Clinical Microbiology: Open Access

Interactions of Viral and Host Proteins: Its Functional Consequences

Saud Alguwaizani^* Department of Life Sciences, Imperial College London, London, United Kingdom
^*Correspondence: Saud Alguwaizani, Department of Life Sciences, Imperial College London, London, United Kingdom, Email:

Received: 28-Oct-2022, Manuscript No. CMO-22-19209; Editor assigned: 01-Nov-2022, Pre QC No. CMO-22-19209(PQ); Reviewed: 17-Nov-2022, QC No. CMO-22-19209; Revised: 22-Nov-2022, Manuscript No. CMO-22-19209(R); Published: 30-Nov-2022, DOI: 10.35248/2327-5073.22.11.315

Description

Information on virus-host protein interactions, viral and host protein structures and repertoires, their specific evolutionary mechanisms, and knowledge of reliable biological data sources are crucial. This article's goal is to give a comprehensive summary of these features [1]. Protein domains are the fundamental building blocks that define how proteins interact. Because of the distinctiveness of viral domain repertoires, their mechanism of evolution, and their functions during viral infection, viruses are fascinating study subjects. Protein electrostatics and structural characteristics can be altered by mutations at protein interfaces to decrease or increase binding affinities. Both pathogen and cellular proteins are constantly vying for binding partners throughout the course of a viral infection [2]. Exogenous interfaces mediating viral-host interactions constantly target and block endogenous interfaces mediating intraspecific interactions, such as viral-viral or hosthost interactions. From a biological standpoint, the primary mechanism underlying antiviral medicines is preventing such connections. The ways in which some proteins interact with their binding partners determine how quickly these "hub proteins" change. "Party hubs" establish simultaneous/stable (domaindomain) contacts, have many interfaces, and have a propensity to evolve gradually. In contrast, "date hubs" have fewer interfaces and can grow more quickly thanks to short linear peptides (15 residues or less) that form temporary or weak (domain-motif) connections. Many Protein-Protein Interactions (PPIs) that are involved in viral infections can be visualised as networks (protein interaction networks, or PINs), where proteins are represented as nodes and interactions as edges [3]. According to some theories, viral proteins prefer to interact with more central, densely linked host proteins. Viral and host proteins are continually altering their interface residues in an evolutionary arms race, either to evade or to improve their binding capacities. Virus-host PINs can also change through gene duplication (paralogy), conservation (orthology), Horizontal Gene Transfer (HGT) (xenology), and molecular mimicry in addition to gaining and losing contacts via rewiring mechanisms (convergence). The latter portions of this study concentrate on PPI experimental methods and their limitations while also giving a summary of the biomolecular data sources available for researching virus-host protein interactions.

All stages of the viral life cycle from infection of the host cell through replication of the viral genome and creation of new viral particles are mediated by interactions between the viral and host proteins [4]. A new area of biology that can clarify the crucial pathways involved in replication, cellular signalling, and cell division is the study of these Protein-Protein Interactions (PPI). Additionally, interactions between viruses and their hosts are a potentially effective target for antiviral medications. One example is the HIV entry inhibitor maraviroc, which prevents viral entry by interacting with the Cellular Receptor (CCR5) and preventing it from interacting with the viral Glycoprotein (gp120).

With an increased focus on distinguishing the true-positives from the false-positives identified during mass spectrometry, recent developments in molecular biology, mass spectrometry, and bioinformatics have increased the throughput of analysis while concurrently decreasing the false-positive rate of interactomic assay results [5]. The main objective of this study is to evaluate the advantages and disadvantages of the most popular interactome identification techniques. After reading this article, readers should be able to create an experiment to obtain an interactomic dataset and screen it for results with a high degree of confidence and the system implications of those results.

Ex situ binding assays, such as Glutathione S-Transferase (GST) pull-downs, yeast two-hybrids, and the Nucleic Acid- Programmable Protein Array (NAPPA), and in situ binding assays, such as Affinity Purification-Mass Spectrometry (AP-MS) and proximity-dependent labelling, are the two main pipelines for collecting interactomic datasets. Typically, a protein of interest is used as bait for a pool of potential prey proteins in these tests. These tests are neither exclusive nor perfect for all proteins. In fact, repeating the experiment using one of the alternative ways is a frequent technique for validating results with high confidence. The identification of the high confidence points in the data remains a persistent challenge in interactomics, despite the fact that the addition of these techniques has enhanced the collection of interactomic data. In order to address this, laboratories have created screening programmes to analyse the data and highlight the most consistently strong results Significance Analysis of INTeractome (SAINT) and Comparative Proteomic Analysis Software Suite. These programmes have been used to map the common falsepositive proteins contaminant repository for affinity purification-mass spectrometry data (CRAPome) and generate screening data (CompPASS). How these interactions impact the biological system is the next topic to be asked once the high confidence results have been established. This issue has been addressed by advancements in systems analysis, which have made it possible for researchers to quickly fit the identified proteins into pathways and identify biological processes that are strongly associated with the dataset (Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and for viralhost PPI Viruses). The likelihood of incorrect protein identifications has decreased as a result of these data analysis advancements, which have also deepened the analysis for interactomic research.

Any virus infection is fundamentally driven by protein-protein interactions. Therefore, thorough knowledge of these interactions is essential for our comprehension of viral illnesses and the creation of new treatments. However, understanding of host-viral protein interactions is heavily skewed toward a tiny subset of viral families. Notably, these families are of the uttermost biological and monetary value while frequently having varied genomes. Likewise, only a limited number of interactions are currently known for many viruses of lesser medical significance, which are insufficient to comprehend their infection mechanisms. Notably, viruses can manufacture several (variant) viral particles by the time the host can mount an immune response, which makes them evolve considerably more quickly than their hosts, particularly in the case of RNA viruses. Thus, although an individual immune system is typically capable of fighting off an infection, viruses can also alter their host-virus interaction interface quicker than a host population can react by mutating their target proteins.

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