[6] Structure-based prediction of binding affinities and molecular design of peptide ligands

1998 ◽  
pp. 100-127 ◽  
Author(s):  
Irene Luque ◽  
Ernesto Freire
Keyword(s):  
Molecular Design ◽  
Peptide Ligands ◽  
Binding Affinities

scholarly journals STUDY OF MAO-B INHIBITOR ANALOUGES FOR PARKINSON’S DISEASE THROUGH CADD APPROACHES

2018 ◽  
Vol 8 (5-s) ◽  
pp. 240-250
Author(s):  
Manish Bachhar ◽  
BK Singh
Keyword(s):  
Molecular Design ◽  
Neurodegenerative Disorder ◽  
Binding Mode ◽  
Docking Studies ◽  
Target Protein ◽  
Binding Affinities ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Mao B ◽  
Pharmacokinetic Properties

New derivatives are designed as target directed MAO-B Inhibitors for medical care of the patients for neurodegenerative disorder. Molecular design and estimated pharmacokinetic properties have been evaluated by using Inventus v 1.1 software. The binding mode of the proposed compounds with target protein i.e. 1S2Q was evaluated and the resulting data from docking studies explained that newly designed derivatives have high and better affinity towards target protein. Based on these properties, the binding affinities are used for speeding up drug discovery process by eliminating less potent compounds from synthesis. Keywords: MAO-B, Inventus, Target protein, Neurodegenerative, Docking.

Electrophoretic affinity measurements on microchip. Determination of binding affinities between diketopiperazine receptors and peptide ligands

2007 ◽  
Vol 28 (11) ◽  
pp. 1832-1838 ◽  
Author(s):  
Alexandra R. Stettler ◽  
Philipp Krattiger ◽  
Helma Wennemers ◽  
Maria A. Schwarz
Keyword(s):  
Peptide Ligands ◽  
Binding Affinities

N-hydroxy-amide analogues of MHC-class I peptide ligands with nanomolar binding affinities

1998 ◽  
Vol 4 (8) ◽  
pp. 471-478 ◽  
Author(s):  
Alberto Bianco ◽  
Claus Zabel ◽  
Peter Walden ◽  
Günther Jung
Keyword(s):  
Mhc Class I ◽  
Peptide Ligands ◽  
Class I ◽  
Binding Affinities

scholarly journals Predicting binding free energies: Frontiers and benchmarks

2016 ◽  
Author(s):  
David L. Mobley ◽  
Michael K. Gilson
Keyword(s):  
Molecular Design ◽  
Early Stage ◽  
Binding Free Energy ◽  
Research Community ◽  
Free Energy Calculations ◽  
Binding Affinities ◽  
Free Energies ◽  
Biomolecular Complexes ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies

Binding free energy calculations based on molecular simulations provide predicted affinities for biomolecular complexes. These calculations begin with a detailed description of a system, including its chemical composition and the interactions between its components. Simulations of the system are then used to compute thermodynamic information, such as binding affinities. Because of their promise for guiding molecular design, these calculations have recently begun to see widespread applications in early stage drug discovery. However, many challenges remain to make them a robust and reliable tool. Here, we briefly explain how the calculations work, highlight key challenges, and argue for the development of accepted benchmark test systems that will help the research community generate and evaluate progress.Manuscript version 1.1.1 pre-release See https://github.com/mobleylab/benchmarksets for all versions.

scholarly journals Analysis of Interactions of Signaling Proteins with Phage-Displayed Ligands by Fluorescence Correlation Spectroscopy

2008 ◽  
Vol 13 (8) ◽  
pp. 766-776
Author(s):  
Helmi R.M. Schlaman ◽  
Kristiane Schmidt ◽  
Dorien Ottenhof ◽  
Maarten H. van Es ◽  
Tjerk H. Oosterkamp ◽  
...  
Keyword(s):  
Good Alternative ◽  
Correlation Spectroscopy ◽  
Peptide Ligands ◽  
Peptide Ligand ◽  
Binding Affinities ◽  
Phage Display Technology ◽  
Force Microscopy ◽  
Fluorescent Correlation Spectroscopy ◽  
Atomic Force ◽  
Display Technology

Fluorescent correlation spectroscopy (FCS) was used to measure binding affinities of ligands to ligates that are expressed by phage-display technology. Using this method we have quantified the binding of the 14-3-3 signaling protein to artificial peptide ligand. As a ligand we used the R18 artificial peptide expressed as a fusion in the cpIII coat protein that is present in 3 to 5 copies in an M13 phage. Comparisons of binding affinities were made with free R18 ligands using FCS. The result showed a relatively high binding affinity for the phage-displayed R18 peptide compared with binding to free fluorescently labeled R18. Quantification was supported by titration of the phage numbers using atomic force microscopy (AFM). AFM was shown to accurately determine phage numbers in solution as a good alternative for electron microscopy. It was shown to give reliable data that correlated perfectly with those of the viable phage numbers determined by classical bacterial infection studies. In conclusion, a very fast and sensitive method for the selection of new peptide ligands or ligates based on a quantitative assay in solution has been developed. ( Journal of Biomolecular Screening 2008:766-776)

scholarly journals Comparison of Ligand Affinity Ranking Using AutoDock-GPU and MM-GBSA Scores in the D3R Grand Challenge 4

2019 ◽  
Author(s):  
Léa El Khoury ◽  
Diogo Santos-Martins ◽  
Sukanya Sasmal ◽  
Jérome Eberhardt ◽  
Giulia Bianco ◽  
...  
Keyword(s):  
Surface Area ◽  
Molecular Design ◽  
Scoring Function ◽  
Grand Challenge ◽  
Binding Affinities ◽  
Design Data ◽  
Generalized Born ◽  
Data Resource ◽  
Ligand Affinity ◽  
Bace 1

Molecular docking has been successfully used in computer-aided molecular design projects for the identification of ligand poses within protein binding sites. However, relying on docking scores to rank different ligands with respect to their experimental affinities might not be sufficient. It is believed that the binding scores calculated using molecular mechanics combined with the Poisson-Boltzman surface area (MM-PBSA) or generalized Born surface area (MM-GBSA) can more accurately predict binding affinities. In this perspective, we decided to take part in Stage 2 in the Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) to compare the performance of a quick scoring function, Autodock4, to that of MM-GBSA in predicting the binding affinities of a set of Beta-Amyloid Cleaving Enzyme 1 (BACE-1) ligands. Our results show that re-scoring docking poses using MM-GBSA did not improve the correlation with experimental affinities. We further did a retrospective analysis of the results and found that our MM-GBSA protocol is sensitive to details in the protein-ligand system: i) neutral ligands are more adapted to MM-GBSA calculations than charged ligands, ii) predicted binding affinities depend on the initial conformation of the BACE-1 receptor, iii) protonating the aspartyl dyad of BACE-1 correctly results in more accurate binding pose and affinity predictions. <br>

Sign in / Sign up

Export Citation Format

Share Document