Mariel Barbachan e Silva

Three-dimensional protein structure prediction

A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction.

APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction.

Tertiary protein structure prediction is one of the most challenging problems in structural bioinformatics. Despite the advances in algorithm development and computational strategies, predicting the folded structure of a protein only from its amino acid sequence remains as an unsolved problem. We present a new computational approach to predict the native-like three-dimensional structure of proteins. Conformational preferences of amino acid residues and secondary structure information were obtained from protein templates stored in the Protein Data Bank and represented as an Angle Probability List. Two knowledge-based prediction methods based on Genetic Algorithms and Particle Swarm Optimization were developed using this information. The proposed method has been tested with twenty-six case studies selected to validate our approach with different classes of proteins and folding patterns. Stereochemical and structural analysis were performed for each predicted three-dimensional structure. Results achieved suggest that the Angle Probability List can improve the effectiveness of metaheuristics used to predicted the three-dimensional structure of protein molecules by reducing its conformational search space.

N-Acetylcysteine administration confers lung protection in different phases of lung ischaemia–reperfusion injury

OBJECTIVES To verify the effects of N-acetylcysteine (NAC) administered before and after ischaemia in an animal model of lung ischaemia-reperfusion (IR) injury. METHODS Twenty-four Wistar rats were subjected to an experimental model of selective left pulmonary hilar clamping for 45 min followed by 2 h of reperfusion. The animals were divided into four groups: control group (SHAM), ischaemia-reperfusion, N-acetylcysteine-preischaemia (NAC-Pre) and NAC-postischaemia (NAC-Post). We recorded the haemodynamic parameters, blood gas analysis and histology. We measured the thiobarbituric acid reactive substances concentration; the expression of superoxide dismutase (SOD), inducible nitric oxide synthase (iNOS), nitrotyrosine, cleaved caspase 3, nuclear factor κB (NF-κB), NF-kappa-B inhibitor alpha (IκB-α), tumour necrosis factor alpha (TNF-α) and interleukin-1 beta (IL-1β); myeloperoxidase activity (MPO). RESULTS No significant differences were observed in the haemodynamic parameters, blood gas analysis and SOD activity among the groups. Lipid peroxidation was significantly higher in the IR and NAC-Pre groups (P < 0.01). The expression of nitrotyrosine, cleaved caspase 3, NF-κB, IκB-α, TNF-α and IL-1β were significantly higher in the IR group when compared with the SHAM and NAC groups (P < 0.01). The NAC-Pre group showed a significantly higher expression of these proteins when compared with the SHAM and NAC-Post groups (P < 0.05). After reperfusion, the expression of iNOS increased almost uniformly in all groups when compared with the SHAM group (P < 0.01). The histological analysis showed fewer inflammatory cells in the NAC groups. CONCLUSIONS The intravenous administration of NAC demonstrated protective properties against lung IR injury. The use of NAC immediately after reperfusion potentiates its protective effects.

The potential role of extracellular regulatory kinase in the survival of patients with early stage adenocarcinoma

BACKGROUND Lung cancer is among the most common types of neoplasias, and adenocarcinoma is the most frequent histological type. There is currently an extensive search for prognostic biomarkers of nonsmall cell lung cancer (NSCLC). METHODS We analyzed the correlation of clinical data and patient survival with the levels of activated extracellular regulatory kinase (ERK) in histological samples of surgically resected early stage lung adenocarcinoma. We randomly selected 36 patients with stage I or II lung adenocarcinoma who underwent pulmonary lobectomy between 1998 and 2004. Patients were divided into the following two groups according to immunohistochemical profile: a group with <15% ERK-positive tumor cells and a group with ≥15% ERK-positive tumor cells. For data comparison, an enrichment analysis of a microarray database was performed (GSE29016, n=72). RESULTS Activated ERK levels were ≥15% and <15% in 21 (58%) and 15 (42%) patients, respectively. There were no statistically significant differences in age, sex, smoking history, and body mass index (BMI) among the groups stratified by ERK levels. The survival rate was lower in the ERK ≥15% group than in the ERK <15% group (P=0.045). Enrichment analyses showed no correlation between variations in gene expression of ERK in patients with adenocarcinoma and survival rates in patients with stage I and combined stage II + III disease. CONCLUSIONS Our findings suggest that high ERK positivity in cells from biological samples of lung adenocarcinoma is related with tumor aggressiveness and a poorer prognosis.

Effect of the systemic administration of methylprednisolone on the lungs of brain-dead donor rats undergoing pulmonary transplantation

OBJECTIVE: Most lung transplants are obtained from brain-dead donors. The physiopathology of brain death involves hemodynamics, the sympathetic nervous system, and inflammatory mechanisms. Administering methylprednisolone 60 min after inducing brain death in rats has been shown to modulate pulmonary inflammatory activity. Our objective was to evaluate the effects of methylprednisolone on transplanted rat lungs from donors treated 60 min after brain death. METHODS: Twelve Wistar rats were anesthetized, and brain death was induced. They were randomly divided into two groups (n = 6), namely a control group, which was administered saline solution, and a methylprednisolone group, which received the drug 60 min after the induction of brain death. All of the animals were observed and ventilated for 2 h prior to being submitted to lung transplantation. We evaluated the hemodynamic and blood gas parameters, histological score, lung tissue levels of thiobarbituric acid-reactive substances, level of superoxide dismutase, level of tumor necrosis factor-alpha, and level of interleukin-1 beta. RESULTS: After transplantation, a significant reduction in the levels of tumor necrosis factor-alpha and IL-1β was observed in the group that received methylprednisolone (p = 0.0084 and p = 0.0155, respectively). There were no significant differences in tumor necrosis factor-alpha and superoxide dismutase levels between the control and methylprednisolone groups (p = 0.2644 and p = 0.7461, respectively). There were no significant differences in the blood gas parameters, hemodynamics, and histological alterations between the groups. CONCLUSION: The administration of methylprednisolone after brain death in donor rats reduces inflammatory activity in transplanted lungs but has no influence on parameters related to oxidative stress.

Improving protein tertiary structure prediction with conformational propensities of amino acid residues

The study of proteins and the prediction of their three-dimensional (3-D) structure is one of the most challenging problems in Structural Bioinformatics. Over the last years, several computational strategies have been proposed as a solution to this problem. As revealed by recent CASP experiments, the best results have been achieved by knowledge-based methods. Despite the advances in the development of computational methods, systems and algorithms for solving this complex problem, further research remains to be done. In this paper, we use a computational strategy to obtain structural information from experimentally determined protein structures called Angle Probability List (APL) combined with a distributed knowledge-based Genetic Algorithm to predict the 3-D structure of proteins. The proposed method has been tested with eight protein sequences. The results show that predicted 3-D structures are topologically comparable to their correspondent experimental ones.