Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

PASSer: Prediction of Allosteric Sites Server.

ABSTRACT: Allostery is considered important in regulating protein's activity. Drug development depends on the understanding of allosteric mechanisms, especially the identification of allosteric sites, which is a prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and protein dynamics. Here, we present an ensemble learning method, consisting of eXtreme gradient boosting (XGBoost) and graph convolutional neural network (GCNN), to predict allosteric sites. Our model can learn physical properties and topology without any prior information, and shows good performance under multiple indicators. Prediction results showed that 84.9% of allosteric pockets in the test set appeared in the top 3 positions. The PASSer: Protein Allosteric Sites Server (https://passer.smu.edu), along with a command line interface (CLI, https://github.com/smutaogroup/passerCLI) provide insights for further analysis in drug discovery.

SUBMITTER: Tian H

PROVIDER: S-EPMC8360383 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

PASSer: Prediction of Allosteric Sites Server.

Tian Hao H Jiang Xi X Tao Peng P

Machine learning: science and technology 20210513 3

Allostery is considered important in regulating protein's activity. Drug development depends on the understanding of allosteric mechanisms, especially the identification of allosteric sites, which is a prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and protein dynamics. Here, we present an ensemble learning method, consisting of eXtreme gradient boosting (XGBoost) and graph convolutional neural networ ...[more]

PMID: 34396127

Similar Datasets

PASSer: fast and accurate prediction of protein allosteric sites.

Project description:Allostery refers to the biological process by which an effector modulator binds to a protein at a site distant from the active site, known as allosteric site. Identifying allosteric sites is essential for discovering allosteric process and is considered a critical factor in allosteric drug development. To facilitate related research, we developed PASSer (Protein Allosteric Sites Server) at https://passer.smu.edu, a web application for fast and accurate allosteric site prediction and visualization. The website hosts three trained and published machine learning models: (i) an ensemble learning model with extreme gradient boosting and graph convolutional neural network, (ii) an automated machine learning model with AutoGluon and (iii) a learning-to-rank model with LambdaMART. PASSer accepts protein entries directly from the Protein Data Bank (PDB) or user-uploaded PDB files, and can conduct predictions within seconds. The results are presented in an interactive window that displays protein and pockets' structures, as well as a table that summarizes predictions of the top three pockets with the highest probabilities/scores. To date, PASSer has been visited over 49 000 times in over 70 countries and has executed over 6 200 jobs.

| S-EPMC10320119 | biostudies-literature

GPS: a comprehensive www server for phosphorylation sites prediction.

Project description:Protein phosphorylation plays a fundamental role in most of the cellular regulatory pathways. Experimental identification of protein kinases' (PKs) substrates with their phosphorylation sites is labor-intensive and often limited by the availability and optimization of enzymatic reactions. Recently, large-scale analysis of the phosphoproteome by the mass spectrometry (MS) has become a popular approach. But experimentally, it is still difficult to distinguish the kinase-specific sites on the substrates. In this regard, the in silico prediction of phosphorylation sites with their specific kinases using protein's primary sequences may provide guidelines for further experimental consideration and interpretation of MS phosphoproteomic data. A variety of such tools exists over the Internet and provides the predictions for at most 30 PK subfamilies. We downloaded the verified phosphorylation sites from the public databases and curated the literature extensively for recently found phosphorylation sites. With the hypothesis that PKs in the same subfamily share similar consensus sequences/motifs/functional patterns on substrates, we clustered the 216 unique PKs in 71 PK groups, according to the BLAST results and protein annotations. Then, we applied the group-based phosphorylation scoring (GPS) method on the data set; here, we present a comprehensive PK-specific prediction server GPS, which could predict kinase-specific phosphorylation sites from protein primary sequences for 71 different PK groups. GPS has been implemented in PHP and is available on a www server at http://973-proteinweb.ustc.edu.cn/gps/gps_web/.

| S-EPMC1160154 | biostudies-literature

STarMir: a web server for prediction of microRNA binding sites.

Project description:STarMir web server predicts microRNA (miRNA) binding sites on a target ribonucleic acid (RNA). STarMir is an implementation of logistic prediction models developed with miRNA binding data from crosslinking immunoprecipitation (CLIP) studies (Liu,C., Mallick, B., Long, D., Rennie, W.A., Wolenc, A., Carmack, C.S. and Ding, Y. (2013). CLIP-based prediction of mammalian microRNA binding sites. Nucleic Acids Res., 41(14), e138). In both intra-dataset and inter-dataset validations, the models showed major improvements over established algorithms in predictions of both seed and seedless sites. General applicability of the models was indicated by good performance in cross-species validations. The input data for STarMir is processed by the web server to perform prediction of miRNA binding sites, compute comprehensive sequence, thermodynamic and target structure features and a logistic probability as a measure of confidence for each predicted site. For each of seed and seedless sites and for all three regions of a mRNA (3' UTR, CDS and 5' UTR), STarMir output includes the computed binding site features, the logistic probability and a publication-quality diagram of the predicted miRNA:target hybrid. The prediction results are available through both an interactive viewer and downloadable text files. As an application module of the Sfold RNA package (http://sfold.wadsworth.org), STarMir is freely available to all at http://sfold.wadsworth.org/starmir.html.

| S-EPMC4086099 | biostudies-literature

Prediction of allosteric sites and signaling: Insights from benchmarking datasets.

Project description:Allostery is a pervasive mechanism that regulates protein activity through ligand binding at a site different from the orthosteric site. The universality of allosteric regulation complemented by the benefits of highly specific and potentially non-toxic allosteric drugs makes uncovering allosteric sites invaluable. However, there are few computational methods to effectively predict them. Bond-to-bond propensity analysis has successfully predicted allosteric sites in 19 of 20 cases using an energy-weighted atomistic graph. We here extended the analysis onto 432 structures of 146 proteins from two benchmarking datasets for allosteric proteins: ASBench and CASBench. We further introduced two statistical measures to account for the cumulative effect of high-propensity residues and the crucial residues in a given site. The allosteric site is recovered for 127 of 146 proteins (407 of 432 structures) knowing only the orthosteric sites or ligands. The quantitative analysis using a range of statistical measures enables better characterization of potential allosteric sites and mechanisms involved.

| S-EPMC8767309 | biostudies-literature

RNAMethPre: A Web Server for the Prediction and Query of mRNA m6A Sites.

Project description:N6-Methyladenosine (m6A) is the most common mRNA modification; it occurs in a wide range of taxon and is associated with many key biological processes. High-throughput experiments have identified m6A-peaks and sites across the transcriptome, but studies of m6A sites at the transcriptome-wide scale are limited to a few species and tissue types. Therefore, the computational prediction of mRNA m6A sites has become an important strategy. In this study, we integrated multiple features of mRNA (flanking sequences, local secondary structure information, and relative position information) and trained a SVM classifier to predict m6A sites in mammalian mRNA sequences. Our method achieves ideal performance in both cross-validation tests and rigorous independent dataset tests. The server also provides a comprehensive database of predicted transcriptome-wide m6A sites and curated m6A-seq peaks from the literature for both human and mouse, and these can be queried and visualized in a genome browser. The RNAMethPre web server provides a user-friendly tool for the prediction and query of mRNA m6A sites, which is freely accessible for public use at http://bioinfo.tsinghua.edu.cn/RNAMethPre/index.html.

| S-EPMC5056760 | biostudies-literature

PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning.

Project description:Allostery is a fundamental process in regulating protein activities. The discovery, design, and development of allosteric drugs demand better identification of allosteric sites. Several computational methods have been developed previously to predict allosteric sites using static pocket features and protein dynamics. Here, we define a baseline model for allosteric site prediction and present a computational model using automated machine learning. Our model, PASSer2.0, advanced the previous results and performed well across multiple indicators with 82.7% of allosteric pockets appearing among the top three positions. The trained machine learning model has been integrated with the Protein Allosteric Sites Server (PASSer) to facilitate allosteric drug discovery.

| S-EPMC9309527 | biostudies-literature

GPS 6.0: an updated server for prediction of kinase-specific phosphorylation sites in proteins.

Project description:Protein phosphorylation, catalyzed by protein kinases (PKs), is one of the most important post-translational modifications (PTMs), and involved in regulating almost all of biological processes. Here, we report an updated server, Group-based Prediction System (GPS) 6.0, for prediction of PK-specific phosphorylation sites (p-sites) in eukaryotes. First, we pre-trained a general model using penalized logistic regression (PLR), deep neural network (DNN), and Light Gradient Boosting Machine (LightGMB) on 490 762 non-redundant p-sites in 71 407 proteins. Then, transfer learning was conducted to obtain 577 PK-specific predictors at the group, family and single PK levels, using a well-curated data set of 30 043 known site-specific kinase-substrate relations in 7041 proteins. Together with the evolutionary information, GPS 6.0 could hierarchically predict PK-specific p-sites for 44046 PKs in 185 species. Besides the basic statistics, we also offered the knowledge from 22 public resources to annotate the prediction results, including the experimental evidence, physical interactions, sequence logos, and p-sites in sequences and 3D structures. The GPS 6.0 server is freely available at https://gps.biocuckoo.cn. We believe that GPS 6.0 could be a highly useful service for further analysis of phosphorylation.

| S-EPMC10320111 | biostudies-literature

m5UPred: A Web Server for the Prediction of RNA 5-Methyluridine Sites from Sequences.

Project description:As one of the widely occurring RNA modifications, 5-methyluridine (m5U) has recently been shown to play critical roles in various biological functions and disease pathogenesis, such as under stress response and during breast cancer development. Precise identification of m5U sites on RNA is vital for the understanding of the regulatory mechanisms of RNA life. We present here m5UPred, the first web server for in silico identification of m5U sites from the primary sequences of RNA. Built upon the support vector machine (SVM) algorithm and the biochemical encoding scheme, m5UPred achieved reasonable prediction performance with the area under the receiver operating characteristic curve (AUC) greater than 0.954 by 5-fold cross-validation and independent testing datasets. To critically test and validate the performance of our newly proposed predictor, the experimentally validated m5U sites were further separated by high-throughput sequencing techniques (miCLIP-Seq and FICC-Seq) and cell types (HEK293 and HAP1). When tested on cross-technique and cross-cell-type validation using independent datasets, m5UPred achieved an average AUC of 0.922 and 0.926 under mature mRNA mode, respectively, showing reasonable accuracy and reliability. The m5UPred web server is freely accessible now and it should make a useful tool for the researchers who are interested in m5U RNA modification.

| S-EPMC7595847 | biostudies-literature

In Silico Prediction and Validation of CB2 Allosteric Binding Sites to Aid the Design of Allosteric Modulators.

Project description:Although the 3D structures of active and inactive cannabinoid receptors type 2 (CB2) are available, neither the X-ray crystal nor the cryo-EM structure of CB2-orthosteric ligand-modulator has been resolved, prohibiting the drug discovery and development of CB2 allosteric modulators (AMs). In the present work, we mainly focused on investigating the potential allosteric binding site(s) of CB2. We applied different algorithms or tools to predict the potential allosteric binding sites of CB2 with the existing agonists. Seven potential allosteric sites can be observed for either CB2-CP55940 or CB2-WIN 55,212-2 complex, among which sites B, C, G and K are supported by the reported 3D structures of Class A GPCRs coupled with AMs. Applying our novel algorithm toolset-MCCS, we docked three known AMs of CB2 including Ec2la (C-2), trans-β-caryophyllene (TBC) and cannabidiol (CBD) to each site for further comparisons and quantified the potential binding residues in each allosteric binding site. Sequentially, we selected the most promising binding pose of C-2 in five allosteric sites to conduct the molecular dynamics (MD) simulations. Based on the results of docking studies and MD simulations, we suggest that site H is the most promising allosteric binding site. We plan to conduct bio-assay validations in the future.

| S-EPMC8781014 | biostudies-literature

Prediction of allosteric sites and mediating interactions through bond-to-bond propensities.

Project description:Allostery is a fundamental mechanism of biological regulation, in which binding of a molecule at a distant location affects the active site of a protein. Allosteric sites provide targets to fine-tune protein activity, yet we lack computational methodologies to predict them. Here we present an efficient graph-theoretical framework to reveal allosteric interactions (atoms and communication pathways strongly coupled to the active site) without a priori information of their location. Using an atomistic graph with energy-weighted covalent and weak bonds, we define a bond-to-bond propensity quantifying the non-local effect of instantaneous bond fluctuations propagating through the protein. Significant interactions are then identified using quantile regression. We exemplify our method with three biologically important proteins: caspase-1, CheY, and h-Ras, correctly predicting key allosteric interactions, whose significance is additionally confirmed against a reference set of 100 proteins. The almost-linear scaling of our method renders it suitable for high-throughput searches for candidate allosteric sites.

| S-EPMC5007447 | 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