Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search

We contribute a novel, ball-histogram approach to DNA-binding propensity prediction of proteins. Unlike state-of-the-art methods based on constructing an ad-hoc set of features describing physicochemical properties of the proteins, the ball-histogram technique enables a systematic, Monte-Carlo exploration of the spatial distribution of amino acids complying with automatically selected properties. This exploration yields a model for the prediction of DNA binding propensity. We validate our method in prediction experiments, improving on state-of-the-art accuracies. Moreover, our method also provides interpretable features involving spatial distributions of selected amino acids

PDNAsite:identification of DNA-binding site from protein sequence by incorporating spatial and sequence context

Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community

Oncogenic ERBB3 Mutations in Human Cancers

SummaryThe human epidermal growth factor receptor (HER) family of tyrosine kinases is deregulated in multiple cancers either through amplification, overexpression, or mutation. ERBB3/HER3, the only member with an impaired kinase domain, although amplified or overexpressed in some cancers, has not been reported to carry oncogenic mutations. Here, we report the identification of ERBB3 somatic mutations in ∼11% of colon and gastric cancers. We found that the ERBB3 mutants transformed colonic and breast epithelial cells in a ligand-independent manner. However, the mutant ERBB3 oncogenic activity was dependent on kinase-active ERBB2. Furthermore, we found that anti-ERBB antibodies and small molecule inhibitors effectively blocked mutant ERBB3-mediated oncogenic signaling and disease progression in vivo

Classifying RNA-Binding Proteins Based on Electrostatic Properties

Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures. The method is based on characterizing unique properties of electrostatic patches on the protein surface. Using an ensemble of general protein features and specific properties extracted from the electrostatic patches, we have trained a support vector machine (SVM) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids. Specifically, the method was applied on proteins possessing the RNA recognition motif (RRM) and successfully classified RNA-binding proteins from RRM domains involved in protein–protein interactions. Overall the method achieves 88% accuracy in classifying RNA-binding proteins, yet it cannot distinguish RNA from DNA binding proteins. Nevertheless, by applying a multiclass SVM approach we were able to classify the RNA-binding proteins based on their RNA targets, specifically, whether they bind a ribosomal RNA (rRNA), a transfer RNA (tRNA), or messenger RNA (mRNA). Finally, we present here an innovative approach that does not rely on sequence or structural homology and could be applied to identify novel RNA-binding proteins with unique folds and/or binding motifs

iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model

DNA-binding proteins play crucial roles in various cellular processes. Developing high throughput tools for rapidly and effectively identifying DNA-binding proteins is one of the major challenges in the field of genome annotation. Although many efforts have been made in this regard, further effort is needed to enhance the prediction power

Participative Leadership and Organizational Identification in SMEs in the MENA Region: Testing the Roles of CSR Perceptions and Pride in Membership

The aim of this research is to explore the process linking participative leadership to organizational identification. The study examines the relationship between participative leadership and internal CSR perceptions of employees and also investigates the role that pride in membership plays in the affiliation of CSR perceptions with organizational identification. By studying these relationships, the paper aspires to contemplate new presumed mediators in the association of participative leadership with organizational identification as well as determine a possible novel antecedent of employee CSR perceptions. Empirical evidence is provided from data that was collected through a survey distributed to employees working for small- and medium-sized enterprises in three countries in the Middle East and North Africa regions, particularly the United Arab Emirates, Lebanon, and Tunisia. Findings show that participative leadership leads to positive internal CSR perceptions of employees and that these CSR perceptions lead to pride in membership which, in turn, results in organizational identification. Implications of these findings are also discussed