GalaxyTongDock: Symmetric and asymmetric ab initio protein–protein docking web server with improved energy parameters

2019 ◽  
Vol 40 (27) ◽  
pp. 2413-2417 ◽  
Author(s):  
Taeyong Park ◽  
Minkyung Baek ◽  
Hasup Lee ◽  
Chaok Seok
Keyword(s):  
Ab Initio ◽  
Web Server ◽  
Protein Docking ◽  
Energy Parameters

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 The pepATTRACT web server for blind, large-scale peptide–protein docking

2017 ◽  
Vol 45 (W1) ◽  
pp. W361-W364 ◽  
Author(s):  
Sjoerd J. de Vries ◽  
Julien Rey ◽  
Christina E. M. Schindler ◽  
Martin Zacharias ◽  
Pierre Tuffery
Keyword(s):  
Large Scale ◽  
Web Server ◽  
Protein Docking

scholarly journals The ClusPro web server for protein–protein docking

2017 ◽  
Vol 12 (2) ◽  
pp. 255-278 ◽  
Author(s):  
Dima Kozakov ◽  
David R Hall ◽  
Bing Xia ◽  
Kathryn A Porter ◽  
Dzmitry Padhorny ◽  
...  
Keyword(s):  
Web Server ◽  
Protein Docking

In Silico Design of Fusion Toxin DT389GCSF and a Comparative Study

2020 ◽  
Vol 16 (3) ◽  
pp. 238-244 ◽  
Author(s):  
Maryam G. Siahmazgi ◽  
Mohammad A.N. Khalili ◽  
Fathollah Ahmadpour ◽  
Sirus Khodadadi ◽  
Mehdi Zeinoddini
Keyword(s):  
Fusion Protein ◽  
Protein Interactions ◽  
In Silico ◽  
Stimulate Factor ◽  
Web Server ◽  
Colony Stimulate Factor ◽  
Protein Docking ◽  
Amino Acid Sequences ◽  
Fusion Toxin ◽  
Normal Tissues

Background: Chemotherapy and radiotherapy have negative effects on normal tissues and they are very expensive and lengthy treatments. These disadvantages have recently attracted researchers to the new methods that specifically affect cancerous tissues and have lower damage to normal tissues. One of these methods is the use of intelligent recombinant fusion toxin. The fusion toxin DTGCSF, which consists of linked Diphtheria Toxin (DT) and Granulocyte Colony Stimulate Factor (GCSF), was first studied by Chadwick et al. in 1993 where HATPL linker provided the linking sequence between GCSF and the 486 amino acid sequences of DT. Methods: In this study, the fusion toxin DT389GCSF is evaluated for functional structure in silico. With the idea of the commercial fusion toxin of Ontak, the DT in this fusion protein is designed incomplete for 389 amino acids and is linked to the beginning of the GCSF cytokine via the SG4SM linker (DT389GCSF). The affinity of the DT389GCSF as a ligand with GCSF-R as receptor was compared with DT486GCSF as a ligand with GCSF-R as receptor. Both DT486GCSF and its receptor GCSF-R have been modeled by Easy Modeler2 software. Our fusion protein (DT389GCSF) and GCSF-R are modeled through Modeller software; all of the structures were confirmed by server MDWEB and VMD software. Then, the interaction studies between two proteins are done using protein-protein docking (HADDOCK 2.2 web server) for both the fusion protein in this study and DT486GCSF. Results: The HADDOCK results demonstrate that the interaction of DT389GCSF with GCSF-R is very different and has a more powerful interaction than DT486GCSF with GCSF-R. Conclusion: HADDOCK web server is operative tools for evaluation of protein–protein interactions, therefore, in silico study of DT389GCSF will help with studying the function and the structure of these molecules. Moreover, DT389GCSF may have important new therapeutic applications.

scholarly journals pyDockWEB: a web server for rigid-body protein–protein docking using electrostatics and desolvation scoring

2013 ◽  
Vol 29 (13) ◽  
pp. 1698-1699 ◽  
Author(s):  
Brian Jiménez-García ◽  
Carles Pons ◽  
Juan Fernández-Recio
Keyword(s):  
Rigid Body ◽  
Web Server ◽  
Protein Docking ◽  
Body Protein

An ab Initio Study of van der Waals Potential Energy Parameters for Silver Clusters

2011 ◽  
Vol 115 (11) ◽  
pp. 2332-2339 ◽  
Author(s):  
Vesa Hänninen ◽  
Markus Korpinen ◽  
Qinghua Ren ◽  
Robert Hinde ◽  
Lauri Halonen
Keyword(s):  
Potential Energy ◽  
Ab Initio ◽  
Van Der Waals ◽  
Silver Clusters ◽  
Ab Initio Study ◽  
Energy Parameters

scholarly journals SPServer: split-statistical potentials for the analysis of protein structures and protein–protein interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Joaquim Aguirre-Plans ◽  
Alberto Meseguer ◽  
Ruben Molina-Fernandez ◽  
Manuel Alejandro Marín-López ◽  
Gaurav Jumde ◽  
...  
Keyword(s):  
Amino Acid ◽  
Solvent Accessibility ◽  
Protein Structures ◽  
Web Server ◽  
Protein Docking ◽  
Scoring Functions ◽  
Statistical Potentials ◽  
Protein Folds ◽  
Protein Interfaces

Abstract Background Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein–protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities. Results Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models. Conclusions While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. Server address https://sbi.upf.edu/spserver/.

scholarly journals Integrating cross-linking experiments with ab initio protein-protein docking

2018 ◽  
Author(s):  
Thom Vreven ◽  
Devin K. Schweppe ◽  
Juan D. Chavez ◽  
Chad R. Weisbrod ◽  
Sayaka Shibata ◽  
...  
Keyword(s):  
Experimental Data ◽  
Ab Initio ◽  
Structure Prediction ◽  
Complex Structure ◽  
Protein Docking ◽  
Cross Linking ◽  
Test Cases ◽  
List Type ◽  
Cross Links ◽  
Chemical Cross Linking

ABSTRACTAb initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 12 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases the rank of the top-scoring near-native prediction was improved by at least two fold compared with docking without the cross-link information, and the success rates for the top 5 and top 10 predictions doubled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.HighlightsIncorporating low-resolution cross-linking experimental data in protein-protein docking algorithms improves performance more than two fold.Integration of protein-protein docking with chemical cross-linking reveals information on the configuration of higher order complexes.Symmetry analysis of protein-protein docking results improves the predictions of multimeric complex structures

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