High-resolution protein fragment interactions using AVA-Seq on a human reference set

Protein-protein interactions (PPIs) are important in understanding numerous aspects of protein function. Here, the recently developed all-vs-all sequencing (AVA-Seq) approach to determine protein-protein interactions was tested on a gold-standard human protein interaction set (hsPRS-v2). Initially, these data were interpreted strictly from a binary PPI perspective to compare AVA-Seq to other binary PPI methods tested on the same hsPRS-v2. AVA-Seq recovered 20 of 47 (43%) binary PPIs from this reference set comparing favorably with other methods. The same experimental data allowed for the determination of >500 known and novel PPIs including interactions between wildtype fragments of tumor protein p53 and minichromosomal maintenance complex proteins 2, and 5 (MCM2 and MCM5) that could be of interest in human disease. Additional results gave a better understanding of why interactions might be missed using AVA-Seq and aide future PPI experimental design for maximum recovery of information.

Identification of COVID-19 Infection-Related Human Genes Based on a Random Walk Model in a Virus–Human Protein Interaction Network

Coronaviruses are specific crown-shaped viruses that were first identified in the 1960s, and three typical examples of the most recent coronavirus disease outbreaks include severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19. Particularly, COVID-19 is currently causing a worldwide pandemic, threatening the health of human beings globally. The identification of viral pathogenic mechanisms is important for further developing effective drugs and targeted clinical treatment methods. The delayed revelation of viral infectious mechanisms is currently one of the technical obstacles in the prevention and treatment of infectious diseases. In this study, we proposed a random walk model to identify the potential pathological mechanisms of COVID-19 on a virus–human protein interaction network, and we effectively identified a group of proteins that have already been determined to be potentially important for COVID-19 infection and for similar SARS infections, which help further developing drugs and targeted therapeutic methods against COVID-19. Moreover, we constructed a standard computational workflow for predicting the pathological biomarkers and related pharmacological targets of infectious diseases.