The Study on Molecular Flexibility Treating and Near-Native Structure Screening in Protein-Protein Docking Approaches

2012 ◽  
Vol 28 (1) ◽  
pp. 15
Author(s):  
Feng YANG ◽  
Libin CAO ◽  
Xinqi GONG ◽  
Shan CHANG ◽  
Weizu CHEN ◽  
...  
Keyword(s):  
Protein Docking ◽  
Native Structure ◽  
Molecular Flexibility

Accurate refinement of docked protein complexes using evolutionary information and deep learning

2016 ◽  
Vol 14 (03) ◽  
pp. 1642002 ◽  
Author(s):  
Bahar Akbal-Delibas ◽  
Roshanak Farhoodi ◽  
Marc Pomplun ◽  
Nurit Haspel
Keyword(s):  
Deep Learning ◽  
Protein Complexes ◽  
Scoring Function ◽  
Protein Docking ◽  
Training Data ◽  
Evolutionary Information ◽  
Native Structure ◽  
Learning Network ◽  
Small Set ◽  
Deep Learning Network

One of the major challenges for protein docking methods is to accurately discriminate native-like structures from false positives. Docking methods are often inaccurate and the results have to be refined and re-ranked to obtain native-like complexes and remove outliers. In a previous work, we introduced AccuRefiner, a machine learning based tool for refining protein–protein complexes. Given a docked complex, the refinement tool produces a small set of refined versions of the input complex, with lower root-mean-square-deviation (RMSD) of atomic positions with respect to the native structure. The method employs a unique ranking tool that accurately predicts the RMSD of docked complexes with respect to the native structure. In this work, we use a deep learning network with a similar set of features and five layers. We show that a properly trained deep learning network can accurately predict the RMSD of a docked complex with 1.40 Å error margin on average, by approximating the complex relationship between a wide set of scoring function terms and the RMSD of a docked structure. The network was trained on 35000 unbound docking complexes generated by RosettaDock. We tested our method on 25 different putative docked complexes produced also by RosettaDock for five proteins that were not included in the training data. The results demonstrate that the high accuracy of the ranking tool enables AccuRefiner to consistently choose the refinement candidates with lower RMSD values compared to the coarsely docked input structures.

RESIDUE CONSERVATION INFORMATION FOR GENERATING NEAR-NATIVE STRUCTURES IN PROTEIN–PROTEIN DOCKING

2006 ◽  
Vol 04 (04) ◽  
pp. 793-806 ◽  
Author(s):  
YUHUA DUAN ◽  
BOOJALA V. B. REDDY ◽  
YIANNIS N. KAZNESSIS
Keyword(s):  
Protein Complexes ◽  
Predictive Ability ◽  
Accessible Surface Area ◽  
Protein Docking ◽  
Solvent Accessible Surface Area ◽  
Complex Structures ◽  
Native Structure ◽  
Interacting Protein ◽  
Residue Conservation ◽  
Shape Complementarity

Motivation: Protein–protein docking algorithms typically generate large numbers of possible complex structures with only a few of them resembling the native structure. Recently (Duan et al., Protein Sci, 14:316–218, 2005), it was observed that the surface density of conserved residue positions is high at the interface regions of interacting protein surfaces, except for antibody–antigen complexes, where a lesser number of conserved positions than average is observed at the interface regions. Using this observation, we identified putative interacting regions on the surface of interacting partners and significantly improved docking results by assigning top ranks to near-native complex structures. In this paper, we combine the residue conservation information with a widely used shape complementarity algorithm to generate candidate complex structures with a higher percentage of near-native structures (hits). What is new in this work is that the conservation information is used early in the generation stage and not only in the ranking stage of the docking algorithm. This results in a significantly larger number of generated hits and an improved predictive ability in identifying the native structure of protein–protein complexes. Results: We report on results from 48 well-characterized protein complexes, which have enough residue conservation information from the same 59 benchmark complexes used in our previous work. We compute conservation indices of residue positions on the surfaces of interacting proteins using available homologous sequences from UNIPROT and calculate the solvent accessible surface area. We combine this information with shape-complementarity scores to generate candidate protein–protein complex structures. When compared with pure shape-complementarity algorithms, performed by FTDock, our method results in significantly more hits, with the improvement being over 100% in many instances. We demonstrate that residue conservation information is useful not only in refinement and scoring of docking solutions, but also helpful in enrichment of near-native-structures during the generation of candidate geometries of complex structures.

The native structure of the eukaryotic ribosome

1983 ◽  
Vol 41 ◽  
pp. 432-433
Author(s):  
R.A. Milligan ◽  
P.N.T. Unwin
Keyword(s):  
Ribosomal Proteins ◽  
Crystal Form ◽  
Native Structure ◽  
X Ray Diffraction ◽  
Eukaryotic Ribosome ◽  
Vitro Analysis ◽  
In Vitro Analysis ◽  
Resolution Structure ◽  
Stable Unit

A detailed understanding of the mechanism of protein synthesis will ultimately depend on knowledge of the native structure of the ribosome. Towards this end we have investigated the low resolution structure of the eukaryotic ribosome embedded in frozen buffer, making use of a system in which the ribosomes crystallize naturally.The ribosomes in the cells of early chicken embryos form crystalline arrays when the embryos are cooled at 4°C. We have developed methods to isolate the stable unit of these arrays, the ribosome tetramer, and have determined conditions for the growth of two-dimensional crystals in vitro, Analysis of the proteins in the crystals by 2-D gel electrophoresis demonstrates the presence of all ribosomal proteins normally found in polysomes. There are in addition, four proteins which may facilitate crystallization. The crystals are built from two oppositely facing P4 layers and the predominant crystal form, accounting for >80% of the crystals, has the tetragonal space group P4212, X-ray diffraction of crystal pellets demonstrates that crystalline order extends to ~ 60Å.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Proteomic Analysis of Medicinal Plant Calotropis Gigantea by insilico Peptide Mass Fingerprinting

2020 ◽  
Vol 16 ◽  
Author(s):  
Saad Ur Rehman ◽  
Muhammad Rizwan ◽  
Sajid Khan ◽  
Azhar Mehmood ◽  
Anum Munir
Keyword(s):  
Medicinal Plant ◽  
Structure Prediction ◽  
3D Structure ◽  
Peptide Mass Fingerprinting ◽  
Protein Docking ◽  
Receptor Protein ◽  
Human Leukemia ◽  
Protein Database ◽  
Calotropis Gigantea ◽  
Peptide Mass

: Medicinal plants are the basic source of medicinal compounds traditionally used for the treatment of human diseases. Calotropis gigantea a medicinal plant belonging to the family of Apocynaceae in the plant kingdom and subfamily Asclepiadaceae usually bearing multiple medicinal properties to cure a variety of diseases. Background: The Peptide Mass Fingerprinting (PMF) identifies the proteins from a reference protein database by comparing the amino acid sequence that is previously stored in a database and identified. Method: The calculation of insilico peptide masses is done through the ExPASy PeptideMass and these masses are used to identify the peptides from MASCOT online server. Anticancer probability is calculated from the iACP server, docking of active peptides is done by CABS-dock the server. Objective: The purpose of the study is to identify the peptides having anti-cancerous properties by in-silico peptide mass fingerprinting. Results : The anti-cancerous peptides are identified with the MASCOT peptide mass fingerprinting server, the identified peptides are screened and only the anti-cancer are selected. De novo peptide structure prediction is used for 3D structure prediction by PEP-FOLD 3 server. The docking results confirm strong bonding with the interacting amino acids of the receptor protein of breast cancer BRCA1 which shows the best peptide binding to the Active chain, the human leukemia protein docking with peptides shows the accurate binding. Conclusion : These peptides are stable and functional and are the best way for the treatment of cancer and many other deadly diseases.

scholarly journals Hide and seek: interplay between influenza viruses and B cells

2020 ◽  
Vol 32 (9) ◽  
pp. 605-611 ◽  
Author(s):  
Masayuki Kuraoka ◽  
Yu Adachi ◽  
Yoshimasa Takahashi
Keyword(s):  
B Cells ◽  
B Cell ◽  
Surface Protein ◽  
Influenza Viruses ◽  
Influenza Vaccines ◽  
Native Structure ◽  
Humoral Immune ◽  
Protective Antibodies ◽  
Major Surface ◽  
And Function

Abstract Influenza virus constantly acquires genetic mutations/reassortment in the major surface protein, hemagglutinin (HA), resulting in the generation of strains with antigenic variations. There are, however, HA epitopes that are conserved across influenza viruses and are targeted by broadly protective antibodies. A goal for the next-generation influenza vaccines is to stimulate B-cell responses against such conserved epitopes in order to provide broad protection against divergent influenza viruses. Broadly protective B cells, however, are not easily activated by HA antigens with native structure, because the virus has multiple strategies to escape from the humoral immune responses directed to the conserved epitopes. One such strategy is to hide the conserved epitopes from the B-cell surveillance by steric hindrance. Technical advancement in the analysis of the human B-cell antigen receptor (BCR) repertoire has dissected the BCRs to HA epitopes that are hidden in the native structure but are targeted by broadly protective antibodies. We describe here the characterization and function of broadly protective antibodies and strategies that enable B cells to seek these hidden epitopes, with potential implications for the development of universal influenza vaccines.

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