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1.  Docking Server for the Identification of Heparin Binding Sites on Proteins 
Many proteins of widely differing functionality and structure are capable of binding heparin and heparan sulfate. Since crystallizing protein–heparin complexes for structure determination is generally difficult, computational docking can be a useful approach for understanding specific interactions. Previous studies used programs originally developed for docking small molecules to well-defined pockets, rather than for docking polysaccharides to highly charged shallow crevices that usually bind heparin. We have extended the program PIPER and the automated protein–protein docking server ClusPro to heparin docking. Using a molecular mechanics energy function for scoring and the fast Fourier transform correlation approach, the method generates and evaluates close to a billion poses of a heparin tetrasaccharide probe. The docked structures are clustered using pairwise root-mean-square deviations as the distance measure. It was shown that clustering of heparin molecules close to each other but having different orientations and selecting the clusters with the highest protein–ligand contacts reliably predicts the heparin binding site. In addition, the centers of the five most populated clusters include structures close to the native orientation of the heparin. These structures can provide starting points for further refinement by methods that account for flexibility such as molecular dynamics. The heparin docking method is available as an advanced option of the ClusPro server at http://cluspro.bu.edu/.
doi:10.1021/ci500115j
PMCID: PMC4184157  PMID: 24974889
2.  How Good is Automated Protein Docking? 
Proteins  2013;81(12):2159-2166.
The protein docking server ClusPro has been participating in CAPRI since its introduction in 2004. This paper evaluates the performance of ClusPro 2.0 for targets 46–58 in rounds 22–27 of CAPRI. The analysis leads to a number of important observations. First, ClusPro reliably yields acceptable or medium accuracy models for targets of moderate difficulty that have also been successfully predicted by other groups, and fails only for targets that have few acceptable models submitted. Second, the quality of automated docking by ClusPro is very close to that of the best human predictor groups, including our own submissions. This is very important, because servers have to submit results within 48 hours and the predictions should be reproducible, whereas human predictors have several weeks and can use any type of information. Third, while we refined the ClusPro results for manual submission by running computationally costly Monte Carlo minimization simulations, we observed significant improvement in accuracy only for two of the six complexes correctly predicted by ClusPro. Fourth, new developments, not seen in previous rounds of CAPRI, are that the top ranked model provided by ClusPro was acceptable or better quality for all these six targets, and that the top ranked model was also the highest quality for five of the six, confirming that ranking models based on cluster size can reliably identify the best near-native conformations.
doi:10.1002/prot.24403
PMCID: PMC3934018  PMID: 23996272
protein-protein docking; structure refinement; method development; CAPRI docking experiment; web based server; user community
3.  FTMAP: extended protein mapping with user-selected probe molecules 
Nucleic Acids Research  2012;40(Web Server issue):W271-W275.
Binding hot spots, protein sites with high-binding affinity, can be identified using X-ray crystallography or NMR by screening libraries of small organic molecules that tend to cluster at such regions. FTMAP, a direct computational analog of the experimental screening approaches, globally samples the surface of a target protein using small organic molecules as probes, finds favorable positions, clusters the conformations and ranks the clusters on the basis of the average energy. The regions that bind several probe clusters predict the binding hot spots, in good agreement with experimental results. Small molecules discovered by fragment-based approaches to drug design also bind at the hot spot regions. To identify such molecules and their most likely bound positions, we extend the functionality of FTMAP (http://ftmap.bu.edu/param) to accept any small molecule as an additional probe. In its updated form, FTMAP identifies the hot spots based on a standard set of probes, and for each additional probe shows representative structures of nearby low energy clusters. This approach helps to predict bound poses of the user-selected molecules, detects if a compound is not likely to bind in the hot spot region, and provides input for the design of larger ligands.
doi:10.1093/nar/gks441
PMCID: PMC3394268  PMID: 22589414
4.  ConSCRIPT 
The aim of the Structural Biology Extensible Visualization Scripting Language (SBEVSL) project is to allow users who are experts in one scripting language to use that language in a second molecular visualization environment without requiring the user to learn a new scripting language. ConSCRIPT, the first SBEVSL release, is a plug-in for PyMOL that accepts RasMol scripting commands either as premade scripts or as line-by-line entries from PyMOL's own command line. The plug-in is available for download at http://sourceforge.net/projects/sbevsl/files in the ConSCRIPT folder.
doi:10.1002/bmb.20450
PMCID: PMC3134254  PMID: 21567873
PyMOL; RasMol; ConSCRIPT; molecular visualization; structural bioinformatics

Results 1-4 (4)