Aplicação de técnicas de aprendizado de máquina no reconhecimento de classes estruturais de proteínas

Nowadays, classifying proteins in structural classes, which concerns the inference of patterns in their 3D conformation, is one of the most important open problems in Molecular Biology. The main reason for this is that the function of a protein is intrinsically related to its spatial conformation. However, such conformations are very difficult to be obtained experimentally in laboratory. Thus, this problem has drawn the attention of many researchers in Bioinformatics. Considering the great difference between the number of protein sequences already known and the number of three-dimensional structures determined experimentally, the demand of automated techniques for structural classification of proteins is very high. In this context, computational tools, especially Machine Learning (ML) techniques, have become essential to deal with this problem. In this work, ML techniques are used in the recognition of protein structural classes: Decision Trees, k-Nearest Neighbor, Naive Bayes, Support Vector Machine and Neural Networks. These methods have been chosen because they represent different paradigms of learning and have been widely used in the Bioinfornmatics literature. Aiming to obtain an improvment in the performance of these techniques (individual classifiers), homogeneous (Bagging and Boosting) and heterogeneous (Voting, Stacking and StackingC) multiclassification systems are used. Moreover, since the protein database used in this work presents the problem of imbalanced classes, artificial techniques for class balance (Undersampling Random, Tomek Links, CNN, NCL and OSS) are used to minimize such a problem. In order to evaluate the ML methods, a cross-validation procedure is applied, where the accuracy of the classifiers is measured using the mean of classification error rate, on independent test sets. These means are compared, two by two, by the hypothesis test aiming to evaluate if there is, statistically, a significant difference between them. With respect to the results obtained with the individual classifiers, Support Vector Machine presented the best accuracy. In terms of the multi-classification systems (homogeneous and heterogeneous), they showed, in general, a superior or similar performance when compared to the one achieved by the individual classifiers used - especially Boosting with Decision Tree and the StackingC with Linear Regression as meta classifier. The Voting method, despite of its simplicity, has shown to be adequate for solving the problem presented in this work. The techniques for class balance, on the other hand, have not produced a significant improvement in the global classification error. Nevertheless, the use of such techniques did improve the classification error for the minority class. In this context, the NCL technique has shown to be more appropriated

Avaliação do efeito da inibição do reparo de sítios abásicos na resposta inflamatória celular

Base excision repair (BER) proteins has been associated with functions beyond DNA repair. Apurynic/apyrimidinic endonuclease 1 (APE1) is a multifunctional protein involved in a plethora of cellular activities, such as redox activation of transcription factors, RNA processing and DNA repair. Some studies have described the action of the protein 8-oxoguanine (OGG1) in correcting oxidized lesions in promoters as a step in the transcription of pro-inflammatory cytokines. Despite being especially important in redox activation of transcription factors such as nuclear factor κB (NF-κB) and AP- 1, the repair activity of APE1 has not yet been associated with the inflammatory response. In this study, experimental and bioinformatic analysis approaches have been used to investigate the relationship between inhibition of the repair of abasic sites in DNA by MX, a synthetic molecule designed to inhibt the repair activity of APE1, and the modulation of the inflammatory response. The results showed that treatment of monocytes with lipopolysaccharide (LPS) and MX reduced the expression of cytokines, chemokines and toll-like receptors, and negatively regulated biological immune processes, as macrophages activation, and NF-κB and tumor necrosis factor (TNF-α) and interferon pathways, without inducing cell death. The transcriptomic analysis suggests that LPS/MX treatment induces mitochondrial dysfunction, endoplasmic reticulum stress and activation of autophagy pathways, probably activated by impairment of cellular energy and/or the accumulation of nuclear and mitochondria DNA damage. Additionally, it is proposed that the repair activity of APE1 is required for transcription of inflammatory genes by interaction with abasic sites at specific promoters and recruitment of transcriptional complexes during inflammatory signaling. This work presents a new perspective on the interactions between the BER activity and the modulation of inflammatory response, and suggests a new activity for APE1 protein as modulator of the immune response in a redox-independent manner.