scholarly journals Identification of selective protein–protein interaction inhibitors using efficient in silico peptide-directed ligand design

2019 ◽  
Vol 10 (16) ◽  
pp. 4502-4508 ◽  
Author(s):  
Andrew M. Beekman ◽  
Marco M. D. Cominetti ◽  
Samuel J. Walpole ◽  
Saurabh Prabhu ◽  
Maria A. O'Connell ◽  
...  
Keyword(s):  
Success Rate ◽  
Protein Interaction ◽  
In Silico ◽  
Ligand Design ◽  
Protein Protein Interaction ◽  
Anticancer Target

Development of selective hDM2/X p53 inhibitors is key to further develop this anticancer target. This method displayed a 50% success rate and identified hDMX selective compounds.

scholarly journals Correction: Identification of selective protein–protein interaction inhibitors using efficient in silico peptide-directed ligand design

2019 ◽  
Vol 10 (22) ◽  
pp. 5849-5850
Author(s):  
Andrew M. Beekman ◽  
Marco M. D. Cominetti ◽  
Samuel J. Walpole ◽  
Saurabh Prabhu ◽  
Maria A. O’Connell ◽  
...  
Keyword(s):  
Protein Interaction ◽  
In Silico ◽  
Ligand Design ◽  
Protein Protein Interaction

Correction for ‘Identification of selective protein–protein interaction inhibitors using efficient in silico peptide-directed ligand design’ by Andrew M. Beekman et al., Chem. Sci., 2019, DOI: 10.1039/c9sc00059c.

scholarly journals In silico peptide-directed ligand design complements experimental peptide-directed binding for protein–protein interaction modulator discovery

2021 ◽  
Author(s):  
Lesley Ann Howell ◽  
Andrew Michael Beekman
Keyword(s):  
Protein Interaction ◽  
In Silico ◽  
Ligand Design ◽  
Protein Protein Interaction

Using the protein–protein interaction of Mcl-1/Noxa, two methods for efficient modulator discovery are directly compared.

scholarly journals In Silico Peptide-Directed Ligand Design Complements Experimental Peptide-Directed Binding for Protein-Protein Interaction Modulator Discovery

2020 ◽  
Author(s):  
Lesley A. Howell ◽  
Andrew Beekman
Keyword(s):  
Protein Interaction ◽  
In Silico ◽  
Ligand Design ◽  
Cellular Activity ◽  
Protein Protein Interaction ◽  
Hit Rate

Using the protein-protein interaction of Mcl-1/Noxa, two methods for efficient modulator discovery are directly compared. In silico peptide-directed ligand design is evaluated against experimental peptide-directed, allowing for the discovery of two new inhibitors of Mcl-1/Noxa with cellular activity. In silico peptide-directed ligand design demonstrates an in vitro hit rate of 80%. The two rapid and efficient methods demonstrate complementary features for protein-protein interaction modulator discovery.

scholarly journals In Silico Peptide-Directed Ligand Design Complements Experimental Peptide-Directed Binding for Protein-Protein Interaction Modulator Discovery

2020 ◽  
Author(s):  
Lesley A. Howell ◽  
Andrew Beekman
Keyword(s):  
Protein Interaction ◽  
In Silico ◽  
Ligand Design ◽  
Cellular Activity ◽  
Protein Protein Interaction ◽  
Hit Rate

Using the protein-protein interaction of Mcl-1/Noxa, two methods for efficient modulator discovery are directly compared. In silico peptide-directed ligand design is evaluated against experimental peptide-directed, allowing for the discovery of two new inhibitors of Mcl-1/Noxa with cellular activity. In silico peptide-directed ligand design demonstrates an in vitro hit rate of 80%. The two rapid and efficient methods demonstrate complementary features for protein-protein interaction modulator discovery.

scholarly journals Identification of an in vivo orally active dual-binding protein-protein interaction inhibitor targeting TNFα through combined in silico/in vitro/in vivo screening

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Hadley Mouhsine ◽  
Hélène Guillemain ◽  
Gabriel Moreau ◽  
Najla Fourati ◽  
Chouki Zerrouki ◽  
...  
Keyword(s):  
Protein Interaction ◽  
In Silico ◽  
Binding Protein ◽  
In Vivo Screening ◽  
Protein Protein Interaction ◽  
Orally Active

scholarly journals Protein-protein interaction networks and protein-ligand docking: Contemporary insights and future perspectives

2021 ◽  
Vol 38 (1) ◽  
pp. 5-17
Author(s):  
Aleksandar Velesinović ◽  
Goran Nikolić
Keyword(s):  
Modeling And Simulation ◽  
Protein Interaction ◽  
In Silico ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Ligand Docking ◽  
In Vivo Models ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks ◽  
In Silico Methods

Traditional research means, such as in vitro and in vivo models, have consistently been used by scientists to test hypotheses in biochemistry. Computational (in silico) methods have been increasingly devised and applied to testing and hypothesis development in biochemistry over the last decade. The aim of in silico methods is to analyze the quantitative aspects of scientific (big) data, whether these are stored in databases for large data or generated with the use of sophisticated modeling and simulation tools; to gain a fundamental understanding of numerous biochemical processes related, in particular, to large biological macromolecules by applying computational means to big biological data sets, and by computing biological system behavior. Computational methods used in biochemistry studies include proteomics-based bioinformatics, genome-wide mapping of protein-DNA interaction, as well as high-throughput mapping of the protein-protein interaction networks. Some of the vastly used molecular modeling and simulation techniques are Monte Carlo and Langevin (stochastic, Brownian) dynamics, statistical thermodynamics, molecular dynamics, continuum electrostatics, protein-ligand docking, protein-ligand affinity calculations, protein modeling techniques, and the protein folding process and enzyme action computer simulation. This paper presents a short review of two important methods used in the studies of biochemistry - protein-ligand docking and the prediction of protein-protein interaction networks.

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