Machine Learning Approaches for Quality Assessment of Protein Structures

Protein structures play a very important role in biomedical research, especially in drug discovery and design, which require accurate protein structures in advance. However, experimental determinations of protein structure are prohibitively costly and time-consuming, and computational predictions of...
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Autor*in:	Jiarui Chen [verfasserIn] Shirley W. I. Siu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	protein structure prediction estimating model quality model quality assessment machine learning deep learning CASP

Übergeordnetes Werk:	In: Biomolecules - MDPI AG, 2013, 10(2020), 4, p 626
Übergeordnetes Werk:	volume:10 ; year:2020 ; number:4, p 626

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/biom10040626

Katalog-ID:	DOAJ047074469

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allfieldsSound	10.3390/biom10040626 doi (DE-627)DOAJ047074469 (DE-599)DOAJ7b8e8a52a16c4407af3674ae28218789 DE-627 ger DE-627 rakwb eng QR1-502 Jiarui Chen verfasserin aut Machine Learning Approaches for Quality Assessment of Protein Structures 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Protein structures play a very important role in biomedical research, especially in drug discovery and design, which require accurate protein structures in advance. However, experimental determinations of protein structure are prohibitively costly and time-consuming, and computational predictions of protein structures have not been perfected. Methods that assess the quality of protein models can help in selecting the most accurate candidates for further work. Driven by this demand, many structural bioinformatics laboratories have developed methods for estimating model accuracy (EMA). In recent years, EMA by machine learning (ML) have consistently ranked among the top-performing methods in the community-wide CASP challenge. Accordingly, we systematically review all the major ML-based EMA methods developed within the past ten years. The methods are grouped by their employed ML approach—support vector machine, artificial neural networks, ensemble learning, or Bayesian learning—and their significances are discussed from a methodology viewpoint. To orient the reader, we also briefly describe the background of EMA, including the CASP challenge and its evaluation metrics, and introduce the major ML/DL techniques. Overall, this review provides an introductory guide to modern research on protein quality assessment and directions for future research in this area. protein structure prediction estimating model quality model quality assessment machine learning deep learning CASP Microbiology Shirley W. I. Siu verfasserin aut In Biomolecules MDPI AG, 2013 10(2020), 4, p 626 (DE-627)735688915 (DE-600)2701262-1 2218273X nnns volume:10 year:2020 number:4, p 626 https://doi.org/10.3390/biom10040626 kostenfrei https://doaj.org/article/7b8e8a52a16c4407af3674ae28218789 kostenfrei https://www.mdpi.com/2218-273X/10/4/626 kostenfrei https://doaj.org/toc/2218-273X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 4, p 626
language	English
source	In Biomolecules 10(2020), 4, p 626 volume:10 year:2020 number:4, p 626
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authorswithroles_txt_mv	Jiarui Chen @@aut@@ Shirley W. I. Siu @@aut@@
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callnumber-first	Q - Science
author	Jiarui Chen
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topic_title	QR1-502 Machine Learning Approaches for Quality Assessment of Protein Structures protein structure prediction estimating model quality model quality assessment machine learning deep learning CASP
topic	misc QR1-502 misc protein structure prediction misc estimating model quality misc model quality assessment misc machine learning misc deep learning misc CASP misc Microbiology
topic_unstemmed	misc QR1-502 misc protein structure prediction misc estimating model quality misc model quality assessment misc machine learning misc deep learning misc CASP misc Microbiology
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title	Machine Learning Approaches for Quality Assessment of Protein Structures
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title_full	Machine Learning Approaches for Quality Assessment of Protein Structures
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title_sort	machine learning approaches for quality assessment of protein structures
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title_auth	Machine Learning Approaches for Quality Assessment of Protein Structures
abstract	Protein structures play a very important role in biomedical research, especially in drug discovery and design, which require accurate protein structures in advance. However, experimental determinations of protein structure are prohibitively costly and time-consuming, and computational predictions of protein structures have not been perfected. Methods that assess the quality of protein models can help in selecting the most accurate candidates for further work. Driven by this demand, many structural bioinformatics laboratories have developed methods for estimating model accuracy (EMA). In recent years, EMA by machine learning (ML) have consistently ranked among the top-performing methods in the community-wide CASP challenge. Accordingly, we systematically review all the major ML-based EMA methods developed within the past ten years. The methods are grouped by their employed ML approach—support vector machine, artificial neural networks, ensemble learning, or Bayesian learning—and their significances are discussed from a methodology viewpoint. To orient the reader, we also briefly describe the background of EMA, including the CASP challenge and its evaluation metrics, and introduce the major ML/DL techniques. Overall, this review provides an introductory guide to modern research on protein quality assessment and directions for future research in this area.
abstractGer	Protein structures play a very important role in biomedical research, especially in drug discovery and design, which require accurate protein structures in advance. However, experimental determinations of protein structure are prohibitively costly and time-consuming, and computational predictions of protein structures have not been perfected. Methods that assess the quality of protein models can help in selecting the most accurate candidates for further work. Driven by this demand, many structural bioinformatics laboratories have developed methods for estimating model accuracy (EMA). In recent years, EMA by machine learning (ML) have consistently ranked among the top-performing methods in the community-wide CASP challenge. Accordingly, we systematically review all the major ML-based EMA methods developed within the past ten years. The methods are grouped by their employed ML approach—support vector machine, artificial neural networks, ensemble learning, or Bayesian learning—and their significances are discussed from a methodology viewpoint. To orient the reader, we also briefly describe the background of EMA, including the CASP challenge and its evaluation metrics, and introduce the major ML/DL techniques. Overall, this review provides an introductory guide to modern research on protein quality assessment and directions for future research in this area.
abstract_unstemmed	Protein structures play a very important role in biomedical research, especially in drug discovery and design, which require accurate protein structures in advance. However, experimental determinations of protein structure are prohibitively costly and time-consuming, and computational predictions of protein structures have not been perfected. Methods that assess the quality of protein models can help in selecting the most accurate candidates for further work. Driven by this demand, many structural bioinformatics laboratories have developed methods for estimating model accuracy (EMA). In recent years, EMA by machine learning (ML) have consistently ranked among the top-performing methods in the community-wide CASP challenge. Accordingly, we systematically review all the major ML-based EMA methods developed within the past ten years. The methods are grouped by their employed ML approach—support vector machine, artificial neural networks, ensemble learning, or Bayesian learning—and their significances are discussed from a methodology viewpoint. To orient the reader, we also briefly describe the background of EMA, including the CASP challenge and its evaluation metrics, and introduce the major ML/DL techniques. Overall, this review provides an introductory guide to modern research on protein quality assessment and directions for future research in this area.
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title_short	Machine Learning Approaches for Quality Assessment of Protein Structures
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