Targeting the Non-Catalytic Functions: a New Paradigm for Kinase Drug Discovery?

Author(s):  
Zhen Wang ◽  
Weixue Huang ◽  
Kaijie Zhou ◽  
Xiaomei Ren ◽  
Ke Ding
BioEssays ◽  
2020 ◽  
Vol 42 (9) ◽  
pp. 2000011
Author(s):  
Steven D. Buckingham ◽  
Harry‐Jack Mann ◽  
Olivia K. Hearnden ◽  
David B. Sattelle

2014 ◽  
Vol 9 (10) ◽  
pp. 1167-1187 ◽  
Author(s):  
Lucia Parrotta ◽  
Francesco Ortuso ◽  
Federica Moraca ◽  
Roberta Rocca ◽  
Giosuè Costa ◽  
...  

MedChemComm ◽  
2017 ◽  
Vol 8 (3) ◽  
pp. 534-550 ◽  
Author(s):  
M. Bernetti ◽  
A. Cavalli ◽  
L. Mollica

Herein, we present an overview of a broad range of physico-chemical approaches able to reveal the details of protein–ligand kinetics.


2018 ◽  
Vol 10 (14) ◽  
pp. 1641-1644 ◽  
Author(s):  
Ivan Babic ◽  
Santosh Kesari ◽  
Elmar Nurmemmedov

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Rosalie Matico ◽  
Lawrence M. Szewczuk ◽  
Beth Pietrak ◽  
Stephanie Chen ◽  
Ed Dul ◽  
...  

Abstract Significant resource is spent by drug discovery project teams to generate numerous, yet unique target constructs for the multiple platforms used to drive drug discovery programs including: functional assays, biophysical studies, structural biology, and biochemical high throughput screening campaigns. To improve this process, we developed Modular Protein Ligation (MPL), a combinatorial reagent platform utilizing Expressed Protein Ligation to site-specifically label proteins at the C-terminus with a variety of cysteine-lysine dipeptide conjugates. Historically, such proteins have been chemically labeled non-specifically through surface amino acids. To demonstrate the feasibility of this approach, we first applied MPL to proteins of varying size in different target classes using different recombinant protein expression systems, which were then evaluated in several different downstream assays. A key advantage to the implementation of this paradigm is that one construct can generate multiple final products, significantly streamlining the reagent generation for multiple early drug discovery project teams.


2013 ◽  
Vol 16 (2) ◽  
pp. 331 ◽  
Author(s):  
Qiong Gu ◽  
Xin Yan ◽  
Jun Xu

Purpose. The Human Genome Project is producing a new biological ‘periodic table’, which defines all genes for making macromolecules (proteins, DNA, RNA, etc) and the relations between genes and their biological functions. We now need to consider whether to initiate a biochemome project aimed at discovering biochemistry’s ‘periodic table’, which would define all molecular parts for making small molecules (natural products) and the relations between the parts and their functions to regulate genes. By understanding the Biochemome, we might be able to design biofunctional molecules based upon a set of molecular parts for drug innovation. Methods. A number of algorithms for processing chemical structures are used to systematically derive chemoyls (natural building blocks) from a database of compounds identified in Traditional Chinese Medicine (TCM). The rules to combine chemoyls for biological activities are then deduced by mining an annotated TCM structure-activity database (ATCMD). Based upon the rules and the basic chemoyls, a chemical library can be biochemically profiled, virtual synthetic routes can be planned, and lead compounds can be identified for a specific drug target. Conclusions. The Biochemome is the complete set of molecular components (chemoyls) in an organism and Biochemomics studies the rules governing their assembly and their evolution, together with the relations between the Biochemome and drug targets. This approach provides a new paradigm for drug discovery that is based on a comprehensive knowledge of the synthetic origins of biochemical diversity, and helps to direct biomimetic syntheses aimed at assembling quasi-natural product libraries for drug screening.   This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.


2017 ◽  
Vol 12 (3) ◽  
pp. 279-291 ◽  
Author(s):  
Rajan Chaudhari ◽  
Zhi Tan ◽  
Beibei Huang ◽  
Shuxing Zhang
Keyword(s):  

2003 ◽  
Vol 31 (2) ◽  
pp. 437-443 ◽  
Author(s):  
E.E. Schadt ◽  
S.A. Monks ◽  
S.H. Friend

Application of statistical genetics approaches to variations in mRNA transcript abundances in segregating populations can be used to identify genes and pathways associated with common human diseases. The combination of this genetic information with gene expression and clinical trait data can also be used to identify subtypes of a disease and the genetic loci specific to each subtype. Here we highlight results from some of our recent work in this area and further explore the many possibilities that exist in employing a more comprehensive genetics and functional genomics approach to the functional annotation of genomes, and in applying such methods to the validation of targets for complex traits in the drug discovery process.


2010 ◽  
Vol 10 (5) ◽  
pp. 503-510 ◽  
Author(s):  
J.K. Nagpal ◽  
R. Rani ◽  
B. Trink ◽  
K.S. Saini
Keyword(s):  

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