A protein sequence meta-functional signature for calcium binding residue prediction
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| Title: | A protein sequence meta-functional signature for calcium binding residue prediction |
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| Authors: | Horst, Jeremy A., Samudrala, Ram ram@compbio.washington.edu |
| Source: | Pattern Recognition Letters. Oct2010, Vol. 31 Issue 14, p2103-2112. 10p. |
| Subjects: | Amino acid sequence, Calcium-binding proteins, Algorithms, Binding sites, Prediction theory, Protein structure |
| Abstract: | Abstract: The diversity of characterized protein functions found amongst experimentally interrogated proteins suggests that a vast array of unknown functions remains undiscovered. These protein functions are imparted by specific geometric distributions of amino acid residue chemical moieties, each contributing a functional interaction. We hypothesize that individual residue function contributions are predictable through sequence analytic knowledge based algorithms, and that they can be recombined to understand composite protein function by predicting spatial relation in tertiary structure. We assess the former by training a meta-functional signature algorithm to specifically predict calcium ion binding residues from protein sequence. We estimate the latter by testing for match between predictive contribution of positions in predicted secondary structures and patterns of side chain proximity forced by secondary structure moieties. Specific training for calcium binding results in 83% area under the receiver operator characteristic curve added value over random (AUCoR) and p <10−300 significance as measured by Kendall’s τ in 10-fold cross validation for parallel sets of 811 residues in 336 proteins and 696 residues in 299 proteins. Training for generalized function results in 63% AUCoR and p ≅10−221 for the same tests. Including inference of side chain proximity improves predictive ability by 2% AUCoR consistently. The results demonstrate that protein meta-functional signatures can be trained to predict specific protein functions by considering amino acid identity and structural features accessible from sequence, laying the groundwork for composite sequence based function site prediction. [Copyright &y& Elsevier] |
| Copyright of Pattern Recognition Letters is the property of Elsevier B.V. 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 53405219 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A protein sequence meta-functional signature for calcium binding residue prediction – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Horst%2C+Jeremy+A%2E%22">Horst, Jeremy A.</searchLink><br /><searchLink fieldCode="AR" term="%22Samudrala%2C+Ram%22">Samudrala, Ram</searchLink><i> ram@compbio.washington.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition+Letters%22">Pattern Recognition Letters</searchLink>. Oct2010, Vol. 31 Issue 14, p2103-2112. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Amino+acid+sequence%22">Amino acid sequence</searchLink><br /><searchLink fieldCode="DE" term="%22Calcium-binding+proteins%22">Calcium-binding proteins</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Binding+sites%22">Binding sites</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+theory%22">Prediction theory</searchLink><br /><searchLink fieldCode="DE" term="%22Protein+structure%22">Protein structure</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: The diversity of characterized protein functions found amongst experimentally interrogated proteins suggests that a vast array of unknown functions remains undiscovered. These protein functions are imparted by specific geometric distributions of amino acid residue chemical moieties, each contributing a functional interaction. We hypothesize that individual residue function contributions are predictable through sequence analytic knowledge based algorithms, and that they can be recombined to understand composite protein function by predicting spatial relation in tertiary structure. We assess the former by training a meta-functional signature algorithm to specifically predict calcium ion binding residues from protein sequence. We estimate the latter by testing for match between predictive contribution of positions in predicted secondary structures and patterns of side chain proximity forced by secondary structure moieties. Specific training for calcium binding results in 83% area under the receiver operator characteristic curve added value over random (AUCoR) and p <10−300 significance as measured by Kendall’s τ in 10-fold cross validation for parallel sets of 811 residues in 336 proteins and 696 residues in 299 proteins. Training for generalized function results in 63% AUCoR and p ≅10−221 for the same tests. Including inference of side chain proximity improves predictive ability by 2% AUCoR consistently. The results demonstrate that protein meta-functional signatures can be trained to predict specific protein functions by considering amino acid identity and structural features accessible from sequence, laying the groundwork for composite sequence based function site prediction. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Pattern Recognition Letters is the property of Elsevier B.V. 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: 10.1016/j.patrec.2010.04.012 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 2103 Subjects: – SubjectFull: Amino acid sequence Type: general – SubjectFull: Calcium-binding proteins Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Binding sites Type: general – SubjectFull: Prediction theory Type: general – SubjectFull: Protein structure Type: general Titles: – TitleFull: A protein sequence meta-functional signature for calcium binding residue prediction Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Horst, Jeremy A. – PersonEntity: Name: NameFull: Samudrala, Ram IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 10 Text: Oct2010 Type: published Y: 2010 Identifiers: – Type: issn-print Value: 01678655 Numbering: – Type: volume Value: 31 – Type: issue Value: 14 Titles: – TitleFull: Pattern Recognition Letters Type: main |
| ResultId | 1 |
