Targeting the interfaces between proteins has huge therapeutic potential, but discovering small-molecule drugs that disrupt protein-protein interactions is an enormous challenge. Several recent success stories, however, indicate that protein-protein interfaces might be more tractable than has been thought. These studies discovered small molecules that bind with drug-like potencies to 'hotspots' on the contact surfaces involved in protein-protein interactions. Remarkably, these small molecules bind deeper within the contact surface of the target protein, and bind with much higher efficiencies, than do the contact atoms of the natural protein partner. Some of these small molecules are now making their way through clinical trials, so this high-hanging fruit might not be far out of reach.

Reaching for high-hanging fruit in drug discovery at protein–protein interfaces

BindingDB (http://www.bindingdb.org) is a publicly accessible database currently containing ∼20 000 experimentally determined binding affinities of protein–ligand complexes, for 110 protein targets including isoforms and mutational variants, and ∼11 000 small molecule ligands. The data are extracted from the scientific literature, data collection focusing on proteins that are drug-targets or candidate drug-targets and for which structural data are present in the Protein Data Bank. The BindingDB website supports a range of query types, including searches by chemical structure, substructure and similarity; protein sequence; ligand and protein names; affinity ranges and molecular weight. Data sets generated by BindingDB queries can be downloaded in the form of annotated SDfiles for further analysis, or used as the basis for virtual screening of a compound database uploaded by the user. The data in BindingDB are linked both to structural data in the PDB via PDB IDs and chemical and sequence searches, and to the literature in PubMed via PubMed IDs.

BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities

We have developed the PDBbind database to provide a comprehensive collection of binding affinities for the protein−ligand complexes in the Protein Data Bank (PDB). This paper gives a full description of the latest version, i.e., version 2003, which is an update to our recently reported work. Out of 23 790 entries in the PDB release No.107 (January 2004), 5897 entries were identified as protein−ligand complexes that meet our definition. Experimentally determined binding affinities (Kd, Ki, and IC50) for 1622 of these were retrieved from the references associated with these complexes. A total of 900 complexes were selected to form a “refined set”, which is of particular value as a standard data set for docking and scoring studies. All of the final data, including binding affinity data, reference citations, and processed structural files, have been incorporated into the PDBbind database accessible on-line at http:// www.pdbbind.org/.

The PDBbind database: methodologies and updates.

Computational methods have become standard in today’s medicinal chemistry tool kit. Like any tool, it is important to periodically evaluate utility and ask how function can be improved. In this section of the Journal, we call attention to the area of calculating molecular interactions, specifically docking, the positioning of a ligand in a protein binding site, and scoring, the quality assessment of docked ligands. As several recent reviews have made clear, 1-3 the technology has been productive for both finding and elaborating bioactive molecules. But has docking and scoring delivered on the promises first made over 20 years ago? To consider that question, we follow up on an extensive symposium held in Philadelphia during the 2004 Fall National Meeting of the American Chemistry Society and on subsequent meetings sponsored by the National Institutes of Health (NIH) and the National Institute of Standards and Technology (NIST) in 2005 and 2006 to address the outcomes of the American Chemical Society symposium. Speakers at the symposium were invited to contribute original manuscripts to be published with this overview to highlight the area of docking and scoring and to identify some of the major gaps yet to be addressed. 4-10

Prediction of protein-ligand interactions. Docking and scoring: successes and gaps.

We describe a general methodology for designing an empirical scoring function and provide smina, a version of AutoDock Vina specially optimized to support high-throughput scoring and user-specified custom scoring functions. Using our general method, the unique capabilities of smina, a set of default interaction terms from AutoDock Vina, and the CSAR (Community Structure–Activity Resource) 2010 data set, we created a custom scoring function and evaluated it in the context of the CSAR 2011 benchmarking exercise. We find that our custom scoring function does a better job sampling low RMSD poses when crossdocking compared to the default AutoDock Vina scoring function. The design and application of our method and scoring function reveal several insights into possible improvements and the remaining challenges when scoring and ranking putative ligands.

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Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

Binding MOAD (Mother of All Data- bases) is the largest collection of high-quality, protein- ligand complexes available from the Protein Data Bank. At this time, Binding MOAD contains 5331 protein-ligand complexes comprised of 1780 unique protein families and 2630 unique ligands. We have searched the crystallography papers for all 5000 structures and compiled binding data for 1375 (26%) of the protein-ligand complexes. The binding-affinity data ranges 13 orders of magnitude. This is the largest collection of binding data reported to date in the literature. We have also addressed the issue of redun- dancy in the data. To create a nonredundant dataset, one protein from each of the 1780 protein families was chosen as a representative. Representatives were cho- sen by tightest binding, best resolution, etc. For the 1780 "best" complexes that comprise the nonredun- dant version of Binding MOAD, 475 (27%) have bind- ing data. This significant collection of protein-ligand complexes will be very useful in elucidating the bio- physical patterns of molecular recognition and enzy- matic regulation. The complexes with binding-affinity data will help in the development of improved scoring functions and structure-based drug discovery tech- niques. The dataset can be accessed at http://www. BindingMOAD.org. Proteins 2005;60:333-340.

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Binding MOAD (Mother of All Databases)

We have recently announced the largest database of protein–ligand complexes, Binding MOAD (Mother of All Databases). After the August 2004 update, Binding MOAD contains 6816 complexes. There are 2220 protein families and 3316 unique ligands. After searching 6000+ crystallography papers, we have obtained binding data for 1793 (27%) of the complexes. We have also created a non-redundant set of complexes with only one complex from each protein family; in that set, 630 (28%) of the unique complexes have binding data. Here, we present information about the data provided at the Binding MOAD website. We also present the results of mining Binding MOAD to map the degree of solvent exposure for binding sites. We have determined that most cavities and ligands (70–85%) are well buried in the complexes. This fits with the common paradigm that a large degree of contact between the ligand and protein is significant in molecular recognition. GoCAV and the GoCAVviewer are the tools we created for this study. To share our data and make our online dataset more useful to other research groups, we have integrated the viewer into the Binding MOAD website ( www.BindingMOAD.org ).

Liegi Hu

Papers

Binding MOAD (Mother of All Databases)

Exploring protein-ligand recognition with Binding MOAD.