De novo design of selective Sortase-A inhibitors: Synthesis, structural and in vitro characterization

2018 ◽  
Vol 15-16 ◽  
pp. 126-133 ◽  
Author(s):  
Kranthi Raj K ◽  
Pardhasaradhi Mathi ◽  
Mutyala Veera Venkata Vara Prasad ◽  
Mahendran Botlagunta ◽  
Ravi M ◽  
...  
Keyword(s):  
De Novo ◽  
De Novo Design ◽  
Sortase A ◽  
In Vitro Characterization

ChemInform Abstract: De Novo Design, Synthesis, and in vitro Evaluation of Inhibitors for Prokaryotic tRNA-Guanine Transglycosylase: A Dramatic Sulfur Effect on Binding Affinity.

ChemInform ◽  
2010 ◽  
Vol 33 (22) ◽  
pp. no-no
Author(s):  
Emmanuel A. Meyer ◽  
Ruth Brenk ◽  
Ronald K. Castellano ◽  
Maya Furler ◽  
Gerhard Klebe ◽  
...  
Keyword(s):  
Binding Affinity ◽  
De Novo ◽  
De Novo Design ◽  
Design Synthesis ◽  
In Vitro Evaluation ◽  
Sulfur Effect

De Novo Design, Synthesis, and In Vitro Evaluation of a New Class of Nonpeptidic Inhibitors of the Malarial Enzyme Plasmepsin II

ChemBioChem ◽  
2002 ◽  
Vol 3 (11) ◽  
pp. 1137-1141 ◽  
Author(s):  
David A. Carcache ◽  
Simone R. Hörtner ◽  
Andreas Bertogg ◽  
Christoph Binkert ◽  
Daniel Bur ◽  
...  
Keyword(s):  
De Novo ◽  
De Novo Design ◽  
Design Synthesis ◽  
In Vitro Evaluation ◽  
New Class ◽  
Plasmepsin Ii

De novo design, synthesis, and in vitro activity of LFA-1 antagonists based on a bicyclic[5.5]hydantoin scaffold

2005 ◽  
Vol 15 (4) ◽  
pp. 1161-1164 ◽  
Author(s):  
Dominique Potin ◽  
Michele Launay ◽  
Eric Nicolai ◽  
Maud Fabreguette ◽  
Patrice Malabre ◽  
...  
Keyword(s):  
De Novo ◽  
De Novo Design ◽  
Vitro Activity ◽  
In Vitro Activity ◽  
Design Synthesis

scholarly journals Escherichia coli PagP Enzyme-Based De Novo Design and In Vitro Activity of Antibacterial Peptide LL-37

2017 ◽  
Vol 23 ◽  
pp. 2558-2564 ◽  
Author(s):  
Hao Yang ◽  
Jingyu Fu ◽  
Youyun Zhao ◽  
Huiping Shi ◽  
Hua Hu ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
De Novo ◽  
De Novo Design ◽  
Antibacterial Peptide ◽  
Vitro Activity ◽  
In Vitro Activity

scholarly journals De Novo Design of Polymeric Carrier to Photothermally Release Singlet Oxygen for Hypoxic Tumor Treatment

Research ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Tianci Huang ◽  
Menglong Zhao ◽  
Qi Yu ◽  
Zheng Feng ◽  
Mingjuan Xie ◽  
...  
Keyword(s):  
Singlet Oxygen ◽  
De Novo ◽  
Inhibitory Effect ◽  
De Novo Design ◽  
Tumor Treatment ◽  
Polymer Carrier ◽  
Hypoxic Environment ◽  
Therapeutic Platform

Intratumoral hypoxia extremely limits the clinic applications of photodynamic therapy (PDT). Endoperoxides allow thermally releasing singlet oxygen (1O2) in a defined quantity and offer promising opportunities for oxygen-independent PDT treatment of hypoxic tumors. However, previous composite systems by combining endoperoxides with photothermal reagents may result in unpredicted side effects and potential harmful impacts during therapy in vivo. Herein, we de novo design an all-in-one polymer carrier, which can photothermally release 1O2. The strategy has been demonstrated to effectively enhance the production of 1O2 and realize the photodamage in vitro, especially in hypoxic environment. Additionally, the polymer carrier accumulates into tumor after intravenous injection via the enhanced permeation and retention effects and accelerates the oxygen-independent generation of 1O2 in tumors. The oxidative damage results in good inhibitory effect on tumor growth. Realization of the strategy in vivo paves a new way to construct photothermal-triggered oxygen-independent therapeutic platform for clinical applications.

scholarly journals De novo design of protein logic gates

Science ◽  
2020 ◽  
Vol 368 (6486) ◽  
pp. 78-84 ◽  
Author(s):  
Zibo Chen ◽  
Ryan D. Kibler ◽  
Andrew Hunt ◽  
Florian Busch ◽  
Jocelynn Pearl ◽  
...  
Keyword(s):  
Protein Function ◽  
De Novo ◽  
Logic Gates ◽  
Disjunctive Normal Form ◽  
Native Mass Spectrometry ◽  
De Novo Design ◽  
Control Logic ◽  
Binding Interaction ◽  
Wide Range

The design of modular protein logic for regulating protein function at the posttranscriptional level is a challenge for synthetic biology. Here, we describe the design of two-input AND, OR, NAND, NOR, XNOR, and NOT gates built from de novo–designed proteins. These gates regulate the association of arbitrary protein units ranging from split enzymes to transcriptional machinery in vitro, in yeast and in primary human T cells, where they control the expression of the TIM3 gene related to T cell exhaustion. Designed binding interaction cooperativity, confirmed by native mass spectrometry, makes the gates largely insensitive to stoichiometric imbalances in the inputs, and the modularity of the approach enables ready extension to three-input OR, AND, and disjunctive normal form gates. The modularity and cooperativity of the control elements, coupled with the ability to de novo design an essentially unlimited number of protein components, should enable the design of sophisticated posttranslational control logic over a wide range of biological functions.

scholarly journals Deep Generative Models for Ligand-based de Novo Design Applied to Multi-parametric Optimization

2021 ◽  
Author(s):  
Quentin Perron ◽  
Olivier Mirguet ◽  
Hamza Tajmouati ◽  
Adam Skiredj ◽  
Anne Rojas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
New Technology ◽  
De Novo Design ◽  
Generative Models ◽  
De Novo Molecular Design

<div> <div> <div> <p>Multi-Parameter Optimization (MPO) is a major challenge in New Chemical Entity (NCE) drug discovery projects, and the inability to identify molecules meeting all the criteria of lead optimization (LO) is an important cause of NCE project failure. Several ligand- and structure-based de novo design methods have been published over the past decades, some of which have proved useful multiobjective optimization. However, there is still need for improvement to better address the chemical feasibility of generated compounds as well as increasing the explored chemical space while tackling the MPO challenge. Recently, promising results have been reported for deep learning generative models applied to de novo molecular design, but until now, to our knowledge, no report has been made of the value of this new technology for addressing MPO in an actual drug discovery project. Our objective in this study was to evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the discovery of an optimized lead compound meeting all in vitro late stage LO criteria. </p> </div> </div> </div>

scholarly journals Deep Generative Models for Ligand-based de Novo Design Applied to Multi-parametric Optimization

2021 ◽  
Author(s):  
Quentin Perron ◽  
Olivier Mirguet ◽  
Hamza Tajmouati ◽  
Adam Skiredj ◽  
Anne Rojas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
New Technology ◽  
De Novo Design ◽  
Generative Models ◽  
De Novo Molecular Design

<div> <div> <div> <p>Multi-Parameter Optimization (MPO) is a major challenge in New Chemical Entity (NCE) drug discovery projects, and the inability to identify molecules meeting all the criteria of lead optimization (LO) is an important cause of NCE project failure. Several ligand- and structure-based de novo design methods have been published over the past decades, some of which have proved useful multiobjective optimization. However, there is still need for improvement to better address the chemical feasibility of generated compounds as well as increasing the explored chemical space while tackling the MPO challenge. Recently, promising results have been reported for deep learning generative models applied to de novo molecular design, but until now, to our knowledge, no report has been made of the value of this new technology for addressing MPO in an actual drug discovery project. Our objective in this study was to evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the discovery of an optimized lead compound meeting all in vitro late stage LO criteria. </p> </div> </div> </div>

Sign in / Sign up

Export Citation Format

Share Document