In Silico Characterization of Meloidogyne Genus Nematode Cellulose Binding Proteins

Abstract Root-knot nematodes are a group of endoparasites species that induce the formation of giant cells in the hosts, by which they guarantee their feeding and development. Meloidogyne species infect over 2000 plant species, and are highly destructive, causing damage to many crops around the world. M. enterolobii is considered the most aggressive species in tropical regions, such as Africa and South America. Phytonematodes are able to penetrate and migrate within plant tissues, establishing a sophisticated interaction with their hosts through parasitism factors, which include a series of cell wall degradation enzymes and plant cell modification. Among the parasitism factors documented in the M. enterolobii species, cellulose binding protein (CBP), a nematode excretion protein that appears to be associated with the breakdown of cellulose present in the plant cell wall. In silico analysis can be of great importance for the identification, structural and functional characterization of genomic sequences, besides making possible the prediction of structures and functions of proteins. The present work characterized 12 sequences of the CBP protein of nematodes of the genus Meloidogyne present in genomic databases. The results showed that all CBP sequences had signal peptide and that, after their removal, they had an isoelectric point that characterized them as unstable in an acid medium. The values of the average hydrophilicity demonstrated the hydrophilic character of the analyzed sequences. Phylogenetic analyzes were also consistent with the taxonomic classification of the nematode species of this study. Five motifs were identified, which are present in all sequences analyzed. These results may provide theoretical grounds for future studies of plant resistance to nematode infection.

Review of Biological Network Data and Its Applications

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

Predicting enzyme class from protein structure using Bayesian classification

Predicting enzyme class from protein structure parameters is a challenging problem in protein analysis. We developed a method to predict enzyme class that combines the strengths of statistical and data-mining methods. This method has a strong mathematical foundation and is simple to implement, achieving an accuracy of 45%. A comparison with the methods found in the literature designed to predict enzyme class showed that our method outperforms the existing methods.