The Development of a Universal In Silico Predictor of Protein-Protein Interactions

Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation. © 2013 Valente et al.

Expression, purification and molecular analysis of the human ZNF706 protein

Background: The ZNF706 gene encodes a protein that belongs to the zinc finger family of proteins and was found to be highly expressed in laryngeal cancer, making the structure and function of ZNF706 worthy of investigation. In this study, we expressed and purified recombinant human ZNF706 that was suitable for structural analysis in Escherichia coli BL21(DH3). Findings. ZNF706 mRNA was extracted from a larynx tissue sample, and cDNA was ligated into a cloning vector using the TOPO method. ZNF706 protein was expressed according to the E. coli expression system procedures and was purified using a nickel-affinity column. The structural qualities of recombinant ZNF706 and quantification alpha, beta sheet, and other structures were obtained by spectroscopy of circular dichroism. ZNF706's structural modeling showed that it is composed of α-helices (28.3%), β-strands (19.4%), and turns (20.9%), in agreement with the spectral data from the dichroism analysis. Conclusions: We used circular dichroism and molecular modeling to examine the structure of ZNF706. The results suggest that human recombinant ZNF706 keeps its secondary structures and is appropriate for functional and structural studies. The method of expressing ZNF706 protein used in this study can be used to direct various functional and structural studies that will contribute to the understanding of its function as well as its relationship with other biological molecules and its putative role in carcinogenesis. © 2013 Colombo et al.; licensee BioMed Central Ltd.

Interação entre o gene TKN2 (KNOX-type I) e o miR156 node durante a transição de fase vegetativa para reprodutiva em tomateiro (Solanum lycopersicum)

Pós-graduação em Ciências Biológicas (Genética) - IBB

microRNA156-targeted SPL/ SBP box transcription factors regulate tomato ovary and fruit development

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Hypusine Modification of the Ribosome-binding Protein eIF5A, a Target for New Anti-Inflammatory Drugs: Understanding the Action of the Inhibitor GC7 on a Murine Macrophage Cell Line

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks

The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.

Protein content of leaf-cutting ant queens before the nuptial flight and during the post-claustral phase

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The use of stable isotopes to investigate the effects of supplemental lysine and methionine on protein turnover and amino acid utilization in pacu, Piaractus mesopotamicus, juveniles

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

An improved interolog mapping-based computational prediction of protein protein interactions with increased network coverage

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Resistance training with excessive training load and insufficient recovery alters skeletal muscle mass-related protein expression

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Effects of feeding corn silage inoculated with microbial additives on the ruminal fermentation, microbial protein yield, and growth performance of lambs

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Energy and protein requirements of male and female saanen goats

Pós-graduação em Zootecnia - FCAV

Gene and protein expression of oestrogen-beta and progesterone receptors in facial melasma and adjacent healthy skin in women

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Extracellular HSP70 levels in diabetic environment in rats

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)