scholarly journals Fluorescent Biosensors for Protein Interactions and Drug Discovery

10.5772/17431 ◽  
2011 ◽  
Author(s):  
Alejandro Sosa-Peinado ◽  
Martn Gonzlez-Andrade
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Fluorescent Biosensors

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

2019 ◽  
Vol 26 (21) ◽  
pp. 3890-3910 ◽  
Author(s):  
Branislava Gemovic ◽  
Neven Sumonja ◽  
Radoslav Davidovic ◽  
Vladimir Perovic ◽  
Nevena Veljkovic
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Experimental Studies ◽  
Protein Protein Interactions ◽  
Web Based ◽  
Prediction Tools ◽  
Physiological Processes ◽  
Modern Drug ◽  
Ppi Prediction

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

Protein Interaction Domains and Post-Translational Modifications: Structural Features and Drug Discovery Applications

2020 ◽  
Vol 27 (37) ◽  
pp. 6306-6355 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Information ◽  
Protein Complexes ◽  
Structural Features ◽  
Protein Protein Interactions ◽  
Modular Architecture ◽  
Post Translational Modifications ◽  
Interaction Domains

Background:: Many pathways regarding healthy cells and/or linked to diseases onset and progression depend on large assemblies including multi-protein complexes. Protein-protein interactions may occur through a vast array of modules known as protein interaction domains (PIDs). Objective:: This review concerns with PIDs recognizing post-translationally modified peptide sequences and intends to provide the scientific community with state of art knowledge on their 3D structures, binding topologies and potential applications in the drug discovery field. Method:: Several databases, such as the Pfam (Protein family), the SMART (Simple Modular Architecture Research Tool) and the PDB (Protein Data Bank), were searched to look for different domain families and gain structural information on protein complexes in which particular PIDs are involved. Recent literature on PIDs and related drug discovery campaigns was retrieved through Pubmed and analyzed. Results and Conclusion:: PIDs are rather versatile as concerning their binding preferences. Many of them recognize specifically only determined amino acid stretches with post-translational modifications, a few others are able to interact with several post-translationally modified sequences or with unmodified ones. Many PIDs can be linked to different diseases including cancer. The tremendous amount of available structural data led to the structure-based design of several molecules targeting protein-protein interactions mediated by PIDs, including peptides, peptidomimetics and small compounds. More studies are needed to fully role out, among different families, PIDs that can be considered reliable therapeutic targets, however, attacking PIDs rather than catalytic domains of a particular protein may represent a route to obtain selective inhibitors.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Mathematical and Computational Techniques for Drug Discovery: Promises and Developments

2019 ◽  
Vol 18 (32) ◽  
pp. 2774-2799 ◽  
Author(s):  
Krishnan Balasubramanian
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Quantum Chemical ◽  
Intrinsically Disordered Proteins ◽  
Hepatitis Virus ◽  
Disordered Proteins ◽  
Computational Techniques ◽  
Large Set ◽  
Structural Protein ◽  
Intrinsically Disordered

We review various mathematical and computational techniques for drug discovery exemplifying some recent works pertinent to group theory of nested structures of relevance to phylogeny, topological, computational and combinatorial methods for drug discovery for multiple viral infections. We have reviewed techniques from topology, combinatorics, graph theory and knot theory that facilitate topological and mathematical characterizations of protein-protein interactions, molecular-target interactions, proteomics, genomics and statistical data reduction procedures for a large set of starting chemicals in drug discovery. We have provided an overview of group theoretical techniques pertinent to phylogeny, protein dynamics especially in intrinsically disordered proteins, DNA base permutations and related algorithms. We consider computational techniques derived from high level quantum chemical computations such as QM/MM ONIOM methods, quantum chemical optimization of geometries complexes, and molecular dynamics methods for providing insights into protein-drug interactions. We have considered complexes pertinent to Hepatitis Virus C non-structural protein 5B polymerase receptor binding of C5-Arylidebne rhodanines, complexes of synthetic potential vaccine molecules with dengue virus (DENV) and HIV-1 virus as examples of various simulation studies that exemplify the utility of computational tools. It is demonstrated that these combinatorial and computational techniques in conjunction with experiments can provide promising new insights into drug discovery. These techniques also demonstrate the need to consider a new multiple site or allosteric binding approach to drug discovery, as these studies reveal the existence of multiple binding sites.

scholarly journals High-Throughput Quantitative Assay Technologies for Accelerating the Discovery and Optimization of Targeted Protein Degradation Therapeutics

2021 ◽  
pp. 247255522098504
Author(s):  
Jeffrey R. Simard ◽  
Linda Lee ◽  
Ellen Vieux ◽  
Reina Improgo ◽  
Trang Tieu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Degradation ◽  
Protein Interactions ◽  
Fluorescent Protein ◽  
De Novo ◽  
Rapid Development ◽  
Dependent Manner ◽  
Western Blots ◽  
Advantages And Disadvantages ◽  
Degradation Rates

The aberrant regulation of protein expression and function can drastically alter cellular physiology and lead to numerous pathophysiological conditions such as cancer, inflammatory diseases, and neurodegeneration. The steady-state expression levels of endogenous proteins are controlled by a balance of de novo synthesis rates and degradation rates. Moreover, the levels of activated proteins in signaling cascades can be further modulated by a variety of posttranslational modifications and protein–protein interactions. The field of targeted protein degradation is an emerging area for drug discovery in which small molecules are used to recruit E3 ubiquitin ligases to catalyze the ubiquitination and subsequent degradation of disease-causing target proteins by the proteasome in both a dose- and time-dependent manner. Traditional approaches for quantifying protein level changes in cells, such as Western blots, are typically low throughput with limited quantification, making it hard to drive the rapid development of therapeutics that induce selective, rapid, and sustained protein degradation. In the last decade, a number of techniques and technologies have emerged that have helped to accelerate targeted protein degradation drug discovery efforts, including the use of fluorescent protein fusions and reporter tags, flow cytometry, time-resolved fluorescence energy transfer (TR-FRET), and split luciferase systems. Here we discuss the advantages and disadvantages associated with these technologies and their application to the development and optimization of degraders as therapeutics.

scholarly journals Assay Optimization and Screening of RNA-Protein Interactions by AlphaScreen

2007 ◽  
Vol 12 (7) ◽  
pp. 946-955 ◽  
Author(s):  
Nicholas L. Mills ◽  
Anang A. Shelat ◽  
R. Kiplin Guy
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
High Throughput ◽  
Protein Interactions ◽  
Proof Of Concept ◽  
Rna Molecules ◽  
Lead Compounds ◽  
Assay Optimization ◽  
Rna Targets ◽  
Hiv 1

The lack of lead compounds that specifically recognize and manipulate the function of RNA molecules limits our ability to consider RNA targets valid for drug discovery. Herein is reported a high-throughput biochemical screen for inhibitors of RNA-protein interactions based on AlphaScreen technology that incorporates several layers of specificity measurements into the primary screen. This screen was used to analyze approximately 5500 compounds from a collection of bioactive small molecules to detect inhibitors of the HIV-1 Rev-RRE and BIV Tat-TAR interactions. This proof-of-concept screen validates the assay as one that accurately identifies hit molecules and determines the selectivity of those hits. ( Journal of Biomolecular Screening 2007: 946-955)

Targeting Protein-Protein Interactions: A Promising Avenue of Anti-HIV Drug Discovery

2010 ◽  
Vol 17 (29) ◽  
pp. 3393-3409 ◽  
Author(s):  
Peng Zhan ◽  
Wenjun Li ◽  
Hongfei Chen ◽  
Xinyong Liu
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Anti Hiv ◽  
Promising Avenue
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