Accounting for conformational changes during protein–protein docking

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Three-dimensional structures of only a small fraction of known protein–protein complexes are currently known. Meanwhile, computational methods are of increasing importance to provide structural models for known protein–protein interactions. Current protein–protein docking methods are often successful if experimentally determined partner proteins undergo little conformational changes upon binding. However, the realistic and computationally efficient treatment of conformational changes especially of the protein backbone during docking remains a challenge. New promising approaches of flexible refinement, ensemble docking and explicit inclusion of flexibility during the entire docking process have been developed. A significant fraction of known protein–protein interactions can be modeled based on homology to known protein–protein complexes which in many cases also requires efficient flexible refinement to provide accurate structural models.

Introduction

The majority of biological processes are mediated or influenced by the interaction of proteins. The binding process of protein partners is driven by the associated change in free energy, which depends on the structural and physicochemical properties of the protein partners. Early ‘lock and key’ concepts of binding reactions proposed by Fischer [1] emphasized the importance of optimal sterical complementarity at binding interfaces as an important factor to achieve affinity and specificity. However, proteins and other interacting biomolecules are not rigid but undergo various types of motions at physiological temperatures. The observation that binding can result in significant conformational changes of partner molecules has lead to the induced-fit concept [2]. During the association process the interacting molecules can adapt to each other. Thus the binding partners induce conformational changes during the binding process that are required for specific binding. It has also been recognized that in principle all possible molecular recognition processes require a certain degree of conformational adaptation.

In recent years extensions of the induced-fit concept, based on ideas from statistical physics, emerged. A pre-existing ensemble of several inter-convertible conformational states being in equilibrium has been postulated [3]. Among these states are structures close to the bound and unbound forms. Binding of a partner molecule to the bound form shifts the equilibrium toward the bound form. Since every conformation is in principle accessible, albeit with a potentially low statistical weight already in the unbound state, the original induced-fit concept is a special case of ensemble selection where only the presence of a ligand gives rise to an appreciable concentration of the bound partner structure.

Experimental studies on detecting all protein–protein interactions in a cell indicate numerous possible interactions ranging from few to several hundred possible binding partners for one protein [4, 5]. Many of these interactions are only transient or only possible in the context of multiple interactions within protein assemblies or may even not be of any functional relevance. A full understanding of cellular functions requires structural knowledge of all these interactions. It is, however, difficult or might even be impossible in the foreseeable future to determine the structure of all detected protein–protein interactions experimentally at high resolution. Hence, reliable computational prediction methods are of increasing importance.

The purpose of computational protein–protein docking methods is to predict the structure of a protein–protein complex based on the structure of the isolated protein partners. Progress in protein–protein docking prediction methods has been monitored with the help of the community wide Critical Assessment of Predicted Interactions (CAPRI) experiment [6, 7]. In this challenge participating groups test the performance of docking methods in blind predictions of protein–protein complex structures. The results of the CAPRI challenge indicate that for protein partners with minor conformational differences between unbound and bound conformation and some experimental hints on the interaction region often quite accurate predictions of complex structures are possible [6, 7]. However, the docking problem becomes much more difficult when protein partners undergo significant conformational changes upon association or for proteins structures based on comparative modeling [7, 8, 9]. The magnitude of possible conformational changes during association can range from local alterations of side chain conformations to global changes of domain geometries and can even involve refolding of protein segments upon association. Computational approaches to realistically predict protein–protein binding geometries need to account for such conformational changes. Recent progress in the area of protein–protein docking with an emphasis on modeling conformational changes and adaptation during protein binding processes will be reviewed. In addition, alternative protein interaction modeling approaches based, for example, on comparative modeling of complexes will also be discussed.

Section snippets

The protein–protein binding process

The process of protein–protein association can be decomposed into phases. During an initial diffusive approach possibly guided by electrostatic interactions the proteins encounter each other many times before an intermediate loosely bound state near the native binding geometry is reached [10] (also termed encounter complex [11]). Upon formation of additional interactions and conformational changes at the interface regions the partners form the native complex with often (but not always) a high

Systematic rigid docking and strategies for scoring and refinement

Similar to the above described process of protein–protein binding most docking procedures distinguish between two or more docking phases [12, 13]. The initial stage consists typically of a systematic docking search keeping partner structures rigid. Subsequently, one or more refinement and scoring steps of a set of preselected rigid docking solutions are added to achieve closer agreement with the native geometry and to recognize near-native docking solutions preferentially as the best or among

The flexible docking problem

A large fraction of experimentally known protein–protein complexes belongs to the class of proteins that undergo little conformational change upon complex formation and can be efficiently treated by a rigid docking protocol combined with a flexible refinement and rescoring step. However, for many interesting docking cases with large associated conformational changes explicit consideration of conformational flexibility during the entire docking procedure or at an early refinement step appear to

Protein–protein complex structures by comparative modeling

It has been recognized that comparative modeling methods used for structure prediction of single proteins could also be used to predict the structure of entire protein–protein complexes [49, 50]. A prerequisite for this approach is the availability of homologous template structures for the complex or at least part of it. Several methods have been published recently to extend available homology modeling methods to allow modeling of protein–protein complexes [51, 52, 53, 54, 55, 56].

In case of

Conclusions

The realistic prediction of binding geometries of protein–protein complexes is highly desirable to provide structural models for the many important protein–protein interactions in a cell. An ultimate aim of protein–protein docking approaches is the application on a systematic proteomic scale. In addition, rational modifications of protein surfaces are increasingly being used to design new protein–protein binding interfaces. Methods of protein–protein docking and interface refinement could help

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

This work has been support by the Deutsche Forschungsgemeinschaft (DFG) and funding under the Sixth Research Framework Programme of the European Union (FP6 STREP ‘BacAbs’, ref. LSHB-CT-2006-037325).

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