scholarly journals MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions

2018 ◽  
Author(s):  
Alexander R. Gawronski ◽  
Michael Uhl ◽  
Yajia Zhang ◽  
Yen-Yi Lin ◽  
Yashar S. Niknafs ◽  
...  
Keyword(s):  
Protein Binding ◽  
Protein Interactions ◽  
Large Scale ◽  
Mechanistic Explanation ◽  
Supplementary Information ◽  
Target Pair ◽  
P Value ◽  
Binding Prediction ◽  
Large Scale Analysis ◽  
Rna Interaction

AbstractMotivationLong non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nucleotides that do not get translated into proteins. Often these transcripts are processed (spliced, capped, polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA.ApproachHere we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined p-value.ResultsWe applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29, ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays.AvailabilityMechRNA is available for download athttps://bitbucket.org/compbio/[email protected],[email protected] informationSupplementary data are available atBioinformaticsonline.

scholarly journals FrustratometeR: an R-package to compute Local frustration in protein structures, point mutants and MD simulations

2020 ◽  
Author(s):  
Atilio O. Rausch ◽  
Maria I. Freiberger ◽  
Cesar O. Leonetti ◽  
Diego M. Luna ◽  
Leandro G. Radusky ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Protein Structures ◽  
Md Simulations ◽  
R Package ◽  
Protein Protein Interactions ◽  
Large Scale Analysis ◽  
Functional Aspects ◽  
Catalytic Sites ◽  
Polypeptide Chains

Once folded natural protein molecules have few energetic conflicts within their polypeptide chains. Many protein structures do however contain regions where energetic conflicts remain after folding, i.e. they have highly frustrated regions. These regions, kept in place over evolutionary and physiological timescales, are related to several functional aspects of natural proteins such as protein-protein interactions, small ligand recognition, catalytic sites and allostery. Here we present FrustratometeR, an R package that easily computes local energetic frustration on a personal computer or a cluster. This package facilitates large scale analysis of local frustration, point mutants and MD trajectories, allowing straightforward integration of local frustration analysis in to pipelines for protein structural analysis.Availability and implementation: https://github.com/proteinphysiologylab/frustratometeR

scholarly journals High throughput flow cytometry based yeast two-hybrid array approach for large-scale analysis of protein-protein interactions

2011 ◽  
Vol 81A (1) ◽  
pp. 90-98 ◽  
Author(s):  
Jun Chen ◽  
Mark B. Carter ◽  
Bruce S. Edwards ◽  
Hong Cai ◽  
Larry A. Sklar
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Protein Interactions ◽  
Large Scale ◽  
Scale Analysis ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Large Scale Analysis ◽  
Two Hybrid

scholarly journals Reliable identification of protein-protein interactions by crosslinking mass spectrometry

2020 ◽  
Author(s):  
Swantje Lenz ◽  
Ludwig R. Sinn ◽  
Francis J. O’Reilly ◽  
Lutz Fischer ◽  
Fritz Wegner ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Large Scale ◽  
Protein Complexes ◽  
Estimation Procedure ◽  
Scale Analysis ◽  
Protein Protein Interactions ◽  
False Discovery ◽  
Large Scale Analysis ◽  
False Discovery Rate Estimation

Crosslinking mass spectrometry is widening its scope from structural analyzes of purified multi-protein complexes towards systems-wide analyzes of protein-protein interactions. Assessing the error in these large datasets is currently a challenge. Using a controlled large-scale analysis of Escherichia coli cell lysate, we demonstrate a reliable false-discovery rate estimation procedure for protein-protein interactions identified by crosslinking mass spectrometry.

scholarly journals Protein interactions and ligand binding: From protein subfamilies to functional specificity

2010 ◽  
Vol 107 (5) ◽  
pp. 1995-2000 ◽  
Author(s):  
Antonio Rausell ◽  
David Juan ◽  
Florencio Pazos ◽  
Alfonso Valencia
Keyword(s):  
Ligand Binding ◽  
Protein Interactions ◽  
Protein Function ◽  
Large Scale ◽  
Protein Families ◽  
Predictive Capacity ◽  
Large Scale Analysis ◽  
Protein Interfaces ◽  
Specificity Determining Positions

The divergence accumulated during the evolution of protein families translates into their internal organization as subfamilies, and it is directly reflected in the characteristic patterns of differentially conserved residues. These specifically conserved positions in protein subfamilies are known as “specificity determining positions” (SDPs). Previous studies have limited their analysis to the study of the relationship between these positions and ligand-binding specificity, demonstrating significant yet limited predictive capacity. We have systematically extended this observation to include the role of differential protein interactions in the segregation of protein subfamilies and explored in detail the structural distribution of SDPs at protein interfaces. Our results show the extensive influence of protein interactions in the evolution of protein families and the widespread association of SDPs with protein interfaces. The combined analysis of SDPs in interfaces and ligand-binding sites provides a more complete picture of the organization of protein families, constituting the necessary framework for a large scale analysis of the evolution of protein function.

Protein interaction interface region prediction by geometric deep learning

2021 ◽  
Author(s):  
Bowen Dai ◽  
Chris Bailey-Kellogg
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
Molecular Surface ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Large Sets ◽  
Interaction Interface

Abstract Motivation Protein–protein interactions drive wide-ranging molecular processes, and characterizing at the atomic level how proteins interact (beyond just the fact that they interact) can provide key insights into understanding and controlling this machinery. Unfortunately, experimental determination of three-dimensional protein complex structures remains difficult and does not scale to the increasingly large sets of proteins whose interactions are of interest. Computational methods are thus required to meet the demands of large-scale, high-throughput prediction of how proteins interact, but unfortunately, both physical modeling and machine learning methods suffer from poor precision and/or recall. Results In order to improve performance in predicting protein interaction interfaces, we leverage the best properties of both data- and physics-driven methods to develop a unified Geometric Deep Neural Network, ‘PInet’ (Protein Interface Network). PInet consumes pairs of point clouds encoding the structures of two partner proteins, in order to predict their structural regions mediating interaction. To make such predictions, PInet learns and utilizes models capturing both geometrical and physicochemical molecular surface complementarity. In application to a set of benchmarks, PInet simultaneously predicts the interface regions on both interacting proteins, achieving performance equivalent to or even much better than the state-of-the-art predictor for each dataset. Furthermore, since PInet is based on joint segmentation of a representation of a protein surfaces, its predictions are meaningful in terms of the underlying physical complementarity driving molecular recognition. Availability and implementation PInet scripts and models are available at https://github.com/FTD007/PInet. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Automated cell tracking using StarDist and TrackMate

2020 ◽  
Author(s):  
Elnaz Fazeli ◽  
Nathan H. Roy ◽  
Gautier Follain ◽  
Romain F. Laine ◽  
Lucas von Chamier ◽  
...  
Keyword(s):  
Open Source Software ◽  
Large Scale ◽  
Cell Tracking ◽  
Quantitative Imaging ◽  
Immune Surveillance ◽  
Supplementary Information ◽  
Large Scale Analysis ◽  
Deep Learning Network ◽  
Cellular Locomotion ◽  
Automatically Tracking

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline’s usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

scholarly journals Automated cell tracking using StarDist and TrackMate

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1279
Author(s):  
Elnaz Fazeli ◽  
Nathan H. Roy ◽  
Gautier Follain ◽  
Romain F. Laine ◽  
Lucas von Chamier ◽  
...  
Keyword(s):  
Open Source Software ◽  
Large Scale ◽  
Cell Tracking ◽  
Quantitative Imaging ◽  
Immune Surveillance ◽  
Supplementary Information ◽  
Large Scale Analysis ◽  
Deep Learning Network ◽  
Cellular Locomotion ◽  
Automatically Tracking

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

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