Literature DB >> 20563611

TI2BioP: Topological Indices to BioPolymers. Its practical use to unravel cryptic bacteriocin-like domains.

Guillermín Agüero-Chapin1, Gisselle Pérez-Machado, Reinaldo Molina-Ruiz, Yunierkis Pérez-Castillo, Aliuska Morales-Helguera, Vítor Vasconcelos, Agostinho Antunes.   

Abstract

Bacteriocins are proteinaceous toxins produced and exported by both gram-negative and gram-positive bacteria as a defense mechanism. The bacteriocin protein family is highly diverse, which complicates the identification of bacteriocin-like sequences using alignment approaches. The use of topological indices (TIs) irrespective of sequence similarity can be a promising alternative to predict proteinaceous bacteriocins. Thus, we present Topological Indices to BioPolymers (TI2BioP) as an alignment-free approach inspired in both the Topological Substructural Molecular Design (TOPS-MODE) and Markov Chain Invariants for Network Selection and Design (MARCH-INSIDE) methodology. TI2BioP allows the calculation of the spectral moments as simple TIs to seek quantitative sequence-function relationships (QSFR) models. Since hydrophobicity and basicity are major criteria for the bactericide activity of bacteriocins, the spectral moments ((HP)μ(k)) were derived for the first time from protein artificial secondary structures based on amino acid clustering into a Cartesian system of hydrophobicity and polarity. Several orders of (HP)μ(k) characterized numerically 196 bacteriocin-like sequences and a control group made up of 200 representative CATH domains. Subsequently, they were used to develop an alignment-free QSFR model allowing a 76.92% discrimination of bacteriocin proteins from other domains, a relevant result considering the high sequence diversity among the members of both groups. The model showed a prediction overall performance of 72.16%, detecting specifically 66.7% of proteinaceous bacteriocins whereas the InterProScan retrieved just 60.2%. As a practical validation, the model also predicted successfully the cryptic bactericide function of the Cry 1Ab C-terminal domain from Bacillus thuringiensis's endotoxin, which has not been detected by classical alignment methods.

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Year:  2010        PMID: 20563611     DOI: 10.1007/s00726-010-0653-9

Source DB:  PubMed          Journal:  Amino Acids        ISSN: 0939-4451            Impact factor:   3.520


  7 in total

1.  An alignment-free approach for eukaryotic ITS2 annotation and phylogenetic inference.

Authors:  Guillermin Agüero-Chapin; Aminael Sánchez-Rodríguez; Pedro I Hidalgo-Yanes; Yunierkis Pérez-Castillo; Reinaldo Molina-Ruiz; Kathleen Marchal; Vítor Vasconcelos; Agostinho Antunes
Journal:  PLoS One       Date:  2011-10-26       Impact factor: 3.240

2.  Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone.

Authors:  Yasser B Ruiz-Blanco; Guillermin Agüero-Chapin; Enrique García-Hernández; Orlando Álvarez; Agostinho Antunes; James Green
Journal:  BMC Bioinformatics       Date:  2017-07-21       Impact factor: 3.169

3.  Graph Theory-Based Sequence Descriptors as Remote Homology Predictors.

Authors:  Guillermin Agüero-Chapin; Deborah Galpert; Reinaldo Molina-Ruiz; Evys Ancede-Gallardo; Gisselle Pérez-Machado; Gustavo A de la Riva; Agostinho Antunes
Journal:  Biomolecules       Date:  2019-12-23

Review 4.  Emerging Computational Approaches for Antimicrobial Peptide Discovery.

Authors:  Guillermin Agüero-Chapin; Deborah Galpert-Cañizares; Dany Domínguez-Pérez; Yovani Marrero-Ponce; Gisselle Pérez-Machado; Marta Teijeira; Agostinho Antunes
Journal:  Antibiotics (Basel)       Date:  2022-07-13

5.  Exploring the adenylation domain repertoire of nonribosomal peptide synthetases using an ensemble of sequence-search methods.

Authors:  Guillermin Agüero-Chapin; Reinaldo Molina-Ruiz; Emanuel Maldonado; Gustavo de la Riva; Aminael Sánchez-Rodríguez; Vitor Vasconcelos; Agostinho Antunes
Journal:  PLoS One       Date:  2013-07-16       Impact factor: 3.240

6.  Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers.

Authors:  Deborah Galpert; Alberto Fernández; Francisco Herrera; Agostinho Antunes; Reinaldo Molina-Ruiz; Guillermin Agüero-Chapin
Journal:  BMC Bioinformatics       Date:  2018-05-03       Impact factor: 3.169

7.  Tensor Algebra-based Geometrical (3D) Biomacro-Molecular Descriptors for Protein Research: Theory, Applications and Comparison with other Methods.

Authors:  Julio E Terán; Yovani Marrero-Ponce; Ernesto Contreras-Torres; César R García-Jacas; Ricardo Vivas-Reyes; Enrique Terán; F Javier Torres
Journal:  Sci Rep       Date:  2019-08-06       Impact factor: 4.379

  7 in total

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