OUP accepted manuscript

Begum, Khodeza; Mohl, Jonathon E.; Ayivor, Fredrick; Perez, Eder E; Leung, Ming-Ying

doi:10.1093/database/baaa087

Cited by 5 publications

References 30 publications

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Rapid transcriptomic and physiological changes in the freshwater pennate diatomMayamaea pseudoterrestrisin response to copper exposure

et al. 2022

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Diatoms function as major primary producers, accumulating large amounts of biomass in most aquatic environments. Given their rapid responses to changes in environmental conditions, diatoms are used for the biological monitoring of water quality and for performing ecotoxicological tests in aquatic ecosystems. However, the molecular basis for their toxicity to chemical compounds remains largely unknown. Here, we sequenced the genome of a freshwater diatom, Mayamaea pseudoterrestris NIES-4280, which has been proposed as an alternative strain of Navicula pelliculosa UTEX 664 for performing the Organisation for Economic Co-operation and Development ecotoxicological test. This study shows that M. pseudoterrestris has a small genome and carries the lowest number of genes among freshwater diatoms. The gene content of M. pseudoterrestris is similar to that of the model marine diatom, Phaeodactylum tricornutum. Genes related to cell motility, polysaccharide metabolism, oxidative stress alleviation, intracellular calcium signaling, and reactive compound detoxification showed rapid changes in their expression patterns in response to copper exposure. Active gliding motility was observed in response to copper addition, and copper exposure decreased intracellular calcium concentration. These findings enhance our understanding of the environmental adaptation of diatoms, and elucidate the molecular basis of toxicity of chemical compounds in algae.

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Rapid transcriptomic and physiological changes in the freshwater pennate diatomMayamaea pseudoterrestrisin response to copper exposure

et al. 2022

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Identifying key amino acid types that distinguish paralogous proteins using Shapley value based feature subset selection

Machingal,

Busi,

Hemachandra

et al. 2024

Preprint

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We view a protein as the composite of the standard 20 amino acids (ignoring their order in the protein sequence) and try to identify a set of important amino acid types whose composition is enough to distinguish two paralogous proteins. For this, we use a linear classifier with amino acid composition as features, and a Shapley value based feature subset selection algorithm. We demonstrate our method using 15 datasets of pairs of paralogous proteins. We find that the amino acid composition feature is adequate to distinguish many paralogous proteins from each other. For a pair of paralogous proteins, we are able to identify a subset of amino acids, referred to as AFS (amino acid feature subset), that are key to distinguish them, for each protein. We validate the ability of the AFS amino acids to discriminate by analyzing multiple sequence alignments of corresponding protein families and/or by providing supporting evidence from literature. We also pair-wise classify sub-families of a protein superfamily and highlight common amino acids identified in the AFS for two pairs with a common sub-family.

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Ion-pumping microbial rhodopsin protein classification by machine learning approach

et al. 2023

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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.

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OUP accepted manuscript

Cited by 5 publications

References 30 publications

Rapid transcriptomic and physiological changes in the freshwater pennate diatomMayamaea pseudoterrestrisin response to copper exposure

Rapid transcriptomic and physiological changes in the freshwater pennate diatomMayamaea pseudoterrestrisin response to copper exposure

Identifying key amino acid types that distinguish paralogous proteins using Shapley value based feature subset selection

Ion-pumping microbial rhodopsin protein classification by machine learning approach

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