TriPepSVM -<i>de novo</i>prediction of RNA-binding proteins based on short amino acid motifs

Bressin, Annkatrin; Schulte-Sasse, Roman; Figini, Davide; Urdaneta, Erika C.; Beckmann, Benedikt M.; Marsico, Annalisa

doi:10.1101/466151

Cited by 4 publications

(3 citation statements)

References 55 publications

(68 reference statements)

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“…6d,S25,S26). Additionally, we recently validated PTex-enriched ClpX and DnaJ as well (72). We furthermore find proteins with known RNA-binding domains (RBDs) such as the nucleic acid binding OB-fold (present in RpsA, RpsL, RplB, CspC, CspE, Pnp, RNaseE, Ssb, NusA) and domains which were also detected in RBPs when screening eukaryotic cells: the afforementioned AAA ATPase fold in the ATP-dependent protease ATPase subunits HslU, ClpB and ClpX, or thioredoxin domains as in AhpC, Thiol:disulfide interchange protein (DsbA) and Bacterioferritin comigratory protein (Bcp) (1,3).…”

Section: A Global Snapshot Of Human Rna-protein Complexesmentioning

confidence: 99%

Purification of Cross-linked RNA-Protein Complexes by Phenol-Toluol Extraction

Urdaneta

Vieira-Vieira

Hick

et al. 2018

Preprint

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Recent methodological advances allowed the identification of an increasing number of RNAbinding proteins (RBPs) and their RNA-binding sites. Most of those methods rely, however, on capturing proteins associated to polyadenylated RNAs which neglects RBPs bound to nonadenylate RNA classes (tRNA, rRNA, pre-mRNA) as well as the vast majority of species that lack poly-A tails in their mRNAs (including all archea and bacteria). To overcome these limitations, we have developed a novel protocol, Phenol Toluol extraction (PTex), that does not rely on a specific RNA sequence or motif for isolation of cross-linked ribonucleoproteins (RNPs), but rather purifies them based entirely on their physicochemical properties. PTex captures RBPs that bind to RNA as short as 30 nt, RNPs directly from animal tissue and can be used to simplify complex workflows such as PAR-CLIP. Finally, we provide a first global RNA-bound proteome of human HEK293 cells and Salmonella Typhimurium as a bacterial species.

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Section: A Global Snapshot Of Human Rna-protein Complexesmentioning

confidence: 99%

Purification of Cross-linked RNA-Protein Complexes by Phenol-Toluol Extraction

Urdaneta

Vieira-Vieira

Hick

et al. 2018

Preprint

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“…The predictive results of AIRBP and RBPPred 571 on three different independent test sets are compared in Table 8. In this section, we compare the performance of AIRBP with two additional predictors, Deep-RBPPred 599 (Zheng, et al, 2018) and TriPepSVM (Bressin, et al, 2019)…”

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confidence: 99%

AIRBP: Accurate identification of RNA-binding proteins using machine learning techniques

Mishra¹,

Khanal

Hoque

2020

Preprint

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11Motivation: Identification of RNA-binding proteins (RBPs) that bind to ribonucleic acid molecules, is an 12 important problem in Computational Biology and Bioinformatics. It becomes indispensable to identify 13 RBPs as they play crucial roles in post-transcriptional control of RNAs and RNA metabolism as well as 14 have diverse roles in various biological processes such as splicing, mRNA stabilization, mRNA 15 localization, and translation, RNA synthesis, folding-unfolding, modification, processing, and degradation. 16The existing experimental techniques for identifying RBPs are time-consuming and expensive. Therefore, 17identifying RBPs directly from the sequence using computational methods can be useful to efficiently 18 annotate RBPs and assist the experimental design. In this work, we present a method, called AIRBP, which 19 is designed using an advanced machine learning technique, called stacking, to effectively predict RBPs by 20 utilizing features extracted from evolutionary information, physiochemical properties, and disordered 2 21 properties. Moreover, our method, AIRBP is trained on the useful feature-subset identified by the 22 evolutionary algorithm (EA). 23 Results: The results show that AIRBP attains Accuracy (ACC), F1-score, and MCC of 95.38%, 0.917, and 24 0.885, respectively, based on the benchmark dataset, using 10-fold cross-validation (CV). Further 25 evaluation of AIRBP on independent test set reveals that it achieves ACC, F1-score, and MCC of 93.04%, 26 0.943, and 0.855, for Human test set; 91.60%, 0.942 and 0.789 for S. cerevisiae test set; and 91.67%, 0.953 27 and 0.594 for A. thaliana test set, respectively. These results indicate that AIRBP outperforms the current 28 state-of-the-art method. Therefore, the proposed top-performing AIRBP can be useful for accurate 29 identification and annotation of RBPs directly from the sequence and help gain valuable insight to treat 30 critical diseases. 50 and study of RBPs and RNP complexes date back to almost half a century ago where experimental methods 51 such as purification of mRNPs from in vitro UV-irradiated polysomal fractions (Greenberg, 1979), from 52 UV-irradiated intact cells (Wagenmakers, et al., 1980) and untreated cells (Lindberg and Sundquist, 1974) 53 revealed the association of a specific set of proteins with mRNA (Baltz, et al., 2012). Recently, cutting-54 edge experimental approaches are developed to recognize numerous RBPs, which include identification of 55 860 RBPs in human HeLa cells (Castello, et al., 2012) using UV crosslinking methods, 797 RBPs in human 56 embryonic kidney cell line (Baltz, et al., 2012) using photoreactive nucleotide-enhanced UV crosslinking 4 57 and oligo(dT) purification approach, 555 mRNA-binding proteins from mouse embryonic stem cells 58 (Kwon, et al., 2013) using UV crosslinking, oligo(dT) and Mass Spectrometry and 120 RBPs from S. 59 cerevisiae cells (Mitchell, et al., 2013) using UV crosslinking and purification methods. These experiments 60for identifying and analyzing of RBPs, have broadened ou...

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“…The investigation of this process requires computational protein domain analyses, complemented with mathematical models that allow the identification of the protein motif responsible for the synthesis of the compound. Several methods can be used to build protein motifs relative to a specific function: recently, many machine learning and deep learning methods have been developed for this purpose, mostly with the aim of identifying binding sites [264][265][266][267] . For our purpose, i.e.…”

Section: Mathematical Models To Improve Vaccine's Safetymentioning

confidence: 99%

Model-driven design of Mycoplasma as a vaccine chassis

Gaspari

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Chapter 1 Introduction Chapter 2 Model-driven design allows growth of Mycoplasma pneumoniae on serum-free media Chapter 3 Design of a fatty acid-prototrophic Mycoplasma pneumoniae strain through metabolic modelling Chapter 4 Galactocerebroside biosynthesis pathways of Mycoplasma species: an antigen triggering Guillain-Barré-Stohl syndrome Chapter 5 Mathematical models of biosafety circuits designed to limit Mycoplasma growth Chapter 6 Discussion

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TriPepSVM -de novoprediction of RNA-binding proteins based on short amino acid motifs

Cited by 4 publications

References 55 publications

Purification of Cross-linked RNA-Protein Complexes by Phenol-Toluol Extraction

Purification of Cross-linked RNA-Protein Complexes by Phenol-Toluol Extraction

AIRBP: Accurate identification of RNA-binding proteins using machine learning techniques

Model-driven design of Mycoplasma as a vaccine chassis

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