Protein-protein interaction site detection software tools | Structure data analysis
The identification of protein-protein interaction sites is an essential intermediate step for mutant design and the prediction of protein networks. In recent years a significant number of methods have been developed to predict these interface residues and here we review the current status of the field.
Identifies RNA binding sites and characterizes DNA-binding protein specificity. DeepBind utilizes a set of sequences and an experimentally determined binding score for each sequence. This software is built on deep learning, a scalable and modular pattern discovery method and doesn’t need common application-specific heuristics like seed finding. It can discover new patterns even when the locations of patterns within sequences are not known.
Provides a suite of methods important for the prediction of protein structural and functional features. predictProtein is a web server that incorporates over 30 tools. This software searches up-to-date public sequence databases, creates alignments, and predicts aspects of protein structure and function. It can help when little is known about the protein in question. For medium-to-high throughput analyses, downloadable software packages and the PredictProtein Machine Image (PPMI) are available.
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.
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.
A flexible, interactive, template-based webserver to predict protein-protein interface of a given monomeric query protein using its close and remote structural neighbors. Potential interfacial residues are identified by iteratively “mapping” interaction sites of each structural neighbor involved in a complex to individual residues in the query protein. Residues which frequently have interactions mapped to them are defined to be interfacial.
Classifies individual protein interfaces. EPPIC employs information about the evolutionary conservation of interface residues to proceed. It is able to consider the crystal lattice as a whole and identify the biological assembly therein. This tool can give a comprehensive enumeration of all valid assemblies in a protein crystal lattice. It takes into account topology and symmetry considerations.
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.
Allows visualization and prediction of potential interaction regions at protein surfaces. ArDock is a web application that allows the manipulation of different proteins and set of protein chains. It includes features for detecting interface residues using a structural information. This tool does not perform explicit clustering of surface residues to predict interaction patches.
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.
Predicts the RNA-, DNA-, and protein-binding residues located in the intrinsically disordered regions. DisoRDPbind is implemented using a runtime-efficient multi-layered design that utilizes information extracted from physiochemical properties of amino acids, sequence complexity, putative secondary structure and disorder, and sequence alignment. Its outputs complement predictions of representative methods that were built using structured DNA- and RNA-binding residues. Predictions of disordered protein-binding residues generated by DisoRDPbind are characterized by strong correlations, better predictive performance and higher runtime when compared with the closest ANCHOR method.
Enables evolutionary conservation analyses of protein interactions within protein quaternary structures. ConPlex automatically identifies protein interfaces and carries out evolutionary conservation analyses for the interface regions. It allows the results of the residue-specific conservation analysis to be displayed on the protein complex structure and provides several options to customize the display output to fit each user’s needs.
Assists users in distinguishing biologically-relevant and crystallographic protein-protein interfaces. IChemPIC is a computational approach that permits users to include hydrogen atoms allowing the utilization of hydrogen-bonds as descriptors for model development. This method can be applied to interfaces presenting post-translational modifications. The area of use covers small biological protein-protein interfaces to larger crystallographic contacts.
Calculates various commonly used structural and physicochemical descriptors and proteochemometrics modeling descriptors for amino acid sequences. Users can select appropriate descriptors calculated by protr or ProtrWeb according to their needs, and conveniently apply various statistical analysis and machine learning methods in R to solve various biological questions concerning the structures, functions and interactions of proteins and peptides.
The server is designed for protein Molecular Recognition Feature (MoRF) prediction. MoRFpred utilizes a novel design in which annotations generated by sequence alignment are fused with predictions generated by a Support Vector Machine (SVM), which uses a custom designed set of sequence-derived features. The features provide information about evolutionary profiles, selected physiochemical properties of amino acids, and predicted disorder, solvent accessibility and B-factors.
Identifies interacting residues from sequence alone. PROFisis is a computational-method that reliably identifies interface residues from sequence could, therefore, be extremely valuable. It identifies protein-protein interaction (PPI) sites. This program can be run via the PredictProtein service.
A meta web server built on three individual web servers: cons-PPISP, PINUP, and Promate. A linear regression method, using the raw scores of the three severs as input, was trained on a set of 35 nonhomologous proteins. Cross validation showed that meta-PPISP outperforms all the three individual servers.
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.
Predicts interacting amino acid residues in proteins that are most likely to interact with other proteins, given the 3D structures of subunits of a protein complex. The prediction method is based on solvent accessible surface area of residues in the isolated subunits, a propensity scale for interface residues and a clustering algorithm to identify surface regions with residues of high interface propensities.