BindUP: a web server for non-homology-based prediction of DNA and RNA binding proteins

Paz, Inbal; Kligun, Efrat; Bengad, Barak; Mandel-Gutfreund, Yael

doi:10.1093/nar/gkw454

Cited by 60 publications

(40 citation statements)

References 49 publications

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“…Computational calculations using Bindup (30) indicate that the JH2 domain harbors a large patch of positive regions that may be involved in protein-RNA binding (Supplemental Figure 8I). The predicted binding site was confirmed by RBRpred (31).…”

Section: Lnc-bm Mediates Jak2/stat3 Signaling That Is Triggered By Osmmentioning

confidence: 99%

JAK2-binding long noncoding RNA promotes breast cancer brain metastasis

Wang¹,

Liang²,

Hu³

et al. 2017

Journal of Clinical Investigation

182

146

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Section: Lnc-bm Mediates Jak2/stat3 Signaling That Is Triggered By Osmmentioning

confidence: 99%

JAK2-binding long noncoding RNA promotes breast cancer brain metastasis

Wang¹,

Liang²,

Hu³

et al. 2017

Journal of Clinical Investigation

182

146

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“…Analysis using Protein Disorder prediction System (PrDOS, (Ishida & Kinoshita, 2007), Figure 5b) also suggested that the disorder at the amino-terminus ( Figure 4b). Analysis of nucleic acid binding using BindUP server (Paz et al, 2016) (Table 2) also strongly indicated the role of the highly basic amino terminal end towards recognition and binding of cognate genome (Figure 6a). A total of 13 basic residues are present amongst the first 30 amino acids at the amino terminus, mostly featuring lysines.…”

Section: In Silico Modelingmentioning

confidence: 88%

“…The best scoring model was considered for further analysis. The disorder was assessed using the PrDOS (Ishida & Kinoshita, 2007), and the nucleic acid binding was assessed using bindUP server (Paz et al, 2016).…”

Section: In Silico Structure Predictionsmentioning

confidence: 99%

“…The nucleic acid binding capacity of the amino terminal 26 residues as predicted by the BindUP server(Paz et al, 2016). POS: Index of amino acid residue, RES: Amino acid residue, S_LBL & S_PRB: SVM prediction of binding label & probability, K_LBL & K_PRB: KLR prediction of binding, label & probability, P_LBL & P_PRB: PLR prediction of binding label & probability, MAJ_CON: Majority consensus, STR_CON: Strict consensus, Labels 0/1 stand for non-binding/binding, respectively, Label NA stands for "label cannot be assigned".…”

mentioning

confidence: 99%

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Towards understanding the structure of the capsid of Banana Bunchy Top Virus

Venkataraman¹,

Sairam

Selvarajan

et al. 2020

Preprint

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Banana is the major staple food crop for approximately 400 million people. Bunchy Top disease of Banana is one of the most devastating diseases caused by Banana Bunchy Top Virus (BBTV) that results in a significant loss of yield, stunting and bunchy appearance of leaves. While many isolates of BBTV from various regions of India have been characterized by different groups, no structural study exists for this important virus. To pursue structural studies, the pET28a clone of coat protein (CP) gene from BBTV isolate of Hill Banana grown in lower Pulney Hills (Virupakshi) of Tamilnadu was expressed in BL21 (DE3) pLysS. Purification of the CP was done using Ni-NTA affinity chromatography. In vitro capsid assembly studied using sucrose density gradient centrifugation suggested that the CP did not assemble as virus like particle (VLPs) but remained as smaller oligomers. Studies using dynamic light scattering (DLS) indicates that the purified protein is poly-dispersed represented majorly as pentamers.Studies using both homology modelling and ab initio structure determination gave useful insights into the probable fold of the CP suggesting it is a β-sandwich fold similar to that seen in majority of plant viruses. In silico capsid reconstruction aided understanding of the quaternary organization of subunits in the capsid and molecular interactions present between the subunits. The location of aphid binding EAG motif was identified on the surface loops close to the pentameric axis indicating their role in vector mediated transmission. Introduction X-ray diffraction studies on single crystals of viruses enable visualization of the structures ofintact virus particles at near-atomic resolution. These studies provide detailed information regarding the CP folding, capsid architecture, molecular interactions between protein subunits, and plausible sites of receptor recognition (Prasad & Schmid, 2012;Venkataraman et al., 2008). Such learning is pivotal in designing strategies for combating infections due to viruses in plants, animals and humans. Banana and plantains are grown in about 120 countries in mixed cropping systems by small holders and occasionally in monoculture (INIBAP, 2002). India is Methodology Cloning studiesThe infected leaf samples of BBTV were collected from the farmer's field near NationalResearch Center for Banana and confirmed by direct antigen coating (DAC) ELISA utilizing polyclonal antiserum of BBTV. The total DNA was extracted from the infected leaves through CTAB method (Selvarajan et al., 2008). The 513 bp gene of CP was amplified through PCR using standard CP specific primers (forward 5'-ATG GCTAGGTATCCGAAGAAATCC-3' and reverse 5' TCAAACATGATATGTAATTCTGTTC -3'). The initial denaturation was for 5min at 94°C; followed by 30s denaturation at the same temperature, annealing for 45 secs at 53°C, and extension at 72°C for 30 cycles. The PCR products were analyzed in agarose gel.The CP gene was purified post PCR and was cloned into pET-28a (+) vector between XhoI and NcoI sites and confirmed by sequencing (

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“…Template-based methods extract significant structural 70 or sequence similarity between the query and a template known to bind RNA to assess the RNA-binding 71 preference of the target sequence (Yang, et al, 2012;. Unlike template-72 based methods, in machine learning methods, the predictive model is created to predict by finding a pattern 73 in the input feature space (Kumar, et al, 2011;Paz, et al, 2016;Shazman and Mandel-Gutfreund, 2008).…”

mentioning

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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BindUP: a web server for non-homology-based prediction of DNA and RNA binding proteins

Cited by 60 publications

References 49 publications

JAK2-binding long noncoding RNA promotes breast cancer brain metastasis

JAK2-binding long noncoding RNA promotes breast cancer brain metastasis

Towards understanding the structure of the capsid of Banana Bunchy Top Virus

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

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