinities for wild-type and family [12]. mutant complexes have been carried out using the protein design algorithm FoldX (http://foldx.embl.de) Quantitative data [16–18]. Examples are the prediction of Ras–effector In many cases, determining the network of interacting interactions [12,19–21], and interactions of PDZ and components is not enough to understand a biological SH3 domains with their targets [22,23]. www.sciencedirect.com Current Opinion in Structural Biology 2007, 17:378–384 380 Sequences and topology Predicting binding affinities and hot-spot residues is also on the affinity alone, or whether individual association and important in rational design, to modify the binding speci- dissociation rate constants are important as well. ficity of ligands. This was successfully done for the TRAIL receptor system, for which DR5-selective TRAIL variants The role of structural proteomics in the were generated that do not induce apoptosis in DR4- post-genomic era responsive cell lines, but show a large increase in biological Recent efforts to map all possible interactions between activity in DR5-responsive cancer cell lines [24]. Other cellular components, in a high-throughput fashion, have examples are the successful creation of new specifically created very large data sets. Properly mined, this data interacting DNase–inhibitor pairs [25,26] and the rational should help us to better understand living cells. Extract- design of ICAM-1 mutants with enhanced affinity for its ing meaningful information from these data sets is, how- antigen (LFA-1) [27]. ever, not a simple task. Most studies of these interactions rely on a simplified network representation, whereby Approaches to predict association rate constants make use components are nodes and connections between them of the principle of electrostatic steering [28]. Based on this are denoted as edges. This has enabled the vast amount of concept, the association rate constant of a protein complex information to be grasped in a formal way, leading to the can be enhanced by increasing the electrostatic charge discovery of important and general global network prop- complementary at the interface and at the edge of the erties [32–34]. interface. The protein design algorithm PARE [29] was successfully developed to specifically enhance the rate of We will, undoubtedly, require much more rich detail to be association, while not affecting the dissociation rate of added to this representation if we are ever to comprehend various protein