Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction.
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| Title: | Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction. |
|---|---|
| Authors: | Nallasamy, Varanavasi1 (AUTHOR) varanavasi.nallasamy@cognizant.com, Seshiah, Malarvizhi2 (AUTHOR) |
| Source: | Neural Computing & Applications. Jan2023, Vol. 35 Issue 2, p1983-2006. 24p. |
| Subjects: | Protein structure prediction, Deep learning, Convolutional neural networks, Nerve tissue proteins, Proteomics, Protein structure |
| Abstract: | In living organisms, proteins are considered as the executants of biological functions. Owing to its pivotal role played in protein folding patterns, comprehension of protein structure is a challenging issue. Moreover, owing to numerous protein sequence exploration in protein data banks and complication of protein structures, experimental methods are found to be inadequate for protein structural class prediction. Hence, it is very much advantageous to design a reliable computational method to predict protein structural classes from protein sequences. In the recent few years there has been an elevated interest in using deep learning to assist protein structure prediction as protein structure prediction models can be utilized to screen a large number of novel sequences. In this regard, we propose a model employing Energy Profile for atom pairs in conjunction with the Legion-Class Bayes function called Energy Profile Legion-Class Bayes Protein Structure Identification model. Followed by this, we use a Thompson Optimized convolutional neural network to extract features between amino acids and then the Thompson Optimized SoftMax function is employed to extract associations between protein sequences for predicting secondary protein structure. The proposed Energy Profile Bayes and Thompson Optimized Convolutional Neural Network (EPB-OCNN) method tested distinct unique protein data and was compared to the state-of-the-art methods, the Template-Based Modeling, Protein Design using Deep Graph Neural Networks, a deep learning-based S-glutathionylation sites prediction tool called a Computational Framework, the Deep Learning and a distance-based protein structure prediction using deep learning. The results obtained when applied with the Biopython tool with respect to protein structure prediction time, protein structure prediction accuracy, specificity, recall, F-measure, and precision, respectively, are measured. The proposed EPB-OCNN method outperformed the state-of-the-art methods, thereby corroborating the objective. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 161207986 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nallasamy%2C+Varanavasi%22">Nallasamy, Varanavasi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> varanavasi.nallasamy@cognizant.com</i><br /><searchLink fieldCode="AR" term="%22Seshiah%2C+Malarvizhi%22">Seshiah, Malarvizhi</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Jan2023, Vol. 35 Issue 2, p1983-2006. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Protein+structure+prediction%22">Protein structure prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Nerve+tissue+proteins%22">Nerve tissue proteins</searchLink><br /><searchLink fieldCode="DE" term="%22Proteomics%22">Proteomics</searchLink><br /><searchLink fieldCode="DE" term="%22Protein+structure%22">Protein structure</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In living organisms, proteins are considered as the executants of biological functions. Owing to its pivotal role played in protein folding patterns, comprehension of protein structure is a challenging issue. Moreover, owing to numerous protein sequence exploration in protein data banks and complication of protein structures, experimental methods are found to be inadequate for protein structural class prediction. Hence, it is very much advantageous to design a reliable computational method to predict protein structural classes from protein sequences. In the recent few years there has been an elevated interest in using deep learning to assist protein structure prediction as protein structure prediction models can be utilized to screen a large number of novel sequences. In this regard, we propose a model employing Energy Profile for atom pairs in conjunction with the Legion-Class Bayes function called Energy Profile Legion-Class Bayes Protein Structure Identification model. Followed by this, we use a Thompson Optimized convolutional neural network to extract features between amino acids and then the Thompson Optimized SoftMax function is employed to extract associations between protein sequences for predicting secondary protein structure. The proposed Energy Profile Bayes and Thompson Optimized Convolutional Neural Network (EPB-OCNN) method tested distinct unique protein data and was compared to the state-of-the-art methods, the Template-Based Modeling, Protein Design using Deep Graph Neural Networks, a deep learning-based S-glutathionylation sites prediction tool called a Computational Framework, the Deep Learning and a distance-based protein structure prediction using deep learning. The results obtained when applied with the Biopython tool with respect to protein structure prediction time, protein structure prediction accuracy, specificity, recall, F-measure, and precision, respectively, are measured. The proposed EPB-OCNN method outperformed the state-of-the-art methods, thereby corroborating the objective. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-022-07868-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 1983 Subjects: – SubjectFull: Protein structure prediction Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Nerve tissue proteins Type: general – SubjectFull: Proteomics Type: general – SubjectFull: Protein structure Type: general Titles: – TitleFull: Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nallasamy, Varanavasi – PersonEntity: Name: NameFull: Seshiah, Malarvizhi IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 01 Text: Jan2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 35 – Type: issue Value: 2 Titles: – TitleFull: Neural Computing & Applications Type: main |
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