Non-linear models based on simple topological indices to identify RNase III protein members

Agüero-Chapin, Guillermin; Riva, G; Ruiz, Reinaldo Molina; Sánchez-Rodríguez, Aminael; Pérez-Machado, Gisselle; Vasconcelos, Vı́tor; Antunes, Agostinho

doi:10.1016/j.jtbi.2010.12.019

Cited by 6 publications

(14 citation statements)

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“…This section summarizes the main results derived from the application of TI2BioP to the functional classification of protein bacteriocins [5], RNase III [69], ITS2 [21] and NRPS Adomains [66]. All these classes show high sequence divergence among their members, which represent a handicap for the good performance of alignment algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…The bactericide action of this domain was only confirmed by experimental evidences; no alignment algorithm could anticipate such activity [5]. In addition, new ITS2 and RNase III members were registered using alignmentfree models based on the same biopolymer representation [21,69]. The predictions of these two members were verified through enzymatic assay for the new RNase III member and by evaluating both queries against profiles HMM ( Table 1).…”

Section: Resultsmentioning

confidence: 99%

“…The predictions of these two members were verified through enzymatic assay for the new RNase III member and by evaluating both queries against profiles HMM ( Table 1). The amino acid clustering strategy according to their physicochemical properties to build 2D Cartesian protein maps was extended to generate a nonclassical profile HMM with higher prediction accuracy to detect RNase III members than classical profiles [69].…”

Section: Resultsmentioning

confidence: 99%

“…Amino acid clustering according to its hydrophobic/charge properties was either effective at the primary level to increase the sensitivity of the profile HMM or at the 2D level to develop highly predictive DTM. Although the nonclassical profile HMM showed a slightly better performance than the alignment-free models, its generation demands programming skills while DTM search resulted in the easiest way to detect the RNase III signature among the diversity of the dataset [69]. The usability of DTM was also shown by predicting a new bacterial RNase III class member that was isolated and subsequently enzymatically tested and registered by our group (Table 1).…”

Section: Discussionmentioning

confidence: 98%

“…We reported, for the first time, a simple and interpretable DTM to identify RNase III members using spectral moments as input predictors. The reported DTM showed a high predictive power (96.07%) using just one spectral moment at different splitting values while ANNs provide a lower predictability (92.15%) (Table 4) [69,82].…”

Section: Discussionmentioning

confidence: 99%

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TI2BioP — Topological Indices to BioPolymers. A Graphical– Numerical Approach for Bioinformatics

Agüero-Chapin¹,

Ruiz²,

Pérez-Machado³

et al. 2016

Recent Advances in Biopolymers

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We developed a new graphical-numerical method called TI2BioP (Topological Indices to BioPolymers) to estimate topological indices (TIs) from two-dimensional (2D) graphical approaches for the natural biopolymers DNA, RNA and proteins The methodology mainly turns long biopolymeric sequences into 2D artificial graphs such as Cartesian and four-color maps but also reads other 2D graphs from the thermodynamic folding of DNA/RNA strings inferred from other programs. The topology of such 2D graphs is either encoded by node or adjacency matrixes for the calculation of the spectral moments as TIs. These numerical indices were used to build up alignment-free models to the functional classification of biosequences and to calculate alignment-free distances for phylogenetic purposes. The performance of the method was evaluated in highly diverse gene/protein classes, which represents a challenge for current bioinformatics algorithms. TI2BioP generally outperformed classical bioinformatics algorithms in the functional classification of Bacteriocins, ribonucleases III (RNases III), genomic internal transcribed spacer II (ITS2) and adenylation domains (A-domains) of nonribosomal peptide synthetases (NRPS) allowing the detection of new members in these target gene/protein classes. TI2BioP classification performance was contrasted and supported by predictions with sensitive alignment-based algorithms and experimental outcomes, respectively. The new ITS2 sequence isolated from Petrakia sp. was used in our graphical-numerical approach to estimate alignment-free distances for phylogenetic inferences. Despite TI2BioP having been developed for application in bioinformatics, it can be extended to predict interesting features of other biopolymers than DNA and protein sequences. TI2BioP version 2.0 is freely available from http://ti2biop.sourceforge.net/.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

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TI2BioP — Topological Indices to BioPolymers. A Graphical– Numerical Approach for Bioinformatics

Agüero-Chapin¹,

Ruiz²,

Pérez-Machado³

et al. 2016

Recent Advances in Biopolymers

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Alignment-Free Methods for the Detection and Specificity Prediction of Adenylation Domains

Agüero-Chapin

Pérez-Machado

Sánchez-Rodríguez

et al. 2016

Methods in Molecular Biology

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Identifying adenylation domains (A-domains) and their substrate specificity can aid the detection of nonribosomal peptide synthetases (NRPS) at genome/proteome level and allow inferring the structure of oligopeptides with relevant biological activities. However, that is challenging task due to the high sequence diversity of A-domains (~10-40 % of amino acid identity) and their selectivity for 50 different natural/unnatural amino acids. Altogether these characteristics make their detection and the prediction of their substrate specificity a real challenge when using traditional sequence alignment methods, e.g., BLAST searches. In this chapter we describe two workflows based on alignment-free methods intended for the identification and substrate specificity prediction of A-domains. To identify A-domains we introduce a graphical-numerical method, implemented in TI2BioP version 2.0 (topological indices to biopolymers), which in a first step uses protein four-color maps to represent A-domains. In a second step, simple topological indices (TIs), called spectral moments, are derived from the graphical representations of known A-domains (positive dataset) and of unrelated but well-characterized sequences (negative set). Spectral moments are then used as input predictors for statistical classification techniques to build alignment-free models. Finally, the resulting alignment-free models can be used to explore entire proteomes for unannotated A-domains. In addition, this graphical-numerical methodology works as a sequence-search method that can be ensemble with homology-based tools to deeply explore the A-domain signature and cope with the diversity of this class (Aguero-Chapin et al., PLoS One 8(7):e65926, 2013). The second workflow for the prediction of A-domain's substrate specificity is based on alignment-free models constructed by transductive support vector machines (TSVMs) that incorporate information of uncharacterized A-domains. The construction of the models was implemented in the NRPSpredictor and in a first step uses the physicochemical fingerprint of the 34 residues lining the active site of the phenylalanine-adenylation domain of gramicidin synthetase A [PDB ID 1 amu] to derive a feature vector. Homologous positions were extracted for A-domains with known and unknown substrate specificities and turned into feature vectors. At the same time, A-domains with known specificities towards similar substrates were clustered by physicochemical properties of amino acids (AA). In a second step, support vector machines (SVMs) were optimized from feature vectors of characterized A-domains in each of the resulting clusters. Later, SVMs were used in the variant of TSVMs that integrate a fraction of uncharacterized A-domains during training to predict unknown specificities. Finally, uncharacterized A-domains were scored by each of the constructed alignment-free models (TSVM) representing each substrate specificity resulting from the clustering. The model producing the largest score for the uncharacterized A-domain assigns t...

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An Alignment-Free Approach for Eukaryotic ITS2 Annotation and Phylogenetic Inference

et al. 2011

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The ITS2 gene class shows a high sequence divergence among its members that have complicated its annotation and its use for reconstructing phylogenies at a higher taxonomical level (beyond species and genus). Several alignment strategies have been implemented to improve the ITS2 annotation quality and its use for phylogenetic inferences. Although, alignment based methods have been exploited to the top of its complexity to tackle both issues, no alignment-free approaches have been able to successfully address both topics. By contrast, the use of simple alignment-free classifiers, like the topological indices (TIs) containing information about the sequence and structure of ITS2, may reveal to be a useful approach for the gene prediction and for assessing the phylogenetic relationships of the ITS2 class in eukaryotes. Thus, we used the TI2BioP (Topological Indices to BioPolymers) methodology [1], [2], freely available at http://ti2biop.sourceforge.net/ to calculate two different TIs. One class was derived from the ITS2 artificial 2D structures generated from DNA strings and the other from the secondary structure inferred from RNA folding algorithms. Two alignment-free models based on Artificial Neural Networks were developed for the ITS2 class prediction using the two classes of TIs referred above. Both models showed similar performances on the training and the test sets reaching values above 95% in the overall classification. Due to the importance of the ITS2 region for fungi identification, a novel ITS2 genomic sequence was isolated from Petrakia sp. This sequence and the test set were used to comparatively evaluate the conventional classification models based on multiple sequence alignments like Hidden Markov based approaches, revealing the success of our models to identify novel ITS2 members. The isolated sequence was assessed using traditional and alignment-free based techniques applied to phylogenetic inference to complement the taxonomy of the Petrakia sp. fungal isolate.

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Non-linear models based on simple topological indices to identify RNase III protein members

Cited by 6 publications

References 50 publications

TI2BioP — Topological Indices to BioPolymers. A Graphical– Numerical Approach for Bioinformatics

TI2BioP — Topological Indices to BioPolymers. A Graphical– Numerical Approach for Bioinformatics

Alignment-Free Methods for the Detection and Specificity Prediction of Adenylation Domains

An Alignment-Free Approach for Eukaryotic ITS2 Annotation and Phylogenetic Inference

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