With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug targets. Compared with inhibition of a single protein, inhibition of protein-protein interaction (PPI) is promising to improve the specificity with fewer adverse side-effects. Also it greatly broadens the drug target search space, which makes the drug target discovery difficult. Computational methods are highly desired to efficiently provide candidates for further experiments and hold the promise to greatly accelerate the discovery of novel drug targets.
Provides functions for inhibitory peptide design. PinaColada extracts a suitable initial peptide from one protein of a protein–protein complex couple, with high affinity to bind to the other. It employs the Ant Colony Optimization algorithm for the optimization task. This tool provides three modes: A Protein–Peptide, a Protein–Protein mode and Protein-Binding site mode. It can be useful to minimize the search space of inhibitory peptide.
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.
Labels the donor/acceptor capacity of each atom and characterizes each H-bond in terms of its atomic chemistry and geometry. Hbind interprets the donor/acceptor capacity of ligand atoms from information in the MOL2 file detailing the hybridization, the order of covalent bonds with neighboring atoms, and the protonation state of these atoms. The software was used to define direct H-bonds and metal bonds with ligands.
Assesses the native-likeness of designed or predicted protein-ligand interfaces. PRI allows measurement of the similarity between H-bonding features in a given complex and the characteristic H-bond trends from crystallographic complexes based on hydrogen-bond interactions identified by Hbind. The software can be used to guide protein mutagenesis and ligand design.
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.
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