Structure-based druggability assessment--identifying suitable targets for small molecule therapeutics

A target is druggable if it can be modulated in vivo by a drug-like molecule. The general properties of oral drugs are summarized by the 'rule of 5' which specifies parameters related to size and lipophilicity. Structure-based target druggability assessment consists of predicting ligand-binding sites on the protein that are complementary to these drug-like properties. Automated identification of ligand-binding sites can use geometrical considerations alone or include specific physicochemical properties of the protein surface. Features of a pocket's size and shape, together with measures of its hydrophobicity, are most informative in identifying suitable drug-binding pockets. The recent availability of several validation sets of druggable versus undruggable targets has helped fuel the development of more elaborate methods.

Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction

MOTIVATION

Protein-ligand binding sites are the active sites on protein surface that perform protein functions. Thus, the identification of those binding sites is often the first step to study protein functions and structure-based drug design. There are many computational algorithms and tools developed in recent decades, such as LIGSITE(cs/c), PASS, Q-SiteFinder, SURFNET, and so on. In our previous work, MetaPocket, we have proved that it is possible to combine the results of many methods together to improve the prediction result.

RESULTS

Here, we continue our previous work by adding four more methods Fpocket, GHECOM, ConCavity and POCASA to further improve the prediction success rate. The new method MetaPocket 2.0 and the individual approaches are all tested on two datasets of 48 unbound/bound and 210 bound structures as used before. The results show that the average success rate has been raised 5% at the top 1 prediction compared with previous work. Moreover, we construct a non-redundant dataset of drug-target complexes with known structure from DrugBank, DrugPort and PDB database and apply MetaPocket 2.0 to this dataset to predict drug binding sites. As a result, >74% drug binding sites on protein target are correctly identified at the top 3 prediction, and it is 12% better than the best individual approach.

AVAILABILITY

The web service of MetaPocket 2.0 and all the test datasets are freely available at http://projects.biotec.tu-dresden.de/metapocket/ and http://sysbio.zju.edu.cn/metapocket.

MSPocket: an orientation-independent algorithm for the detection of ligand binding pockets

MOTIVATION

Identification of ligand binding pockets on proteins is crucial for the characterization of protein functions. It provides valuable information for protein-ligand docking and rational engineering of small molecules that regulate protein functions. A major number of current prediction algorithms of ligand binding pockets are based on cubic grid representation of proteins and, thus, the results are often protein orientation dependent.

RESULTS

We present the MSPocket program for detecting pockets on the solvent excluded surface of proteins. The core algorithm of the MSPocket approach does not use any cubic grid system to represent proteins and is therefore independent of protein orientations. We demonstrate that MSPocket is able to achieve an accuracy of 75% in predicting ligand binding pockets on a test dataset used for evaluating several existing methods. The accuracy is 92% if the top three predictions are considered. Comparison to one of the recently published best performing methods shows that MSPocket reaches similar performance with the additional feature of being protein orientation independent. Interestingly, some of the predictions are different, meaning that the two methods can be considered complementary and combined to achieve better prediction accuracy. MSPocket also provides a graphical user interface for interactive investigation of the predicted ligand binding pockets. In addition, we show that overlap criterion is a better strategy for the evaluation of predicted ligand binding pockets than the single point distance criterion.

AVAILABILITY

The MSPocket source code can be downloaded from http://appserver.biotec.tu-dresden.de/MSPocket/. MSPocket is also available as a PyMOL plugin with a graphical user interface.

3V: cavity, channel and cleft volume calculator and extractor

As larger macromolecular structures become available, there is a growing need to understand their ‘internal’ volumes—such as deep clefts, channels and cavities—as these often play critical roles in their function. The 3V web server can automatically extract and comprehensively analyze all the internal volumes from input RNA and protein structures. It rapidly finds internal volumes by taking the difference between two rolling-probe solvent-excluded surfaces, one with as large as possible a probe radius and the other with a solvent radius (typically 1.5 Å for water). The outputs are volumetric representations, both as images and downloadable files, which can be used for further analysis.

The 3V server and source code are available from http://3vee.molmovdb.org.