Background: Infectious diseases are a cruel assassin with millions of victims around the world each year. Understanding infectious mechanism of viruses is indispensable for their inhibition. One of the best ways of unveiling this mechanism is to investigate the host-pathogen protein-protein interaction network. In this paper we try to disclose many properties of this network. We focus on human as host and integrate experimentally 32,859 interaction between human proteins and virus proteins from several databases. We investigate different properties of human proteins targeted by virus proteins and find that most of them have a considerable high centrality scores in human intra protein-protein interaction network. Investigating human proteins network properties which are targeted by different virus proteins can help us to design multipurpose drugs. Results: As host-pathogen protein-protein interaction network is a bipartite network and centrality measures for this type of networks are scarce, we proposed seven new centrality measures for analyzing bipartite networks. Applying them to different virus strains reveals unrandomness of attack strategies of virus proteins which could help us in drug design hence elevating the quality of life. They could also be used in detecting host essential proteins. Essential proteins are those whose functions are critical for survival of its host. One of the proposed centralities named diversity of predators, outperforms the other existing centralities in terms of detecting essential proteins and could be used as an optimal essential proteins’ marker. Conclusions: Different centralities were applied to analyze human protein-protein interaction network and to detect characteristics of human proteins targeted by virus proteins. Moreover, seven new centralities were proposed to analyze host-pathogen protein-protein interaction network and to detect pathogens’ favorite host protein victims. Comparing different centralities in detecting essential proteins reveals that diversity of predator (one of the proposed centralities) is the best essential protein marker.

Gluten proteins are known as immunological triggers for inflammation resulting in mucosal lesions in patients with coeliac disease (CD). Adherence to a strict gluten-free diet (GFD) is currently known as the only effective treatment for CD. In this study, we performed a systematic review and dose-response meta-analysis on data from previous studies to investigate the association between different gluten doses administered and the risk of CD relapse. Electronic databases were systematically searched to retrieve studies that investigated the response of CD patients to different amounts of gluten intake and evaluated the clinical, serologic, and/or histologic evidence to recognize disease relapse. Study-specific relative risks (RRs) were combined using a random effects model. A total of 440 identified published papers were screened, of which 7 records were selected following full-text reviewing and eligibility assessment for dose-response meta-analysis. According to our analysis, the risk of CD relapse is estimated to be 0.2% (RR: 1.002; 95% CI: 1.001 to 1.004) following the consumption of 6 mg gluten/day, which was increased to 7% (RR: 1.07; 95% CI: 1.03 to 1.10), 50% (RR: 1.50; 95% CI: 1.23 to 1.82), 80% (RR: 1.80; 95% CI: 1.36 to 2.38), and 100% (RR: 2.00; 95% CI: 1.43 to 2.78) by the daily intake of 150, 881, 1276, and 1505 mg gluten, respectively. Although good adherence to a GFD can adequately control CD-related symptoms, disease relapse might happen even with a very low dose of gluten, and the duration of exposure to gluten is also an important matter. The current literature has substantial limitations, such as relying on the data from just a few countries that were different in terms of the amount of gluten administered, the duration of the challenge, etc. Therefore, more randomized clinical trials using a standardized gluten challenge protocol are needed to confirm the findings of the present study.

Peptihub (https://bioinformaticscollege.ir/peptihub/) is a meticulously curated repository of cancer-related peptides (CRPs) that have been documented in scientific literature. A diverse collection of CRPs is included in the PeptiHub, showcasing a spectrum of effects and activities. While some peptides demonstrated significant anticancer efficacy, others exhibited no discernible impact, and some even possessed alternative non-drug functionalities, including drug carrier or carcinogenic attributes. Presently, Peptihub houses 874 CRPs, subjected to evaluation across 10 distinct organism categories, 26 organs, and 438 cell lines. Each entry in the database is accompanied by easily accessible 3D conformations, obtained either experimentally or through predictive methodology. Users are provided with three search frameworks offering basic, advanced, and BLAST sequence search options. Furthermore, precise annotations of peptides enable users to explore CRPs based on their specific activities (anticancer, no effect, insignificant effect, carcinogen, and others) and their effectiveness (rate and IC₅₀) under cancer conditions, specifically within individual organs. This unique property facilitates the construction of robust training and testing datasets. Additionally, PeptiHub offers 1141 features with the convenience of selecting the most pertinent features to address their specific research questions. Features include aaindex1 (in six main subcategories: alpha propensities, beta propensity, composition indices, hydrophobicity, physicochemical properties, and other properties), amino acid composition (Amino acid Composition and Dipeptide Composition), and Grouped Amino Acid Composition (Grouped amino acid composition, Grouped dipeptide composition, and Conjoint triad) categories. These utilities not only speed up machine learning-based peptide design but also facilitate peptide classification. Database URL: https://bioinformaticscollege.ir/peptihub/

Combining chemotherapeutic (CT) and chemopreventive (CP) agents for cancer treatment is controversial, and the issue has not yet been conclusively resolved. In this study, by integrating text mining and protein-protein interaction (PPI), the combined effects of these two kinds of agents in cancer treatment were investigated. First, text mining was performed by the Pathway Studio database to study the effects of various agents (CP and CT) on cancer-related processes. Then, each group’s most important hub genes were obtained by calculating different centralities. Finally, the results of in silico analysis were validated by examining the combined effects of hesperetin (Hst) and vincristine (VCR) on MCF-7 cells. In general, the results of the in silico analysis revealed that the combination of these two kinds of agents could be useful for treating cancer. However, the PPI analysis revealed that there were a few important proteins that could be targeted for intelligent therapy while giving treatment with these agents. In vitro experiments confirmed the results of the in silico analysis. Also, Hst and VCR had good harmony in modulating the hub genes obtained from the in silico analysis and inducing apoptosis in the MCF-7 cell line.