DOCK/PIERR: Web Server for Structure Prediction of Protein–Protein Complexes

2014 ◽  
pp. 199-207 ◽  
Author(s):  
Shruthi Viswanath ◽  
D. V. S. Ravikant ◽  
Ron Elber
Keyword(s):  
Structure Prediction ◽  
Protein Complexes ◽  
Web Server

scholarly journals FALCON2: a web server for high-quality prediction of protein tertiary structures

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lupeng Kong ◽  
Fusong Ju ◽  
Haicang Zhang ◽  
Shiwei Sun ◽  
Dongbo Bu
Keyword(s):  
Protein Structure ◽  
Ab Initio ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Web Server ◽  
High Quality ◽  
Tertiary Structures ◽  
Target Proteins ◽  
High Quality Protein ◽  
Protein Functions

Abstract Background Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a template-based modeling approach (called ProALIGN) and an ab initio prediction approach (called ProFOLD). Briefly speaking, ProALIGN aligns a target protein with templates through exploiting the patterns of context-specific alignment motifs and then builds the final structure with reference to the homologous templates. In contrast, ProFOLD uses an end-to-end neural network to estimate inter-residue distances of target proteins and builds structures that satisfy these distance constraints. These two approaches emphasize different characteristics of target proteins: ProALIGN exploits structure information of homologous templates of target proteins while ProFOLD exploits the co-evolutionary information carried by homologous protein sequences. Recent progress has shown that the combination of template-based modeling and ab initio approaches is promising. Results In the study, we present FALCON2, a web server that integrates ProALIGN and ProFOLD to provide high-quality protein structure prediction service. For a target protein, FALCON2 executes ProALIGN and ProFOLD simultaneously to predict possible structures and selects the most likely one as the final prediction result. We evaluated FALCON2 on widely-used benchmarks, including 104 CASP13 (the 13th Critical Assessment of protein Structure Prediction) targets and 91 CASP14 targets. In-depth examination suggests that when high-quality templates are available, ProALIGN is superior to ProFOLD and in other cases, ProFOLD shows better performance. By integrating these two approaches with different emphasis, FALCON2 server outperforms the two individual approaches and also achieves state-of-the-art performance compared with existing approaches. Conclusions By integrating template-based modeling and ab initio approaches, FALCON2 provides an easy-to-use and high-quality protein structure prediction service for the community and we expect it to enable insights into a deep understanding of protein functions.

scholarly journals Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 432 ◽  
Author(s):  
Chandran Nithin ◽  
Pritha Ghosh ◽  
Janusz Bujnicki
Keyword(s):  
Rna Splicing ◽  
Structure Prediction ◽  
Protein Complexes ◽  
3D Structure ◽  
Structural Models ◽  
Computational Prediction ◽  
Computational Docking ◽  
3D Structure Prediction ◽  
Rna Protein Complexes

RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.

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

2019 ◽  
Vol 20 (17) ◽  
pp. 4116 ◽  
Author(s):  
Jun Wang ◽  
Jian Wang ◽  
Yanzhao Huang ◽  
Yi Xiao
Keyword(s):  
Structure Prediction ◽  
3D Structure ◽  
Web Server ◽  
Structure Optimization ◽  
Optimization Procedure ◽  
Biological Functions ◽  
Rna Sequences ◽  
3D Structures ◽  
3D Structure Prediction ◽  
Sampling Structure

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.

On the Use of low-resolution Data to Improve Structure Prediction of Proteins and Protein Complexes

2009 ◽  
Vol 5 (11) ◽  
pp. 3129-3137 ◽  
Author(s):  
Marco D’Abramo ◽  
Tim Meyer ◽  
Pau Bernadó ◽  
Carles Pons ◽  
Juan Fernández Recio ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Protein Complexes ◽  
Low Resolution ◽  
Resolution Data

CSSP(Consensus Secondary Structure Prediction): a web-based server for structural biologists

2009 ◽  
Vol 42 (2) ◽  
pp. 336-338 ◽  
Author(s):  
Ankit Gupta ◽  
Avnish Deshpande ◽  
Janardhan Kumar Amburi ◽  
Radhakrishnan Sabarinathan ◽  
Ramaswamy Senthilkumar ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Three Dimensional ◽  
Web Server ◽  
Dimensional Structure ◽  
Structural Elements ◽  
Web Based ◽  
Consensus Secondary Structure ◽  
Helical Wheel

Sequence–structure correlation studies are important in deciphering the relationships between various structural aspects, which may shed light on the protein-folding problem. The first step of this process is the prediction of secondary structure for a protein sequence of unknown three-dimensional structure. To this end, a web server has been created to predict the consensus secondary structure using well known algorithms from the literature. Furthermore, the server allows users to see the occurrence of predicted secondary structural elements in other structure and sequence databases and to visualize predicted helices as a helical wheel plot. The web server is accessible at http://bioserver1.physics.iisc.ernet.in/cssp/.

scholarly journals TRFolder-W: a web server for telomerase RNA structure prediction in yeast genomes

2012 ◽  
Vol 28 (20) ◽  
pp. 2696-2697
Author(s):  
D. Zhang ◽  
X. Xue ◽  
R. L. Malmberg ◽  
L. Cai
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Web Server ◽  
Telomerase Rna ◽  
Rna Structure Prediction ◽  
Yeast Genomes

scholarly journals LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Charles Christoffer ◽  
Vijay Bharadwaj ◽  
Ryan Luu ◽  
Daisuke Kihara
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo ◽  
Protein Complexes ◽  
Protein Sequences ◽  
Data Bank ◽  
Protein Docking ◽  
Functional Mechanisms ◽  
Established Technique

Protein-protein docking is a useful tool for modeling the structures of protein complexes that have yet to be experimentally determined. Understanding the structures of protein complexes is a key component for formulating hypotheses in biophysics regarding the functional mechanisms of complexes. Protein-protein docking is an established technique for cases where the structures of the subunits have been determined. While the number of known structures deposited in the Protein Data Bank is increasing, there are still many cases where the structures of individual proteins that users want to dock are not determined yet. Here, we have integrated the AttentiveDist method for protein structure prediction into our LZerD webserver for protein-protein docking, which enables users to simply submit protein sequences and obtain full-complex atomic models, without having to supply any structure themselves. We have further extended the LZerD docking interface with a symmetrical homodimer mode. The LZerD server is available at https://lzerd.kiharalab.org/.

scholarly journals PPI-MASS: An Interactive Web Server to Identify Protein-Protein Interactions From Mass Spectrometry-Based Proteomics Data

2021 ◽  
Vol 8 ◽  
Author(s):  
Mariela González-Avendaño ◽  
Simón Zúñiga-Almonacid ◽  
Ian Silva ◽  
Boris Lavanderos ◽  
Felipe Robinson ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Web Server ◽  
Target Protein ◽  
Protein Protein Interactions ◽  
Proteomics Data ◽  
Functional Protein ◽  
Web Platform ◽  
The Web

Mass spectrometry-based proteomics methods are widely used to identify and quantify protein complexes involved in diverse biological processes. Specifically, tandem mass spectrometry methods represent an accurate and sensitive strategy for identifying protein-protein interactions. However, most of these approaches provide only lists of peptide fragments associated with a target protein, without performing further analyses to discriminate physical or functional protein-protein interactions. Here, we present the PPI-MASS web server, which provides an interactive analytics platform to identify protein-protein interactions with pharmacological potential by filtering a large protein set according to different biological features. Starting from a list of proteins detected by MS-based methods, PPI-MASS integrates an automatized pipeline to obtain information of each protein from freely accessible databases. The collected data include protein sequence, functional and structural properties, associated pathologies and drugs, as well as location and expression in human tissues. Based on this information, users can manipulate different filters in the web platform to identify candidate proteins to establish physical contacts with a target protein. Thus, our server offers a simple but powerful tool to detect novel protein-protein interactions, avoiding tedious and time-consuming data postprocessing. To test the web server, we employed the interactome of the TRPM4 and TMPRSS11a proteins as a use case. From these data, protein-protein interactions were identified, which have been validated through biochemical and bioinformatic studies. Accordingly, our web platform provides a comprehensive and complementary tool for identifying protein-protein complexes assisting the future design of associated therapies.

scholarly journals Leri: a web-server for identifying protein functional networks from evolutionary couplings

2020 ◽  
Author(s):  
Ngaam J. Cheung ◽  
Arun T. John Peter ◽  
Benoit Kornmann
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Protein Function ◽  
Structure Prediction ◽  
High Performance ◽  
Web Server ◽  
Functional Networks ◽  
Computational Results ◽  
High Quality ◽  
Recent Developments

ABSTRACTInformation on the co-evolution of amino acid pairs in a protein can be used for endeavors such as protein engineering, mutation design, and structure prediction. Here we report a method that captures significant determinants of proteins using estimated co-evolution information to identify networks of residues, termed “residue communities”, relevant to protein function. By taking advantage of recent developments in high-performance and parallel computing, we constructed a web-server, Leri, that identifies relevant residue communities to allow researchers to investigate how a protein evolves and folds for function(s). All the data of the computational results including high-quality images can be downloaded and presented for publication. This web-server, written in C++, is sufficiently rapid to enable the studies on proteins of up to 400 amino acids.

Sign in / Sign up

Export Citation Format

Share Document