scholarly journals Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1111
Author(s):  
Francesco Raimondi ◽  
Joshua G. Burkhart ◽  
Matthew J. Betts ◽  
Robert B. Russell ◽  
Guanming Wu
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Software Tool ◽  
Cascade Reactions ◽  
Data Types ◽  
Biochemical Reactions ◽  
3D Structures ◽  
Multi Scale ◽  
Specific Cancer ◽  
Cancer Types

Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease.

scholarly journals Complex+: Aided Decision-Making for the Study of Protein Complexes

2019 ◽  
Author(s):  
Mehrnoosh Oghbaie ◽  
Petr Šulc ◽  
David Fenyö ◽  
Michael Pennock ◽  
John LaCava
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Cell Biology ◽  
Protein Complexes ◽  
Conclusive Evidence ◽  
Computational Techniques ◽  
Protein Assemblies ◽  
Data Types ◽  
Research Strategies ◽  
Protein Interactome

AbstractProteins are the chief effectors of cell biology and their functions are typically carried out in the context of multi-protein assemblies; large collections of such interacting protein assemblies are often referred to as interactomes. Knowing the constituents of protein complexes is therefore important for investigating their molecular biology. Many experimental methods are capable of producing data of use for detecting and inferring the existence of physiological protein complexes. Each method has associated pros and cons, affecting the potential quality and utility of the data. Numerous informatic resources exist for the curation, integration, retrieval, and processing of protein interactions data. While each resource may possess different merits, none are definitive and few are wieldy, potentially limiting their effective use by non-experts. In addition, contemporary analyses suggest that we may still be decades away from a comprehensive map of a human protein interactome. Taken together, we are currently unable to maximally impact and improve biomedicine from a protein interactome perspective – motivating the development of experimental and computational techniques that help investigators to address these limitations. Here, we present a resource intended to assist investigators in (i) navigating the cumulative knowledge concerning protein complexes and (ii) forming hypotheses concerning protein interactions that may yet lack conclusive evidence, thus (iii) directing future experiments to address knowledge gaps. To achieve this, we integrated multiple data-types/different properties of protein interactions from multiple sources and after applying various methods of regularization, compared the protein interaction networks computed to those available in the EMBL-EBI Complex Portal, a manually curated, gold-standard catalog of macromolecular complexes. As a result, our resource provides investigators with reliable curation of bona fide and candidate physical interactors of their protein or complex of interest, prompting due scrutiny and further validation when needed. We believe this information will empower a wider range of experimentalists to conduct focused protein interaction studies and to better select research strategies that explicitly target missing information.

scholarly journals Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Ru Shen ◽  
Xiaosheng Wang ◽  
Chittibabu Guda
Keyword(s):  
Protein Interaction ◽  
Cellular Systems ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Cancer Type ◽  
Functional Modules ◽  
Protein Protein Interaction ◽  
Specific Cancer ◽  
Cancer Types ◽  
Protein Protein Interaction Networks

Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein-protein interaction networks carry vital information about molecular interactions in cellular systems, and identification of functional modules (subgraphs) in these networks is one of the most important applications of biological network analysis.Results. In this study, we developed a new graph theory based method to identify distinct functional modules from nine different cancer protein-protein interaction networks. The method is composed of three major steps: (i) extracting modules from protein-protein interaction networks using network clustering algorithms; (ii) identifying distinct subgraphs from the derived modules; and (iii) identifying distinct subgraph patterns from distinct subgraphs. The subgraph patterns were evaluated using experimentally determined cancer-specific protein-protein interaction data from the Ingenuity knowledgebase, to identify distinct functional modules that are specific to each cancer type.Conclusion. We identified cancer-type specific subgraph patterns that may represent the functional modules involved in the molecular pathogenesis of different cancer types. Our method can serve as an effective tool to discover cancer-type specific functional modules from large protein-protein interaction networks.

scholarly journals EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA–protein interaction prediction

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jingjing Wang ◽  
Yanpeng Zhao ◽  
Weikang Gong ◽  
Yang Liu ◽  
Mei Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Tertiary Structure ◽  
Short Term Memory ◽  
Biological Information ◽  
Sequence Features ◽  
Multi Scale ◽  
Non Coding Rna ◽  
Increasing Demand

Abstract Background Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA–protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA–protein interactions. Results In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA–protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA–protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA–protein networks of Mus musculus successfully. Conclusions In general, our proposed method EDLMFC improved the accuracy of ncRNA–protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC.

scholarly journals EDLMFC: An Ensemble Deep Learning Framework with Multi-scale Features Combination for ncRNA-protein Interaction Prediction

2021 ◽  
Author(s):  
Jingjing Wang ◽  
Yanpeng Zhao ◽  
Weikang Gong ◽  
Yang Liu ◽  
Mei Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Tertiary Structure ◽  
Short Term Memory ◽  
Biological Information ◽  
Sequence Features ◽  
Multi Scale ◽  
Non Coding Rna ◽  
Increasing Demand

Abstract Background: Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA-protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA-protein interactions.Results: In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA-protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under 5-fold cross-validation, EDLMFC shows the best performance with accuracy of 94.3% and 90.0% on RPI1807 and NPInter v2.0 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA-protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA-protein networks of Mus musculus successfully.Conclusions: In general, our proposed method EDLMFC improved the accuracy of ncRNA-protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research.The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC.

Cancer in agricultural populations

2020 ◽  
pp. 837-837
Author(s):  
K. . Togawa
Keyword(s):  
Risk Factors ◽  
General Population ◽  
Cohort Studies ◽  
Health Effects ◽  
Cancer Incidence ◽  
Lower Prevalence ◽  
International Consortium ◽  
Adverse Health Effects ◽  
Specific Cancer ◽  
Cancer Types

Agricultural workers can be exposed to a wide variety of agents (e.g. pesticides), some of which may have adverse health effects, such as cancer. To study the health effects of agricultural exposures, an international consortium of agricultural cohort studies, AGRICOH, was established. The present analysis compared cancer incidence between the AGRICOH cohorts and the general population and found lower overall cancer incidence in the AGRICOH cohorts, with some variation across cohorts for specific cancer types. The observed lower cancer incidence may be due to healthy worker bias or lower prevalence of risk factors in the agricultural populations. Further analysis is underway.

An efficient transient assays system using Agrobacterium-mediated transformation of onion (Allium cepa) epidermal cells

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Yu-Miao Zhang ◽  
Jun Wang ◽  
Tao Wu
Keyword(s):  
Subcellular Localization ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Epidermal Cells ◽  
Cyclin Dependent Kinase ◽  
Protein Subcellular Localization ◽  
Protein Protein Interactions ◽  
Efficient System ◽  
Protein Protein Interaction ◽  
Onion Epidermal Cells

In this study, the Agrobacterium infection medium, infection duration, detergent, and cell density were optimized. The sorghum-based infection medium (SbIM), 10-20 min infection time, addition of 0.01% Silwet L-77, and Agrobacterium optical density at 600 nm (OD600), improved the competence of onion epidermal cells to support Agrobacterium infection at >90% efficiency. Cyclin-dependent kinase D-2 (CDKD-2) and cytochrome c-type biogenesis protein (CYCH), protein-protein interactions were localized. The optimized procedure is a quick and efficient system for examining protein subcellular localization and protein-protein interaction.

Protein Interaction Domains and Post-Translational Modifications: Structural Features and Drug Discovery Applications

2020 ◽  
Vol 27 (37) ◽  
pp. 6306-6355 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Information ◽  
Protein Complexes ◽  
Structural Features ◽  
Protein Protein Interactions ◽  
Modular Architecture ◽  
Post Translational Modifications ◽  
Interaction Domains

Background:: Many pathways regarding healthy cells and/or linked to diseases onset and progression depend on large assemblies including multi-protein complexes. Protein-protein interactions may occur through a vast array of modules known as protein interaction domains (PIDs). Objective:: This review concerns with PIDs recognizing post-translationally modified peptide sequences and intends to provide the scientific community with state of art knowledge on their 3D structures, binding topologies and potential applications in the drug discovery field. Method:: Several databases, such as the Pfam (Protein family), the SMART (Simple Modular Architecture Research Tool) and the PDB (Protein Data Bank), were searched to look for different domain families and gain structural information on protein complexes in which particular PIDs are involved. Recent literature on PIDs and related drug discovery campaigns was retrieved through Pubmed and analyzed. Results and Conclusion:: PIDs are rather versatile as concerning their binding preferences. Many of them recognize specifically only determined amino acid stretches with post-translational modifications, a few others are able to interact with several post-translationally modified sequences or with unmodified ones. Many PIDs can be linked to different diseases including cancer. The tremendous amount of available structural data led to the structure-based design of several molecules targeting protein-protein interactions mediated by PIDs, including peptides, peptidomimetics and small compounds. More studies are needed to fully role out, among different families, PIDs that can be considered reliable therapeutic targets, however, attacking PIDs rather than catalytic domains of a particular protein may represent a route to obtain selective inhibitors.

scholarly journals Novel Roles for the Sirtuin Deacylase SIRT5 in Normal Physiology and in Cancer

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 741-741
Author(s):  
David Lombard
Keyword(s):  
Mitochondrial Matrix ◽  
Genomic Integrity ◽  
Biological Functions ◽  
Protein Targets ◽  
Post Translational Modifications ◽  
Catalytic Activities ◽  
Cellular Processes ◽  
Specific Cancer ◽  
Cancer Types ◽  
Chromatin Biology

Abstract Sirtuins are NAD+-dependent deacylases that regulate diverse cellular processes such as metabolic homeostasis and genomic integrity. Mammals possess seven sirtuin family members, SIRT1-SIRT7, that display diverse subcellular localization patterns, catalytic activities, protein targets, and biological functions. Three sirtuins, SIRT3, SIRT4, and SIRT5, are primarily located in the mitochondrial matrix. SIRT5 is a very inefficient deacetylase, instead removing negatively charged post-translational modifications (succinyl, glutaryl, and malonyl groups) from lysines of its target proteins, in mitochondria and throughout the cell. SIRT5 plays only modest known roles in normal physiology, with its major functions occurring in the heart under stress conditions. In contrast, in specific cancer types, including melanoma, we have identified a major pro-survival role for SIRT5. We have traced this function of SIRT5 to novel roles for this protein in regulating chromatin biology. New insights into mechanisms of SIRT5 action in cancer, and in normal myocardium, will be discussed.

scholarly journals Mutations and Protein Interaction Landscape Reveal Key Cellular Events Perturbed in Upper Motor Neurons with HSP and PLS

2021 ◽  
Vol 11 (5) ◽  
pp. 578
Author(s):  
Oge Gozutok ◽  
Benjamin Ryan Helmold ◽  
P. Hande Ozdinler
Keyword(s):  
Motor Neurons ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Cortical Neurons ◽  
Therapeutic Interventions ◽  
Functional Identification ◽  
Protein Protein Interaction ◽  
Cellular Events ◽  
Interaction Domains ◽  
Upper Motor Neurons

Hereditary spastic paraplegia (HSP) and primary lateral sclerosis (PLS) are rare motor neuron diseases, which affect mostly the upper motor neurons (UMNs) in patients. The UMNs display early vulnerability and progressive degeneration, while other cortical neurons mostly remain functional. Identification of numerous mutations either directly linked or associated with HSP and PLS begins to reveal the genetic component of UMN diseases. Since each of these mutations are identified on genes that code for a protein, and because cellular functions mostly depend on protein-protein interactions, we hypothesized that the mutations detected in patients and the alterations in protein interaction domains would hold the key to unravel the underlying causes of their vulnerability. In an effort to bring a mechanistic insight, we utilized computational analyses to identify interaction partners of proteins and developed the protein-protein interaction landscape with respect to HSP and PLS. Protein-protein interaction domains, upstream regulators and canonical pathways begin to highlight key cellular events. Here we report that proteins involved in maintaining lipid homeostasis and cytoarchitectural dynamics and their interactions are of great importance for UMN health and stability. Their perturbation may result in neuronal vulnerability, and thus maintaining their balance could offer therapeutic interventions.

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