Computational approach to biological validation of protein-protein interactions discovered using literature mining

A Computational Approach for Protein-Protein Interactions of Bacterial Surface Layer Protein with Human Erb3 and αIIB-β3 Receptors

Biointerface Research in Applied Chemistry ◽

10.33263/briac121.420430 ◽

2021 ◽

Vol 12 (1) ◽

pp. 420-430

Keyword(s):

Surface Layer ◽

Molecular Docking ◽

Protein Interactions ◽

Protein A ◽

Computational Approach ◽

Microbial Interactions ◽

Protein Protein Interactions ◽

Epithelial Surface ◽

Surface Receptors ◽

Surface Layer Protein

Host microbial interactions had significant factors in maintains homeostasis and immune-related activity. One such interaction made by Lactobacillus sp. with Surface layer proteins (Slps) had been studied through a computational approach. Erb3 and αIIB-β3, which are epithelial surface layer receptors, are subjected to interact with the Slp homology model. Both cell surface receptors were subjected to interact through computational docking, followed by molecular dynamics simulations through the coarse-grain method to explore the conformational stability. Through the implementation of the molecular docking for the surface layer protein A, we have shown the surface layer protein A, protein-protein interactions are higher in cellular receptors with epidermal growth factor receptor at an -34.45 ΔG and -51.19 ΔG through molecular docking with Erb3 and αIIB-β3. This study shows the unique interaction of Slp with the epithelial surface receptors like Erb3 and αIIB-β3, which are multipurpose applications in microbial-based drug therapeutics.

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An Integrative Computational Approach for the Prediction of Human-Plasmodium Protein-Protein Interactions

BioMed Research International ◽

10.1155/2020/2082540 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Kais Ghedira ◽

Yosr Hamdi ◽

Abir El Béji ◽

Houcemeddine Othman

Keyword(s):

Protein Interactions ◽

Computational Approach ◽

Protein Protein Interactions ◽

Human Red Blood Cells ◽

Pharmacological Targets ◽

Host Pathogen ◽

Human Proteins ◽

Life Threatening ◽

Similarity Method ◽

Human Plasmodium

Host-pathogen molecular cross-talks are critical in determining the pathophysiology of a specific infection. Most of these cross-talks are mediated via protein-protein interactions between the host and the pathogen (HP-PPI). Thus, it is essential to know how some pathogens interact with their hosts to understand the mechanism of infections. Malaria is a life-threatening disease caused by an obligate intracellular parasite belonging to the Plasmodium genus, of which P. falciparum is the most prevalent. Several previous studies predicted human-plasmodium protein-protein interactions using computational methods have demonstrated their utility, accuracy, and efficiency to identify the interacting partners and therefore complementing experimental efforts to characterize host-pathogen interaction networks. To predict potential putative HP-PPIs, we use an integrative computational approach based on the combination of multiple OMICS-based methods including human red blood cells (RBC) and Plasmodium falciparum 3D7 strain expressed proteins, domain-domain based PPI, similarity of gene ontology terms, structure similarity method homology identification, and machine learning prediction. Our results reported a set of 716 protein interactions involving 302 human proteins and 130 Plasmodium proteins. This work provides a list of potential human-Plasmodium interacting proteins. These findings will contribute to better understand the mechanisms underlying the molecular determinism of malaria disease and potentially to identify candidate pharmacological targets.

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Determining rewiring effects of alternatively spliced isoforms on protein-protein interactions using a computational approach

10.1101/256834 ◽

2018 ◽

Author(s):

Oleksandr Narykov ◽

Nathan Johnson ◽

Dmitry Korkin

Keyword(s):

Protein Interactions ◽

Molecular Mechanisms ◽

Critical Role ◽

Computational Approach ◽

Superior Performance ◽

Large Set ◽

Protein Protein Interactions ◽

Specific Expression ◽

Alternatively Spliced ◽

Specific Regulation

AbstractThe critical role of alternative splicing (AS) in cell functioning has recently become apparent, whether in studying tissue-or cell-specific regulation, or understanding molecular mechanisms governing a complex disorder. Studying the rewiring, or edgetic, effects of alternatively spliced isoforms on protein interactome can provide system-wide insights into these questions. Unfortunately, high-throughput experiments for such studies are expensive and time-consuming, hence the need to develop an in-silico approach. Here, we formulated the problem of characterization the edgetic effects of AS on protein-protein interactions (PPIs) as a binary classification problem and introduced a first computational approach to solve it. We first developed a supervised feature-based classifier that benefited from the traditional features describing a PPI, the problem-specific features that characterized the difference between the reference and alternative isoforms, and a novel domain interaction potential that allowed pinpointing the domains employed during a specific PPI. We then expanded this approach by including a large set of unlabeled interactomics data and developing a semi-supervised learning method. Our method called AS-IN (Alternatively Splicing INteraction prediction) Tool was compared with the state-of-the-art PPI prediction tools and showed a superior performance, achieving 0.92 in precision and recall. We demonstrated the utility of AS-IN Tool by applying it to the transcriptomic data obtained from the brain and liver tissues of a healthy mouse and western diet fed mouse that developed type two diabetes. We showed that the edgetic effects of differentially expressed transcripts associated with the disease condition are system-wide and unlikely to be detected by looking only at the gene-specific expression levels.

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Using biomedical literature mining to consolidate the set of known human protein-protein interactions

10.3115/1641484.1641491 ◽

2005 ◽

Cited By ~ 3

Author(s):

Arun Ramani ◽

Edward Marcotte ◽

Razvan Bunescu ◽

Raymond Mooney

Keyword(s):

Protein Interactions ◽

Biomedical Literature ◽

Human Protein ◽

Literature Mining ◽

Protein Protein Interactions ◽

Biomedical Literature Mining

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Protein interaction studies for understanding the tremor pathway in Parkinson’s disease.

CNS & Neurological Disorders - Drug Targets ◽

10.2174/1871527319666200905115548 ◽

2020 ◽

Vol 19 ◽

Author(s):

Nitu Dogra ◽

Ruchi Jakhmola Mani ◽

Deepshikha Pande Katare

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Protein Interaction ◽

Protein Interactions ◽

Interaction Network ◽

Literature Mining ◽

Protein Protein Interactions ◽

Calcium Ion Channels ◽

Progression Of Disease ◽

Different Sources

Background: Tremor is one of the most noticeable features, which occurs during the early stages of Parkinson’s disease (PD). It is one of the major pathological hallmarks and does not have any interpreted mechanism. In this study we have framed a hypothesis and deciphered protein-protein interactions between the proteins involved in impairment in sodium and calcium ion channels and thus cause synaptic plasticity leading to a tremor. Methods: Literature mining for retrieval of proteins was done using Science Direct, PubMed Central, SciELO and JSTOR databases. A well thought approach was used and a list of differentially expressed proteins in PD was collected from different sources. A total of 71 proteins were retrieved and a protein interaction network was constructed between them by using Cytoscape.v.3.7. The network was further analysed using BiNGO plugin for retrieval of overrepresented biological processes in Tremor-PD datasets. Hub nodes were also generated in the network. Results: The Tremor-PD pathway was deciphered which demonstrates the cascade of protein interactions that might lead to tremors in PD. Major proteins involved were LRRK2, TUBA1A, TRAF6, HSPA5, ADORA2A, DRD1, DRD2, SNCA, ADCY5, TH etc. Conclusion: In the current study it is predicted that ADORA2A and DRD1/DRD2 are equally contributing to the progression of disease by inhibiting the activity of adenylyl cyclase and thereby increases the permeability of the blood brain barrier causing an influx of neurotransmitters and together they alter the level of dopamine in the brain which eventually leads to tremor.

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Detecting Protein-Protein Interactions with a Novel Matrix-Based Protein Sequence Representation and Support Vector Machines

BioMed Research International ◽

10.1155/2015/867516 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 26

Author(s):

Zhu-Hong You ◽

Jianqiang Li ◽

Xin Gao ◽

Zhou He ◽

Lin Zhu ◽

...

Keyword(s):

Protein Interactions ◽

Protein Sequence ◽

Molecular Mechanisms ◽

Computational Approach ◽

Support Vector ◽

Biological Processes ◽

Protein Protein Interactions ◽

Technological Advances ◽

Vector Machines ◽

Sequence Representation

Proteins and their interactions lie at the heart of most underlying biological processes. Consequently, correct detection of protein-protein interactions (PPIs) is of fundamental importance to understand the molecular mechanisms in biological systems. Although the convenience brought by high-throughput experiment in technological advances makes it possible to detect a large amount of PPIs, the data generated through these methods is unreliable and may not be completely inclusive of all possible PPIs. Targeting at this problem, this study develops a novel computational approach to effectively detect the protein interactions. This approach is proposed based on a novel matrix-based representation of protein sequence combined with the algorithm of support vector machine (SVM), which fully considers the sequence order and dipeptide information of the protein primary sequence. When performed on yeast PPIs datasets, the proposed method can reach 90.06% prediction accuracy with 94.37% specificity at the sensitivity of 85.74%, indicating that this predictor is a useful tool to predict PPIs. Achieved results also demonstrate that our approach can be a helpful supplement for the interactions that have been detected experimentally.

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A computational approach to structural properties of glycoside hydrolase family 4 from bacteria.

Acta Biochimica Polonica ◽

10.18388/abp.2013_2020 ◽

2013 ◽

Vol 60 (4) ◽

Author(s):

Dana Craciun ◽

Beatrice Vlad-Oros ◽

Nicoleta Filimon ◽

Vasile Ostafe ◽

Adriana Isvoran

Keyword(s):

Protein Interactions ◽

Thermotoga Maritima ◽

Structural Characteristics ◽

Specific Protein ◽

Computational Approach ◽

Glycoside Hydrolase Family ◽

Protein Protein Interactions ◽

Sequence Identity ◽

Enzyme Family ◽

Hydrolase Family

Structural bioinformatics approaches applied to the alpha- and beta-glycosidases from the GH4 enzyme family reveal that, despite low sequence identity, these enzymes possess quite similar global structural characteristics reflecting a common reaction mechanism. Locally, there are a few distinctive structural characteristics of GH4 alpha- and beta-glycosidases, namely, surface cavities with different geometric characteristics and two regions with highly dissimilar structural organizations and distinct physicochemical properties in the alpha- and beta-glucosidases from Thermotoga maritima. We suggest that these structurally dissimilar regions may be involved in specific protein-protein interactions and this hypothesis is sustained by the predicted distinct functional partners of the investigated proteins. Also, we predict that alpha- and beta-glycosidases from the GH4 enzyme family interact with difenoconazole, a fungicide, but there are different features of these interactions especially concerning the identified structurally distinct regions of the investigated proteins.

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