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A Novel In Silico Benchmarked Pipeline Capable of Complete Protein Analysis: A Possible Tool for Potential Drug Discovery

SIMPLE SUMMARY: Protein interactions govern the majority of an organism’s biological processes. Therefore, to fully understand the functionality of an organism, we must know how proteins work at a molecular level. This study assembled a protocol that enables scientists to construct a protein’s terti...

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Autores principales: Perera, D. D. B. D., Perera, K. Minoli L., Peiris, Dinithi C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615085/
https://www.ncbi.nlm.nih.gov/pubmed/34827106
http://dx.doi.org/10.3390/biology10111113
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author Perera, D. D. B. D.
Perera, K. Minoli L.
Peiris, Dinithi C.
author_facet Perera, D. D. B. D.
Perera, K. Minoli L.
Peiris, Dinithi C.
author_sort Perera, D. D. B. D.
collection PubMed
description SIMPLE SUMMARY: Protein interactions govern the majority of an organism’s biological processes. Therefore, to fully understand the functionality of an organism, we must know how proteins work at a molecular level. This study assembled a protocol that enables scientists to construct a protein’s tertiary structure easily and subsequently to investigate its mechanism and function. Each step involved in prediction, validation, and functional analysis of a protein is crucial to obtain an accurate result. We have dubbed this the trifecta analysis. It was clear early in our research that no single study in the literature had previously encompassed the complete trifecta analysis. In particular, studies that recommend free, open-source tools that have been benchmarked for each step are lacking. The present study ensures that predictions are accurate and validated and will greatly benefit new and experienced scientists alike in obtaining a strong understanding of the trifecta analysis, resulting in a domino effect that could lead to drug development. ABSTRACT: Current in silico proteomics require the trifecta analysis, namely, prediction, validation, and functional assessment of a modeled protein. The main drawback of this endeavor is the lack of a single protocol that utilizes a proper set of benchmarked open-source tools to predict a protein’s structure and function accurately. The present study rectifies this drawback through the design and development of such a protocol. The protocol begins with the characterization of a novel coding sequence to identify the expressed protein. It then recognizes and isolates evolutionarily conserved sequence motifs through phylogenetics. The next step is to predict the protein’s secondary structure, followed by the prediction, refinement, and validation of its three-dimensional tertiary structure. These steps enable the functional analysis of the macromolecule through protein docking, which facilitates the identification of the protein’s active site. Each of these steps is crucial for the complete characterization of the protein under study. We have dubbed this process the trifecta analysis. In this study, we have proven the effectiveness of our protocol using the cystatin C and AChE proteins. Beginning with just their sequences, we have characterized both proteins’ structures and functions, including identifying the cystatin C protein’s seven-residue active site and the AChE protein’s active-site gorge via protein–protein and protein–ligand docking, respectively. This process will greatly benefit new and experienced scientists alike in obtaining a strong understanding of the trifecta analysis, resulting in a domino effect that could expand drug development.
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spelling pubmed-86150852021-11-26 A Novel In Silico Benchmarked Pipeline Capable of Complete Protein Analysis: A Possible Tool for Potential Drug Discovery Perera, D. D. B. D. Perera, K. Minoli L. Peiris, Dinithi C. Biology (Basel) Article SIMPLE SUMMARY: Protein interactions govern the majority of an organism’s biological processes. Therefore, to fully understand the functionality of an organism, we must know how proteins work at a molecular level. This study assembled a protocol that enables scientists to construct a protein’s tertiary structure easily and subsequently to investigate its mechanism and function. Each step involved in prediction, validation, and functional analysis of a protein is crucial to obtain an accurate result. We have dubbed this the trifecta analysis. It was clear early in our research that no single study in the literature had previously encompassed the complete trifecta analysis. In particular, studies that recommend free, open-source tools that have been benchmarked for each step are lacking. The present study ensures that predictions are accurate and validated and will greatly benefit new and experienced scientists alike in obtaining a strong understanding of the trifecta analysis, resulting in a domino effect that could lead to drug development. ABSTRACT: Current in silico proteomics require the trifecta analysis, namely, prediction, validation, and functional assessment of a modeled protein. The main drawback of this endeavor is the lack of a single protocol that utilizes a proper set of benchmarked open-source tools to predict a protein’s structure and function accurately. The present study rectifies this drawback through the design and development of such a protocol. The protocol begins with the characterization of a novel coding sequence to identify the expressed protein. It then recognizes and isolates evolutionarily conserved sequence motifs through phylogenetics. The next step is to predict the protein’s secondary structure, followed by the prediction, refinement, and validation of its three-dimensional tertiary structure. These steps enable the functional analysis of the macromolecule through protein docking, which facilitates the identification of the protein’s active site. Each of these steps is crucial for the complete characterization of the protein under study. We have dubbed this process the trifecta analysis. In this study, we have proven the effectiveness of our protocol using the cystatin C and AChE proteins. Beginning with just their sequences, we have characterized both proteins’ structures and functions, including identifying the cystatin C protein’s seven-residue active site and the AChE protein’s active-site gorge via protein–protein and protein–ligand docking, respectively. This process will greatly benefit new and experienced scientists alike in obtaining a strong understanding of the trifecta analysis, resulting in a domino effect that could expand drug development. MDPI 2021-10-28 /pmc/articles/PMC8615085/ /pubmed/34827106 http://dx.doi.org/10.3390/biology10111113 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Perera, D. D. B. D.
Perera, K. Minoli L.
Peiris, Dinithi C.
A Novel In Silico Benchmarked Pipeline Capable of Complete Protein Analysis: A Possible Tool for Potential Drug Discovery
title A Novel In Silico Benchmarked Pipeline Capable of Complete Protein Analysis: A Possible Tool for Potential Drug Discovery
title_full A Novel In Silico Benchmarked Pipeline Capable of Complete Protein Analysis: A Possible Tool for Potential Drug Discovery
title_fullStr A Novel In Silico Benchmarked Pipeline Capable of Complete Protein Analysis: A Possible Tool for Potential Drug Discovery
title_full_unstemmed A Novel In Silico Benchmarked Pipeline Capable of Complete Protein Analysis: A Possible Tool for Potential Drug Discovery
title_short A Novel In Silico Benchmarked Pipeline Capable of Complete Protein Analysis: A Possible Tool for Potential Drug Discovery
title_sort novel in silico benchmarked pipeline capable of complete protein analysis: a possible tool for potential drug discovery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615085/
https://www.ncbi.nlm.nih.gov/pubmed/34827106
http://dx.doi.org/10.3390/biology10111113
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