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Motivation Disease gene prioritization methods assign scores to genes or proteins according to their likely relevance for a given disease based on a provided set of seed genes. This scoring can be used to find new biologically relevant genes or proteins for many diseases. Although methods based on classical random walks have proven to yield competitive results, quantum walk methods have not been explored to this end. Results We propose a new algorithm for disease gene prioritization based on continuous-time quantum walks using the adjacency matrix of a protein–protein interaction (PPI) network. We demonstrate the success of our proposed quantum walk method by comparing it to several well-known gene prioritization methods on three disease sets, across seven different PPI networks. In order to compare these methods, we use cross-validation and examine the mean reciprocal ranks of recall and average precision values. We further validate our method by performing an enrichment analysis of the predicted genes for coronary artery disease. Availability and implementation The data and code for the methods can be accessed at https://github.com/markgolds/qdgp. [ABSTRACT FROM AUTHOR]

Germline mutations involving small mothers against decapentaplegic-transforming growth factor-β (SMAD-TGF-β) signaling are an important but rare cause of pulmonary arterial hypertension (PAH), which is a disease characterized, in part, by vascular fibrosis and hyperaldosteronism (ALDO). We developed and analyzed a fibrosis protein-protein network (fibrosome) in silico, which predicted that the SMAD3 target neural precursor cell expressed developmentally down-regulated 9 (NEDD9) is a critical ALDO-regulated node underpinning pathogenic vascular fibrosis. Bioinformatics and microscale thermophoresis demonstrated that oxidation of Cys18 in the SMAD3 docking region of NEDD9 impairs SMAD3-NEDD9 protein-protein interactions in vitro. This effect was reproduced by ALDO-induced oxidant stress in cultured human pulmonary artery endothelial cells (HPAECs), resulting in impaired NEDD9 proteolytic degradation, increased NEDD9 complex formation with Nk2 homeobox 5 (NKX2-5), and increased NKX2-5 binding to COL3A1 Up-regulation of NEDD9-dependent collagen III expression corresponded to changes in cell stiffness measured by atomic force microscopy. HPAEC-derived exosomal signaling targeted NEDD9 to increase collagen I/III expression in human pulmonary artery smooth muscle cells, identifying a second endothelial mechanism regulating vascular fibrosis. ALDO-NEDD9 signaling was not affected by treatment with a TGF-β ligand trap and, thus, was not contingent on TGF-β signaling. Colocalization of NEDD9 with collagen III in HPAECs was observed in fibrotic pulmonary arterioles from PAH patients. Furthermore, NEDD9 ablation or inhibition prevented fibrotic vascular remodeling and pulmonary hypertension in animal models of PAH in vivo. These data identify a critical TGF-β-independent posttranslational modification that impairs SMAD3-NEDD9 binding in HPAECs to modulate vascular fibrosis and promote PAH.

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), the cause of coronavirus disease 2019 (COVID‐19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS‐CoV‐2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS‐CoV‐2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS‐CoV‐2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC‐AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS‐CoV‐2 infection. Our systems pharmacology–based machine learning framework can be used to classify other existing drugs for SARS‐CoV‐2 infection and can easily be adapted to drug effect prediction for future viral outbreaks. [ABSTRACT FROM AUTHOR]