scholarly journals Syntaxin 1A inhibits CFTR chloride channels by means of domain-specific protein-protein interactions

1998 ◽  
Vol 95 (18) ◽  
pp. 10972-10977 ◽  
Author(s):  
A. P. Naren ◽  
M. W. Quick ◽  
J. F. Collawn ◽  
D. J. Nelson ◽  
K. L. Kirk
Keyword(s):  
Protein Interactions ◽  
Chloride Channels ◽  
Specific Protein ◽  
Protein Protein Interactions ◽  
Syntaxin 1A ◽  
Domain Specific

Structure, Function, Involvement in Diseases and Targeting of 14-3-3 Proteins: An Update

2018 ◽  
Vol 25 (1) ◽  
pp. 5-21 ◽  
Author(s):  
Ylenia Cau ◽  
Daniela Valensin ◽  
Mattia Mori ◽  
Sara Draghi ◽  
Maurizio Botta
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Physiological Role ◽  
Specific Protein ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Protein Partners ◽  
High Sequence Homology ◽  
Small Molecule Modulators

14-3-3 is a class of proteins able to interact with a multitude of targets by establishing protein-protein interactions (PPIs). They are usually found in all eukaryotes with a conserved secondary structure and high sequence homology among species. 14-3-3 proteins are involved in many physiological and pathological cellular processes either by triggering or interfering with the activity of specific protein partners. In the last years, the scientific community has collected many evidences on the role played by seven human 14-3-3 isoforms in cancer or neurodegenerative diseases. Indeed, these proteins regulate the molecular mechanisms associated to these diseases by interacting with (i) oncogenic and (ii) pro-apoptotic proteins and (iii) with proteins involved in Parkinson and Alzheimer diseases. The discovery of small molecule modulators of 14-3-3 PPIs could facilitate complete understanding of the physiological role of these proteins, and might offer valuable therapeutic approaches for these critical pathological states.

Metallocluster transactions: dynamic protein interactions guide the biosynthesis of Fe–S clusters in bacteria

2018 ◽  
Vol 46 (6) ◽  
pp. 1593-1603 ◽  
Author(s):  
Chenkang Zheng ◽  
Patricia C. Dos Santos
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Specific Protein ◽  
Biological Synthesis ◽  
Cluster Assembly ◽  
Sequence Motifs ◽  
Protein Protein Interactions ◽  
Cysteine Desulfurase ◽  
Wide Range ◽  
Domains Of Life

Iron–sulfur (Fe–S) clusters are ubiquitous cofactors present in all domains of life. The chemistries catalyzed by these inorganic cofactors are diverse and their associated enzymes are involved in many cellular processes. Despite the wide range of structures reported for Fe–S clusters inserted into proteins, the biological synthesis of all Fe–S clusters starts with the assembly of simple units of 2Fe–2S and 4Fe–4S clusters. Several systems have been associated with the formation of Fe–S clusters in bacteria with varying phylogenetic origins and number of biosynthetic and regulatory components. All systems, however, construct Fe–S clusters through a similar biosynthetic scheme involving three main steps: (1) sulfur activation by a cysteine desulfurase, (2) cluster assembly by a scaffold protein, and (3) guided delivery of Fe–S units to either final acceptors or biosynthetic enzymes involved in the formation of complex metalloclusters. Another unifying feature on the biological formation of Fe–S clusters in bacteria is that these systems are tightly regulated by a network of protein interactions. Thus, the formation of transient protein complexes among biosynthetic components allows for the direct transfer of reactive sulfur and Fe–S intermediates preventing oxygen damage and reactions with non-physiological targets. Recent studies revealed the importance of reciprocal signature sequence motifs that enable specific protein–protein interactions and consequently guide the transactions between physiological donors and acceptors. Such findings provide insights into strategies used by bacteria to regulate the flow of reactive intermediates and provide protein barcodes to uncover yet-unidentified cellular components involved in Fe–S metabolism.

scholarly journals Bi-directional protein-protein interactions control liquid-liquid phase separation of PSD-95 and its interaction partners

2021 ◽  
Author(s):  
Nikolaj Riis Christensen ◽  
Christian Parsbæk Pedersen ◽  
Vita Sereikaite ◽  
Jannik Nedergaard Pedersen ◽  
Maria Vistrup-Parry ◽  
...  
Keyword(s):  
Phase Separation ◽  
Liquid Phase ◽  
Protein Interactions ◽  
Postsynaptic Density ◽  
Specific Protein ◽  
Liquid Phase Separation ◽  
Protein Protein Interactions ◽  
New Perspective ◽  
Liquid Liquid Phase Separation

SUMMARYThe organization of the postsynaptic density (PSD), a protein-dense semi-membraneless organelle, is mediated by numerous specific protein-protein interactions (PPIs) which constitute a functional post-synapse. Postsynaptic density protein 95 (PSD-95) interacts with a manifold of proteins, including the C-terminal of transmembrane AMPA receptor (AMAPR) regulatory proteins (TARPs). Here, we uncover the minimal essential peptide responsible for the stargazin (TARP-γ2) mediated liquid-liquid phase separation (LLPS) formation of PSD-95 and other key protein constituents of the PSD. Furthermore, we find that pharmacological inhibitors of PSD-95 can facilitate formation of LLPS. We found that in some cases LLPS formation is dependent on multivalent interactions while in other cases short peptides carrying a high charge are sufficient to promote LLPS in complex systems. This study offers a new perspective on PSD-95 interactions and their role in LLPS formation, while also considering the role of affinity over multivalency in LLPS systems.

scholarly journals Coevolution-based prediction of protein–protein interactions in polyketide biosynthetic assembly lines

2020 ◽  
Vol 36 (19) ◽  
pp. 4846-4853 ◽  
Author(s):  
Yan Wang ◽  
Miguel Correa Marrero ◽  
Marnix H Medema ◽  
Aalt D J van Dijk
Keyword(s):  
Protein Interactions ◽  
Antitumor Agents ◽  
Acyl Carrier Protein ◽  
Specific Protein ◽  
Combinatorial Biosynthesis ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Assembly Lines ◽  
Specific Order ◽  
Encoding Genes

Abstract Motivation Polyketide synthases (PKSs) are enzymes that generate diverse molecules of great pharmaceutical importance, including a range of clinically used antimicrobials and antitumor agents. Many polyketides are synthesized by cis-AT modular PKSs, which are organized in assembly lines, in which multiple enzymes line up in a specific order. This order is defined by specific protein–protein interactions (PPIs). The unique modular structure and catalyzing mechanism of these assembly lines makes their products predictable and also spurred combinatorial biosynthesis studies to produce novel polyketides using synthetic biology. However, predicting the interactions of PKSs, and thereby inferring the order of their assembly line, is still challenging, especially for cases in which this order is not reflected by the ordering of the PKS-encoding genes in the genome. Results Here, we introduce PKSpop, which uses a coevolution-based PPI algorithm to infer protein order in PKS assembly lines. Our method accurately predicts protein orders (93% accuracy). Additionally, we identify new residue pairs that are key in determining interaction specificity, and show that coevolution of N- and C-terminal docking domains of PKSs is significantly more predictive for PPIs than coevolution between ketosynthase and acyl carrier protein domains. Availability and implementation The code is available on http://www.bif.wur.nl/ (under ‘Software’). Supplementary information Supplementary data are available at Bioinformatics online.

The study of protein–protein interactions in bacteria

2012 ◽  
Vol 58 (11) ◽  
pp. 1241-1257 ◽  
Author(s):  
Roberto Velasco-García ◽  
Rocío Vargas-Martínez
Keyword(s):  
Hybrid System ◽  
High Throughput ◽  
Protein Interactions ◽  
Specific Protein ◽  
False Positives ◽  
Interaction Networks ◽  
Protein Protein Interactions ◽  
Extensive Data ◽  
False Positives And Negatives

Many of the functions fulfilled by proteins in the cell require specific protein–protein interactions (PPI). During the last decade, the use of high-throughput experimental technologies, primarily based on the yeast 2-hybrid system, generated extensive data currently located in public databases. This information has been used to build interaction networks for different species. Unfortunately, due to the nature of the yeast 2-hybrid system, these databases contain many false positives and negatives, thus they require purging. A method for confirming these PPI is to test them using a technique that operates in vivo and detects binary PPI. This article comprises an overview of the study of PPI and describes the main techniques that have been used to identify bacterial PPI, prioritizing those that can be used for their verification, and it also mentions a number of PPI that have been identified or confirmed using these methods.

scholarly journals Design of Disruptors of the Hsp90–Cdc37 Interface

Molecules ◽  
2020 ◽  
Vol 25 (2) ◽  
pp. 360 ◽  
Author(s):  
Ilda D’Annessa ◽  
Naama Hurwitz ◽  
Valentina Pirota ◽  
Giovanni Luca Beretta ◽  
Stella Tinelli ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Human Cancer ◽  
Specific Protein ◽  
Cancer Cell Proliferation ◽  
Protein Protein Interactions ◽  
Suitable Candidate ◽  
Structural Stabilization ◽  
Human Cancer Cells ◽  
Theoretical Approaches ◽  
Competitive Inhibitors

The molecular chaperone Hsp90 is a ubiquitous ATPase-directed protein responsible for the activation and structural stabilization of a large clientele of proteins. As such, Hsp90 has emerged as a suitable candidate for the treatment of a diverse set of diseases, such as cancer and neurodegeneration. The inhibition of the chaperone through ATP-competitive inhibitors, however, was shown to lead to undesirable side effects. One strategy to alleviate this problem is the development of molecules that are able to disrupt specific protein–protein interactions, thus modulating the activity of Hsp90 only in the particular cellular pathway that needs to be targeted. Here, we exploit novel computational and theoretical approaches to design a set of peptides that are able to bind Hsp90 and compete for its interaction with the co-chaperone Cdc37, which is found to be responsible for the promotion of cancer cell proliferation. In spite of their capability to disrupt the Hsp90–Cdc37 interaction, no important cytotoxicity was observed in human cancer cells exposed to designed compounds. These findings imply the need for further optimization of the compounds, which may lead to new ways of interfering with the Hsp90 mechanisms that are important for tumour growth.

New advances in extracting and learning from protein–protein interactions within unstructured biomedical text data

2019 ◽  
Vol 3 (4) ◽  
pp. 357-369
Author(s):  
J. Harry Caufield ◽  
Peipei Ping
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Historical Context ◽  
Specific Protein ◽  
Basic Unit ◽  
Biomedical Text ◽  
Protein Protein Interactions ◽  
Text Data ◽  
Ppi Networks ◽  
Protein Functions

Abstract Protein–protein interactions, or PPIs, constitute a basic unit of our understanding of protein function. Though substantial effort has been made to organize PPI knowledge into structured databases, maintenance of these resources requires careful manual curation. Even then, many PPIs remain uncurated within unstructured text data. Extracting PPIs from experimental research supports assembly of PPI networks and highlights relationships crucial to elucidating protein functions. Isolating specific protein–protein relationships from numerous documents is technically demanding by both manual and automated means. Recent advances in the design of these methods have leveraged emerging computational developments and have demonstrated impressive results on test datasets. In this review, we discuss recent developments in PPI extraction from unstructured biomedical text. We explore the historical context of these developments, recent strategies for integrating and comparing PPI data, and their application to advancing the understanding of protein function. Finally, we describe the challenges facing the application of PPI mining to the text concerning protein families, using the multifunctional 14-3-3 protein family as an example.

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