Computational predictions of hostpathogen interactions using domain and sequence signature Pathogens have specialized proteins to invade and infect the host by interacting with the host proteins. Despite several experimental studies, knowledge about hostpathogen interactions (HPIs) is limited. However, this knowledge is essential to understand disease mechanism and identify potential targets for disease prevention and intervention. In this study, we propose a pipeline for identifying hostpathogen proteinprotein interactions. The pipeline consists of a biological knowledgebased filter, a domainbased statistical filter followed by a sequencesignature based machine learning method. Interspecies proteinprotein interaction data between eukaryotic pathogens and human was used to build the domainbased statistical model. Known hostpathogen interactions of all eukaryotic pathogens from HPIDB and noninteracting protein data from Negatome were used as positive and negative training sets, respectively to train the machine learning model. We applied our pipeline to predict HPIs between human and malarial parasite, P. falciparum. Several biologically relevant features like tissue specificity, protein annotations and functions were used to construct a primary list of possible HPIs. Next, the statistical and machine learning based models were used as filters on the initial list to predict novel proteinprotein interactions between human and P. falciparum during intraerythrocytic stages. We have predicted several HPIs that are involved in host erythrocyte cytoskeleton remodeling, signaling and immune response. The proposed method can be used to find novel HPIs between human cells and any eukaryotic pathogens.

Systematic comparison of the proteinprotein interaction databases from a users perspective Abstract   In absence of periodic systematic comparisons, biologistsbioinformaticians may be forced to make a subjective selection among the many proteinprotein interaction (PPI) databases and tools. We conducted a comprehensive compilation and comparison of such resources. We compiled 375 PPI resources, shortlisted 125 important ones (both lists are available at startbioinfo.com), and compared the features and coverage of 16 carefullyselected databases related to human PPIs. We quantitatively compared the coverage of u0027experimentally verifiedu0027 as well as u0027totalu0027 (experimentally verified as well as predicted) PPIs for these 16 databases. Coverage was compared in two ways: (a) PPIs obtained in response to gene queries using the web interfaces were compared. As a query set, 108 genes expressed differently across tissues (specific to kidney, testis, and uterus, and ubiquitous  i.e., expressed in 43 human normal tissues) or associated with certain diseases (breast cancer, lung cancer, Alzheimerxe2x80x99s, cystic fibrosis, diabetes, and cardiomyopathy) were chosen. The coverage was also compared for the wellstudied genes versus the lessstudied ones. The coverage of the databases for highquality interactions was separately assessed using a set of literature curated experimentallyproven PPIs (gold standard PPIset); (b) the backenddata from 15 PPI databases was downloaded and compared. Combined results from STRING and UniHI covered around 84% of xe2x80x98experimentally verifiedxe2x80x99 PPIs. Approximately 94% of the xe2x80x98totalxe2x80x99 PPIs available across the databases were retrieved by the combined use of hPRINT, STRING, and IID. Among the experimentally verified PPIs found exclusively in each database, STRING contributed around 71% of the hits. The coverage of certain databases was skewed for some genetypes. Analysis with the goldstandard PPIset revealed that GPSProt, STRING, APID, and HIPPIE, each covered xe2x88xbc70% of the curated interactions. The database usage frequencies did not always correlate with their respective advantages, thereby justifying the need for more frequent studies of this nature.