Faculty Opinions recommendation of A systematic method for identifying small-molecule modulators of protein-protein interactions.

2004 ◽  
Author(s):  
Thomas Kodadek
Keyword(s):  
Small Molecule ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Systematic Method ◽  
Small Molecule Modulators

Polyamines: Naturally occurring small molecule modulators of electrostatic protein–protein interactions

2010 ◽  
Vol 104 (2) ◽  
pp. 118-125 ◽  
Author(s):  
Anja Berwanger ◽  
Susanne Eyrisch ◽  
Inge Schuster ◽  
Volkhard Helms ◽  
Rita Bernhardt
Keyword(s):  
Small Molecule ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Naturally Occurring ◽  
Small Molecule Modulators

Small Molecule Modulators of Protein–Protein Interactions: Selected Case Studies

2014 ◽  
Vol 114 (9) ◽  
pp. 4640-4694 ◽  
Author(s):  
Madhu Aeluri ◽  
Srinivas Chamakuri ◽  
Bhanudas Dasari ◽  
Shiva Krishna Reddy Guduru ◽  
Ravikumar Jimmidi ◽  
...  
Keyword(s):  
Case Studies ◽  
Small Molecule ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Small Molecule Modulators

scholarly journals A Dual Luciferase Multiplexed High-Throughput Screening Platform for Protein-Protein Interactions

2003 ◽  
Vol 8 (6) ◽  
pp. 676-684 ◽  
Author(s):  
Bart W. Nieuwenhuijsen ◽  
Youping Huang ◽  
Yuren Wang ◽  
Fernando Ramirez ◽  
Gary Kalgaonkar ◽  
...  
Keyword(s):  
Reporter Gene ◽  
Small Molecule ◽  
High Throughput ◽  
Protein Interactions ◽  
Renilla Luciferase ◽  
Luciferase Reporter ◽  
Rgs Proteins ◽  
Protein Protein Interactions ◽  
Luciferase Reporter Gene ◽  
Small Molecule Modulators

To study the biology of regulators of G-protein signaling (RGS) proteins and to facilitate the identification of small molecule modulators of RGS proteins, the authors recently developed an advanced yeast 2-hybrid (YTH) assay format for GαZand RGS-Z1. Moreover, they describe the development of a multiplexed luciferase-based assay that has been successfully adapted to screen large numbers of small molecule modulators of protein-protein interactions. They generated and evaluated 2 different luciferase reporter gene systems for YTH interactions, a Gal4 responsive firefly luciferase reporter gene and a Gal4 responsive Renilla luciferase reporter gene. Both the firefly and Renilla luciferase reporter genes demonstrated a 40-to 50-fold increase in luminescence in strains expressing interacting YTH fusion proteins versus negative control strains. Because the firefly and Renilla luciferase proteins have different substrate specificity, the assays were multiplexed. The multiplexed luciferase-based YTH platform adds speed, sensitivity, simplicity, quantification, and efficiency to YTH high-throughput applications and therefore greatly facilitates the identification of small molecule modulators of protein-protein interactions as tools or potential leads for drug discovery efforts.

scholarly journals SMMPPI: a machine learning-based approach for prediction of modulators of protein–protein interactions and its application for identification of novel inhibitors for RBD:hACE2 interactions in SARS-CoV-2

2021 ◽  
Author(s):  
Priya Gupta ◽  
Debasisa Mohanty
Keyword(s):  
Machine Learning ◽  
Small Molecule ◽  
Protein Interactions ◽  
Large Data ◽  
Docking Studies ◽  
Protein Protein Interactions ◽  
Data Set ◽  
Drug Candidates ◽  
Test Sets ◽  
Small Molecule Modulators

Abstract Small molecule modulators of protein–protein interactions (PPIs) are being pursued as novel anticancer, antiviral and antimicrobial drug candidates. We have utilized a large data set of experimentally validated PPI modulators and developed machine learning classifiers for prediction of new small molecule modulators of PPI. Our analysis reveals that using random forest (RF) classifier, general PPI Modulators independent of PPI family can be predicted with ROC-AUC higher than 0.9, when training and test sets are generated by random split. The performance of the classifier on data sets very different from those used in training has also been estimated by using different state of the art protocols for removing various types of bias in division of data into training and test sets. The family-specific PPIM predictors developed in this work for 11 clinically important PPI families also have prediction accuracies of above 90% in majority of the cases. All these ML-based predictors have been implemented in a freely available software named SMMPPI for prediction of small molecule modulators for clinically relevant PPIs like RBD:hACE2, Bromodomain_Histone, BCL2-Like_BAX/BAK, LEDGF_IN, LFA_ICAM, MDM2-Like_P53, RAS_SOS1, XIAP_Smac, WDR5_MLL1, KEAP1_NRF2 and CD4_gp120. We have identified novel chemical scaffolds as inhibitors for RBD_hACE PPI involved in host cell entry of SARS-CoV-2. Docking studies for some of the compounds reveal that they can inhibit RBD_hACE2 interaction by high affinity binding to interaction hotspots on RBD. Some of these new scaffolds have also been found in SARS-CoV-2 viral growth inhibitors reported recently; however, it is not known if these molecules inhibit the entry phase.

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