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WIREs Comput Mol Sci
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Prediction of protein binding sites and hot spots

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Abstract Protein–protein interactions are involved in the majority of cell processes, and their detailed structural and functional characterization has become one of the most important challenges in current structural biology. The first ideal goal is to determine the structure of the specific complex formed upon interaction of two or more given proteins. However, since this is not always technically possible, the practical approach is often to locate and characterize the protein residues that are involved in the interaction. This can be achieved by experimental means at expense of time and cost, so a growing number of computer tools are becoming available to complement experimental efforts. Reported methods for interface prediction are based on sequence information or on structural data, and make use of a variety of evolutionary, geometrical, and physicochemical parameters. As we show here, computer predictions can achieve a high degree of success, and they are of practical use to guide mutational experiments as well as to explain functional and mechanistic aspects of the interaction. Interestingly, it has been found that typically only a few of the interacting residues contribute significantly to the binding energy. The identification of such hot‐spot residues is important for understanding basic aspects of protein association. In addition, these residues have received recent attention as possible targets for drug design, so several computer methods have been developed to predict them. We will review here existing computer approaches for the prediction of protein binding sites and hot‐spot residues, with a discussion on their applicability and limitations. © 2011 John Wiley & Sons, Ltd. WIREs Comput Mol Sci 2011 1 680–698 DOI: 10.1002/wcms.45 This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics

Scheme of a typical drug discovery process. Computer prediction of protein binding sites and hot‐spot residues are essential for steps 2 and 4, respectively.

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Examples of hot‐spot predictions by pyDockNIP method (in red predicted residues, left panel). (a) SEC3 super antigen (hot spots for interaction with T‐Cell β‐chain; complex PDB 1JCK); (b) D1.3 IgG1 (hot spots for interaction with lysozyme; complex PDB 1VFB); (c) IL‐4 receptor α‐chain (hot‐spots for interaction with IL‐4; complex PDB 1IAR). In the right panel, the experimentally known hot spots (in red) and the non‐hot spots (in blue) are shown. Residues in white have not been tested experimentally.

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Scheme of the normalized interface propensity method for prediction of interface and hot‐spot residues. The rigid‐body docking poses are sorted by binding energy and mapped onto the surface of the interacting proteins, by computing the average relative buried surface per residue. The residues more often involved in the docking interfaces are shown in red and indicate hot‐spot residues.

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An example of the application of the optimal docking area prediction method to a case of biological interest. Interface predictions (in red) on different carboxypeptidases show that their surfaces contribute differently (in shape and intensity) to the interaction with the corresponding partners (inhibitors and/or N‐terminal pro domains), according to each sub‐family. The interest here is to show that interface predictions can help in protein classification. In green is shown the contour of the real interface (if known) for comparison purposes. Positive predictive values of the predictions are 60, 80, 87, 90, and 90%, from left to right and top to bottom, respectively.94

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Examples of optimal docking area binding site predictions on a variety of unbound proteins: (a) bovine cyclin A3 (PDB 1VIN); (b) antibody Fab D44.1 (PDB 1MLB); (c) acetylcholinesterase (PDB 1ACL); (d) barstar (PDB 1A19). The position of the partner molecule in the complex structure is shown in green ribbon, for comparison purposes. Figure prepared with ICM software (www.molsoft.com).

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Scheme of the optimal docking area (ODA) method for interface prediction. (a) Starting points are defined in the center of each residue side‐chain; (b) representation of starting points around a protein; (c) for each point, surface patches of different size are defined and their desolvation energy calculated until finding the best value. Each starting point gets the optimal desolvation (ODA) value of the optimal patch generated from it.

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