scholarly journals Predicted protein interactions of IFITMs which inhibit Zika virus infection

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1919 ◽  
Author(s):  
Madhavi K. Ganapathiraju
Keyword(s):  
Protein Interactions ◽  
Zika Virus ◽  
Axonal Guidance ◽  
Neural Tube Closure ◽  
Future Research ◽  
Viral Immunity ◽  
Interferon Signaling ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction

After the first reported case of Zika virus in Brazil, in 2015, a significant increase in the reported cases of microcephaly was observed. Microcephaly is a neurological condition in which the infant’s head is significantly smaller with complications in brain development. Recently, two small membrane-associated interferon-inducible transmembrane proteins (IFITM1 and IFITM3) have been shown to repress members of the flaviviridae family which includes the Zika virus. However, the exact mechanisms leading to the inhibition of the virus are yet unknown. Here, we assembled an interactome of IFITM1 and IFITM3 with known protein-protein interactions (PPIs) collected from publicly available databases and novel PPIs predicted using High-confidence Protein-Protein Interaction Prediction (HiPPIP) model. We analyzed the functional and pathway associations of the interacting proteins, and found that there are several immunity pathways (interferon signaling, cd28 signaling in T-helper cells crosstalk between dendritic cells and natural killer cells), neuronal pathways (axonal guidance signaling, neural tube closure and actin cytoskeleton signaling) and developmental pathways that are associated with these interactors. These results could help direct future research in elucidating the mechanisms underlying the viral immunity to Zika virus and other flaviviruses.

scholarly journals Predicted protein interactions of IFITMs may shed light on mechanisms of Zika virus-induced microcephaly and host invasion

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 1919
Author(s):  
Madhavi K. Ganapathiraju ◽  
Kalyani B. Karunakaran ◽  
Josefina Correa-Menéndez
Keyword(s):  
Neural Tube ◽  
Protein Interactions ◽  
Zika Virus ◽  
Axonal Guidance ◽  
Neural Tube Closure ◽  
Ependymal Cells ◽  
Future Research ◽  
Shed Light ◽  
Tube Closure ◽  
Host Invasion

After the first reported case of Zika virus (ZIKV) in Brazil, in 2015, a significant increase in the reported cases of microcephaly was observed. Microcephaly is a neurological condition in which the infant’s head is significantly smaller with complications in brain development. Recently, two small membrane-associated interferon-inducible transmembrane proteins (IFITM1 and IFITM3) have been shown to repress members of the flaviviridae family which includes ZIKV. However, the exact mechanisms leading to the inhibition of the virus are yet unknown. Here, we assembled an interactome of IFITM1 and IFITM3 with known protein-protein interactions (PPIs) collected from publicly available databases and novel PPIs predicted using the High-confidence Protein-Protein Interaction Prediction (HiPPIP) model. We analyzed the functional and pathway associations of the interacting proteins, and found that there are several immunity pathways (toll-like receptor signaling, cd28 signaling in T-helper cells, crosstalk between dendritic cells and natural killer cells), neuronal pathways (axonal guidance signaling, neural tube closure and actin cytoskeleton signaling) and developmental pathways (neural tube closure, embryonic skeletal system development) that are associated with these interactors. Our novel PPIs associate cilia dysfunction in ependymal cells to microcephaly, and may also shed light on potential targets of ZIKV for host invasion by immunosuppression and cytoskeletal rearrangements. These results could help direct future research in elucidating the mechanisms underlying host defense to ZIKV and other flaviviruses.

In Silico Recognition of Protein-Protein Interaction

2011 ◽  
pp. 248-268
Author(s):  
Byung-Hoon Park ◽  
Phuongan Dam ◽  
Chongle Pan ◽  
Ying Xu ◽  
Al Geist ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Regulatory Networks ◽  
Machine Learning Techniques ◽  
Translation Regulation ◽  
Future Research ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Protein Levels ◽  
Protein Functions ◽  
Model Protein

Protein-protein interactions are fundamental to cellular processes. They are responsible for phenomena like DNA replication, gene transcription, protein translation, regulation of metabolic pathways, immunologic recognition, signal transduction, etc. The identification of interacting proteins is therefore an important prerequisite step in understanding their physiological functions. Due to the invaluable importance to various biophysical activities, reliable computational methods to infer protein-protein interactions from either structural or genome sequences are in heavy demand lately. Successful predictions, for instance, will facilitate a drug design process and the reconstruction of metabolic or regulatory networks. In this chapter, we review: (a) high-throughput experimental methods for identification of protein-protein interactions, (b) existing databases of protein-protein interactions, (c) computational approaches to predicting protein-protein interactions at both residue and protein levels, (d) various statistical and machine learning techniques to model protein-protein interactions, and (e) applications of protein-protein interactions in predicting protein functions. We also discuss intrinsic drawbacks of the existing approaches and future research directions.

scholarly journals RAPPPID: Towards Generalisable Protein Interaction Prediction with AWD-LSTM Twin Networks

2021 ◽  
Author(s):  
Joseph Szymborski ◽  
Amin Emad
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Information Leakage ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Training Time ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction ◽  
Time To Learn

Motivation: Computational methods for the prediction of protein-protein interactions, while important tools for researchers, are plagued by challenges in generalising to unseen proteins. Datasets used for modelling protein-protein predictions are particularly predisposed to information leakage and sampling biases. Results: In this study, we introduce RAPPPID, a method for the Regularised Automatic Prediction of Protein-Protein Interactions using Deep Learning. RAPPPID is a twin AWD-LSTM network which employs multiple regularisation methods during training time to learn generalised weights. Testing on stringent interaction datasets composed of proteins not seen during training, RAPPPID outperforms state-of-the-art methods. Further experiments show that RAPPPID's performance holds regardless of the particular proteins in the testing set and its performance is higher for biologically supported edges. This study serves to demonstrate that appropriate regularisation is an important component of overcoming the challenges of creating models for protein-protein interaction prediction that generalise to unseen proteins. Availability and Implementation: Code and datasets are freely available at https://github.com/jszym/rapppid. Contact: [email protected] Supplementary Information: Online-only supplementary data is available at the journal's website.

scholarly journals Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction

2020 ◽  
Vol 21 (6) ◽  
pp. 454-463 ◽  
Author(s):  
Mst. Shamima Khatun ◽  
Watshara Shoombuatong ◽  
Md. Mehedi Hasan ◽  
Hiroyuki Kurata
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Disease Incidence ◽  
Computational Prediction ◽  
Future Perspective ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
High Throughput Technology ◽  
Protein Interaction Prediction ◽  
Therapy Design

Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

scholarly journals An Mtb-Human Protein-Protein Interaction Map Reveals that Bacterial LpqN Antagonizes CBL, a Host Ubiquitin Ligase that Regulates the Balance Between Anti-Viral and Anti-Bacterial Responses

2017 ◽  
Author(s):  
Bennett H. Penn ◽  
Zoe Netter ◽  
Jeffrey R. Johnson ◽  
John Von Dollen ◽  
Gwendolyn M. Jang ◽  
...  
Keyword(s):  
Ubiquitin Ligase ◽  
Protein Interactions ◽  
Human Protein ◽  
Viral Immunity ◽  
Protein Protein Interactions ◽  
Affinity Tag ◽  
Intrinsic Polarization ◽  
Protein Protein Interaction ◽  
Human Ubiquitin ◽  
Interaction Map

SUMMARYAlthough macrophages are armed with potent anti-bacterial functions, Mycobacterium tuberculosis (Mtb) replicates inside these innate immune cells. Determinants of macrophage-intrinsic bacterial control, and the Mtb strategies to overcome them are poorly understood. To further study these processes, we used a systematic affinity tag purification mass spectrometry (AP-MS) approach to identify 187 Mtb-human protein-protein interactions (PPIs) involving 34 secreted Mtb proteins. This interaction map revealed two new factors involved in Mtb pathogenesis - the secreted Mtb protein, LpqN, and its binding partner, the human ubiquitin ligase CBL. We discovered that an lpqN Mtb mutant is attenuated in macrophages, but growth is restored when CBL is removed. Conversely, Cbl-/- macrophages are resistant to viral infection, indicating that CBL regulates cell-intrinsic polarization between anti-bacterial and anti-viral immunity. Collectively, these findings illustrate the utility of this Mtb-human PPI map as a resource for developing a deeper understanding of the intricate interactions between Mtb and its host.

scholarly journals A multitask transfer learning framework for the prediction of virus-human protein–protein interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Thi Ngan Dong ◽  
Graham Brogden ◽  
Gisa Gerold ◽  
Megha Khosla
Keyword(s):  
Transfer Learning ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Sequences ◽  
Human Protein ◽  
Interaction Patterns ◽  
Protein Protein Interactions ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction

Abstract Background Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein–protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses. Results We developed a multitask transfer learning approach that exploits the information of around 24 million protein sequences and the interaction patterns from the human interactome to counter the problem of small training datasets. Instead of using hand-crafted protein features, we utilize statistically rich protein representations learned by a deep language modeling approach from a massive source of protein sequences. Additionally, we employ an additional objective which aims to maximize the probability of observing human protein–protein interactions. This additional task objective acts as a regularizer and also allows to incorporate domain knowledge to inform the virus-human protein–protein interaction prediction model. Conclusions Our approach achieved competitive results on 13 benchmark datasets and the case study for the SARS-CoV-2 virus receptor. Experimental results show that our proposed model works effectively for both virus-human and bacteria-human protein–protein interaction prediction tasks. We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/multitask-transfer.

scholarly journals Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

2020 ◽  
Author(s):  
Kalyani B. Karunakaran ◽  
N. Balakrishnan ◽  
Madhavi K. Ganapathiraju
Keyword(s):  
Viral Infection ◽  
Protein Interactions ◽  
Keyword Search ◽  
Protein Protein Interactions ◽  
Host Proteins ◽  
Viral Genomes ◽  
Protein Protein Interaction ◽  
Viral Budding ◽  
Human Proteins ◽  
Protein Interaction Prediction

Abstract World over, people are looking for solutions to tackle the pandemic coronavirus disease (COVID-19) caused by the virus SARS-CoV-2/nCoV-19. Notable contributions in biomedical field have been characterizing viral genomes, host transcriptomes and proteomes, repurposable drugs and vaccines. In one such study, 332 human proteins targeted by nCoV19 were identified. We expanded this set of host proteins by constructing their protein interactome, including in it not only the known protein-protein interactions (PPIs) but also novel, hitherto unknown PPIs predicted with our High-precision Protein-Protein Interaction Prediction (HiPPIP) model that was shown to be highly accurate. In fact, one of the earliest discoveries made possible by HiPPIP is related to activation of immunity upon viral infection. We found that several interactors of the host proteins are differentially expressed upon viral infection, are related to highly relevant pathways, and that the novel interaction of NUP98 with CHMP5 may activate an antiviral mechanism leading to disruption of viral budding. We are making the interactions available as downloadable files to facilitate future systems biology studies and also on a web-server at http://hagrid.dbmi.pitt.edu/corona that allows not only keyword search but also queries such as “PPIs where one protein is associated with ‘virus’ and the interactors with 'pulmonary'”.

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.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

Sign in / Sign up

Export Citation Format

Share Document