Ontology-based validation and identification of regulatory phenotypes

AbstractMotivationFunction annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out; creating the annotations additionally requires a curator to make an assertion based on reported evidence. Support to validate the mutual consistency of functional and phenotype annotations as well as a computational method to predict phenotypes from function annotations, would greatly improve the utility of function annotations.ResultsWe developed a novel ontology-based method to validate the mutual consistency of function and phenotype annotations. We apply our method to mouse and human annotations, and identify several inconsistencies that can be resolved to improve overall annotation quality. Our method can also be applied to the rule-based prediction of phenotypes from functions. We show that the predicted phenotypes can be utilized for identification of protein-protein interactions and gene-disease associations. Based on experimental functional annotations, we predict phenotypes for 1,986 genes in mouse and 7,301 genes in human for which no experimental phenotypes have yet been determined.Availabilityhttps://github.com/bio-ontology-research-group/[email protected]

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Gene multifunctionality scoring using gene ontology

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720018400188 ◽

2018 ◽

Vol 16 (05) ◽

pp. 1840018 ◽

Cited By ~ 4

Author(s):

Hisham Al-Mubaid

Keyword(s):

Gene Ontology ◽

Protein Interactions ◽

Evaluation Criteria ◽

Computational Method ◽

Protein Protein Interactions ◽

Validity And Reliability ◽

Functional Annotations ◽

Gene Sets ◽

Human Genes ◽

Disease Associations

Multifunctional genes are important genes because of their essential roles in human cells. Studying and analyzing multifunctional genes can help understand disease mechanisms and drug discovery. We propose a computational method for scoring gene multifunctionality based on functional annotations of the target gene from the Gene Ontology. The method is based on identifying pairs of GO annotations that represent semantically different biological functions and any gene annotated with two annotations from one pair is considered multifunctional. The proposed method can be employed to identify multifunctional genes in the entire human genome using solely the GO annotations. We evaluated the proposed method in scoring multifunctionality of all human genes using four criteria: gene-disease associations; protein–protein interactions; gene studies with PubMed publications; and published known multifunctional gene sets. The evaluation results confirm the validity and reliability of the proposed method for identifying multifunctional human genes. The results across all four evaluation criteria were statistically significant in determining multifunctionality. For example, the method confirmed that multifunctional genes tend to be associated with diseases more than other genes, with significance [Formula: see text]. Moreover, consistent with all previous studies, proteins encoded by multifunctional genes, based on our method, are involved in protein–protein interactions significantly more ([Formula: see text]) than other proteins.

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Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

Current Gene Therapy ◽

10.2174/1566523219666190917155959 ◽

2019 ◽

Vol 19 (4) ◽

pp. 232-241 ◽

Cited By ~ 5

Author(s):

Xuegong Chen ◽

Wanwan Shi ◽

Lei Deng

Keyword(s):

Protein Interactions ◽

Experimental Studies ◽

Treatment Strategies ◽

Computational Method ◽

Biological Information ◽

Support Vector ◽

Protein Protein Interactions ◽

Efficient Treatment ◽

Disease Associations ◽

Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

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Entamoeba histolytica: Proteomics Bioinformatics Reveal Predictive Functions and Protein–Protein Interactions of Differentially Abundant Membrane and Cytosolic Proteins

Membranes ◽

10.3390/membranes11060376 ◽

2021 ◽

Vol 11 (6) ◽

pp. 376

Author(s):

Norhidayah Azmi ◽

Nurulhasanah Othman

Keyword(s):

Entamoeba Histolytica ◽

Protein Interactions ◽

Biological Process ◽

Protein Protein Interactions ◽

Differential Abundance ◽

Cytosolic Proteins ◽

Functional Annotations ◽

Predictive Functions ◽

Differentially Abundant Proteins ◽

Abundance Proteins

Amoebiasis is caused by Entamoeba histolytica and ranked second for parasitic diseases causing death after malaria. E. histolytica membrane and cytosolic proteins play important roles in the pathogenesis. Our previous study had shown several cytosolic proteins were found in the membrane fraction. Therefore, this study aimed to quantify the differential abundance of membrane and cytosolic proteins in membrane versus cytosolic fractions and analyze their predicted functions and interaction. Previous LC-ESI-MS/MS data were analyzed by PERSEUS software for the differentially abundant proteins, then they were classified into their functional annotations and the protein networks were summarized using PantherDB and STRiNG, respectively. The results showed 24 (44.4%) out of the 54 proteins that increased in abundance were membrane proteins and 30 were cytosolic proteins. Meanwhile, 45 cytosolic proteins were found to decrease in abundance. Functional analysis showed differential abundance proteins involved in the molecular function, biological process, and cellular component with 18.88%, 33.04% and, 48.07%, respectively. The STRiNG server predicted that the decreased abundance proteins had more protein–protein network interactions compared to increased abundance proteins. Overall, this study has confirmed the presence of the differentially abundant membrane and cytosolic proteins and provided the predictive functions and interactions between them.

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Structure, Function, Involvement in Diseases and Targeting of 14-3-3 Proteins: An Update

Current Medicinal Chemistry ◽

10.2174/0929867324666170426095015 ◽

2018 ◽

Vol 25 (1) ◽

pp. 5-21 ◽

Cited By ~ 25

Author(s):

Ylenia Cau ◽

Daniela Valensin ◽

Mattia Mori ◽

Sara Draghi ◽

Maurizio Botta

Keyword(s):

Protein Interactions ◽

Molecular Mechanisms ◽

Physiological Role ◽

Specific Protein ◽

Protein Protein Interactions ◽

Cellular Processes ◽

Protein Partners ◽

High Sequence Homology ◽

Small Molecule Modulators

14-3-3 is a class of proteins able to interact with a multitude of targets by establishing protein-protein interactions (PPIs). They are usually found in all eukaryotes with a conserved secondary structure and high sequence homology among species. 14-3-3 proteins are involved in many physiological and pathological cellular processes either by triggering or interfering with the activity of specific protein partners. In the last years, the scientific community has collected many evidences on the role played by seven human 14-3-3 isoforms in cancer or neurodegenerative diseases. Indeed, these proteins regulate the molecular mechanisms associated to these diseases by interacting with (i) oncogenic and (ii) pro-apoptotic proteins and (iii) with proteins involved in Parkinson and Alzheimer diseases. The discovery of small molecule modulators of 14-3-3 PPIs could facilitate complete understanding of the physiological role of these proteins, and might offer valuable therapeutic approaches for these critical pathological states.

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Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

Methods in Molecular Biology - Proteomics for Drug Discovery ◽

10.1007/978-1-4939-7201-2_15 ◽

2017 ◽

pp. 221-236 ◽

Cited By ~ 11

Author(s):

Alexander Goncearenco ◽

Minghui Li ◽

Franco L. Simonetti ◽

Benjamin A. Shoemaker ◽

Anna R. Panchenko

Keyword(s):

Cancer Therapy ◽

Protein Interactions ◽

In Silico ◽

Drug Targets ◽

Protein Protein Interactions ◽

Anti Cancer

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Quantitative analysis of the interactions between HIV-1 integrase and retroviral reverse transcriptases

Biochemical Journal ◽

10.1042/bj20071279 ◽

2008 ◽

Vol 412 (1) ◽

pp. 163-170 ◽

Cited By ~ 21

Author(s):

Alon Herschhorn ◽

Iris Oz-Gleenberg ◽

Amnon Hizi

Keyword(s):

Conformational Changes ◽

Protein Interactions ◽

Molecular Mechanisms ◽

Reaction Model ◽

Protein Protein Interactions ◽

Common Site ◽

Leukaemia Virus ◽

Reverse Transcriptases ◽

Interaction Kinetics ◽

Hiv 1

The RT (reverse transcriptase) of HIV-1 interacts with HIV-1 IN (integrase) and inhibits its enzymatic activities. However, the molecular mechanisms underling these interactions are not well understood. In order to study these mechanisms, we have analysed the interactions of HIV-1 IN with HIV-1 RT and with two other related RTs: those of HIV-2 and MLV (murine-leukaemia virus). All three RTs inhibited HIV-1 IN, albeit to a different extent, suggesting a common site of binding that could be slightly modified for each one of the studied RTs. Using surface plasmon resonance technology, which monitors direct protein–protein interactions, we performed kinetic analyses of the binding of HIV-1 IN to these three RTs and observed interesting binding patterns. The interaction of HIV-1 RT with HIV-1 IN was unique and followed a two-state reaction model. According to this model, the initial IN–RT complex formation was followed by a conformational change in the complex that led to an elevation of the total affinity between these two proteins. In contrast, HIV-2 and MLV RTs interacted with IN in a simple bi-molecular manner, without any apparent secondary conformational changes. Interestingly, HIV-1 and HIV-2 RTs were the most efficient inhibitors of HIV-1 IN activity, whereas HIV-1 and MLV RTs showed the highest affinity towards HIV-1 IN. These modes of direct protein interactions, along with the apparent rate constants calculated and the correlations of the interaction kinetics with the capacity of the RTs to inhibit IN activities, are all discussed.

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Significance of thiol-disulfide balance in SARS-CoV-2 infection

Medicinski podmladak ◽

10.5937/mp72-32874 ◽

2021 ◽

Vol 72 (3) ◽

pp. 30-36

Author(s):

Tatjana Simić

Keyword(s):

Cell Surface ◽

Host Cell ◽

Protein Interactions ◽

Viral Entry ◽

Molecular Mechanisms ◽

Alkylating Agents ◽

Protein S ◽

Protein Protein Interactions ◽

Virus Surface ◽

Host Cell Surface

Studies of the molecular mechanisms regarding interaction of different viruses with receptors on the host cell surface have shown that the viral entry depends on the specific relationship between free thiol (SH) groups and disulfides on the virus surface, as well as the thiol disulfide balance on the host cell surface. The presence of oxidizing compounds or alkylating agents, which disturb the thiol-disulfide balance on the surface of the virus, can also affect its infectious potential. Disturbed thiol-disulfide balance may also influence protein-protein interactions between SARS-CoV-2 protein S and ACE2 receptors of the host cell. This review presents the basic mechanisms of maintaining intracellular and extracellular thiol disulfide balance and previous experimental and clinical evidence in favor of impaired balance in SARS-CoV-2 infection. Besides, the results of the clinical application or experimental analysis of compounds that induce changes in the thiol disulfide balance towards reduction of disulfide bridges in proteins of interest in COVID-19 infection are presented.

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