Protein-RNA interaction data analysis software tools
Interactions between proteins and RNA play essential roles for life. For example, protein-RNA interactions mediate RNA metabolic processes such as splicing, polyadenylation, messenger RNA stability, localization and translation. Furthermore, many of these RNA-binding proteins are involved in human diseases.
Finds docking transformations that yield good molecular shape complementarity. A wide interface is ensured to include several matched local features of the docked molecules. PatchDock divides the Connolly dot surface representation of the molecules into concave, convex and flat patches. Then, complementary patches are matched to generate candidate transformations. Each candidate is further evaluated by a scoring function that considers both geometric fit and atomic desolvation. An root mean square deviation clustering is applied to the candidate solutions to discard redundant solutions. PatchDock performs structure prediction of protein–protein and protein–small molecule complexes.
An information-driven flexible docking approach for the modeling of biomolecular complexes. HADDOCK distinguishes itself from ab-initio docking methods in the fact that it encodes information from identified or predicted protein interfaces in ambiguous interaction restraints (AIRs) to drive the docking process. HADDOCK can deal with a large class of modeling problems including protein-protein, protein-nucleic acids and protein-ligand complexes.
Discretises two molecules onto orthogonal grids and performs a global scan of translational and rotational space. In order to scan rotational space it is necessary to rediscretise one of the molecules (for speed the smaller) for each rotation. The scoring method is primarily a surface complementarity score between the two grids. To speed up the surface complementarity calculations, which are convolutions of two grids, Fourier Transforms are used.
Applies support vector machines (SVMs) to prediction of DNA and RNA-binding residues from sequence features, including the side chain pKa value, hydrophobicity index and molecular mass of an amino acid. The SVM classifiers have been constructed using two curated datasets (PDNA-62 and PRINR25) from the Protein Data Bank. For DNA-binding residues, the prediction accuracy estimated from cross-validation is about 70% with equal sensitivity and specificity. For RNA-binding residues, the estimated accuracy is approximately 68%.
A general binding score for predicting the nucleic acid binding probability in proteins. The score is directly derived from physicochemical and evolutionary features and integrates a residue neighboring network approach.
Predicts the location of RNA-binding residues (RBRs) in protein sequences. The goal of functional annotation of sequences in the field of RNA binding is to provide predictions of high accuracy that require only small numbers of targeted mutations for verification. The PiRaNhA server uses a support vector machine (SVM), with position-specific scoring matrices, residue interface propensity, predicted residue accessibility and residue hydrophobicity as features. The server allows the submission of up to 10 protein sequences, and the predictions for each sequence are provided on a web page and via email.