scholarly journals Utilizing Yeast Surface Human Proteome Display Libraries to Identify Small Molecule-Protein Interactions

2015 ◽  
pp. 203-214 ◽  
Author(s):  
Scott Bidlingmaier ◽  
Bin Liu
Keyword(s):  
Small Molecule ◽  
Protein Interactions ◽  
Human Proteome ◽  
Yeast Surface

scholarly journals Yeast Surface Two-hybrid for Quantitative in Vivo Detection of Protein-Protein Interactions via the Secretory Pathway

2009 ◽  
Vol 284 (24) ◽  
pp. 16369-16376 ◽  
Author(s):  
Xuebo Hu ◽  
Sungkwon Kang ◽  
Xiaoyue Chen ◽  
Charles B. Shoemaker ◽  
Moonsoo M. Jin
Keyword(s):  
Protein Interactions ◽  
Secretory Pathway ◽  
Coiled Coil ◽  
Affinity Maturation ◽  
Antibody Binding ◽  
Soluble Form ◽  
Protein Protein Interactions ◽  
Yeast Surface ◽  
Two Hybrid

A quantitative in vivo method for detecting protein-protein interactions will enhance our understanding of protein interaction networks and facilitate affinity maturation as well as designing new interaction pairs. We have developed a novel platform, dubbed “yeast surface two-hybrid (YS2H),” to enable a quantitative measurement of pairwise protein interactions via the secretory pathway by expressing one protein (bait) anchored to the cell wall and the other (prey) in soluble form. In YS2H, the prey is released either outside of the cells or remains on the cell surface by virtue of its binding to the bait. The strength of their interaction is measured by antibody binding to the epitope tag appended to the prey or direct readout of split green fluorescence protein (GFP) complementation. When two α-helices forming coiled coils were expressed as a pair of prey and bait, the amount of the prey in complex with the bait progressively decreased as the affinity changes from 100 pm to 10 μm. With GFP complementation assay, we were able to discriminate a 6-log difference in binding affinities in the range of 100 pm to 100 μm. The affinity estimated from the level of antibody binding to fusion tags was in good agreement with that measured in solution using a surface plasmon resonance technique. In contrast, the level of GFP complementation linearly increased with the on-rate of coiled coil interactions, likely because of the irreversible nature of GFP reconstitution. Furthermore, we demonstrate the use of YS2H in exploring the nature of antigen recognition by antibodies and activation allostery in integrins and in isolating heavy chain-only antibodies against botulinum neurotoxin.

Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases

2018 ◽  
Vol 18 (20) ◽  
pp. 1719-1736 ◽  
Author(s):  
Sharanya Sarkar ◽  
Khushboo Gulati ◽  
Manikyaprabhu Kairamkonda ◽  
Amit Mishra ◽  
Krishna Mohan Poluri
Keyword(s):  
Small Molecule ◽  
Protein Interactions ◽  
Small Molecule Inhibitors ◽  
Computational Techniques ◽  
Protein Protein Interactions ◽  
Computational Tools ◽  
Computational Approaches ◽  
Protein Protein Interaction ◽  
Wide Range ◽  
Therapeutic Measures

Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Results: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. Conclusion: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.

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

Targeting Disease with Small Molecule Inhibitors of Protein-Protein Interactions

2010 ◽  
pp. 215-238
Author(s):  
Fedor Forafonov ◽  
Elena Miranda ◽  
Ida Karin Nordgren ◽  
Ali Tavassoli
Keyword(s):  
Small Molecule ◽  
Protein Interactions ◽  
Small Molecule Inhibitors ◽  
Protein Protein Interactions

scholarly journals A computational interactome and functional annotation for the human proteome

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
José Ignacio Garzón ◽  
Lei Deng ◽  
Diana Murray ◽  
Sagi Shapira ◽  
Donald Petrey ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Enrichment Analysis ◽  
Human Proteome ◽  
Gene Set Enrichment Analysis ◽  
Protein Protein Interactions ◽  
Functional Relationships ◽  
Structural Relationships ◽  
Human Proteins ◽  
Validation Tests

We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function.

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