Allosteric site detection software tools | Protein structure data analysis
Allostery is a fundamental process that regulates a protein’s functional activity through the induction of changes in its conformation and dynamics in response to the perturbation of an effector at a site distinct from the active site, also termed the allosteric site. Structure-based allosteric site prediction tools analyze protein structure to identify potential effector binding sites and allosteric communication between sites.
Analyzes protein structure, finds potential functional/effector binding sites, and shows allosteric communication between the sites. It is possible to explore, for instance, potential effector binding sites in a given structure as targets for allosteric drugs.
Provides an effective tool for the prediction of key residues that mediate the allosteric communication in an ensemble of pathways and functionally plausible residues. The centrality parameters also provided by the MCPath server labels the plausible functional regions of the structure, such as active sites or catalytic sites and also the regions with a role in allosteric communication.
Retrieves allosteric modulators and evaluates biological functions by mapping allosterome data. AlloFinder uncovers putative allosteric sites for a query protein using feature and dynamic perturbations followed by in silico screening of potential allosteric modulators at the predicted allosteric site according to accurate allosteric scoring function. It can automatically discover allosteric modulators for a particular protein.
Uses elegant algorithms such as pocket-based analysis and support vector machine (SVM) classifier to predict the location of allosteric sites in proteins. Allosite’s model uses a rigorous selection of high-quality datasets for training and exhibits a high accuracy of ∼95% on the test set. More importantly, the prediction of novel allosteric sites for several proteins using Allosite was experimentally supported by mutagenesis.
A server for the prediction of allosteric and regulatory sites on protein structures. PARS queries protein dynamics and structural conservation to identify pockets that may exert a regulatory effect upon binding of a small-molecule ligand.
Provides a variety of interfaces and graphical visualizations to facilitate the viewing and analysis of allosteric sites in the benchmarking sets, including structural and pharmacophoric properties. ASBench allows for browsing the sets and provides a search filter for flexible query. A full (or query) list of allosteric sites in the benchmarking sets integrated within a panel is displayed by clicking from the homepage of entries. Then, checking the selected site in the panel opens a new browser window with a detailed view of the representative allosteric complex containing the site. In addition, other complex structures containing the same site and different allosteric sites within the same protein are hyperlinked under “Related Structures”. Finally, all data in the benchmarking sets can be downloaded using the "Download" link, while the statistical results of datasets are diagramed and displayed in "Statistics" page.
Provides a user-friendly interface to predict the binding affinities of allosteric ligand-protein interactions. Furthermore, critical energy contributions that contribute to allosteric binding are offered. Alloscore exhibits prominent performance in describing allosteric binding and could be useful in allosteric virtual screening and the structural optimization of allosteric agonists/antagonists.
Allows users to analyze allosteric signaling in proteins. AlloSigMA is a web platform supplying an estimate of the allosteric free energy thanks to a structure-based statistical mechanical model. The program can be used for investigating various proteins including small monomeric structures or even large protein complexes. Besides, it also can be employed for searching latent regulatory exosites or analyzing of clinical high-throughput data on mutations.
A computational method that predicts a pathway of residues that mediate protein allosteric communication. The pathway is predicted using only a combination of distance constraints between contiguous residues and evolutionary data. AlloPathFinder provides a simple, computationally efficient means of predicting a set of residues that mediate allosteric communication.
A modular and scalable modelling methodology that alleviates the regulatory as well as the combinatorial complexity of biochemical networks. ANC can provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology.
Discriminates and/or ranks allosteric pockets against orthosteric pockets and non-allosteric pockets. ALLO consists of (1) Naive Bayes Classifier (NBC) models and (2) artificial neural networks (ANN) models. The software uses DoGSiteScorer to identify pockets and to generate descriptors and then trains NBC and ANN models. It can be used to select or prioritize a set of conformations from molecular dynamics (MD) simulations.
Predicts allosteric pockets on proteins, was developed. AlloPred uses perturbation of normal modes alongside pocket descriptors in a machine learning approach that ranks the pockets on a protein. AlloPred ranked an allosteric pocket top for 23 out of 40 known allosteric proteins, showing comparable and complementary performance to two existing methods. The AlloPred web server allows visualisation and analysis of predictions.
Calculates area compressibility moduli of lipid bilayers and their individual leaflets. This algorithm yields elastic moduli that are in agreement with available experimental data for both single and multi-component bilayers composed of saturated, unsaturated lipids and cholesterol and simulated at different temperatures. This method analyzes the area compressibility of bilayers under tension.
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