scholarly journals IL-12 and IL-23—Close Relatives with Structural Homologies but Distinct Immunological Functions

Cells ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 2184
Author(s):  
Doreen M. Floss ◽  
Jens M. Moll ◽  
Jürgen Scheller
Keyword(s):  
Protein Interactions ◽  
Intracellular Signaling ◽  
Signaling Cascades ◽  
Structural Knowledge ◽  
Nucleotide Polymorphisms ◽  
Protein Protein Interactions ◽  
Single Nucleotide ◽  
Mediated Effects ◽  
Protein Functions ◽  
Close Relatives

Cytokines of the IL-12 family show structural similarities but have distinct functions in the immune system. Prominent members of this cytokine family are the pro-inflammatory cytokines IL-12 and IL-23. These two cytokines share cytokine subunits and receptor chains but have different functions in autoimmune diseases, cancer and infections. Accordingly, structural knowledge about receptor complex formation is essential for the development of new therapeutic strategies preventing and/or inhibiting cytokine:receptor interaction. In addition, intracellular signaling cascades can be targeted to inhibit cytokine-mediated effects. Single nucleotide polymorphisms can lead to alteration in the amino acid sequence and thereby influencing protein functions or protein–protein interactions. To understand the biology of IL-12 and IL-23 and to establish efficient targeting strategies structural knowledge about cytokines and respective receptors is crucial. A highly efficient therapy might be a combination of different drugs targeting extracellular cytokine:receptor assembly and intracellular signaling pathways.

Functional nonsynonymous single nucleotide polymorphisms from the TGF-β protein interaction network

2007 ◽  
Vol 29 (2) ◽  
pp. 109-117 ◽  
Author(s):  
Sevtap Savas ◽  
Ian W. Taylor ◽  
Jeff L. Wrana ◽  
Hilmi Ozcelik
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Protein Interactions ◽  
Transforming Growth Factor ◽  
Conservation Status ◽  
Genetic Variations ◽  
Phosphorylation Sites ◽  
Nucleotide Polymorphisms ◽  
Protein Protein Interactions ◽  
Single Nucleotide ◽  
Putative Protein

Protein complexes mediated by protein-protein interactions are essential for many cellular functions. Transforming growth factor (TGF)-β signaling involves a cascade of protein-protein interactions and malfunctioning of this pathway has been implicated in human diseases. Using an in silico approach, we analyzed the naturally occurring human genetic variations from the proteins involved in the TGF-β signaling (10 TGF-β proteins and 242 other proteins interacting with them) to identify the ones that have potential biological consequences. All proteins were searched in the dbSNP database for the presence of nonsynonymous single nucleotide polymorphisms (nsSNPs). A total of 118 validated nsSNPs from 63 proteins were retrieved and analyzed in terms of 1) evolutionary conservation status, 2) being located in a functional protein domain or motif, and 3) altering putative protein motif or phosphorylation sites. Our results indicated the presence of 31 nsSNPs that occurred at evolutionarily conserved residues, 37 nsSNPs were located in protein domains, motifs, or repeats, and 46 nsSNPs were predicted to either create or abolish putative protein motifs or phosphorylation sites. We undertook this study to analyze the human genetic variations that can affect the protein function and the TGF-β signaling. The nsSNPs reported in here can be characterized by experimental approaches to elucidate their exact biological roles and whether they are related to human disease.

scholarly journals Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach

2021 ◽  
Vol 10 (8) ◽  
pp. 1666
Author(s):  
Micaela F. Beckman ◽  
Farah Bahrani Mougeot ◽  
Jean-Luc C. Mougeot
Keyword(s):  
Protein Interactions ◽  
Association Studies ◽  
Meta Analysis ◽  
Holistic Approach ◽  
Gene Interaction ◽  
Snp Analysis ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Protein Protein Interactions ◽  
Data Mining Approach

The COVID-19 pandemic has led to over 2.26 million deaths for almost 104 million confirmed cases worldwide, as of 4 February 2021 (WHO). Risk factors include pre-existing conditions such as cancer, cardiovascular disease, diabetes, and obesity. Although several vaccines have been deployed, there are few alternative anti-viral treatments available in the case of reduced or non-existent vaccine protection. Adopting a long-term holistic approach to cope with the COVID-19 pandemic appears critical with the emergence of novel and more infectious SARS-CoV-2 variants. Our objective was to identify comorbidity-associated single nucleotide polymorphisms (SNPs), potentially conferring increased susceptibility to SARS-CoV-2 infection using a computational meta-analysis approach. SNP datasets were downloaded from a publicly available genome-wide association studies (GWAS) catalog for 141 of 258 candidate COVID-19 comorbidities. Gene-level SNP analysis was performed to identify significant pathways by using the program MAGMA. An SNP annotation program was used to analyze MAGMA-identified genes. Differential gene expression was determined for significant genes across 30 general tissue types using the Functional and Annotation Mapping of GWAS online tool GENE2FUNC. COVID-19 comorbidities (n = 22) from six disease categories were found to have significant associated pathways, validated by Q–Q plots (p < 0.05). Protein–protein interactions of significant (p < 0.05) differentially expressed genes were visualized with the STRING program. Gene interaction networks were found to be relevant to SARS and influenza pathogenesis. In conclusion, we were able to identify the pathways potentially affected by or affecting SARS-CoV-2 infection in underlying medical conditions likely to confer susceptibility and/or the severity of COVID-19. Our findings have implications in future COVID-19 experimental research and treatment development.

scholarly journals Discovery, development and application of drugs targeting BCL-2 pro-survival proteins in cancer

2021 ◽  
Author(s):  
Erinna F. Lee ◽  
W. Douglas Fairlie
Keyword(s):  
Structural Biology ◽  
Protein Interactions ◽  
Basic Science ◽  
Great Success ◽  
Protein Protein Interactions ◽  
New Class ◽  
Success Stories ◽  
History Of ◽  
Approved Drugs ◽  
Close Relatives

The discovery of a new class of small molecule compounds that target the BCL-2 family of anti-apoptotic proteins is one of the great success stories of basic science leading to translational outcomes in the last 30 years. The eponymous BCL-2 protein was identified over 30 years ago due to its association with cancer. However, it was the unveiling of the biochemistry and structural biology behind it and its close relatives’ mechanism(s)-of-action that provided the inspiration for what are now known as ‘BH3-mimetics’, the first clinically approved drugs designed to specifically inhibit protein–protein interactions. Herein, we chart the history of how these drugs were discovered, their evolution and application in cancer treatment.

scholarly journals Chemical toolbox for ‘live’ biochemistry to understand enzymatic functions in living systems

2019 ◽  
Author(s):  
Toru Komatsu ◽  
Yasuteru Urano
Keyword(s):  
Environmental Factors ◽  
Protein Interactions ◽  
Posttranslational Modifications ◽  
Live Cell ◽  
Living Systems ◽  
Protein Protein Interactions ◽  
Redox States ◽  
Recent Advances ◽  
Protein Functions ◽  
Research Tools

Abstract In this review, we present an overview of the recent advances in chemical toolboxes that are used to provide insights into ‘live’ protein functions in living systems. Protein functions are mediated by various factors inside of cells, such as protein−protein interactions, posttranslational modifications, and they are also subject to environmental factors such as pH, redox states and crowding conditions. Obtaining a true understanding of protein functions in living systems is therefore a considerably difficult task. Recent advances in research tools have allowed us to consider ‘live’ biochemistry as a valid approach to precisely understand how proteins function in a live cell context.

In Silico Recognition of Protein-Protein Interaction

2011 ◽  
pp. 248-268
Author(s):  
Byung-Hoon Park ◽  
Phuongan Dam ◽  
Chongle Pan ◽  
Ying Xu ◽  
Al Geist ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Regulatory Networks ◽  
Machine Learning Techniques ◽  
Translation Regulation ◽  
Future Research ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Protein Levels ◽  
Protein Functions ◽  
Model Protein

Protein-protein interactions are fundamental to cellular processes. They are responsible for phenomena like DNA replication, gene transcription, protein translation, regulation of metabolic pathways, immunologic recognition, signal transduction, etc. The identification of interacting proteins is therefore an important prerequisite step in understanding their physiological functions. Due to the invaluable importance to various biophysical activities, reliable computational methods to infer protein-protein interactions from either structural or genome sequences are in heavy demand lately. Successful predictions, for instance, will facilitate a drug design process and the reconstruction of metabolic or regulatory networks. In this chapter, we review: (a) high-throughput experimental methods for identification of protein-protein interactions, (b) existing databases of protein-protein interactions, (c) computational approaches to predicting protein-protein interactions at both residue and protein levels, (d) various statistical and machine learning techniques to model protein-protein interactions, and (e) applications of protein-protein interactions in predicting protein functions. We also discuss intrinsic drawbacks of the existing approaches and future research directions.

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