Modern Approaches to Protein Constructions: A Comprehensive Review of Computational Tools and Databases for De Novo Protein Design and Engineering

ABSTRACT The field of protein engineering has witnessed transformative advancements, with computational tools and databases driving novel innovations in de novo protein design. This review consolidates and critiques a comprehensive range of modern computational resources, offering a unique focus on...

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Vydáno v:Engineering reports (Hoboken, N.J.) Ročník 7; číslo 2
Hlavní autoři: Mia, Md. Mojnu, Sultana, Habiba, Amin, Md. Al, Hossain, Md. Sakhawat, Imam, Hasan, Mohiuddin, A. K. M., Mahmud, Shahin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 01.02.2025
Wiley
Témata:
Accuracy
Algorithms
Amino acids
Artificial intelligence
Bioinformatics
Biomedical materials
Datasets
Design
Energy
Engineering
Machine learning
Mutagenesis
Mutation
Neural networks
NMR
Nuclear magnetic resonance
protein computational resources
protein design
protein engineering
protein engineering tools
Proteins
Research methodology
Software
Stability
ISSN:2577-8196, 2577-8196
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Abstract ABSTRACT The field of protein engineering has witnessed transformative advancements, with computational tools and databases driving novel innovations in de novo protein design. This review consolidates and critiques a comprehensive range of modern computational resources, offering a unique focus on their applications across diverse domains, including protein stability prediction, posttranslational modification analysis, and mutation effect evaluation. Key contributions include a detailed examination of tools integrating machine learning and artificial intelligence to enhance predictive accuracy and streamline protein engineering workflows. By highlighting underexplored tools and novel methodologies, such as advanced protein–ligand interaction predictors and neural network–based stability assessment models, this study establishes itself as a unique reference for researchers aiming to develop tailored proteins for therapeutic, industrial, and biomedical applications. Illustration of modern computational approaches in protein engineering, encompassing mutation analysis, protein dynamics, high‐throughput screening, and enzyme optimization to enhance de novo protein design and functional understanding
AbstractList ABSTRACT The field of protein engineering has witnessed transformative advancements, with computational tools and databases driving novel innovations in de novo protein design. This review consolidates and critiques a comprehensive range of modern computational resources, offering a unique focus on their applications across diverse domains, including protein stability prediction, posttranslational modification analysis, and mutation effect evaluation. Key contributions include a detailed examination of tools integrating machine learning and artificial intelligence to enhance predictive accuracy and streamline protein engineering workflows. By highlighting underexplored tools and novel methodologies, such as advanced protein–ligand interaction predictors and neural network–based stability assessment models, this study establishes itself as a unique reference for researchers aiming to develop tailored proteins for therapeutic, industrial, and biomedical applications.
ABSTRACT The field of protein engineering has witnessed transformative advancements, with computational tools and databases driving novel innovations in de novo protein design. This review consolidates and critiques a comprehensive range of modern computational resources, offering a unique focus on their applications across diverse domains, including protein stability prediction, posttranslational modification analysis, and mutation effect evaluation. Key contributions include a detailed examination of tools integrating machine learning and artificial intelligence to enhance predictive accuracy and streamline protein engineering workflows. By highlighting underexplored tools and novel methodologies, such as advanced protein–ligand interaction predictors and neural network–based stability assessment models, this study establishes itself as a unique reference for researchers aiming to develop tailored proteins for therapeutic, industrial, and biomedical applications. Illustration of modern computational approaches in protein engineering, encompassing mutation analysis, protein dynamics, high‐throughput screening, and enzyme optimization to enhance de novo protein design and functional understanding
The field of protein engineering has witnessed transformative advancements, with computational tools and databases driving novel innovations in de novo protein design. This review consolidates and critiques a comprehensive range of modern computational resources, offering a unique focus on their applications across diverse domains, including protein stability prediction, posttranslational modification analysis, and mutation effect evaluation. Key contributions include a detailed examination of tools integrating machine learning and artificial intelligence to enhance predictive accuracy and streamline protein engineering workflows. By highlighting underexplored tools and novel methodologies, such as advanced protein–ligand interaction predictors and neural network–based stability assessment models, this study establishes itself as a unique reference for researchers aiming to develop tailored proteins for therapeutic, industrial, and biomedical applications.
Author Amin, Md. Al
Mia, Md. Mojnu
Sultana, Habiba
Mahmud, Shahin
Hossain, Md. Sakhawat
Mohiuddin, A. K. M.
Imam, Hasan
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  surname: Mahmud
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  email: shahin018mbstu@gmail.com, shahinbge@mbstu.ac.bd
  organization: Mawlana Bhashani Science and Technology University
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Md. Mojnu Mia and Habiba Sultana equally contributed as first author.
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2018; 8
1985; 18
January 2011; 39
May 2014; 30
April 2005; 21
2010; 28
2024; 7
May 2011; 39
October 2007; 23
2007; 8
2014; 15
April 2023; 101
2007; 4
2022; 31
2007; 3
July 2013; 41
2018; 34
2022; 38
2014; 11
2021; 2021
2018; 36
2018; 35
2019; 7
December 2022; 38
2023; 51
2010; 31
2019; 3
January 2006; 34
2015; 51
2024; 128
2019; 1
1997; 25
2015; 55
2011; 75
August 2007; 24
2022; 119
1996; 14
January 2014; 42
2003; 332
2003; 31
October 2012; 7
2018; 27
2014; 42
April 2007; 8
January 2003; 19
January 2019; 47
January 2022; 130
2022; 14
July 2005; 33
July 2016; 44
January 2022; 3
2018; 16
2022; 16
2018; 14
February 2020; 18
2021; 22
July 2005; 21
2015; 31
March 2023; 24
2011; 12
2005; 26
2021; 30
1990; 265
October 2016; 32
2006; 62
July 2012; 40
2015; 43
2019; 116
November 2019; 36
September 2003; 19
May 2013; 32
September 2003; 13
2005; 33
September 2009; 25
2015; 16
2022; 271
2022; 50
January 2015; 43
2017; 1561
2006; 7
April 2020; 173
2011; 32
2022; 44
2011; 39
1983; 219
July 2006; 34
2021; 13
2009; 30
July 2017; 45
2018; 1685
November 2022
June 2010; 5
January 2015; 32
August 2015; 31
January 2021; 22
2012; 279
2023; 93
2009; 37
2022; 147
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Snippet ABSTRACT The field of protein engineering has witnessed transformative advancements, with computational tools and databases driving novel innovations in de...
The field of protein engineering has witnessed transformative advancements, with computational tools and databases driving novel innovations in de novo protein...
ABSTRACT The field of protein engineering has witnessed transformative advancements, with computational tools and databases driving novel innovations in de...
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SubjectTerms Accuracy
Algorithms
Amino acids
Artificial intelligence
Bioinformatics
Biomedical materials
Datasets
Design
Energy
Engineering
Machine learning
Mutagenesis
Mutation
Neural networks
NMR
Nuclear magnetic resonance
protein computational resources
protein design
protein engineering
protein engineering tools
Proteins
Research methodology
Software
Stability
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Title Modern Approaches to Protein Constructions: A Comprehensive Review of Computational Tools and Databases for De Novo Protein Design and Engineering
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