scholarly journals Cellinker: a platform of ligand–receptor interactions for intercellular communication analysis

2021 ◽  
Author(s):  
Yang Zhang ◽  
Tianyuan Liu ◽  
Jing Wang ◽  
Bohao Zou ◽  
Le Li ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Intercellular Communication ◽  
Rapid Development ◽  
Cell Communication ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Communication Analysis ◽  
Mediate Cell ◽  
Intercellular Communications ◽  
Cell Cell

Abstract Motivation Ligand–receptor (L–R) interactions mediate cell adhesion, recognition and communication and play essential roles in physiological and pathological signaling. With the rapid development of single-cell RNA sequencing (scRNA-seq) technologies, systematically decoding the intercellular communication network involving L–R interactions has become a focus of research. Therefore, construction of a comprehensive, high-confidence and well-organized resource to retrieve L–R interactions in order to study the functional effects of cell–cell communications would be of great value. Results In this study, we developed Cellinker, a platform of literature-supported L–R interactions that play roles in cell–cell communication. We aimed to provide a useful platform for studies on cell–cell communication mediated by L–R interactions. The current version of Cellinker documents over 3700 human and 3200 mouse L–R protein–protein interactions (PPIs) and embeds a practical and convenient webserver with which researchers can decode intercellular communications based on scRNA-seq data. And over 400 endogenous small molecule (sMOL) related L–R interactions were collected as well. Moreover, to help with research on coronavirus (CoV) infection, Cellinker collects information on 16L–R PPIs involved in CoV–human interactions (including 12L–R PPIs involved in SARS-CoV-2 infection). In summary, Cellinker provides a user-friendly interface for querying, browsing and visualizing L–R interactions as well as a practical and convenient web tool for inferring intercellular communications based on scRNA-seq data. We believe this platform could promote intercellular communication research and accelerate the development of related algorithms for scRNA-seq studies. Availability and implementation Cellinker is available at http://www.rna-society.org/cellinker/ Supplementary information Supplementary data are available at Bioinformatics online.

CellTalkDB: a manually curated database of ligand–receptor interactions in humans and mice

2020 ◽  
Author(s):  
Xin Shao ◽  
Jie Liao ◽  
Chengyu Li ◽  
Xiaoyan Lu ◽  
Junyun Cheng ◽  
...  
Keyword(s):  
Single Cell ◽  
Protein Interactions ◽  
Cell Communication ◽  
Systematic Investigation ◽  
Protein Protein Interactions ◽  
Biological Phenomena ◽  
Type Composition ◽  
Dependent Cell ◽  
Multicellular Organisms ◽  
Cell Cell

Abstract Cell–cell communications in multicellular organisms generally involve secreted ligand–receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell–cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell–cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein–protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell–cell communications, which might provide new insights into the mechanism underlying biological processes.

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

2021 ◽  
Author(s):  
Qianmu Yuan ◽  
Jianwen Chen ◽  
Huiying Zhao ◽  
Yaoqi Zhou ◽  
Yuedong Yang
Keyword(s):  
Protein Interactions ◽  
Spatial Information ◽  
Screening Tools ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Convolutional Network ◽  
Source Codes ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Mapping Techniques

Abstract Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Coevolution-based prediction of protein–protein interactions in polyketide biosynthetic assembly lines

2020 ◽  
Vol 36 (19) ◽  
pp. 4846-4853 ◽  
Author(s):  
Yan Wang ◽  
Miguel Correa Marrero ◽  
Marnix H Medema ◽  
Aalt D J van Dijk
Keyword(s):  
Protein Interactions ◽  
Antitumor Agents ◽  
Acyl Carrier Protein ◽  
Specific Protein ◽  
Combinatorial Biosynthesis ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Assembly Lines ◽  
Specific Order ◽  
Encoding Genes

Abstract Motivation Polyketide synthases (PKSs) are enzymes that generate diverse molecules of great pharmaceutical importance, including a range of clinically used antimicrobials and antitumor agents. Many polyketides are synthesized by cis-AT modular PKSs, which are organized in assembly lines, in which multiple enzymes line up in a specific order. This order is defined by specific protein–protein interactions (PPIs). The unique modular structure and catalyzing mechanism of these assembly lines makes their products predictable and also spurred combinatorial biosynthesis studies to produce novel polyketides using synthetic biology. However, predicting the interactions of PKSs, and thereby inferring the order of their assembly line, is still challenging, especially for cases in which this order is not reflected by the ordering of the PKS-encoding genes in the genome. Results Here, we introduce PKSpop, which uses a coevolution-based PPI algorithm to infer protein order in PKS assembly lines. Our method accurately predicts protein orders (93% accuracy). Additionally, we identify new residue pairs that are key in determining interaction specificity, and show that coevolution of N- and C-terminal docking domains of PKSs is significantly more predictive for PPIs than coevolution between ketosynthase and acyl carrier protein domains. Availability and implementation The code is available on http://www.bif.wur.nl/ (under ‘Software’). Supplementary information Supplementary data are available at Bioinformatics online.

High-throughput proteomics and the fight against pathogens

2016 ◽  
Vol 12 (8) ◽  
pp. 2373-2384 ◽  
Author(s):  
Anita Horvatić ◽  
Josipa Kuleš ◽  
Nicolas Guillemin ◽  
Asier Galan ◽  
Vladimir Mrljak ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Animal Welfare ◽  
High Throughput ◽  
Protein Interactions ◽  
Rapid Development ◽  
Protein Protein Interactions ◽  
Major Threat ◽  
Host Pathogen ◽  
Pathogen Protein

Pathogens pose a major threat to human and animal welfare. Understanding the interspecies host–pathogen protein–protein interactions could lead to the development of novel strategies to combat infectious diseases through the rapid development of new therapeutics.

scholarly journals Inhibition of  a v b 3 integrin impairs adhesion and uptake of tumor-derived small extracellular vesicles

2020 ◽  
Author(s):  
Wanessa Altei ◽  
Bianca Pachane ◽  
Patty K. Santos ◽  
Ligia Ribeiro ◽  
Bong Hwan Sung ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Extracellular Vesicles ◽  
Cell Communication ◽  
Organ Specific ◽  
Breast Cells ◽  
Mediate Cell ◽  
First Time ◽  
Cd63 Expression ◽  
Cell Cell

Abstract Background: Extracellular vesicles (EVs) are lipid-bound particles that are naturally released from cells and mediate cell-cell communication. Integrin adhesion receptors are enriched in small EVs (SEVs) and SEV-carried integrins have been shown to promote cancer cell migration and to mediate organ-specific metastasis; however, how integrins mediate these effects is not entirely clear and could represent a combination of EV binding to extracellular matrix and cells.Methods: To probe integrin role in EVs binding and uptake, we employed a disintegrin inhibitor (DisBa-01) of integrin binding with specificity for avb3 integrin. EVs were purified from MDA-MB-231 cells conditioned media by serial centrifugation method. Isolated EVs were characterized by different techniques and further employed in adhesion, uptake and co-culture experiments.Results: We find that SEVs secreted from MDA-MB-231 breast cancer cells carry avb3 integrin and bind directly to fibronectin-coated plates, which is inhibited by DisBa-01. SEV coating on tissue culture plates also induces adhesion of MDA-MB-231 cells, which is inhibited by DisBa-01 treatment. Analysis of EV uptake and interchange between cells reveals that the amount of CD63-positive EVs delivered from malignant MDA-MB-231 breast cells to non-malignant MCF10A breast epithelial cells is reduced by DisBa-01 treatment. Inhibition of avb3 integrin decreases CD63 expression in cancer cells suggesting an effect on SEV content.Conclusion: In summary, our findings demonstrate for the first time a key role of avb3 integrin in cell-cell communication through SEVs.

scholarly journals Inhibition of  a v b 3 integrin impairs adhesion and uptake of tumor-derived small extracellular vesicles

2020 ◽  
Author(s):  
Wanessa Altei ◽  
Bianca Pachane ◽  
Patty K. Santos ◽  
Ligia Ribeiro ◽  
Bong Hwan Sung ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Extracellular Vesicles ◽  
Cell Communication ◽  
Αvβ3 Integrin ◽  
Organ Specific ◽  
Breast Cells ◽  
Mediate Cell ◽  
First Time ◽  
Cell Cell

Abstract Background: Extracellular vesicles (EVs) are lipid-bound particles that are naturally released from cells and mediate cell-cell communication. Integrin adhesion receptors are enriched in small EVs (SEVs) and SEV-carried integrins have been shown to promote cancer cell migration and to mediate organ-specific metastasis; however, how integrins mediate these effects is not entirely clear and could represent a combination of EV binding to extracellular matrix and cells. Methods: To probe integrin role in EVs binding and uptake, we employed a disintegrin inhibitor (DisBa-01) of integrin binding with specificity for αvβ3 integrin. EVs were purified from MDA-MB-231 cells conditioned media by serial centrifugation method. Isolated EVs were characterized by different techniques and further employed in adhesion, uptake and co-culture experiments. Results: We find that SEVs secreted from MDA-MB-231 breast cancer cells carry αvβ3 integrin and bind directly to fibronectin-coated plates, which is inhibited by DisBa-01. SEV coating on tissue culture plates also induces adhesion of MDA-MB-231 cells, which is inhibited by DisBa-01 treatment. Analysis of EV uptake and interchange between cells reveals that the amount of CD63-positive EVs delivered from malignant MDA-MB-231 breast cells to non-malignant MCF10A breast epithelial cells is reduced by DisBa-01 treatment. Inhibition of αvβ3 integrin decreases CD63 expression in cancer cells suggesting an effect on SEV content. Conclusion: In summary, our findings demonstrate for the first time a key role of αvβ3 integrin in cell-cell communication through SEVs.

scholarly journals Enhanced Waddington landscape model with cell–cell communication can explain molecular mechanisms of self-organization

2019 ◽  
Vol 35 (20) ◽  
pp. 4081-4088
Author(s):  
Hosein Fooladi ◽  
Parsa Moradi ◽  
Ali Sharifi-Zarchi ◽  
Babak Hosein Khalaj
Keyword(s):  
Molecular Mechanisms ◽  
Embryonic Stem ◽  
Cell Communication ◽  
Self Organization ◽  
Supplementary Information ◽  
Embryonic Cells ◽  
Human Escs ◽  
Germ Layers ◽  
Cell Cell

Abstract Motivation The molecular mechanisms of self-organization that orchestrate embryonic cells to create astonishing patterns have been among major questions of developmental biology. It is recently shown that embryonic stem cells (ESCs), when cultured in particular micropatterns, can self-organize and mimic the early steps of pre-implantation embryogenesis. A systems-biology model to address this observation from a dynamical systems perspective is essential and can enhance understanding of the phenomenon. Results Here, we propose a multicellular mathematical model for pattern formation during in vitro gastrulation of human ESCs. This model enhances the basic principles of Waddington epigenetic landscape with cell–cell communication, in order to enable pattern and tissue formation. We have shown the sufficiency of a simple mechanism by using a minimal number of parameters in the model, in order to address a variety of experimental observations such as the formation of three germ layers and trophectoderm, responses to altered culture conditions and micropattern diameters and unexpected spotted forms of the germ layers under certain conditions. Moreover, we have tested different boundary conditions as well as various shapes, observing that the pattern is initiated from the boundary and gradually spreads towards the center. This model provides a basis for in-silico modeling of self-organization. Availability and implementation https://github.com/HFooladi/Self_Organization. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions

2019 ◽  
Vol 35 (22) ◽  
pp. 4794-4796 ◽  
Author(s):  
Qingzhen Hou ◽  
Paul F G De Geest ◽  
Christian J Griffioen ◽  
Sanne Abeln ◽  
Jaap Heringa ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Sequence Data ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Interaction Sites ◽  
Expert User ◽  
Interaction Interface ◽  
Experimental Annotation ◽  
Protein Sequence Data

Abstract Motivation Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random forest method for protein–protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein–protein interactions. Here, we present a webserver that implements this method efficiently. Results With the aim of accelerating our previous approach, we obtained sequence conservation profiles by re-mastering the alignment of homologous sequences found by PSI-BLAST. This yielded a more than 10-fold speedup and at least the same accuracy, as reported previously for our method; these results allowed us to offer the method as a webserver. The web-server interface is targeted to the non-expert user. The input is simply a sequence of the protein of interest, and the output a table with scores indicating the likelihood of having an interaction interface at a certain position. As the method is sequence-based and not sensitive to the type of protein interaction, we expect this webserver to be of interest to many biological researchers in academia and in industry. Availability and implementation Webserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document