Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences

Author(s):  
Saurabh Agrawal ◽  
Dilip Singh Sisodia ◽  
Naresh Kumar Nagwani
2012 ◽  
Vol 33 (2) ◽  
pp. 258-266 ◽  
Author(s):  
Jun Shi ◽  
Yi-Bing Zhang ◽  
Ting-Kai Liu ◽  
Fan Sun ◽  
Jian-Fang Gui

2020 ◽  
Vol 21 (S8) ◽  
Author(s):  
Castrense Savojardo ◽  
Pier Luigi Martelli ◽  
Giacomo Tartari ◽  
Rita Casadio

Abstract Background The prediction of protein subcellular localization is a key step of the big effort towards protein functional annotation. Many computational methods exist to identify high-level protein subcellular compartments such as nucleus, cytoplasm or organelles. However, many organelles, like mitochondria, have their own internal compartmentalization. Knowing the precise location of a protein inside mitochondria is crucial for its accurate functional characterization. We recently developed DeepMito, a new method based on a 1-Dimensional Convolutional Neural Network (1D-CNN) architecture outperforming other similar approaches available in literature. Results Here, we explore the adoption of DeepMito for the large-scale annotation of four sub-mitochondrial localizations on mitochondrial proteomes of five different species, including human, mouse, fly, yeast and Arabidopsis thaliana. A significant fraction of the proteins from these organisms lacked experimental information about sub-mitochondrial localization. We adopted DeepMito to fill the gap, providing complete characterization of protein localization at sub-mitochondrial level for each protein of the five proteomes. Moreover, we identified novel mitochondrial proteins fishing on the set of proteins lacking any subcellular localization annotation using available state-of-the-art subcellular localization predictors. We finally performed additional functional characterization of proteins predicted by DeepMito as localized into the four different sub-mitochondrial compartments using both available experimental and predicted GO terms. All data generated in this study were collected into a database called DeepMitoDB (available at http://busca.biocomp.unibo.it/deepmitodb), providing complete functional characterization of 4307 mitochondrial proteins from the five species. Conclusions DeepMitoDB offers a comprehensive view of mitochondrial proteins, including experimental and predicted fine-grain sub-cellular localization and annotated and predicted functional annotations. The database complements other similar resources providing characterization of new proteins. Furthermore, it is also unique in including localization information at the sub-mitochondrial level. For this reason, we believe that DeepMitoDB can be a valuable resource for mitochondrial research.


2011 ◽  
Vol 9 (2) ◽  
pp. 347-351 ◽  
Author(s):  
A. R. Paolacci ◽  
M. Ciaffi ◽  
A. P. Dhanapal ◽  
O. A. Tanzarella ◽  
E. Porceddu ◽  
...  

The deduced amino-acid sequences of 17 protein disulphide isomerase (PDI) and PDI-like cDNAs of wheat assigned to nine homoeologous groups were searched for conserved motives by comparison with characterized sequences in different protein databases. The wheat protein sequences encoded by genes of different homoelogous groups showed a high level of structural similarity with the corresponding protein sequences of other species clustering into the same phylogenetic group. The proteins of five groups (I–V) share two thioredoxin-like active domains and show structural similarities with the corresponding proteins of higher eukaryotes, whereas those of the remaining three groups (VI–VIII) contain a single thioredoxin-like active domain. The expression analysis of the nine non-homoeologous wheat genes, which was carried out by quantitative RT-PCR in developing caryopses and in seedlings subjected to temperature stresses, showed their constitutive although highly variable transcription rate. The comprehensive structural and transcriptional characterization of the PDI and PDI-like genes of wheat performed in this study represents a basis for future functional characterization of the PDI gene family in the hexaploid context of bread wheat.


2019 ◽  
Vol 17 (04) ◽  
pp. 1950025 ◽  
Author(s):  
Sovan Saha ◽  
Abhimanyu Prasad ◽  
Piyali Chatterjee ◽  
Subhadip Basu ◽  
Mita Nasipuri

Computational prediction of functional annotation of proteins is an uphill task. There is an ever increasing gap between functional characterization of protein sequences and deluge of protein sequences generated by large-scale sequencing projects. The dynamic nature of protein interactions is frequently observed which is mostly influenced by any new change of state or change in stimuli. Functional characterization of proteins can be inferred from their interactions with each other, which is dynamic in nature. In this work, we have used a dynamic protein–protein interaction network (PPIN), time course gene expression data and protein sequence information for prediction of functional annotation of proteins. During progression of a particular function, it has also been observed that not all the proteins are active at all time points. For unannotated active proteins, our proposed methodology explores the dynamic PPIN consisting of level-1 and level-2 neighboring proteins at different time points, filtered by Damerau–Levenshtein edit distance to estimate the similarity between two protein sequences and coefficient variation methods to assess the strength of an edge in a network. Finally, from the filtered dynamic PPIN, at each time point, functional annotations of the level-2 proteins are assigned to the unknown and unannotated active proteins through the level-1 neighbor, following a bottom-up strategy. Our proposed methodology achieves an average precision, recall and F-Score of 0.59, 0.76 and 0.61 respectively, which is significantly higher than the reported state-of-the-art methods.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1717-1717
Author(s):  
Maria Paola Martelli ◽  
Roberta Rossi ◽  
Emanuela Varasano ◽  
Giorgina Specchia ◽  
Francesco Di Raimondo ◽  
...  

Abstract Introduction. Nucleophosmin (NPM1) gene mutations occur in 50-60% of adult AML with normal karyotype (Falini et al, NEJM 2005). About 50 NPM1 mutations have been so far identified, all clustering in exon-12 (Falini et al, Blood 2007) but few sporadic cases involving either exon-9 (one) (Mariano et al, Oncogene 2006) or exon-11 (two) (Albiero et al, Leukemia 2007). In spite of molecular heterogeneity, all mutations cause common changes at the C-terminus of NPM1 mutants, i.e. loss of tryptophans 288 and 290 (or 290 alone) and creation of a new nuclear export signal (NES) motif (Falini et al, Blood 2006a).As a consequence, all NPM1 mutants aberrantly accumulates in the cytoplasm of leukemic cells and can be detected by immunohistochemistry, which is fully predictive of NPM1 gene mutations (Falini et al, Blood 2006b). Methods. From 2005 to 2015, 702 AML patients samples were analyzed at diagnosis by both immunohistochemistry (IHC) for NPM1 subcellular localization and western blot (WB) with anti-NPM1 mutant specific rabbit polyclonal antibodies antibodies, produced in our laboratory (Martelli et al, Leukemia 2008). Discordant cases were further analyzed by NPM1 gene Sanger sequencing. Newly discovered NPM1 mutated genes were subcloned in pEGFP-C1 vector and transiently expressed in NIH-3T3 adherent cells to study the NPM1 mutant subcellular localization by immunofluorescence microscopy. The NESbase version 1.0 program was used to identify putative NES within the new protein sequence, and their efficiency was evaluated by the pREV1.4-based NES efficiency assay, as previously described (Bolli et al, Cancer Res 2007). Results. At IHC and WB analyses, concordance in diagnosis was obtained in 695/702 samples (291 NPM1-mutated and 404 NPM1-unmutated AML). In 7/702 (1%), IHC detected cytoplasmic NPM1 whilst WB with anti-NPM1 mutant antibodies was negative. Unfortunately, in 3 out of these cases, the original patient sample was not available for further analyses. In the other 4, exon-12 NPM1 gene sequence was wild-type (WT), in keeping with the negative WB results. One of these cases harbored the previously described exon-11 NPM1 mutation, in 1 case no mutation was detected (further studies are ongoing), and in 2 cases new mutations involving exon-6 were discovered. Strikingly, in the latter cases, WB analysis with different anti-NPM1 antibodies revealed a new band at different molecular weight (MW) than NPM1-WT. Indeed, in 1 case an in frame 21 nucleotides insertion at exon-6 lead to a 7 aa longer than WT protein, whilst in the other a 19 nucleotides insertion created a new stop codon leading to a truncated protein. In both cases, a new NES motif was created. Importantly, cell transfection experiments confirmed that the new NPM1 mutants localized at least partly in the cytoplasm, and the pREV1.4-based NES efficiency assay showed the new NES were active. Conclusions. Here, we report on the identification and functional characterization of two novel NPM1 mutations in AML. Our observations further support the view that cytoplasmic NPM1 dislocation is a critical step in leukemogenesis, and that immunohistochemistry, that detects, through cytoplasmic dislocation on NPM, 'all types' of NPM1 mutations, might be used as first step for directing further molecular studies. Disclosures Di Raimondo: Janssen-Cilag: Honoraria.


Oncogene ◽  
2002 ◽  
Vol 21 (44) ◽  
pp. 6766-6771 ◽  
Author(s):  
Carolina Abramovich ◽  
Elizabeth A Chavez ◽  
Peter M Lansdorp ◽  
R Keith Humphries

Biomolecules ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 670 ◽  
Author(s):  
Jiahui Wang ◽  
Yang Yang ◽  
Lingzi Liao ◽  
Jiawei Xu ◽  
Xiao Liang ◽  
...  

The phosphate transporter (PHT) family mediates the uptake and translocation of the essential macronutrient phosphorus (P) in plants. In this study, 27 PHT proteins in Sorghum were identified via bioinformatics tools. Phylogenetic analysis of their protein sequences in comparison with those family proteins from Arabidopsis and rice indicated that these proteins could be clustered into five typical subfamilies. There are 12 SbPHT1 members, one SbPHT2, six SbPHT3s, six SbPHT4s, and two SbPHOs in Sorghum. Further analysis of the gene structure, conserved motifs, subcellular localization, and transmembrane domains suggested that these features are relatively conserved within each subfamily. Meanwhile, the qRT-PCR assay implied that SbPHT1;2, SbPHT1;11, and SbPHT4;6 were significantly upregulated in roots when exposed to low-phosphate conditions, suggesting that these genes might be involved in P uptake in low-phosphate conditions. Our study will increase our understanding of the roles of phosphate transporters in Sorghum.


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