Browse
Submit Data
Databases
API
Help

Dataset Information

62 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

PRISM: a web server and repository for prediction of protein-protein interactions and modeling their 3D complexes.

ABSTRACT: The PRISM web server 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. The PRISM web server is user friendly, free and open to all users at http://cosbi.ku.edu.tr/prism.

SUBMITTER: Baspinar A

PROVIDER: S-EPMC4086120 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

PRISM: a web server and repository for prediction of protein-protein interactions and modeling their 3D complexes.

Baspinar Alper A Cukuroglu Engin E Nussinov Ruth R Keskin Ozlem O Gursoy Attila A

Nucleic acids research 20140514 Web Server issue

The PRISM web server 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 resi ...[more]

PMID: 24829450

Similar Datasets

Protinfo PPC: a web server for atomic level prediction of protein complexes.

Project description:'Protinfo PPC' (Prediction of Protein Complex) is a web server that predicts atomic level structures of interacting proteins from their amino-acid sequences. It uses the interolog method to search for experimental protein complex structures that are homologous to the input sequences submitted by a user. These structures are then used as starting templates to generate protein complex models, which are returned to the user in Protein Data Bank format via email. The server supports modeling of both homo and hetero multimers and generally produces full atomic level models (including insertion/deletion regions) of protein complexes as long as at least one putative homologous template for the query sequences is found. The modeling pipeline behind Protinfo PPC has been rigorously benchmarked and proven to produce highly accurate protein complex models. The fully automated all atom comparative modeling service for protein complexes provided by Protinfo PPC server offers wide capabilities ranging from prediction of protein complex interactions to identification of possible interaction sites, which will be useful for researchers studying these topics. The Protinfo PPC web server is available at http://protinfo.compbio.washington.edu/ppc/.

| S-EPMC2703994 | biostudies-literature

omiXcore: a web server for prediction of protein interactions with large RNA.

Project description:SummaryHere we introduce omiXcore, a server for calculations of protein binding to large RNAs (> 500 nucleotides). Our webserver allows (i) use of both protein and RNA sequences without size restriction, (ii) pre-compiled library for exploration of human long intergenic RNAs interactions and (iii) prediction of binding sites.ResultsomiXcore was trained and tested on enhanced UV Cross-Linking and ImmunoPrecipitation data. The method discriminates interacting and non-interacting protein-RNA pairs and identifies RNA binding sites with Areas under the ROC curve > 0.80, which suggests that the tool is particularly useful to prioritize candidates for further experimental validation.Availability and implementationomiXcore is freely accessed on the web at http://service.tartaglialab.com/grant_submission/omixcore.Contactgian.tartaglia@crg.es.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC5870566 | biostudies-literature

3dRNA v2.0: An Updated Web Server for RNA 3D Structure Prediction.

Project description:3D structures of RNAs are the basis for understanding their biological functions. However, experimentally solved RNA 3D structures are very limited in comparison with known RNA sequences up to now. Therefore, many computational methods have been proposed to solve this problem, including our 3dRNA. In recent years, 3dRNA has been greatly improved by adding several important features, including structure sampling, structure ranking and structure optimization under residue-residue restraints. Particularly, the optimization procedure with restraints enables 3dRNA to treat pseudoknots in a new way. These new features of 3dRNA can greatly promote its performance and have been integrated into the 3dRNA v2.0 web server. Here we introduce these new features in the 3dRNA v2.0 web server for the users.

| S-EPMC6747482 | biostudies-literature

PCFamily: a web server for searching homologous protein complexes.

Project description:The proteins in a cell often assemble into complexes to carry out their functions and play an essential role of biological processes. The PCFamily server identifies template-based homologous protein complexes [called protein complex family (PCF)] and infers functional modules of the query proteins. This server first finds homologous structure complexes of the query using BLASTP to search the structural template database (11,263 complexes). PCFamily then searches the homologous complexes of the templates (query) from a complete genomic database (Integr8 with 6,352,363 protein sequences in 2274 species). According to these homologous complexes across multiple species, this sever infers binding models (e.g. hydrogen-bonds and conserved amino acids in the interfaces), functional modules, and the conserved interacting domains and Gene Ontology annotations of the PCF. Experimental results demonstrate that the PCFamily server can be useful for binding model visualizations and annotating the query proteins. We believe that the server is able to provide valuable insights for determining functional modules of biological networks across multiple species. The PCFamily sever is available at http://pcfamily.life.nctu.edu.tw.

| S-EPMC2896147 | biostudies-literature

RaptorX-Property: a web server for protein structure property prediction.

Project description:RaptorX Property (http://raptorx2.uchicago.edu/StructurePropertyPred/predict/) is a web server predicting structure property of a protein sequence without using any templates. It outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile (i.e. carries little evolutionary information). This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO). DeepCNF not only models complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent property labels. Our experimental results show that, tested on CASP10, CASP11 and the other benchmarks, this server can obtain ?84% Q3 accuracy for 3-state SS, ?72% Q8 accuracy for 8-state SS, ?66% Q3 accuracy for 3-state solvent accessibility, and ?0.89 area under the ROC curve (AUC) for disorder prediction.

| S-EPMC4987890 | biostudies-other

SimRNAweb: a web server for RNA 3D structure modeling with optional restraints.

Project description:RNA function in many biological processes depends on the formation of three-dimensional (3D) structures. However, RNA structure is difficult to determine experimentally, which has prompted the development of predictive computational methods. Here, we introduce a user-friendly online interface for modeling RNA 3D structures using SimRNA, a method that uses a coarse-grained representation of RNA molecules, utilizes the Monte Carlo method to sample the conformational space, and relies on a statistical potential to describe the interactions in the folding process. SimRNAweb makes SimRNA accessible to users who do not normally use high performance computational facilities or are unfamiliar with using the command line tools. The simplest input consists of an RNA sequence to fold RNA de novo. Alternatively, a user can provide a 3D structure in the PDB format, for instance a preliminary model built with some other technique, to jump-start the modeling close to the expected final outcome. The user can optionally provide secondary structure and distance restraints, and can freeze a part of the starting 3D structure. SimRNAweb can be used to model single RNA sequences and RNA-RNA complexes (up to 52 chains). The webserver is available at http://genesilico.pl/SimRNAweb.

| S-EPMC4987879 | biostudies-literature

Vfold-Pipeline: a web server for RNA 3D structure prediction from sequences.

Project description:SummaryRNA 3D structures are critical for understanding their functions and for RNA-targeted drug design. However, experimental determination of RNA 3D structures is laborious and technically challenging, leading to the huge gap between the number of sequences and the availability of RNA structures. Therefore, the computer-aided structure prediction of RNA 3D structures from sequences becomes a highly desirable solution to this problem. Here, we present a pipeline server for RNA 3D structure prediction from sequences that integrates the Vfold2D, Vfold3D and VfoldLA programs. The Vfold2D program can incorporate the SHAPE experimental data in 2D structure prediction. The pipeline can also automatically extract 2D structural constraints from the Rfam database. Furthermore, with a significantly expanded 3D template database for various motifs, this Vfold-Pipeline server can efficiently return accurate 3D structure predictions or reliable initial 3D structures for further refinement.Availability and implementationhttp://rna.physics.missouri.edu/vfoldPipeline/index.html. The data underlying this article have been provided in the article and in its online supplementary material.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC9364377 | biostudies-literature

PPAI: a web server for predicting protein-aptamer interactions.

Project description:BackgroundThe interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is necessary and feasible to construct an accurate and effective computational model to predict aptamers binding to certain interested proteins and protein-aptamer interactions, which is beneficial for understanding mechanisms of protein-aptamer interactions and improving aptamer-based therapies.ResultsIn this study, a novel web server named PPAI is developed to predict aptamers and protein-aptamer interactions with key sequence features of proteins/aptamers and a machine learning framework integrated adaboost and random forest. A new method for extracting several key sequence features of both proteins and aptamers is presented, where the features for proteins are extracted from amino acid composition, pseudo-amino acid composition, grouped amino acid composition, C/T/D composition and sequence-order-coupling number, while the features for aptamers are extracted from nucleotide composition, pseudo-nucleotide composition (PseKNC) and normalized Moreau-Broto autocorrelation coefficient. On the basis of these feature sets and balanced the samples with SMOTE algorithm, we validate the performance of PPAI by the independent test set. The results demonstrate that the Area Under Curve (AUC) is 0.907 for prediction of aptamer, while the AUC reaches 0.871 for prediction of protein-aptamer interactions.ConclusionThese results indicate that PPAI can query aptamers and proteins, predict aptamers and predict protein-aptamer interactions in batch mode precisely and efficiently, which would be a novel bioinformatics tool for the research of protein-aptamer interactions. PPAI web-server is freely available at http://39.96.85.9/PPAI.

| S-EPMC7285591 | biostudies-literature

LigDig: a web server for querying ligand-protein interactions.

Project description:UnlabelledLigDig is a web server designed to answer questions that previously required several independent queries to diverse data sources. It also performs basic manipulations and analyses of the structures of protein-ligand complexes. The LigDig webserver is modular in design and consists of seven tools, which can be used separately, or via linking the output from one tool to the next, in order to answer more complex questions. Currently, the tools allow a user to: (i) perform a free-text compound search, (ii) search for suitable ligands, particularly inhibitors, of a protein and query their interaction network, (iii) search for the likely function of a ligand, (iv) perform a batch search for compound identifiers, (v) find structures of protein-ligand complexes, (vi) compare three-dimensional structures of ligand binding sites and (vii) prepare coordinate files of protein-ligand complexes for further calculations.Availability and implementationLigDig makes use of freely available databases, including ChEMBL, PubChem and SABIO-RK, and software programs, including cytoscape.js, PDB2PQR, ProBiS and Fconv. LigDig can be used by non-experts in bio- and chemoinformatics. LigDig is available at: http://mcm.h-its.org/ligdig.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC4382906 | biostudies-literature

ICM Web: the interactive chromatin modeling web server.

Project description:The interactive chromatin modeling web server (ICM Web) is an interactive tool that allows users to rapidly assess nucleosome stability and fold sequences of DNA into putative chromatin templates. ICM Web takes a sequence composed of As, Cs, Gs, and Ts as input and generates (i) a nucleosome energy level diagram, (ii) coarse-grained representations of free DNA and chromatin and (iii) plots of the helical parameters (Tilt, Roll, Twist, Shift, Slide and Rise) as a function of position. The user can select from several different energy models, nucleosome structures and methods for placing nucleosomes in the energy landscape. Alternatively, if nucleosome footprints are known from experiment, ICM Web can use these positions to create a nucleosome array. The default energy model achieves a correlation coefficient of 0.7 with 100 experimentally determined values of stability and properly predicts the location of six positioned nucleosomes in the mouse mammary tumor virus (MMTV) promoter. ICM Web is suitable for interactively investigating nucleosome stability and chromatin folding for sequences up to tens of kilobases in length. No login is required to use ICM Web.

| S-EPMC2896153 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data