Investigation of protein binding affinity and preferred orientations in ion exchange systems using a homologous protein library

2009 ◽  
Vol 102 (3) ◽  
pp. 869-881 ◽  
Author(s):  
Wai Keen Chung ◽  
Ying Hou ◽  
Alexander Freed ◽  
Melissa Holstein ◽  
George I. Makhatadze ◽  
...  
Keyword(s):  
Ion Exchange ◽  
Protein Binding ◽  
Binding Affinity ◽  
Homologous Protein ◽  
Protein Library

Investigation of protein binding affinity in multimodal chromatographic systems using a homologous protein library

2010 ◽  
Vol 1217 (2) ◽  
pp. 191-198 ◽  
Author(s):  
Wai Keen Chung ◽  
Ying Hou ◽  
Melissa Holstein ◽  
Alexander Freed ◽  
George I. Makhatadze ◽  
...  
Keyword(s):  
Protein Binding ◽  
Binding Affinity ◽  
Homologous Protein ◽  
Protein Library

scholarly journals Learning protein binding affinity using privileged information

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Wajid Arshad Abbasi ◽  
Amina Asif ◽  
Asa Ben-Hur ◽  
Fayyaz ul Amir Afsar Minhas
Keyword(s):  
Protein Binding ◽  
Binding Affinity ◽  
Privileged Information

Evaluation for Protein Binding Affinity of Maghemite and Magnetite Nanoparticles

2007 ◽  
Vol 7 (11) ◽  
pp. 3706-3708 ◽  
Author(s):  
Se Chan Kang ◽  
Yong Jun Jo ◽  
Jong Phil Bak ◽  
Ki-Chul Kim ◽  
Young-Sung Kim
Keyword(s):  
Protein Binding ◽  
Binding Affinity ◽  
Magnetite Nanoparticles ◽  
Polyacrylamide Gel Electrophoresis ◽  
Sodium Dodecyl ◽  
Human Lung Cancer ◽  
Maghemite Nanoparticles ◽  
Binding Ability ◽  
Sds Page ◽  
Lung Cancer A549 Cell

We investigated the protein binding affinity of magnetite (Fe3O4) and maghemite (γ-Fe2O3) nanoparticles with against non-characterized protein from human lung cancer A549 cell line on sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). The binding ability of maghemite was 400 ng/mg. According to the SDS-PAGE results, the protein binding affinity of maghemite nanoparticles is stronger than magnetite nanoparticles. These data suggest that a protein can be detected with maghemite nanoparticles.

Using an oxidized porous silicon interferometer for determination of relative protein binding affinity through non-covalent capture probe immobilization

2007 ◽  
Vol 204 (5) ◽  
pp. 1444-1448 ◽  
Author(s):  
Michael P. Schwartz ◽  
Christine Yu ◽  
Sara D. Alvarez ◽  
Benjamin Migliori ◽  
Denis Godin ◽  
...  
Keyword(s):  
Porous Silicon ◽  
Protein Binding ◽  
Binding Affinity ◽  
Capture Probe ◽  
Oxidized Porous Silicon

scholarly journals RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features

2020 ◽  
Author(s):  
Stefan Holderbach ◽  
Lukas Adam ◽  
Bhyravabhotla Jayaram ◽  
Rebecca Wade ◽  
Goutam Mukherjee
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Protein Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Rapid Screening ◽  
Scoring Functions ◽  
Energy Prediction ◽  
Molecular Features ◽  
Physicochemical Descriptors

The virtual screening of large numbers of compounds against target protein binding sites has become an integral component of drug discovery workflows. This screening is often done by computationally docking ligands into a protein binding site of interest, but this has the drawback that a large number of poses must be evaluated to obtain accurate estimates of protein-ligand binding affinity. We here introduce a fast prefiltering method for ligand prioritization that is based on a set of machine learning models and uses simple pose-invariant physicochemical descriptors of the ligands and the protein binding pocket. Our method, Rapid Screening with Physicochemical Descriptors + machine learning (RASPD+), is trained on PDBbind data and achieves a regression performance better than for the original RASPD method and comparable to traditional scoring functions on a range of different test sets without the need for generating ligand poses. Additionally, we use RASPD+ to identify molecular features important for binding affinity and assess the ability of RASPD+ to enrich active molecules from decoys.

Tuning carbohydrate density enhances protein binding and inhibition by glycosylated β-sheet peptide nanofibers

2018 ◽  
Vol 6 (9) ◽  
pp. 2327-2335 ◽  
Author(s):  
Antonietta Restuccia ◽  
Gregory A. Hudalla
Keyword(s):  
Protein Binding ◽  
Binding Affinity ◽  
Binding Proteins ◽  
Carbohydrate Binding ◽  
Peptide Nanofibers ◽  
Carbohydrate Binding Proteins ◽  
Β Sheet

The efficacy of glycosylated β-sheet peptide nanofibers for inhibiting carbohydrate-binding proteins can be increased by tuning carbohydrate density to maximize protein binding affinity.

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