scholarly journals RNAInter in 2020: RNA interactome repository with increased coverage and annotation

2019 ◽  
Vol 48 (D1) ◽  
pp. D189-D197 ◽  
Author(s):  
Yunqing Lin ◽  
Tianyuan Liu ◽  
Tianyu Cui ◽  
Zhao Wang ◽  
Yuncong Zhang ◽  
...  
Keyword(s):  
Rna Editing ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Interaction Data ◽  
Biological Functions ◽  
Additional Species ◽  
Dynamic Expression ◽  
Regulatory Landscape ◽  
The Way

Abstract Research on RNA-associated interactions has exploded in recent years, and increasing numbers of studies are not limited to RNA–RNA and RNA–protein interactions but also include RNA–DNA/compound interactions. To facilitate the development of the interactome and promote understanding of the biological functions and molecular mechanisms of RNA, we updated RAID v2.0 to RNAInter (RNA Interactome Database), a repository for RNA-associated interactions that is freely accessible at http://www.rna-society.org/rnainter/ or http://www.rna-society.org/raid/. Compared to RAID v2.0, new features in RNAInter include (i) 8-fold more interaction data and 94 additional species; (ii) more definite annotations organized, including RNA editing/localization/modification/structure and homology interaction; (iii) advanced functions including fuzzy/batch search, interaction network and RNA dynamic expression and (iv) four embedded RNA interactome tools: RIscoper, IntaRNA, PRIdictor and DeepBind. Consequently, RNAInter contains >41 million RNA-associated interaction entries, involving more than 450 thousand unique molecules, including RNA, protein, DNA and compound. Overall, RNAInter provides a comprehensive RNA interactome resource for researchers and paves the way to investigate the regulatory landscape of cellular RNAs.

scholarly journals In Silico Designing and Analysis of Inhibitors against Target Protein Identified through Host-Pathogen Protein Interactions in Malaria

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Monika Samant ◽  
Nidhi Chadha ◽  
Anjani K. Tiwari ◽  
Yasha Hasija
Keyword(s):  
Plasmodium Falciparum ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Biological Significance ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Pathogen Protein ◽  
Complete Protein

Malaria, a life-threatening blood disease, has been a major concern in the field of healthcare. One of the severe forms of malaria is caused by the parasite Plasmodium falciparum which is initiated through protein interactions of pathogen with the host proteins. It is essential to analyse the protein-protein interactions among the host and pathogen for better understanding of the process and characterizing specific molecular mechanisms involved in pathogen persistence and survival. In this study, a complete protein-protein interaction network of human host and Plasmodium falciparum has been generated by integration of the experimental data and computationally predicting interactions using the interolog method. The interacting proteins were filtered according to their biological significance and functional roles. α-tubulin was identified as a potential protein target and inhibitors were designed against it by modification of amiprophos methyl. Docking and binding affinity analysis showed two modified inhibitors exhibiting better docking scores of −10.5 kcal/mol and −10.43 kcal/mol and an improved binding affinity of −83.80 kJ/mol and −98.16 kJ/mol with the target. These inhibitors can further be tested and validated in vivo for their properties as an antimalarial drug.

Reshaping biological membranes in endocytosis: crossing the configurational space of membrane-protein interactions

2014 ◽  
Vol 395 (3) ◽  
pp. 275-283 ◽  
Author(s):  
Mijo Simunovic ◽  
Patricia Bassereau
Keyword(s):  
Membrane Protein ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Full Range ◽  
Biological Membranes ◽  
Membrane Properties ◽  
Physical Parameters ◽  
Membrane Remodeling ◽  
Experimental Conditions ◽  
The Way

Abstract Lipid membranes are highly dynamic. Over several decades, physicists and biologists have uncovered a number of ways they can change the shape of membranes or alter their phase behavior. In cells, the intricate action of membrane proteins drives these processes. Considering the highly complex ways proteins interact with biological membranes, molecular mechanisms of membrane remodeling still remain unclear. When studying membrane remodeling phenomena, researchers often observe different results, leading them to disparate conclusions on the physiological course of such processes. Here we discuss how combining research methodologies and various experimental conditions contributes to the understanding of the entire phase space of membrane-protein interactions. Using the example of clathrin-mediated endocytosis we try to distinguish the question ‘how can proteins remodel the membrane?’ from ‘how do proteins remodel the membrane in the cell?’ In particular, we consider how altering physical parameters may affect the way membrane is remodeled. Uncovering the full range of physical conditions under which membrane phenomena take place is key in understanding the way cells take advantage of membrane properties in carrying out their vital tasks.

scholarly journals PINA 3.0: mining cancer interactome

2020 ◽  
Vol 49 (D1) ◽  
pp. D1351-D1357
Author(s):  
Yang Du ◽  
Meng Cai ◽  
Xiaofang Xing ◽  
Jiafu Ji ◽  
Ence Yang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Cancer Type ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
Specific Context ◽  
Human Interactome ◽  
Interactive Network ◽  
Cancer Types

Abstract Protein–protein interactions (PPIs) are crucial to mediate biological functions, and understanding PPIs in cancer type-specific context could help decipher the underlying molecular mechanisms of tumorigenesis and identify potential therapeutic options. Therefore, we update the Protein Interaction Network Analysis (PINA) platform to version 3.0, to integrate the unified human interactome with RNA-seq transcriptomes and mass spectrometry-based proteomes across tens of cancer types. A number of new analytical utilities were developed to help characterize the cancer context for a PPI network, which includes inferring proteins with expression specificity and identifying candidate prognosis biomarkers, putative cancer drivers, and therapeutic targets for a specific cancer type; as well as identifying pairs of co-expressing interacting proteins across cancer types. Furthermore, a brand-new web interface has been designed to integrate these new utilities within an interactive network visualization environment, which allows users to quickly and comprehensively investigate the roles of human interacting proteins in a cancer type-specific context. PINA is freely available at https://omics.bjcancer.org/pina/.

scholarly journals Towards a structurally resolved human protein interaction network

2021 ◽  
Author(s):  
David F Burke ◽  
Patrick Bryant ◽  
Inigo Barrio-Hernandez ◽  
Danish Memon ◽  
Gabriele Pozzati ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Cell Biology ◽  
Molecular Mechanisms ◽  
Protein Complexes ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Cellular Functions ◽  
Human Interactions ◽  
Pathogenic Variants ◽  
Binary Complexes

All cellular functions are governed by complex molecular machines that assemble through protein-protein interactions. Their atomic details are critical to the study of their molecular mechanisms but fewer than 5% of hundreds of thousands of human interactions have been structurally characterized. Here, we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human interactions. We show that higher confidence models are enriched in interactions supported by affinity or structure based methods and can be orthogonally confirmed by spatial constraints defined by cross-link data. We identify 3,137 high confidence models, of which 1,371 have no homology to a known structure, from which we identify interface residues harbouring disease mutations, suggesting potential mechanisms for pathogenic variants. We find groups of interface phosphorylation sites that show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple interactions as signalling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies. Accurate prediction of protein complexes promises to greatly expand our understanding of the atomic details of human cell biology in health and disease.

scholarly journals The wiring of protein networks: Computational approaches for predicting protein interaction networks

2011 ◽  
Vol 33 (1) ◽  
pp. 8-11
Author(s):  
Hung Xuan Ta ◽  
Liisa Holm
Keyword(s):  
Computational Methods ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Experimental Techniques ◽  
Biological Functions ◽  
Protein Protein Interactions ◽  
Computational Approaches ◽  
Experimental Approaches

A great number of cellular behaviours are mediated by proteins which always carry out their functions by interacting with each other. Unravelling protein–protein interactions (PPIs) is one of the central goals in proteomics, which will decipher the molecular mechanisms underlying the biological functions and thereby help to understand human diseases on a system-wide level. A number of experimental techniques, especially high-throughput approaches, have resulted in a large amount of PPI data that still suffer from incompleteness and contradiction. Moreover, these experimental techniques are expensive, time-consuming and labour-intensive. Computational methods have emerged as complementary tools to experimental approaches to discover PPIs. Promisingly, computational methods can guide, assess and validate experimental data and finally predict novel PPIs.

scholarly journals Towards a structurally resolved human protein interaction network

2021 ◽  
Author(s):  
Arne Elofsson ◽  
David Burke ◽  
Patrick Bryant ◽  
Inigo Barrio-Hernandez ◽  
Danish Memon ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Cell Biology ◽  
Molecular Mechanisms ◽  
Protein Complexes ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Cellular Functions ◽  
Human Interactions ◽  
Pathogenic Variants ◽  
Binary Complexes

Abstract All cellular functions are governed by complex molecular machines that assemble through protein-protein interactions. Their atomic details are critical to the study of their molecular mechanisms but fewer than 5% of hundreds of thousands of human interactions have been structurally characterized. Here, we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human interactions. We show that higher confidence models are enriched in interactions supported by affinity or structure based methods and can be orthogonally confirmed by spatial constraints defined by cross-link data. We identify 3,137 high confidence models, of which 1,371 have no homology to a known structure, from which we identify interface residues harbouring disease mutations, suggesting potential mechanisms for pathogenic variants. We find groups of interface phosphorylation sites that show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple interactions as signalling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies. Accurate prediction of protein complexes promises to greatly expand our understanding of the atomic details of human cell biology in health and disease.

Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

2019 ◽  
Vol 19 (4) ◽  
pp. 216-223 ◽  
Author(s):  
Tianyi Zhao ◽  
Donghua Wang ◽  
Yang Hu ◽  
Ningyi Zhang ◽  
Tianyi Zang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Feature Vector ◽  
Mirna Gene ◽  
Interaction Network ◽  
Gene Interaction ◽  
Proteinprotein Interaction ◽  
Synaptic Structures

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms. Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis. Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers). Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

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.

scholarly journals Adhesion and Growth of Neuralized Mouse Embryonic Stem Cells on Parylene-C/SiO2 Substrates

Materials ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3174
Author(s):  
Alan F. Murray ◽  
Evangelos Delivopoulos
Keyword(s):  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Embryonic Stem ◽  
Mouse Embryonic Stem Cells ◽  
Primary Neurons ◽  
Tissue Engineering Scaffolds ◽  
Parylene C ◽  
Unexpected Outcome

Neuronal patterning on microfabricated architectures has developed rapidly over the past few years, together with the emergence of soft biocompatible materials and tissue engineering scaffolds. Previously, we introduced a patterning technique based on serum and the biopolymer parylene-C, achieving highly compliant growth of primary neurons and astrocytes on different geometries. Here, we expanded this technique and illustrated that neuralized cells derived from mouse embryonic stem cells (mESCs) followed stripes of variable widths with conformity equal to or higher than that of primary neurons and astrocytes. Our results indicate the presence of undifferentiated mESCs, which also conformed to the underlying patterns to a high degree. This is an exciting and unexpected outcome, as molecular mechanisms governing cell and ECM protein interactions are different in stem cells and primary cells. Our study enables further investigations into the development and electrophysiology of differentiating patterned neural stem cells.

scholarly journals Frequent assembly of chimeric complexes in the protein interaction network of an interspecies yeast hybrid

2020 ◽  
Author(s):  
Rohan Dandage ◽  
Caroline M Berger ◽  
Isabelle Gagnon-Arsenault ◽  
Kyung-Mee Moon ◽  
Richard Greg Stacey ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Molecular Level ◽  
Yeast Species ◽  
Protein Complexes ◽  
Mitochondrial Protein ◽  
Chimeric Protein ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Extreme Phenotypes ◽  
Yeast Hybrid

Abstract Hybrids between species often show extreme phenotypes, including some that take place at the molecular level. In this study, we investigated the phenotypes of an interspecies diploid hybrid in terms of protein-protein interactions inferred from protein correlation profiling. We used two yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum, which are interfertile, but yet have proteins diverged enough to be differentiated using mass spectrometry. Most of the protein-protein interactions are similar between hybrid and parents, and are consistent with the assembly of chimeric complexes, which we validated using an orthogonal approach for the prefoldin complex. We also identified instances of altered protein-protein interactions in the hybrid, for instance in complexes related to proteostasis and in mitochondrial protein complexes. Overall, this study uncovers the likely frequent occurrence of chimeric protein complexes with few exceptions, which may result from incompatibilities or imbalances between the parental proteins.

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