A novel method for identifying disease associated protein complexes based on functional similarity protein complex networks

Similarity Calculation, Enrichment Analysis, and Ontology Visualization of Biomedical Ontologies using UFO

The rapid growth of biomedical ontologies observed in recent years has been reported to be useful in various applications. In this article, we propose two main-function protocols-term-related and entity-related-with the three most common ontology analyses, including similarity calculation, enrichment analysis, and ontology visualization, which can be done by separate methods. Many previously developed tools implementing those methods run on different platforms and implement a limited number of the methods for similarity calculation and enrichment analysis tools for a specific type of biomedical ontology, although any type can be acceptable. Moreover, depending on each application, methods have distinct advantages; thus, the greater the number of methods a tool has, the better decisions that users make. The protocol here implements all the analyses above using an advanced popular tool called UFO. UFO is a Cytoscape app that unifies most of the semantic similarity measures for between-term and between-entity similarity calculation for biomedical ontologies in OBO format, which can calculate the similarity between two sets of entities and weigh imported entity networks, as well as generate functional similarity networks. The complete protocol can be performed in 30 min and is designed for use by biologists with no prior bioinformatics training. © 2021 Wiley Periodicals LLC. Basic Protocol: Running UFO using a list of input Gene Ontology, Disease Ontology, or Human Phenotype Ontology data.

UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization

Background

Biomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomedical entities, analyzing enrichment for a set of biomedical entities. Many semantic similarity calculation and enrichment analysis methods have been proposed for such applications. Also, a number of tools implementing the methods have been developed on different platforms. However, these tools have implemented a small number of the semantic similarity calculation and enrichment analysis methods for a certain type of biomedical ontology. Note that the methods can be applied to all types of biomedical ontologies. More importantly, each method can be dominant in different applications; thus, users have more choice with more number of methods implemented in tools. Also, more functions would facilitate their task with ontology.

Results

In this study, we developed a Cytoscape app, named UFO, which unifies most of the semantic similarity measures for between-term and between-entity similarity calculation for all types of biomedical ontologies in OBO format. Based on the similarity calculation, UFO can calculate the similarity between two sets of entities and weigh imported entity networks as well as generate functional similarity networks. Besides, it can perform enrichment analysis of a set of entities by different methods. Moreover, UFO can visualize structural relationships between ontology terms, annotating relationships between entities and terms, and functional similarity between entities. Finally, we demonstrated the ability of UFO through some case studies on finding the best semantic similarity measures for assessing the similarity between human disease phenotypes, constructing biomedical entity functional similarity networks for predicting disease-associated biomarkers, and performing enrichment analysis on a set of similar phenotypes.

Conclusions

Taken together, UFO is expected to be a tool where biomedical ontologies can be exploited for various biomedical applications.

Availability

UFO is distributed as a Cytoscape app, and can be downloaded freely at Cytoscape App (http://apps.cytoscape.org/apps/ufo) for non-commercial use

Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines

Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. Stoichiometric relationships of interacting proteins for DNA replication, repair, the chromatin remodeling NuRD complex, β-catenin, RNA metabolism, and prefoldins are more evident than that at the mRNA level. The data are available in CellMiner (discover.nci.nih.gov/cellminercdb and discover.nci.nih.gov/cellminer), allowing casual users to test hypotheses and perform integrative, cross-database analyses of multi-omic drug response correlations for over 20,000 drugs. We demonstrate the value of proteome data in predicting drug response for over 240 clinically relevant chemotherapeutic and targeted therapies. In summary, we present a novel proteome resource for the NCI-60, together with relevant software tools, and demonstrate the benefit of proteome analyses.

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High-quality NCI-60 proteotypes created using pressure cycling technology and SWATH-MS

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Proteotypes improve drug response prediction in multi-omics regression analysis

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∼3000 measured proteins allow investigation into protein complex stoichiometry

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CellMinerCDB (discover.nci.nih.gov/cellminercdb) portal allows dataset exploration

Drug Repositioning by Integrating Known Disease-Gene and Drug-Target Associations in a Semi-supervised Learning Model

Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs and diseases. These methods approach the problem using either machine learning- or network-based models with an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarities between drugs and between diseases are usually used as inputs. In addition, known drug-disease associations are also needed for the methods as prior information. It should be noted that those associations are still not well established due to the fact that many of marketed drugs have been withdrawn and this could affect the outcome of the methods. In this study, we propose a novel method named RLSDR (Regularized Least Square for Drug Repositioning) to find new uses of drugs. More specifically, it relies on a semi-supervised learning model, Regularized Least Square, thus it does not require definition of non-drug-disease associations as previously proposed machine learning-based methods. In addition, the similarity between drugs measured by chemical structures of drug compounds and the similarity between diseases which share phenotypes can be represented in a form of either similarity network or similarity matrix as inputs of the method. Moreover, instead of using a gold-standard set of known drug-disease associations, we construct an artificial set of the associations based on known disease-gene and drug-target associations. Experiment results demonstrate that RLSDR achieves better prediction performance on the artificial set of drug-disease associations than that on the gold-standard ones in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC). In addition, it outperforms two representative network-based methods irrespective of the prior information of drug-disease associations. Novel indications for a number of drugs are also identified and validated by evidences from a different data resource.

Protein complex finding and ranking: An application to Alzheimer's disease

Protein complexes are known to play a major role in controlling cellular activity in a living being. Identifying complexes from raw protein-protein interactions (PPIs) is an important area of research. Earlier work has been limited mostly to yeast and a few other model organisms. Such protein complex identification methods, when applied to large human PPIs often give poor performance. We introduce a novel method called ComFiR to detect such protein complexes and further rank diseased complexes based on a query disease. We have shown that it has better performance in identifying protein complexes from human PPI data. This method is evaluated in terms of positive predictive value, sensitivity and accuracy. We have introduced a ranking approach and showed its application on Alzheimer's disease.

Toward Large-Scale Computational Prediction of Protein Complexes

Cellular functions are often performed by multiprotein structures called protein complexes. These complexes are dynamic structures that evolve during the cell cycle or in response to external and internal stimuli, and are tightly regulated by protein expression in different tissues resulting in quantitative and qualitative variation of protein complexes. Advances in high-throughput techniques, such as mass-spectrometry and yeast two-hybrid provided a large amount of data on protein-protein interactions. This sparked the development of computational methods able to predict protein complex formation under a variety of biological and clinical conditions. However, the challenges that need to be addressed for successful computational protein complex prediction are highly complex.The post-genomic era saw an emerging number of algorithms and software, which are able to predict protein complexes from protein-protein interaction networks and a variety of other sources. Despite the high capacity of these methods to qualitatively predict protein complexes, they could provide only limited or no quantitative information of the predicted complexes. Recently, a new large-scale simulation of protein complexes was able to achieve this task by simulating protein complex formation on the proteome scale.In this chapter, we review representative methods that can predict multiple protein complexes at different scales and discuss how these can be combined with emerging sources of data in order to improve protein complex characterization.

Study on material base and action mechanism of compound Danshen dripping pills for treatment of atherosclerosis based on modularity analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicine (TCM) has been widely used in China and its surrounding countries in clinical treatments for centuries-long time. However, due to the complexity of TCM constituents, both action mechanism and material base of TCM remain nearly unknown.

AIM OF THE STUDY

The present study was designed to uncover the action mechanism and material base of TCM in a low-cost manner.

MATERIALS AND METHODS

Compound Danshen dripping pills (DSP) is a widely used TCM for treatment of atherosclerosis, and was researched here to demonstrate the effectiveness of our method. We constructed a heterogeneous network for DSP, identified the significant network module, and analyzed the primary pharmacological units by performing GO and pathways enrichment analysis.

RESULTS

Two significant network modules were identified from the heterogeneous network of DSP, and three compounds out of four hub nodes in the network were found to intervene in the process of atherosclerosis. Moreover, 13 out of 20 enriched pathways that were ranked in top 10 corresponding to both the two pharmacological units were found to be involved in the process of atherosclerosis.

CONCLUSIONS

Quercetin, luteolin and apigenin may be the main active compounds which modulate the signaling pathways, such as metabolism of xenobiotics by cytochrome P450, retinol metabolism, etc. The present method helps reveal the action mechanism and material base of DSP for treatment of atherosclerosis.

Using contrast patterns between true complexes and random subgraphs in PPI networks to predict unknown protein complexes

Most protein complex detection methods utilize unsupervised techniques to cluster densely connected nodes in a protein-protein interaction (PPI) network, in spite of the fact that many true complexes are not dense subgraphs. Supervised methods have been proposed recently, but they do not answer why a group of proteins are predicted as a complex, and they have not investigated how to detect new complexes of one species by training the model on the PPI data of another species. We propose a novel supervised method to address these issues. The key idea is to discover emerging patterns (EPs), a type of contrast pattern, which can clearly distinguish true complexes from random subgraphs in a PPI network. An integrative score of EPs is defined to measure how likely a subgraph of proteins can form a complex. New complexes thus can grow from our seed proteins by iteratively updating this score. The performance of our method is tested on eight benchmark PPI datasets and compared with seven unsupervised methods, two supervised and one semi-supervised methods under five standards to assess the quality of the predicted complexes. The results show that in most cases our method achieved a better performance, sometimes significantly.