Disulfide bonding detection software tools | Protein structure data analysis
Disulfide bonds play an important role in protein folding and structure stability. Accurately predicting disulfide bonds from protein sequences is important for modeling the structural and functional characteristics of many proteins.
Offers a platform for determining protein structural features and tertiary structures. SCRATCH is a web application including ten modules for determining three and eight class: (1) secondary structure, (2) relative solvent accessibility, (3) domain boundaries, (4) disordered regions, (5) disulfide bridges, (6) the effect of single amino acid mutation on stability, (7) residue-residue contact maps, and (8) tertiary structures as well as contacts with other residues compared to average.
Provides prediction of disulfide bonding connectivity pattern without the prior knowledge of the bonding state of cysteines. The method used in this server improves the accuracy of disulfide connectivity pattern prediction (Qp) over the previous studies reported in the literature.
Predicts bonding state and connectivity patterns of cysteine residues in a protein chain. DisLocate is based on machine learning methods that are Grammatical-Restrained Hidden Conditional Random Fields (GRHCRFs) and Support Vector Regression (SVR). It is composed of two steps: the first one predicts cysteine bonding-state, and the second predicts connectivity pattern. The tool is uses protein subcellular localization in Eukaryotes to work.
A disulfide bond predictor. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared to purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins.
A framework for disulphide bridge predictions. DIpro provides graphical models and recursive neural networks to predict the bonding probability of each pair of cysteines, leveraging in addition secondary structure and relative solvent accessibility information. DIpro infers the disulphide bridge connectivity of each protein chain, which in turn yields a solution for both the bridge and residue classification problems, even in the case where the bonding state of individual cysteines is not known.
Assists users in experimental mapping studies. DIMPL is a web application that authorizes to obtain fragment sequence associated to mass data derived from permutations tests leads on proteases of interest. These outputs can then be used for running a comparative analysis with mass spectroscopy (MS), after classification, in order to ease dipeptide identification.
Computes more than 50 structure-based features for any given protein structure. PDBparam can deal with inter residue interactions, amino acid propensities, physicochemical properties, and binding sites. It allows to understand the structure and functions of proteins and their complexes. The tool provides deep insights into its structure-function relationship. It can be used for largescale analysis of different types of proteins to explore potential interactions and contacts.
A tool for finding modifications on polypeptide sequences. The modifications can be affecting single amino acids (e.g. phosphorylation or oxidation) or cross-linking two amino acids (e.g. disulfide bonds or chemical cross-linking reagents).
Allows users to detect potential disulfide bonds in sequences of unknown structure. GDAP is a web platform that provides two different features: (i) a repository compiling pre-calculated disulfide bond predictions for more than 100 microbial genomes that can be filtered and gathered; (ii) a protein disulfide bond predictor with settings for specifying the search program and the annotation types that have to be applied.
Allows representation of a variety of ion products resulted from both single cleavage and multiple cleavages on the disulfide bonding structure. DISC is capable of characterizing post-translational modifications (PTMs). It can report the disulfide linkage structures that contains modified amino acid residues.
A tool designed for biologists that attempt to determine the three-dimensional structure of protein molecules. Based on the fact that disulfide bridges add strong constraints to the native structure, the main function of x3CysBridges is to predict the disulfide bonding probability of each cysteine-pair of a protein and to propose a disulfide connectivity pattern that maximizes the sum of these probabilities.
Predicts protein disulfide connectivity. TargerDisulfide is based on a random forest regression model to proceed prediction and discriminative features derived from the predicted protein 3D structural information. It is able to achieve good performance and outperforms many existing sequence-based predictors. Users can specify if they want to give the bonding states of cysteines in the protein or not.