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Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing. [ABSTRACT FROM AUTHOR]

Ubiquitylation is a universal mechanism for controlling cellular functions. A large family of ubiquitin E3 ligases (E3) mediates Ubiquitin (Ub) modification. To facilitate Ub transfer, RING E3 ligases bind both the substrate and ubiquitin E2 conjugating enzyme (E2) linked to Ub via a thioester bond to form a catalytic complex. The mechanism of Ub transfer catalyzed by RING E3 remains elusive. By employing a combined computational approach including molecular modeling, molecular dynamics (MD) simulations, and quantum mechanics/molecular mechanics (QM/MM) calculations, we characterized this catalytic mechanism in detail. The three-dimensional model of dimeric RING E3 ligase RNF4 RING, E2 ligase UbcH5A, Ub and the substrate SUMO2 shows close contact between the substrate and Ub transfer catalytic center. Deprotonation of the substrate lysine by D117 on UbcH5A occurs with almost no energy barrier as calculated by MD and QM/MM calculations. Then, the side chain of the activated lysine gets close to the thioester bond via a conformation change. The Ub transfer pathway begins with a nucleophilic addition that forms an oxyanion intermediate of a 4.23 kcal/mol energy barrier followed by nucleophilic elimination, resulting in a Ub modified substrate by a 5.65 kcal/mol energy barrier. These results provide insight into the mechanism of RING-catalyzed Ub transfer guiding the discovery of Ub system inhibitors. [ABSTRACT FROM AUTHOR]

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science. [ABSTRACT FROM AUTHOR]

Chromosomal translocation, which generates fusion proteins in blood tumor or solid tumor, is considered as one of the major causes leading to cancer. Recent studies suggested that the disordered fragments in a fusion protein might contribute to its carcinogenicity. Here, we investigated the sequence feature near the breakpoints in the fusion partner genes, the structure features of breakpoints in fusion proteins, and the posttranslational modification preference in the fusion proteins. Results show that the breakpoints in the fusion partner genes have both sequence preference and structural preference. At the sequence level, nucleotide combination AG is preferred before the breakpoint and GG is preferred at the breakpoint. At the structural level, the breakpoints in the fusion proteins prefer to be located in the disordered regions. Further analysis suggests the phosphorylation sites at serine, threonine, and the methylation sites at arginine are enriched in disordered regions of the fusion proteins. Using EML4-ALK as an example, we further explained how the fusion protein leads to the protein disorder and contributes to its carcinogenicity. The sequence and structural features of the fusion proteins may help the scientific community to predict novel breakpoints in fusion genes and better understand the structure and function of fusion proteins. [ABSTRACT FROM AUTHOR]

Background: Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression. Results: Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets. Conclusions: Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors. [ABSTRACT FROM AUTHOR]

Abstract: Quercetin-3-O-amino acid-esters, a new type of quercetin derivatives, were successfully prepared for the first time. Different from quercetin, the novel compounds show higher selectivity as inhibitors against Src tyrosine kinase (IC50 values ranging from 3.2μM to 9.9μM) than against EGFR tyrosine kinase. Molecular docking reveals that both hydrophobic and hydrogen bonding interactions are important to the selectivity. Therefore, this study provides a new promising scaffold for further development of new anticancer drugs targeting Src tyrosine kinase. [Copyright &y& Elsevier]

We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention. • Analysis of serial blood from 139 COVID-19 patients reveals immune coordination • A major immunological shift is seen between mild and moderate infection • Moderate and severe cases exhibit inflammation and a sharp drop in blood nutrients • Novel immune cell subsets emerge in moderate cases and increase with severity Using serial blood draws from COVID-19 patients, Su et al. present an extensive multi-omics dataset of plasma and single PBMCs covering the first week of infection following clinical diagnosis, which includes information on plasma proteins, metabolites, and on PBMC transcriptomic and surface-protein data, immune receptor sequences, secreted proteins, and electronic health record data. Their integrated analysis identifies a major immunological shift between mild and moderate infection, which includes an increase in inflammation, drop in blood nutrients, and the emergence of novel immune cell subpopulations that intensify with disease severity. [ABSTRACT FROM AUTHOR]

Biological and biomedical research relies on comprehensive understanding of protein-coding transcripts. However, the total number of human proteins is still unknown due to the prevalence of alternative splicing. In this paper, we detected 31,566 novel transcripts with coding potential by filtering our ab initio predictions with 50 RNA-seq datasets from diverse tissues/cell lines. PCR followed by MiSeq sequencing showed that at least 84.1% of these predicted novel splice sites could be validated. In contrast to known transcripts, the expression of these novel transcripts were highly tissue-specific. Based on these novel transcripts, at least 36 novel proteins were detected from shotgun proteomics data of 41 breast samples. We also showed L1 retrotransposons have a more significant impact on the origin of new transcripts/genes than previously thought. Furthermore, we found that alternative splicing is extraordinarily widespread for genes involved in specific biological functions like protein binding, nucleoside binding, neuron projection, membrane organization and cell adhesion. In the end, the total number of human transcripts with protein-coding potential was estimated to be at least 204,950. [ABSTRACT FROM AUTHOR]