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PRISM / Protein Interactions By Structural Matching
Enables fast and accurate prediction of protein-protein interactions (PPIs). The prediction algorithm is knowledge-based. It combines structural similarity and accounts for evolutionary conservation in the template interfaces. The predicted models are stored in its repository. Given two protein structures, PRISM will provide a structural model of their complex if a matching template interface is available. Users can download the complex structure, retrieve the interface residues and visualize the complex model.
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Provides a user-friendly interface to calculate various features of biological molecules and complex interaction samples conveniently. BioTriangle is a comprehensive molecular representation platform to emphasize the integration of cheminformatics and bioinformatics into a molecular informatics platform for computational biology study. It contains a feature-rich toolkit used for the characterization of various biological molecules and complex interaction samples including chemicals, proteins, DNAs/RNAs and even their interactions.
webPIPSA / Protein Interaction Property Similarity Analysis
Permits the classification of proteins according to their interaction properties. webPIPSA is a web server that enables the use of Protein Interaction Property Similarity Analysis (PIPSA) to compare and analyze protein electrostatic potentials. This allows non-expert users to perform PIPSA for their protein datasets. Currently, webPIPSA provides a description and categorization of the electrostatic potential differences between the input protein structures.
PSAIA / Protein Structure and Interaction Analyzer
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Computes geometric parameters for large sets of protein structures in order to predict and investigate protein-protein interaction (PPI) sites. PSAIA offers a PIADA (Protein Interaction Atom Distance Algorithm) method for the determination of residue interaction pairs. It includes capacity to combine different methods to detect the locations and types of interactions between residues and its ability, without any further automation steps, to handle large numbers of protein structures and complexes.
Predicts protein-protein, protein-peptide, protein-DNA and protein-RNA binding sites. Multi-VORFFIP utilizes a wide range of structural, evolutionary, experimental and energy-based information that is integrated into a common probabilistic framework by means of a Random Forest (RF) ensemble classifier. It is a centralized resource for the prediction of functional sites and is interfaced by a powerful web application tailored to facilitate the use of the method and analysis of predictions to non-expert end-users.
RF PPI / Random Forest Protein-Protein Interaction
Captures the common properties of both homodimer and heterodimer interfaces. RF PPI contains the backbone flexibility score predicted by DynaMine which is the first direct predictor of dynamics from sequence. This sequence-based interface predictor trained on the homomeric dataset which can gain a better performance than other methods on homodimer interface residues prediction. RF interface predictor is able to distinguish interface from non-interface residues with an area under the ROC curve of 0.72 in a homomeric test-set.
Detects protein-protein interaction (PPI) sites. IntPred uses sequence and structure information to create features. It returns a prediction label at either the surface patch- or residue-level. It is based on a random forest (RF) machine learning method. This tool uses 11 features for learning and prediction which can be divided into sequence features and structural features. It has been cross-validated on a large set of structures obtained from Protein, Interfaces, Structures and Assemblies (PISA).
Infers possible interacting regions between two proteins by searching minimal common sequence-fragments of the interacting protein pairs. iFrag is a sequence-based computational method that derives a two-dimensional matrix computing a score for each pair of residues that relates to the presence of similar regions in interolog protein pairs. This method does not require three-dimensional structural information or multiple sequence alignments and can even predict small interaction sites consisting only of few residues.
A method for predicting free energy changes (ΔΔG) of protein-protein binding interactions upon mutations of residues at the interface. BindProf is an interface binding profile score, formed from an aligned ensemble of structurally similar interfaces, that has accuracy as a standalone feature similar to, or in most cases, better than many composite all-atom potentials. BindProf adopts a multi-scale approach using a variety of features at different levels of structural resolution using machine learning with sequence and structure based features to learn the correct weighting between terms using a regression tree classifier. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies.
MFPred / Mean-Field theory-based Prediction
Expresses specificity as a sitewise probability distribution function for protein-peptide interfaces. MFPred can obtain multi-specificity predictions for diverse classes of protein-recognition domains (PRDs). It is able to recapitulate experimentally determined changes in specificity profiles due to receptor-side mutations. This tool was tested on a dataset consisting of a variety of non-protease PRDs that had high-resolution crystal structures as protein-peptide complexes. It is a part of the Rosetta suite.
Exploits the dynamics of protein complexes in order to detect obligatory vs. non-obligatory interactions among the subunits. The global perspective of interactions across the subunits interface is described mainly by the dynamics of the structural complex, which is not easily accessible by studying only local sequence and structural properties. The dynamic domains, the motions of sub-structural units and how they cooperate with respect to the interacting chains, i.e. the dynamic infrastructure provided by the interacting chains, overall captures the global connectivity similarity for the obligatory and non-obligatory complex structures. To this end, the dynamics of protein complexes in terms of the motions of their dynamic domains and how they are dynamically coupled for their function is of importance for design and function modification of proteins.
The Interactorium
A tool in which a Virtual Cell is used as the context for the seamless visualisation of the yeast protein interaction network, protein complexes and protein 3-D structures. The Interactorium has been designed to display very complex networks of up to 40 000 proteins or 6000 multiprotein complexes and has a series of toolboxes and menus to allow real-time data manipulation and control the manner in which data are displayed. It incorporates new algorithms that reduce the complexity of the visualisation by the generation of putative new complexes from existing data and by the reduction of edges through the use of protein ‘‘twins’’ when they occur in multiple locations.
3DIANA / 3D Domain Interaction Analysis
A web based environment designed to integrate bioinformatics-like information for the analysis of protein interactions and quaternary structure modellling. 3DIANA is specially targeted to the cases in which the electron microscopy map resolution is medium or low and additional experimental structural information is scarce or even lacking. In this way, 3DIANA statistically evaluates proposed/potential contacts between protein domains, presents a complete catalog of both structurally resolved and predicted interacting regions involving these domains and, finally, suggests structural templates to model the interaction between them. The evaluation of the proposed interactions is computed with DIMERO, a new method that scores physical binding sites based on the topology of protein interaction networks, which has recently shown the capability to increase by 200% the number of domain-domain interactions predicted in interactomes as compared to previous approaches.
cons-PPISP / consensus Protein-Protein Interaction Site Predictor
A consensus neural network method for predicting protein-protein interaction sites. Given the structure of a protein, cons-PPISP will predict the residues that will likely form the binding site for another protein. The inputs to the neural network include position-specific sequence profiles and solvent accessibilities of each residue and its spatial neighbors. The neural network is trained on known structures of protein-protein complexes.
A sequence-based protein-protein interaction (PPI) site predictor which takes the combined features of position-specific scoring matrices, averaged cumulative hydropathy, and predicted relative solvent accessibility as model inputs. Our results from one cross-validation dataset and two independent validation datasets demonstrated that the proposed CRF-PPI outperformed the state-of-the-art sequence-based PPIs predictors and can be applied as a complement to existing PPI predictors.
Predicts in a robust manner amino acids that constitute protein-protein interfaces (PPIs). PresCont reaches state-of-the-art classification quality on the basis of only four residue properties that can be readily deduced from the 3D structure of an individual protein and a multiple sequence alignment (MSA) composed of homologs. The core of PresCont is a support vector machine, which assesses solvent-accessible surface area, hydrophobicity, conservation, and the local environment of each amino acid on the protein surface.
PrISE / Prediction of protein-protein Interface residues using Structural Elements
Predicts interface residues using local surface structural similarity. PrISe represents a local surface structure using structural elements. A structural element represents the surface of a central residue and its surrounding residues on the protein structure. PrISe decomposes the surface of a query protein in as many structural elements in the surface of the protein and search for similar elements in a data base containing all the structural elements in PDB. The central residue in each element in the database has been label as interface or non-interface. These labels are used to predict whether the central residue of each structural element in the query protein is or not an interface residue.
CaMELS / CalModulin intEraction Learning System
Allows biologists to working on analyzing the functions and interaction behavior of CaM (Calmodulin) and its target proteins. CaMELS uses protein sequence information only and offers state of the art accuracy both for interaction and binding site prediction. This shows that sequence information of the whole protein is predictive of its interaction with CaM. It algorithm gives state of the art classification results and is available as a cloud based webserver.
VORFFIP / Voronoi Random Forest Feedback Interface Predictor
A structure-based computational method for prediction of protein binding sites. VORFFIP makes use of broad set of heterogeneous data and defined of residue environment, by means of Voronoi Diagrams that are integrated by a two-steps Random Forest ensemble classifier. Four sets of residue features (structural, energy terms, sequence conservation, and crystallographic B-factors) used in different combinations together with three definitions of residue environment (Voronoi Diagrams, sequence sliding window, and Euclidian distance) have been analyzed in order to maximize the performance of the method.
Identifies protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition. Cross-validation tests indicate that iPPBS-PseAAC is very promising, meaning that many important key features, which are deeply hidden in complicated protein sequences, can be extracted via the wavelets transform approach, quite consistent with the facts that many important biological functions of proteins can be elucidated with their low-frequency internal motions.
SPRINGS / Sequence-based predictor of PRotein-protein interactING Sites
A computational approach using artificial neural networks for predicting protein-protein interaction sites based on evolutionary conservation, averaged cumulative hydropathy and predicted relative solvent accessibility of protein sequences. SPRINGS uses protein evolutionary information, averaged cumulative hydropathy and predicted relative solvent accessibility from amino acid chains in artificial neural network architecture with a promising performance for protein-protein interactions sites based research and applications.
Employs a series of attributes to re-rank docking conformations, including residue-level interface probabilities, protein docking contact potentials, and energy-based scores. eRankPPI is an algorithm for the selection of correct docking conformations constructed by rigid-body protein docking. It uses not only experimental monomer structures but also protein models. This package has a high tolerance to structural imperfections in computer-generated protein models. Therefore, it opens up a possibility to conduct the exhaustive structure-based reconstruction of protein-protein interaction (PPI) networks across proteomes.
Calculates amino acid contact distances in proteins at different distance threshold from the 3 Dimensional (3D)-structure of the protein. ContPro calculates the distance between selected protein chain residue atoms and interacting partner atoms, and when this distance falls below or equal to the selected distance threshold, this residue is considered as binding residue. It parses the multi model Protein Data Bank (PDB) file, sequence of selected protein chain from the 3D-structure of protein and gives the atomic details of contacts.
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