Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone
Abstract Background Computational prediction of protein function constitutes one of the more complex problems in Bioinformatics, because of the diversity of functions and mechanisms in that proteins exert in nature. This issue is reinforced especially for proteins that share very low primary or tert...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2017-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s12859-017-1758-x |
| _version_ | 1811318530984902656 |
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| author | Yasser B. Ruiz-Blanco Guillermin Agüero-Chapin Enrique García-Hernández Orlando Álvarez Agostinho Antunes James Green |
| author_facet | Yasser B. Ruiz-Blanco Guillermin Agüero-Chapin Enrique García-Hernández Orlando Álvarez Agostinho Antunes James Green |
| author_sort | Yasser B. Ruiz-Blanco |
| collection | DOAJ |
| description | Abstract Background Computational prediction of protein function constitutes one of the more complex problems in Bioinformatics, because of the diversity of functions and mechanisms in that proteins exert in nature. This issue is reinforced especially for proteins that share very low primary or tertiary structure similarity to existing annotated proteomes. In this sense, new alignment-free (AF) tools are needed to overcome the inherent limitations of classic alignment-based approaches to this issue. We have recently introduced AF protein-numerical-encoding programs (TI2BioP and ProtDCal), whose sequence-based features have been successfully applied to detect remote protein homologs, post-translational modifications and antibacterial peptides. Here we aim to demonstrate the applicability of 4 AF protein descriptor families, implemented in our programs, for the identification enzyme-like proteins. At the same time, the use of our novel family of 3D–structure-based descriptors is introduced for the first time. The Dobson & Doig (D&D) benchmark dataset is used for the evaluation of our AF protein descriptors, because of its proven structural diversity that permits one to emulate an experiment within the twilight zone of alignment-based methods (pair-wise identity <30%). The performance of our sequence-based predictor was further assessed using a subset of formerly uncharacterized proteins which currently represent a benchmark annotation dataset. Results Four protein descriptor families (sequence-composition-based (0D), linear-topology-based (1D), pseudo-fold-topology-based (2D) and 3D–structure features (3D), were assessed using the D&D benchmark dataset. We show that only the families of ProtDCal’s descriptors (0D, 1D and 3D) encode significant information for enzymes and non-enzymes discrimination. The obtained 3D–structure-based classifier ranked first among several other SVM-based methods assessed in this dataset. Furthermore, the model leveraging 1D descriptors, showed a higher success rate than EzyPred on a benchmark annotation dataset from the Shewanella oneidensis proteome. Conclusions The applicability of ProtDCal as a general-purpose-AF protein modelling method is illustrated through the discrimination between two comprehensive protein functional classes. The observed performances using the highly diverse D&D dataset, and the set of formerly uncharacterized (hard-to-annotate) proteins of Shewanella oneidensis, places our methodology on the top range of methods to model and predict protein function using alignment-free approaches. |
| first_indexed | 2024-04-13T12:26:40Z |
| format | Article |
| id | doaj.art-ff0a29516e424b4d854ec40d0e209808 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T12:26:40Z |
| publishDate | 2017-07-01 |
| publisher | BMC |
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| series | BMC Bioinformatics |
| spelling | doaj.art-ff0a29516e424b4d854ec40d0e2098082022-12-22T02:46:59ZengBMCBMC Bioinformatics1471-21052017-07-0118111410.1186/s12859-017-1758-xExploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zoneYasser B. Ruiz-Blanco0Guillermin Agüero-Chapin1Enrique García-Hernández2Orlando Álvarez3Agostinho Antunes4James Green5Facultad de Química y Farmacia, Universidad Central “Marta Abreu” de Las VillasCIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de LeixõesInstituto de Química, Universidad Nacional Autónoma de México (UNAM)Centro de Bioactivos Químicos (CBQ), Universidad Central ¨Marta Abreu¨ de Las Villas (UCLV)CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de LeixõesDepartment of Systems and Computer Engineering, Carleton UniversityAbstract Background Computational prediction of protein function constitutes one of the more complex problems in Bioinformatics, because of the diversity of functions and mechanisms in that proteins exert in nature. This issue is reinforced especially for proteins that share very low primary or tertiary structure similarity to existing annotated proteomes. In this sense, new alignment-free (AF) tools are needed to overcome the inherent limitations of classic alignment-based approaches to this issue. We have recently introduced AF protein-numerical-encoding programs (TI2BioP and ProtDCal), whose sequence-based features have been successfully applied to detect remote protein homologs, post-translational modifications and antibacterial peptides. Here we aim to demonstrate the applicability of 4 AF protein descriptor families, implemented in our programs, for the identification enzyme-like proteins. At the same time, the use of our novel family of 3D–structure-based descriptors is introduced for the first time. The Dobson & Doig (D&D) benchmark dataset is used for the evaluation of our AF protein descriptors, because of its proven structural diversity that permits one to emulate an experiment within the twilight zone of alignment-based methods (pair-wise identity <30%). The performance of our sequence-based predictor was further assessed using a subset of formerly uncharacterized proteins which currently represent a benchmark annotation dataset. Results Four protein descriptor families (sequence-composition-based (0D), linear-topology-based (1D), pseudo-fold-topology-based (2D) and 3D–structure features (3D), were assessed using the D&D benchmark dataset. We show that only the families of ProtDCal’s descriptors (0D, 1D and 3D) encode significant information for enzymes and non-enzymes discrimination. The obtained 3D–structure-based classifier ranked first among several other SVM-based methods assessed in this dataset. Furthermore, the model leveraging 1D descriptors, showed a higher success rate than EzyPred on a benchmark annotation dataset from the Shewanella oneidensis proteome. Conclusions The applicability of ProtDCal as a general-purpose-AF protein modelling method is illustrated through the discrimination between two comprehensive protein functional classes. The observed performances using the highly diverse D&D dataset, and the set of formerly uncharacterized (hard-to-annotate) proteins of Shewanella oneidensis, places our methodology on the top range of methods to model and predict protein function using alignment-free approaches.http://link.springer.com/article/10.1186/s12859-017-1758-xEnzymeAlignment-free protein analysisProtein descriptorsSupport vector machinesProtDCalTI2BioP |
| spellingShingle | Yasser B. Ruiz-Blanco Guillermin Agüero-Chapin Enrique García-Hernández Orlando Álvarez Agostinho Antunes James Green Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone BMC Bioinformatics Enzyme Alignment-free protein analysis Protein descriptors Support vector machines ProtDCal TI2BioP |
| title | Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone |
| title_full | Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone |
| title_fullStr | Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone |
| title_full_unstemmed | Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone |
| title_short | Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone |
| title_sort | exploring general purpose protein features for distinguishing enzymes and non enzymes within the twilight zone |
| topic | Enzyme Alignment-free protein analysis Protein descriptors Support vector machines ProtDCal TI2BioP |
| url | http://link.springer.com/article/10.1186/s12859-017-1758-x |
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