scholarly journals Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins

2019 ◽  
Author(s):  
Andrés López-Cortés ◽  
Alejandro Cabrera-Andrade ◽  
José M. Vázquez-Naya ◽  
Alejandro Pazos ◽  
Humberto Gonzáles-Díaz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Cancer Immunotherapy ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Operating Characteristics ◽  
Associated Proteins ◽  
Artificial Neural ◽  
New Biomarkers

ABSTRACTBackgroundBreast cancer (BC) is a heterogeneous disease characterized by an intricate interplay between different biological aspects such as ethnicity, genomic alterations, gene expression deregulation, hormone disruption, signaling pathway alterations and environmental determinants. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design.MethodsThis work is proposing accurate prediction classifier for BC proteins using six sets of protein sequence descriptors and 13 machine learning methods. After using a univariate feature selection for the mix of five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial neural network) and 300 features.ResultsThe performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980 ± 0.0037 and accuracy of 0.936 ± 0.0056 (3-fold cross-validation). Regarding the prediction of 4504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1.ConclusionsThis powerful model predicts several BC-related proteins which should be deeply studied to find new biomarkers and better therapeutic targets. The script and the results are available as a free repository at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.

scholarly journals Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Andrés López-Cortés ◽  
Alejandro Cabrera-Andrade ◽  
José M. Vázquez-Naya ◽  
Alejandro Pazos ◽  
Humberto Gonzáles-Díaz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rna Binding ◽  
Molecular Descriptors ◽  
Artificial Neural

scholarly journals SPACE exploration of chromatin proteome to reveal associated RNA-binding proteins

2020 ◽  
Author(s):  
Mahmoud-Reza Rafiee ◽  
Julian A Zagalak ◽  
Giulia Tyzack ◽  
Rickie Patani ◽  
Jernej Ule ◽  
...  
Keyword(s):  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Genome Maintenance ◽  
Purification Method ◽  
Comprehensive Knowledge ◽  
Associated Proteins ◽  
Versatile Technique ◽  
Dna Binders ◽  
Lateral Sclerosis

AbstractChromatin is composed of many proteins that mediate intermolecular transactions with the genome. Comprehensive knowledge of these components and their interactions is necessary for insights into gene regulation and other activities; however, reliable identification of chromatin-associated proteins remains technically challenging. Here, we present SPACE (Silica Particle Assisted Chromatin Enrichment), a stringent and straightforward chromatin-purification method that helps identify direct DNA-binders separately from chromatin-associated proteins. We demonstrate SPACE’s unique strengths in three experimental set-ups: the sensitivity to detect novel chromatin-associated proteins, the quantitative nature to measure dynamic protein use across distinct cellular conditions, and the ability to handle 10-25 times less starting material than competing methods. In doing so, we reveal an unforeseen scale of association between over 500 nuclear RNA-binding proteins (RBPs) with chromatin and DNA, providing new insights into their roles as important regulators of genome maintenance and chromatin composition. Applied to iPSC-derived neural precursors, we discover a new role for the amyotrophic lateral sclerosis (ALS)-causing Valosin Containing Protein (VCP) in recruiting DNA-damage components to chromatin, thus paving the way for molecular mechanistic insights into the disease. SPACE is a fast and versatile technique with many applications.

scholarly journals GraphProt2: A novel deep learning-based method for predicting binding sites of RNA-binding proteins

2019 ◽  
Author(s):  
Michael Uhl ◽  
Van Dinh Tran ◽  
Rolf Backofen
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Deep Learning ◽  
Binding Sites ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
State Of The Art ◽  
Prediction Method ◽  
Superior Performance

AbstractCLIP-seq is the state-of-the-art technique to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression which can be highly variable between conditions, and thus cannot provide a complete picture of the RBP binding landscape. This necessitates the use of computational methods to predict missing binding sites. Here we present GraphProt2, a computational RBP binding site prediction method based on graph convolutional neural networks (GCN). In contrast to current CNN methods, GraphProt2 supports variable length input as well as the possibility to accurately predict nucleotide-wise binding profiles. We demonstrate its superior performance compared to GraphProt and a CNN-based method on single as well as combined CLIP-seq datasets.

Abstract PD15-07: Myc regulates alternative splicing through a network of RNA binding proteins in breast cancer

2021 ◽  
Author(s):  
Laura Urbanski ◽  
Brittany Angarola ◽  
Mattia Brugiolo ◽  
Marina Yurieva ◽  
Sunghee Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Alternative Splicing ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins

scholarly journals Genetic polymorphisms in RNA binding proteins contribute to breast cancer survival

2012 ◽  
Vol 132 (3) ◽  
pp. E128-E138 ◽  
Author(s):  
Rohit Upadhyay ◽  
Sandhya Sanduja ◽  
Vimala Kaza ◽  
Dan A. Dixon
Keyword(s):  
Breast Cancer ◽  
Genetic Polymorphisms ◽  
Binding Proteins ◽  
Cancer Survival ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Breast Cancer Survival

scholarly journals Essential PBP1-associated proteins of Trypanosoma brucei

2020 ◽  
Author(s):  
L. Nascimento ◽  
M. Terrao ◽  
KK. Marucha ◽  
B. Liu ◽  
F. Egler ◽  
...  
Keyword(s):  
Trypanosoma Brucei ◽  
Ribosomal Proteins ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Low Complexity ◽  
Control Of Gene Expression ◽  
C Terminus ◽  
Associated Proteins ◽  
A Minor

AbstractControl of gene expression in kinetoplastids depends heavily on RNA-binding proteins that influence mRNA decay and translation. We previously showed that MKT1 interacts with PBP1, which in turn recruits LSM12 and poly(A) binding protein. MKT1 is recruited to mRNA by sequence-specific RNA-binding proteins, resulting in stabilisation of mRNA. We here show that PBP1, LSM12 and an additional 117-residue protein, XAC1 (Tb927.7.2780), are present in complexes that contain either MKT1 or MKT1L (Tb927.10.1490). All five proteins are present predominantly in the complexes, and there was evidence for a minor subset of complexes that contained both MKT1 and MKT1L. MKT1 appeared to be associated with many mRNAs, with the exception of those encoding ribosomal proteins. XAC1-containing complexes reproducibly contained RNA-binding proteins that were previously found associated with MKT1. In addition, however, XAC1- or MKT1-containing complexes specifically recruit one of the six translation initiation complexes, EIF4E6-EIF4G5; and yeast 2-hybrid assay results indicated that MKT1 interacts with EIF4G5. The C-terminus of MKT1L resembles MKT1: it contains MKT1 domains and a PIN domain that is probably not active as an endonuclease. MKT1L, however, also has an N-terminal extension with regions of low-complexity. Although MKT1L depletion inhibited cell proliferation, we found no evidence for specific interactions with RNA-binding proteins or mRNA. Deletion of the N-terminal extension, however, enabled MKT1L to interact with EIF4E6. We speculate that MKT1L may either enhance or inhibit the functions of MKT1-containing complexes.

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