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PrePPItar / Predict PPI target

A computational method to predict PPIs as particular drug targets by uncovering the potential associations between drugs and PPIs. PrePPItar is firstly based on a database surveyed and a manually constructed gold-standard positive dataset for drug and PPI interactions. Secondly, we characterize drugs by profiling in chemical structure, drug ATC-code annotation, and side effect space and represent PPI similarity by a symmetrical S-kernel based on protein amino acid sequences. Then the drugs and PPIs are correlated by Kronecker product kernel. Finally, a support vector machine (SVM), is trained to predict novel associations between drugs and PPIs. We validate our PrePPItar method on the well established gold-standard dataset by cross-validation. We find that all chemical structure, drug ATC-code, and side-effect information sources are predictive for PPI target.

HBonanza / Hydrogen-Bond analyzer

A package which, given a protein structure, can greatly facilitate the identification, analysis and visualization of hydrogen-bond networks. HBonanza can be used to analyze single protein structures or entire molecular-dynamics trajectories. Unlike many other freely available hydrogen-bond analysis tools, HBonanza generates not only a text-based table describing the hydrogen-bond network, but also a Tcl script to facilitate visualization in VMD, a molecular visualization program.