scholarly journals De-novo protein function prediction using DNA binding and RNA binding proteins as a test case

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Sapir Peled ◽  
Olga Leiderman ◽  
Rotem Charar ◽  
Gilat Efroni ◽  
Yaron Shav-Tal ◽  
...  
Keyword(s):  
Dna Binding ◽  
Protein Function ◽  
Binding Proteins ◽  
Rna Binding ◽  
De Novo ◽  
Rna Binding Proteins ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Test Case

scholarly journals De novo design of RNA-binding proteins with a prion-like domain related to ALS/FTD proteinopathies

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Kana Mitsuhashi ◽  
Daisuke Ito ◽  
Kyoko Mashima ◽  
Munenori Oyama ◽  
Shinichi Takahashi ◽  
...  
Keyword(s):  
Binding Proteins ◽  
Rna Binding ◽  
De Novo ◽  
Rna Binding Proteins ◽  
De Novo Design

scholarly journals Molecular dynamics of FMRP and other RNA-binding proteins in MEG-01 differentiation: the role of mRNP complexes in non-neuronal development

2016 ◽  
Vol 94 (6) ◽  
pp. 597-608 ◽  
Author(s):  
M. McCoy ◽  
D. Poliquin-Duchesneau ◽  
F. Corbin
Keyword(s):  
Protein Content ◽  
Binding Proteins ◽  
Rna Binding ◽  
De Novo ◽  
Rna Binding Proteins ◽  
Morphological Changes ◽  
Fragile X ◽  
Cellular Protein ◽  
The Body ◽  
Total Protein Content

Asymmetrically differentiating cells are formed with the aid of RNA-binding proteins (RBPs), which can bind, stabilize, regulate, and transport target mRNAs. The loss of RBPs in neurons may lead to severe neurodevelopmental diseases such as the Fragile X Syndrome with the absence of the Fragile X Mental Retardation Protein (FMRP). Because the latter is ubiquitous and shares many similarities with other RBPs involved in the development of peripheral cells, we suggest that FMRP would have a role in the differentiation of all tissues where it is expressed. A MEG-01 differentiation model was, therefore, established to study the global developmental functions of FMRP. PMA induction of MEG-01 cells causes important morphological changes driven by cytoskeletal dynamics. Cytoskeleton change and colocalization analyses were performed by confocal microscopy and sucrose gradient fractionation. Total cellular protein content and de novo synthesis were also analyzed. Microtubular transport mediates the displacement of FMRP and other RBP-containing mRNP complexes towards regions of the cell in development. De novo protein synthesis decreases significantly upon differentiation and total protein content composition is altered. Because those results are comparable with those obtained in neurons, the absence of FMRP would have significant consequences in cells everywhere in the body. The latter should be further investigated to give a better understanding of the systemic implications of imbalances of FMRP and other functionally similar RBPs.

scholarly journals 2P303 FCANAL : A structure based protein function prediction method. Application to enzymes and binding proteins

2005 ◽  
Vol 45 (supplement) ◽  
pp. S195
Author(s):  
A. Suzuki ◽  
T. Ando ◽  
A. Matsumura ◽  
H. Sakao ◽  
I. Yamato ◽  
...  
Keyword(s):  
Protein Function ◽  
Binding Proteins ◽  
Protein Function Prediction ◽  
Prediction Method ◽  
Function Prediction

scholarly journals RBPMetaDB: A comprehensive annotation of mouse RNA-Seq datasets with perturbations of RNA-binding proteins

2018 ◽  
Author(s):  
Jin Li ◽  
Su-Ping Deng ◽  
Jacob Vieira ◽  
James Thomas ◽  
Valerio Costa ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Dna Binding ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Critical Role ◽  
Experimental Studies ◽  
Easy Access ◽  
Rna Seq ◽  
Mrna Stabilization

AbstractRNA-binding proteins may play a critical role in gene regulation in various diseases or biological processes by controlling post-transcriptional events such as polyadenylation, splicing, and mRNA stabilization via binding activities to RNA molecules. Due to the importance of RNA-binding proteins in gene regulation, a great number of studies have been conducted, resulting in a large amount of RNA-Seq datasets. However, these datasets usually do not have structured organization of metadata, which limits their potentially wide use. To bridge this gap, the metadata of a comprehensive set of publicly available mouse RNA-Seq datasets with perturbed RNA-binding proteins were collected and integrated into a database called RBPMetaDB. This database contains 278 mouse RNA-Seq datasets for a comprehensive list of 163 RNA-binding proteins. These RNA-binding proteins account for only ∼10% of all known RNA-binding proteins annotated in Gene Ontology, indicating that most are still unexplored using high-throughput sequencing. This negative information provides a great pool of candidate RNA-binding proteins for biologists to conduct future experimental studies. In addition, we found that DNA-binding activities are significantly enriched among RNA-binding proteins in RBPMetaDB, suggesting that prior studies of these DNA- and RNA-binding factors focus more on DNA-binding activities instead of RNA-binding activities. This result reveals the opportunity to efficiently reuse these data for investigation of the roles of their RNA-binding activities. A web application has also been implemented to enable easy access and wide use of RBPMetaDB. It is expected that RBPMetaDB will be a great resource for improving understanding of the biological roles of RNA-binding proteins.Database URL: http://rbpmetadb.yubiolab.org

scholarly journals A look back at the quality of Protein Function Prediction tools in CAFA

2018 ◽  
Author(s):  
Morteza Pourreza Shahri ◽  
Madhusudan Srinivasan ◽  
Diane Bimczok ◽  
Upulee Kanewala ◽  
Indika Kahanda
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Computational Models ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Test Case ◽  
Test Cases ◽  
Metamorphic Testing ◽  
Main Challenge ◽  
Scale Experiment

The Critical Assessment of protein Function Annotation algorithms (CAFA) is a large-scale experiment for assessing the computational models for automated function prediction (AFP). The models presented in CAFA have shown excellent promise in terms of prediction accuracy, but quality assurance has been paid relatively less attention. The main challenge associated with conducting systematic testing on AFP software is the lack of a test oracle, which determines passing or failing of a test case; unfortunately, the exact expected outcomes are not well defined for the AFP task. Thus, AFP tools face the oracle problem. Metamorphic testing (MT) is a technique used to test programs that face the oracle problem using metamorphic relations (MRs). A MR determines whether a test has passed or failed by specifying how the output should change according to a specific change made to the input. In this work, we use MT to test nine CAFA2 AFP tools by defining a set of MRs that apply input transformations at the protein-level. According to our initial testing, we observe that several tools fail all the test cases and two tools pass all the test cases on different GO ontologies.

scholarly journals Author Correction: De novo design of RNA-binding proteins with a prion-like domain related to ALS/FTD proteinopathies

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kana Mitsuhashi ◽  
Daisuke Ito ◽  
Kyoko Mashima ◽  
Munenori Oyama ◽  
Shinichi Takahashi ◽  
...  
Keyword(s):  
Binding Proteins ◽  
Rna Binding ◽  
De Novo ◽  
Rna Binding Proteins ◽  
De Novo Design

scholarly journals The design and structural characterization of a synthetic pentatricopeptide repeat protein

2015 ◽  
Vol 71 (2) ◽  
pp. 196-208 ◽  
Author(s):  
Benjamin S. Gully ◽  
Kunal R. Shah ◽  
Mihwa Lee ◽  
Kate Shearston ◽  
Nicole M. Smith ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Binding Proteins ◽  
Rna Binding ◽  
De Novo ◽  
Rna Binding Proteins ◽  
Specific Binding ◽  
Consensus Sequence ◽  
Pentatricopeptide Repeat ◽  
Ppr Protein ◽  
Ppr Proteins

Proteins of the pentatricopeptide repeat (PPR) superfamily are characterized by tandem arrays of a degenerate 35-amino-acid α-hairpin motif. PPR proteins are typically single-stranded RNA-binding proteins with essential roles in organelle biogenesis, RNA editing and mRNA maturation. A modular, predictable code for sequence-specific binding of RNA by PPR proteins has recently been revealed, which opens the door to thede novodesign of bespoke proteins with specific RNA targets, with widespread biotechnological potential. Here, the design and production of a synthetic PPR protein based on a consensus sequence and the determination of its crystal structure to 2.2 Å resolution are described. The crystal structure displays helical disorder, resulting in electron density representing an infinite superhelical PPR protein. A structural comparison with related tetratricopeptide repeat (TPR) proteins, and with native PPR proteins, reveals key roles for conserved residues in directing the structure and function of PPR proteins. The designed proteins have high solubility and thermal stability, and can form long tracts of PPR repeats. Thus, consensus-sequence synthetic PPR proteins could provide a suitable backbone for the design of bespoke RNA-binding proteins with the potential for high specificity.

scholarly journals A Survey of Computational Intelligence Techniques in Protein Function Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-22 ◽  
Author(s):  
Arvind Kumar Tiwari ◽  
Rajeev Srivastava
Keyword(s):  
Gene Expression ◽  
Computational Intelligence ◽  
Protein Function ◽  
Rna Binding ◽  
Protein Function Prediction ◽  
Interaction Network ◽  
Heterogeneous Data ◽  
Function Prediction ◽  
Ensemble Classifiers ◽  
The Past

During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction.

scholarly journals A look back at the quality of Protein Function Prediction tools in CAFA

2018 ◽  
Author(s):  
Morteza Pourreza Shahri ◽  
Madhusudan Srinivasan ◽  
Diane Bimczok ◽  
Upulee Kanewala ◽  
Indika Kahanda
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Computational Models ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Test Case ◽  
Test Cases ◽  
Metamorphic Testing ◽  
Main Challenge ◽  
Scale Experiment

The Critical Assessment of protein Function Annotation algorithms (CAFA) is a large-scale experiment for assessing the computational models for automated function prediction (AFP). The models presented in CAFA have shown excellent promise in terms of prediction accuracy, but quality assurance has been paid relatively less attention. The main challenge associated with conducting systematic testing on AFP software is the lack of a test oracle, which determines passing or failing of a test case; unfortunately, the exact expected outcomes are not well defined for the AFP task. Thus, AFP tools face the oracle problem. Metamorphic testing (MT) is a technique used to test programs that face the oracle problem using metamorphic relations (MRs). A MR determines whether a test has passed or failed by specifying how the output should change according to a specific change made to the input. In this work, we use MT to test nine CAFA2 AFP tools by defining a set of MRs that apply input transformations at the protein-level. According to our initial testing, we observe that several tools fail all the test cases and two tools pass all the test cases on different GO ontologies.

scholarly journals Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins

2021 ◽  
Author(s):  
Salma Sohrabi-Jahromi ◽  
Johannes Söding
Keyword(s):  
Binding Proteins ◽  
Rna Binding ◽  
De Novo ◽  
Rna Binding Proteins ◽  
Sequence Specificity ◽  
Rna Sequences ◽  
Link Type ◽  
Binding Domains ◽  
Regulatory Processes ◽  
Multivalent Binding

AbstractMotivationUnderstanding how proteins recognize their RNA targets is essential to elucidate regulatory processes in the cell. Many RNA-binding proteins (RBPs) form complexes or have multiple domains that allow them to bind to RNA in a multivalent, cooperative manner. They can thereby achieve higher specificity and affinity than proteins with a single RNA-binding domain. However, current approaches to de-novo discovery of RNA binding motifs do not take multivalent binding into account.ResultsWe present Bipartite Motif Finder (BMF), which is based on a thermodynamic model of RBPs with two cooperatively binding RNA-binding domains. We show that bivalent binding is a common strategy among RBPs, yielding higher affinity and sequence specificity. We furthermore illustrate that the spatial geometry between the binding sites can be learned from bound RNA sequences. These discovered bipartite motifs are consistent with previously known motifs and binding behaviors. Our results demonstrate the importance of multivalent binding for RNA-binding proteins and highlight the value of bipartite motif models in representing the multivalency of protein-RNA interactions.AvailabilityBMF source code is available at https://github.com/soedinglab/bipartite_motif_finder under a GPL license. The BMF web server is accessible at https://bmf.soedinglab.org.

Sign in / Sign up

Export Citation Format

Share Document