Target Protein Identification on Photocatalyst‐Functionalized Magnetic Affinity Beads

2020 ◽  
Vol 101 (1) ◽  
Author(s):  
Michihiko Tsushima ◽  
Shinichi Sato ◽  
Keita Nakane ◽  
Hiroyuki Nakamura
Keyword(s):  
Protein Identification ◽  
Target Protein ◽  
Affinity Beads

From noncovalent to covalent bonds: a paradigm shift in target protein identification

2013 ◽  
Vol 9 (4) ◽  
pp. 544 ◽  
Author(s):  
Jongmin Park ◽  
Minseob Koh ◽  
Seung Bum Park
Keyword(s):  
Paradigm Shift ◽  
Protein Identification ◽  
Target Protein ◽  
Covalent Bonds

Selective purification and chemical labeling of a target protein on ruthenium photocatalyst-functionalized affinity beads

2017 ◽  
Vol 53 (35) ◽  
pp. 4838-4841 ◽  
Author(s):  
Michihiko Tsushima ◽  
Shinichi Sato ◽  
Hiroyuki Nakamura
Keyword(s):  
Target Protein ◽  
Chemical Labeling ◽  
Protein Mixture ◽  
Affinity Beads

Simultaneous selective purification and chemical labeling of a target protein were achieved on the surface of affinity beads functionalized with a ruthenium photocatalyst and a ligand in a protein mixture.

scholarly journals Putative drug and vaccine target protein identification using comparative genomic analysis of KEGG annotated metabolic pathways of Mycoplasma hyopneumoniae

Genomics ◽  
2013 ◽  
Vol 102 (1) ◽  
pp. 47-56 ◽  
Author(s):  
Dereje Damte ◽  
Joo-Won Suh ◽  
Seung-Jin Lee ◽  
Sileshi Belew Yohannes ◽  
Md. Akil Hossain ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Protein Identification ◽  
Genomic Analysis ◽  
Mycoplasma Hyopneumoniae ◽  
Comparative Genomic Analysis ◽  
Target Protein ◽  
Comparative Genomic ◽  
Vaccine Target

scholarly journals Carbohydrate-Encapsulated Gold Nanoparticles for Rapid Target-Protein Identification and Binding-Epitope Mapping

ChemBioChem ◽  
2005 ◽  
Vol 6 (7) ◽  
pp. 1169-1173 ◽  
Author(s):  
Yu-Ju Chen ◽  
Shu-Hua Chen ◽  
Yuh-Yih Chien ◽  
Yu-Wan Chang ◽  
Hsin-Kai Liao ◽  
...  
Keyword(s):  
Gold Nanoparticles ◽  
Epitope Mapping ◽  
Protein Identification ◽  
Target Protein ◽  
Binding Epitope

scholarly journals PatchSearch: a web server for off-target protein identification

2019 ◽  
Vol 47 (W1) ◽  
pp. W365-W372 ◽  
Author(s):  
Julien Rey ◽  
Inès Rasolohery ◽  
Pierre Tufféry ◽  
Frédéric Guyon ◽  
Gautier Moroy
Keyword(s):  
Protein Identification ◽  
Protein Structures ◽  
Scoring Function ◽  
Web Server ◽  
Binding Mode ◽  
Target Protein ◽  
Local Alignment ◽  
Protein Surfaces ◽  
Target Proteins ◽  
Potential Interactions

Abstract The large number of proteins found in the human body implies that a drug may interact with many proteins, called off-target proteins, besides its intended target. The PatchSearch web server provides an automated workflow that allows users to identify structurally conserved binding sites at the protein surfaces in a set of user-supplied protein structures. Thus, this web server may help to detect potential off-target protein. It takes as input a protein complexed with a ligand and identifies within user-defined or predefined collections of protein structures, those having a binding site compatible with this ligand in terms of geometry and physicochemical properties. It is based on a non-sequential local alignment of the patch over the entire protein surface. Then the PatchSearch web server proposes a ligand binding mode for the potential off-target, as well as an estimated affinity calculated by the Vinardo scoring function. This novel tool is able to efficiently detects potential interactions of ligands with distant off-target proteins. Furthermore, by facilitating the discovery of unexpected off-targets, PatchSearch could contribute to the repurposing of existing drugs. The server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PatchSearch.

An Automated Model for Target Protein prediction in PPI

2020 ◽  
Vol 15 ◽  
Author(s):  
G. Naveen Sundar ◽  
D. Narmadha
Keyword(s):  
Protein Identification ◽  
Research Work ◽  
Target Protein ◽  
Centrality Measures ◽  
Essential Proteins ◽  
Machine Learning Model ◽  
Computer Based ◽  
Living Organisms ◽  
Protein Essentiality ◽  
Sensitivity Specificity

Background: Essential proteins play a crucial role in most of the living organisms. The computer-based task of predicting essential proteins is important for target protein identification, disease treatment and suitable drug development. Objective: Traditionally many experimental and centrality measures have been proposed by researchers to predict protein essentiality. Methods: The prediction accuracy, sensitivity, specificity identified by the traditional methods is very low. Results and Discussion: In this research work, a novel computational based approach such NC-KNN model has been proposed to identify the most essential proteins. The proposed work uses a combination of network topology measure and machine learning model to predict the essential proteins. Conclusion: The proposed work shows a remarkable improvement than seven traditional centrality based measures such as DC, BC, CC, EC, NC, ECC and SC in terms of the metrics such as accuracy(A1), precision(P1), recall(R1), sensitivity(SE) and specificity(SP).

Advances in identification and validation of protein targets of natural products without chemical modification

2016 ◽  
Vol 33 (5) ◽  
pp. 719-730 ◽  
Author(s):  
J. Chang ◽  
Y. Kim ◽  
H. J. Kwon
Keyword(s):  
Natural Products ◽  
Chemical Modification ◽  
Case Studies ◽  
Protein Identification ◽  
Target Protein ◽  
Label Free ◽  
Protein Targets ◽  
Identification Methods ◽  
Drug Candidates ◽  
New Drug

This review focuses on and reports case studies of the latest advances in target protein identification methods for label-free natural products. The integration of newly developed technologies will provide new insights and highlight the value of natural products for use as biological probes and new drug candidates.

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