Catálogo de publicaciones - libros

This work presents the application of the omni-aiNet algorithm - an immune-inspired algorithm originally developed to solve single and multiobjective optimization problems - to the construction of phylogenetic trees. The main goal of this work is to automatically evolve a population of phylogenetic unrooted trees, possibly with distinct topologies, by minimizing at the same time the minimal evolution and the mean-squared error criteria. The obtained set of phylogenetic trees contains non-dominated individuals that form the Pareto front and that represent the trade-off of the two conflicting objectives. The proposal of multiple non-dominated solutions in a single run gives to the user the possibility of having distinct explanations for the difference observed in the terminal nodes of the tree, and also indicates the restrictive feedback provided by the individual application of well-known algorithms for phylogenetic reconstruction that takes into account both optimization criteria, like Neighbor Joining.

Proteins are the main building blocks of the cell, and perform almost all the functions related to cell activity. Despite the recent advances in Molecular Biology, the function of a large amount of proteins is still unknown. The use of algorithms able to induce classification models is a promising approach for the functional prediction of proteins, whose classes are usually organized hierarchically. Among the machine learning techniques that have been used in hierarchical classification problems, one may highlight the Decision Trees. This paper describes the main characteristics of hierarchical classification models for Bioinformatics problems and applies three hierarchical methods based on the use of Decision Trees to protein functional classification datasets.

An important task in the area of gene discovery is the correct prediction of the translation initiation site (TIS). The TIS can correspond to the first AUG, but this is not always the case. This task can be modeled as a classification problem between positive (TIS) and negative patterns. Here we have used Support Vector Machine working with data processed by the class balancing method called Smote (Synthetic Minority Over-sampling Technique). Smote was used because the average imbalance has a positive/negative pattern ratio of around 1:28 for the databases used in this work. As a result we have attained accuracy, precision, sensitivity and specificity values of 99% on average.

Evidences that non-coding RNAs exert functions in organisms accumulate in the literature. Both computational predictions and experimental results have shown that, albeit not coding for a protein product, these transcripts play roles as diverse as catalytic activities and complex gene regulations, suggesting its therapeutic potential when applied to the study of pathogenic organisms. A target for such approach is the fungus (Pb), the ethyological agent of paracoccidioidomycosis, whose transcriptome has recently been elucidated. This work reports the compiling of a large training set and implementation of a framework of programs for sequence feature extraction, generating input for a Support Vector Machines algorithm for characterizing the coding potential of transcripts from a transcriptome.

This work presents a preliminary comparative study of some tools for mapping annotated contigs onto a close-related complete annotated genome. This kind of mapping could help scientists in generating additional sequences to fill in gaps in finishing genome projects, or even in getting relevant functional information, specially when annotations of the complete genome and contigs are available.

Nearly 100 years after the discovery of , the parasitic agent of Chagas’ disease, there are no appropriate therapies that lead to cure the acute or the chronic phases of this disease. Among the enzymes of , already considered as molecular targets for Chagas’ disease treatment, the cysteine proteases had been extensively studied by experimental approaches. In the present work, the isoforms 1 and 2 of cruzipain were investigated by molecular dynamics simulations (MD) at 25°C and 37°C temperatures, using as control papain, the representative enzyme of cysteine proteases family C1. The main results showed that the presence of a negatively charged amino acid at the 158 position (papain numbering) in the catalytic site, could induces a structural reorganisation, susceptible to temperature variations, in the catalytic residues CYS25 and HIS159.

The determination of the three-dimensional structure of a protein is one of the most challenging problems of modern science. A genetic algorithm (GA) was developed to find low energy conformations under an atomist protein model. A crowding method was used for parental replacement. The comparison criterion between individuals was the absolute RMSD of the C positions’ of the residues. The GROMOS96 force field potential energy function was used to evaluate the energy of the conformations. We tested the performance of the GA against poly-alanine sequences of lengths 18 and 23 in a situation where the global minimum was an alpha helix, and also when it was some other compact structure. The GA proved very efficient by having a 100% success ratio in finding both the global minimum and the alpha helix conformation in all situations.

The pharmaceutical industry is hunting for high-affinity inhibitors of medical targets, but most of them fail in clinical trials because of severe side effects. On the other hand, there is a growing knowledge about multiple targets and their role in various signalling pathways. Therefore, the integration of experimental data, literature knowledge about drugs, targets, their metabolism, ontology, and related pathways is an important task to achieve better understanding of drug mechanisms on a systems biological level.

Coffee is one of the most important commodities worldwide. For this reason, the sequencing in large scale of expressed sequence tags (ESTs) from different tissues of the coffee tree was performed and resulted in the formation of the Brazilian Coffee Genome EST database (CafEST). There is a raising interest of genetic breeding programs in developing varieties of with increased resistance to nematodes, pests, and diseases. A high number of plant resistance genes ( genes) have already been isolated and classified into six categories denoted as class 1 to class 6. In this study, we show results of a screening of the coffee transcriptome database for class 3 LLR/NBS/TIR-like gene related sequences within the ESTs from the CafEST database. Based on searches for sequence similarities, we selected a total of 293 ESTs coding for class 3 R proteins, putatively related to disease resistance in . Among these reads, 101 ESTs, representing the RPP4 subclass, were grouped into 56 clusters. We found 93 reads representing the RPP5 subclass, which were grouped into 46 clusters. In addition, we also found 99 reads representing the RPS4 subclass, which were grouped into 54 clusters. However, no matches were found with other subclasses of genes (L, M, N, P, and RPP1) so far. These studies should contribute to the elucidation of the recognition and resistance cascades elicited by genes. These results may provide relevant information to be applied on coffee breeding programs and on the development of new strategies to obtain genetic durable resistance for plants against pathogens, resulting in positive impacts on the coffee agribusiness.