Identification of protein complexes is of great importance in the understanding of cellular organization and functions. Traditional computational protein complex prediction methods mainly rely on the topology of protein-protein interaction (PPI) networks but seldom take biological information of proteins (such as Gene Ontology (GO)) into consideration. Meanwhile, the environment relevant analys...

Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redund...

Methamphetamine (Meth) is recognized as one of the most important new distributedabused drug that causes severe damage to the different parts of the brain, especiallyhippocampus. Previous studies have demonstrated that Meth can induce apoptosis and celldeath in the brain. In this study, we evaluated the long-term effects of Meth abuse in theCA1 region of postmortem hippo...

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking ...

The structure and connectivity of protein-protein interaction (PPI) networks are maintained throughout evolution by coordinated changes (coevolution) of network proteins. Despite extensive research, relatively little is known regarding the molecular basis and functional implications of the coevolution of PPI networks. Here, we used proliferating cell nuclear antigen, a hub protein that mediates...

Protein-protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method ...

Reconstruction of signaling pathways is crucial for understanding cellular mechanisms. A pathway is represented as a path of a signaling cascade involving a series of proteins to perform a particular function. Since a protein pair involved in signaling and response have a strong interaction, putative pathways can be detected from protein-protein interaction (PPI) networks. However, predicting d...

First, we analyzed protein-protein interaction (PPI) datasets from three different species (Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans) to investigate the extent to which alternative paths are present in PPI datasets. We focus here only on interactomes that are derived by the popular high-throughput assays such as Y2H. Then, we provide some actual examples in ...

As the amount of biological research literature increases, finding information is becoming a daunting task. Since machine learning techniques could alleviate this problem, we propose a machine learning framework to identify protein-protein interaction sentences from research papers. This machine learning technique is one of the basic components needed to automatically extract biological informa...

Considering the roles of protein complexes in many biological processes in the cell, detection of protein complexes from available protein-protein interaction (PPI) networks is a key challenge in the post genome era. Despite high dynamicity of cellular systems and dynamic interaction between proteins in a cell, most computational methods have focused on static networks which cannot represent th...