Dynamically searching for a domain for protein function prediction

The availability of large amounts of protein-protein interaction (PPI) data makes it feasible to use computational approaches to predict protein functions. The base of existing computational approaches is to exploit the known function information of annotated proteins in the PPI data to predict functions of un-annotated proteins. However, these approaches consider the prediction domain (i.e. the set of proteins from which the functions are predicted) as unchangeable during the prediction procedure. This may lead to valuable information being overwhelmed by the unavoidable noise information in the PPI data when predicting protein functions, and in turn, the prediction results will be distorted. In this paper, we propose a novel method to dynamically predict protein functions from the PPI data. Our method regards the function prediction as a dynamic process of finding a suitable prediction domain, from which representative functions of the domain are selected to predict functions of un-annotated proteins. Our method exploits the topological structural information of a PPI network and the semantic relationship between protein functions to measure the relationship between proteins, dynamically select a suitable prediction domain and predict functions. The evaluation on real PPI datasets demonstrated the effectiveness of our proposed method, and generated better prediction results.

Progressive clustering based method for protein function prediction

In recent years, significant effort has been given to predicting protein functions from protein interaction data generated from high throughput techniques. However, predicting protein functions correctly and reliably still remains a challenge. Recently, many computational methods have been proposed for predicting protein functions. Among these methods, clustering based methods are the most promising. The existing methods, however, mainly focus on protein relationship modeling and the prediction algorithms that statically predict functions from the clusters that are related to the unannotated proteins. In fact, the clustering itself is a dynamic process and the function prediction should take this dynamic feature of clustering into consideration. Unfortunately, this dynamic feature of clustering is ignored in the existing prediction methods. In this paper, we propose an innovative progressive clustering based prediction method to trace the functions of relevant annotated proteins across all clusters that are generated through the progressive clustering of proteins. A set of prediction criteria is proposed to predict functions of unannotated proteins from all relevant clusters and traced functions. The method was evaluated on real protein interaction datasets and the results demonstrated the effectiveness of the proposed method compared with representative existing methods.

Transcriptomics and systems biology analysis in identification of specific pathways involved in cacao resistance and susceptibility to witches' broom disease

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Enhanced stabilization of aerosol-OT surfactant monolayer upon interaction with small amounts of bovine serum albumin at the air-water interface

An investigation is made of the influence from small amounts of the protein bovine serum albumin (BSA) on the lateral organization of low molecular weight surfactant sodium bis-2-ethylhexyl sulfosuccinate (AOT) at the air-water interface. Surface pressure (pi - A), surface potential (DeltaV - A) and Brewster angle microscopy (BAM) experiments were carried out, with particular emphasis on the monolayer stability under successive compression-expansion cycles. AOT monolayer is not stable at the air-water interface, which means that the majority of AOT molecules go into the aqueous subphase as monomers and/or normal micelles. When a waiting time elapses between spreading and compression, the surfactant monolayer tends to reorganize partially at the air-water interface, with a monolayer expansion being observed for waiting times as large as 12 h. The incorporation of very small amount of BSA (10(-9) M) at the interface, also inferred from BAM, increases the monolayer stability as revealed by pi - A and DeltaV - A results. For a waiting time of circa 3 h, the mixed monolayer reaches its maximum stability. This must be related to protein (and/or protein-surfactant complexes) adsorbed onto the AOT monolayer, thus altering the BSA conformation to accommodate its hydrophobic/hydrophilic residues. Furthermore, the effects from such small amounts of BSA in the monolayer formation and stabilization mean that the AOT monolayer responds cooperatively to BSA. (C) 2004 Elsevier B.V. All rights reserved.

Interaction of small amounts of bovine serum albumin with phospholipid monolayers investigated by surface pressure and atomic force microscopy

The influence of small amounts of bovine serum albumin (BSA) (nM concentration) on the lateral organization of phospholipid monolayers at the air-water interface and transferred onto solid substrates as one-layer Langmuir-Blodgett (LB) films was investigated. The kinetics of adsorption of BSA onto the phospholipid monolayers was monitored with surface pressure isotherms in a Langmuir trough, for the zwitterionic dipalmitoylphosphatidyl ethanolamine (N,N-dimethyl-PE) and the anionic dimyristoylphosphatidic acid (DMPA). A monolayer of N,N-dimethyl-PE or DMPA incorporating BSA was transferred onto a solid substrate using the Langmuir-Blodgett technique. Atomic force microscopy (AFM) images of one-layer LB films displayed protein-phospholipid domains, whose morphology was characterized using dynamic scaling theories to calculate roughness exponents. For DMPA-BSA films the surface is characteristic of self-affine fractals, which may be described with the Kardar-Parisi-Zhang (KPZ) equation. on the other hand, for N,N-dimethyl-PE-BSA films, the results indicate a relatively flat surface within the globule. The height profile and the number and size of globules varied with the type of phospholipid. The overall results, from kinetics of adsorption on Langmuir monolayers and surface morphology in LB films, could be interpreted in terms of the higher affinity of BSA to the anionic DMPA than to the zwitterionic N,N-dimethyl-PE. Furthermore, the effects from such small amounts of BSA in the monolayer point to a cooperative response of DMPA and N,N-dimethyl-PE monolayers to the protein. (c) 2005 Elsevier B.V. All rights reserved.

Transporter protein genes are differentially expressed in Acidithiobacillus ferrooxidans LR maintained in contact with covellite

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

Conserved sequences in the β subunit of archaeal and eukaryal translation initiation factor 2 (elF2), absent from elF5, mediate interaction with elF2γ

The eukaryotic translation initiation factor 2 (eIF2) binds the methionyl-initiator tRNA in a GTP-dependent mode. This complex associates with the 40 S ribosomal particle, which then, with the aid of other factors, binds to the 5' end of the mRNA and migrates to the first AUG codon, where eIF5 promotes GTP hydrolysis, followed by the formation of the 80 S ribosome. Here we provide a comparative sequence analysis of the β subunit of eIF2 and its archaeal counterpart (aIF2β). aIF2β differs from eIF2β in not possessing an N-terminal extension implicated in binding RNA, eIF5 and eIF2B. The remaining sequences are highly conserved, and are shared with eIF5. Previously isolated mutations in the yeast eIF2β, which allow initiation of translation at UUG codons due to the uncovering of an intrinsic GTPase activity in eIF2, involve residues that are conserved in aIF2β, but not in eIF5. We show that the sequence of eIF2B homologous to aIF2β is sufficient for binding eIF2γ, the only subunit with which it interacts, and comprises, at the most, 78 residues, eIF5 does not interact with eIF2γ, despite its similarity with eIF2β, probably because of a gap in homology in this region. These observations have implications for the evolution of the mechanism of translation initiation.

Genetic interactions of yeast eukaryotic translation initiation factor 5a (eIF5A) reveal connections to poly(A)-binding protein and protein kinase C signaling

The highly conserved eukaryotic translation initiation factor eIF5A has been proposed to have various roles in the cell, from translation to mRNA decay to nuclear protein export. To further our understanding of this essential protein, three temperature-sensitive alleles of the yeast TIF51A gene have been characterized. Two mutant eIF5A proteins contain mutations in a proline residue at the junction between the two eIFSA domains and the third, strongest allele encodes a protein with a single mutation in each domain, both of which are required for the growth defect. The stronger tif51A alleles cause defects in degradation of short-lived mRNAs, supporting a role for this protein in mRNA decay. A multicopy suppressor screen revealed six genes, the overexpression of which allows growth of a tif51A-1 strain at high temperature; these genes include PAB1, PKC1, and PKC1 regulators WSC1, WSC2, and WSC3. Further results suggest that eIFSA may also be involved in ribosomal synthesis and the WSC/PKC1 signaling pathway for cell wall integrity or related processes.

Human eosinophil adhesion and degranulation stimulated with eotaxin and RANTES in vitro: Lack of interaction with nitric oxide

Background: Airway eosinophilia is considered a central event in the pathogenesis of asthma. The toxic components of eosinophils are thought to be important in inducing bronchial mucosal injury and dysfunction. Previous studies have suggested an interaction between nitric oxide (NO) and chemokines in modulating eosinophil functions, but this is still conflicting. In the present study, we have carried out functional assays (adhesion and degranulation) and flow cytometry analysis of adhesion molecules (VLA-4 and Mac-1 expression) to evaluate the interactions between NO and CC-chemokines (eotaxin and RANTES) in human eosinophils. Methods: Eosinophils were purified using a percoll gradient followed byimmunomagnetic cell separator. Cell adhesion and degranulation were evaluated by measuring eosinophil peroxidase (EPO) activity, whereas expression of Mac-1 and VLA-4 was detected using flow cytometry. Results: At 4 h incubation, both eotaxin (100 ng/ml) and RANTES (1000 ng/ml) increased by 133% and 131% eosinophil adhesion, respectively. L-NAME alone (but not D-NAME) also increased the eosinophil adhesion, but the co-incubation of L-NAME with eotaxin or RANTES did not further affect the increased adhesion seen with chemokines alone. In addition, L-NAME alone (but not D-NAME) caused a significant cell degranulation, but it did not affect the CC-chemokine-induced cell degranulation. Incubation of eosinophils with eotaxin or RANTES, in absence or presence of L-NAME, did not affect the expression of VLA-4 and Mac-1 on eosinophil surface. Eotaxin and RANTES (100 ng/ml each) also failed to elevate the cyclic GMP levels above baseline in human eosinophils. Conclusion: Eotaxin and RANTES increase the eosinophil adhesion to fibronectin-coated plates and promote cell degranulation by NO-independent mechanisms. The failure of CC-chemokines to affect VLA-4 and Mac-1 expression suggests that changes in integrin function (avidity or affinity) are rather involved in the enhanced adhesion. © 2008 Lintomen et al; licensee BioMed Central Ltd.

Interaction of Bacillus thuringiensis Cry1 and Vip3A proteins with Spodoptera frugiperda midgut binding sites

Vip3Aa, Vip3Af, Cry1Ab, and Cry1Fa were tested for their toxicities and binding interactions. Vip3A proteins were more toxic than Cry1 proteins. Binding assays showed independent specific binding sites for Cry1 and Vip3A proteins. Cry1Ab and Cry1Fa competed for the same binding sites, whereas Vip3Aa competed for those of Vip3Af. Copyright © 2009, American Society for Microbiology. All Rights Reserved.

An Integrative Genomic and Transcriptomic Analysis Reveals Potential Targets Associated with Cell Proliferation in Uterine Leiomyomas

Background: Uterine Leiomyomas (ULs) are the most common benign tumours affecting women of reproductive age. ULs represent a major problem in public health, as they are the main indication for hysterectomy. Approximately 40-50% of ULs have non-random cytogenetic abnormalities, and half of ULs may have copy number alterations (CNAs). Gene expression microarrays studies have demonstrated that cell proliferation genes act in response to growth factors and steroids. However, only a few genes mapping to CNAs regions were found to be associated with ULs. Methodology: We applied an integrative analysis using genomic and transcriptomic data to identify the pathways and molecular markers associated with ULs. Fifty-one fresh frozen specimens were evaluated by array CGH (JISTIC) and gene expression microarrays (SAM). The CONEXIC algorithm was applied to integrate the data. Principal Findings: The integrated analysis identified the top 30 significant genes (P<0.01), which comprised genes associated with cancer, whereas the protein-protein interaction analysis indicated a strong association between FANCA and BRCA1. Functional in silico analysis revealed target molecules for drugs involved in cell proliferation, including FGFR1 and IGFBP5. Transcriptional and protein analyses showed that FGFR1 (P = 0.006 and P<0.01, respectively) and IGFBP5 (P = 0.0002 and P = 0.006, respectively) were up-regulated in the tumours when compared with the adjacent normal myometrium. Conclusions: The integrative genomic and transcriptomic approach indicated that FGFR1 and IGFBP5 amplification, as well as the consequent up-regulation of the protein products, plays an important role in the aetiology of ULs and thus provides data for potential drug therapies development to target genes associated with cellular proliferation in ULs. © 2013 Cirilo et al.

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.