Nglyc: A Random Forest Method for Prediction of N-Glycosylation Sites in Eukaryotic Protein Sequence

An efficient strategy with a synergistic effect of hydrophilic and electrostatic interactions for simultaneous enrichment of N- and O-glycopeptides

N- and O-glycosylation modifications of proteins are closely linked to the onset and development of many diseases and have gained widespread attention as potential targets for therapy and diagnosis. However, the low abundance and low ionization efficiency of glycopeptides as well as the high heterogeneity make glycosylation analysis challenging. Here, an enrichment strategy, using Knoevenagel copolymers modified with polydopamine-adenosine (denoted as PDA-ADE@KCP), was firstly proposed for simultaneous enrichment of N- and O-glycopeptides through the synergistic effects of hydrophilic and electrostatic interactions. The adjustable charged surface and hydrophilic properties endow the material with the capability to achieve effective enrichment of intact N- and O-glycopeptides. The experimental results exhibited excellent selectivity (1 : 5000) and sensitivity (0.1 fmol μL-1) of the prepared material for N-glycopeptides from standard protein digest samples. Moreover, it was further applied to simultaneous capturing of N- and O-glycopeptides from mouse liver protein digests. Compared to the commercially available zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) material, the number of glycoproteins corresponding to all N- and O-glycopeptides enriched with PDA-ADE@KCP was much more than that with ZIC-HILIC. Furthermore, PDA-ADE@KCP captured more O-glycopeptides than ZIC-HILIC, revealing its superior performance in O-glycopeptide enrichment. All these results indicated that the strategy holds immense potential in characterizing N- and O-intact glycopeptides in the field of proteomics.

Ion-pumping microbial rhodopsin protein classification by machine learning approach

BACKGROUND

Rhodopsin is a seven-transmembrane protein covalently linked with retinal chromophore that absorbs photons for energy conversion and intracellular signaling in eukaryotes, bacteria, and archaea. Haloarchaeal rhodopsins are Type-I microbial rhodopsin that elicits various light-driven functions like proton pumping, chloride pumping and Phototaxis behaviour. The industrial application of Ion-pumping Haloarchaeal rhodopsins is limited by the lack of full-length rhodopsin sequence-based classifications, which play an important role in Ion-pumping activity. The well-studied Haloarchaeal rhodopsin is a proton-pumping bacteriorhodopsin that shows promising applications in optogenetics, biosensitized solar cells, security ink, data storage, artificial retinal implant and biohydrogen generation. As a result, a low-cost computational approach is required to identify Ion-pumping Haloarchaeal rhodopsin sequences and its subtype.

RESULTS

This study uses a support vector machine (SVM) technique to identify these ion-pumping Haloarchaeal rhodopsin proteins. The haloarchaeal ion pumping rhodopsins viz., bacteriorhodopsin, halorhodopsin, xanthorhodopsin, sensoryrhodopsin and marine prokaryotic Ion-pumping rhodopsins like actinorhodopsin, proteorhodopsin have been utilized to develop the methods that accurately identified the ion pumping haloarchaeal and other type I microbial rhodopsins. We achieved overall maximum accuracy of 97.78%, 97.84% and 97.60%, respectively, for amino acid composition, dipeptide composition and hybrid approach on tenfold cross validation using SVM. Predictive models for each class of rhodopsin performed equally well on an independent data set. In addition to this, similar results were achieved using another machine learning technique namely random forest. Simultaneously predictive models performed equally well during five-fold cross validation. Apart from this study, we also tested the own, blank, BLAST dataset and annotated whole-genome rhodopsin sequences of PWS haloarchaeal isolates in the developed methods. The developed web server ( https://bioinfo.imtech.res.in/servers/rhodopred ) can identify the Ion Pumping Haloarchaeal rhodopsin proteins and their subtypes. We expect this web tool would be useful for rhodopsin researchers.

CONCLUSION

The overall performance of the developed method results show that it accurately identifies the Ionpumping Haloarchaeal rhodopsin and their subtypes using known and unknown microbial rhodopsin sequences. We expect that this study would be useful for optogenetics, molecular biologists and rhodopsin researchers.

Computational method for aromatase-related proteins using machine learning approach

Human aromatase enzyme is a microsomal cytochrome P450 and catalyzes aromatization of androgens into estrogens during steroidogenesis. For breast cancer therapy, third-generation aromatase inhibitors (AIs) have proven to be effective; however patients acquire resistance to current AIs. Thus there is a need to predict aromatase-related proteins to develop efficacious AIs. A machine learning method was established to identify aromatase-related proteins using a five-fold cross validation technique. In this study, different SVM approach-based models were built using the following approaches like amino acid, dipeptide composition, hybrid and evolutionary profiles in the form of position-specific scoring matrix (PSSM); with maximum accuracy of 87.42%, 84.05%, 85.12%, and 92.02% respectively. Based on the primary sequence, the developed method is highly accurate to predict the aromatase-related proteins. Prediction scores graphs were developed using the known dataset to check the performance of the method. Based on the approach described above, a webserver for predicting aromatase-related proteins from primary sequence data was developed and implemented at https://bioinfo.imtech.res.in/servers/muthu/aromatase/home.html. We hope that the developed method will be useful for aromatase protein related research.

The genetic characterization of hemagglutinin (HA), neuraminidase (NA) and polymerase acidic (PA) genes of H3N2 influenza viruses circulated in Guangdong Province of China during 2019-2020

The evolution of seasonal influenza viruses, which can cause virus antigenic drift to escape human herd immunity, is a significant public health problem. Here, we obtained hemagglutinin (HA), neuraminidase (NA), and polymerase acidic protein (PA) the gene sequences of 84 influenza virus isolates collected in Guangdong Province during the 2019-2020 influenza season. Phylogenetic analyses revealed all these isolates were genetically similar to the viruses of clade 3C2a A1b, specifically those within subclades of A1b 137F (59 cases), A1b 186D (19 cases), and A1b 94 N (6 cases). The influenza virus isolates were distinct from the World Health Organization recommended influenza A vaccine virus for the 2019-2020 Northern Hemisphere season (A/Kansas/14/2017; H3N2). Phylogenies inferred from the individual gene segment sequences revealed that one reassortment event occurred among these clades. The genetic variation involved mutations within viral antigenic epitopes and two N-glycosylation site alterations. The novel mutation sites of G202D and D206N in the HA gene, E344K in the NA gene, and K626R in the PA gene which may affect the spread of the virus were observed. We investigated the evolution of these genes and found that the HA and NA genes were under greater pressure than PA gene. Mutations associated with conferring resistance to NA inhibitors or baloxavir acid were not found. Our results suggest that a rapid evolution of the H3N2 influenza virus occurred, thus continuous monitoring is critical for establishing appropriate vaccine formulations or drug delivery for targeting influenza.

DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction

Protein N-linked glycosylation is a post-translational modification that plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of glycosylation sites. Most of the existing predictors for N-linked glycosylation utilize the information that the glycosylation site occurs at the N-X-[S/T] sequon, where X is any amino acid except proline. Not all N-X-[S/T] sequons are glycosylated, thus the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In that regard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. Here, we report DeepNGlyPred a deep learning-based approach that encodes the positive and negative sequences in the human proteome dataset (extracted from N-GlycositeAtlas) using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. DeepNGlyPred produces SN, SP, MCC, and ACC of 88.62%, 73.92%, 0.60, and 79.41%, respectively on N-GlyDE independent test set, which is better than the compared approaches. These results demonstrate that DeepNGlyPred is a robust computational technique to predict N-Linked glycosylation sites confined to N-X-[S/T] sequon. DeepNGlyPred will be a useful resource for the glycobiology community.

Genome-wide identification and expression profiling of the COBRA-like genes reveal likely roles in stem strength in rapeseed (Brassica napus L.)

The COBRA-like (COBL) genes play key roles in cell anisotropic expansion and the orientation of microfibrils. Mutations in these genes cause the brittle stem and induce pathogen responsive phenotypes in Arabidopsis and several crop plants. In this study, an in silico genome-wide analysis was performed to identify the COBL family members in Brassica. We identified 44, 20 and 23 COBL genes in B. napus and its diploid progenitor species B. rapa and B. oleracea, respectively. All the predicted COBL genes were phylogenetically clustered into two groups: the AtCOB group and the AtCOBL7 group. The conserved chromosome locations of COBLs in Arabidopsis and Brassica, together with clustering, indicated that the expansion of the COBL gene family in B. napus was primarily attributable to whole-genome triplication. Among the BnaCOBLs, 22 contained all the conserved motifs and derived from 9 of 12 subgroups. RNA-seq analysis was used to determine the tissue preferential expression patterns of various subgroups. BnaCOBL9, BnaCOBL35 and BnaCOBL41 were highly expressed in stem with high-breaking resistance, which implies these AtCOB subgroup members may be involved in stem development and stem breaking resistance of rapeseed. Our results of this study may help to elucidate the molecular properties of the COBRA gene family and provide informative clues for high stem-breaking resistance studies.

Recent Advances in the Microbial Synthesis of Hemoglobin

Hemoglobin is a cofactor-containing protein with heme that plays important roles in transporting and storing oxygen. Hemoglobins have been widely applied as acellular oxygen carriers, bioavailable iron-supplying agents, and food-grade coloring and flavoring agents. To meet increasing demands and overcome the drawbacks of chemical extraction, the biosynthesis of hemoglobin has become an attractive alternative. Several hemoglobins have recently been synthesized by various microorganisms through metabolic engineering and synthetic biology. In this review, we summarize the novel strategies that have been used to biosynthesize hemoglobin. These strategies can also serve as references for producing other heme-binding proteins.