scholarly journals Conservation of glutathione S-transferase mRNA and protein sequences similar to human and horse Alpha class GST A3-3 across dog, goat, and opossum species

2020 ◽  
Author(s):  
Shawna M. Hubert ◽  
Paul B. Samollow ◽  
Helena Lindström ◽  
Bengt Mannervik ◽  
Nancy H. Ing
Keyword(s):  
Active Sites ◽  
Mammalian Species ◽  
Protein Sequences ◽  
Fertility Control ◽  
Gene Duplications ◽  
Glutathione S Transferase ◽  
Ketosteroid Isomerase ◽  
Isomerase Activity ◽  
Steroid Dependent ◽  
High Level

AbstractRecently, the glutathione S-transferase A3-3 (GST A3-3) homodimeric enzyme was identified as the most efficient enzyme that catalyzes isomerization of the precursors of testosterone, estradiol, and progesterone in the gonads of humans and horses. However, the presence of GST A3-3 orthologs with equally high ketosteroid isomerase activity has not been verified in other mammalian species, even though pig and cattle homologs have been cloned and studied. Identifying GSTA3 genes is a challenge because of multiple GSTA gene duplications (12 in the human genome), so few genomes have a corresponding GSTA3 gene annotated. To improve our understanding of GSTA3 gene products and their functions across diverse mammalian species, we cloned homologs of the horse and human GSTA3 mRNAs from the testes of a dog, goat, and gray short-tailed opossum, with those current genomes lacking GSTA3 gene annotations. The resultant novel GSTA3 mRNA and inferred protein sequences had a high level of conservation with human GSTA3 mRNA and protein sequences (≥ 70% and ≥ 64% identities, respectively). Sequence conservation was also apparent for the 13 residues of the “H-site” in the 222 amino acid GSTA3 protein that is known to interact with the steroid substrates. Modeling predicted that the dog GSTA3-3 is a more active ketosteroid isomerase than the goat or opossum enzymes. Our results help us understand the active sites of mammalian GST A3-3 enzymes, and their inhibitors may be useful for reducing steroidogenesis for medical purposes, such as fertility control or treatment of steroid-dependent diseases.

scholarly journals Alien chromatin but not Fhb7 confers Fusarium head blight resistance in wheat breeding

2021 ◽  
Author(s):  
Xianrui Guo ◽  
Qinghua Shi ◽  
Jing Yuan ◽  
Mian Wang ◽  
Jing Wang ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Fusarium Head Blight Resistance ◽  
Resistance Breeding ◽  
Wheat Breeding ◽  
Glutathione S Transferase ◽  
Head Blight ◽  
Fhb Resistance ◽  
Translocation Lines ◽  
High Level ◽  
Thinopyrum Elongatum

AbstractFusarium head blight (FHB), caused by Fusarium species, seriously threaten global wheat production. Three wheat-Th.elongatum FHB resistant translocation lines have been developed and used for breeding. Transcriptomic analysis identified a derivative glutathione S-transferase transcript T26102, which was homologous to Fhb7 and induced dramatically by Fusarium graminearum. Homologs of Fhb7 were detected in several genera in Triticeae, including Thinopyrum, Elymus, Leymus, Pseudoroegeria and Roegeria. Several wheat-Thinopyrum translocation lines carrying Fhb7 remain susceptible to FHB, and transgenic plants overexpressing the T26102 on different backgrounds did not improve the FHB resistance. Taken as a whole, we show the application of the chromatin derived from diploid Thinopyrum elongatum successfully conferring wheat with high level FHB resistance independent of the Fhb7.One Sentence SummaryThinopyrum elongatum chromatin from 7EL was successfully applied to wheat FHB resistance breeding, but the resistant gene other than the reported Fhb7 remained unknown.

scholarly journals BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information

2019 ◽  
Vol 20 (4) ◽  
pp. 978 ◽  
Author(s):  
Zhao-Hui Zhan ◽  
Li-Na Jia ◽  
Yong Zhou ◽  
Li-Ping Li ◽  
Hai-Cheng Yi
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Prediction Accuracy ◽  
Sparse Matrices ◽  
Protein Sequences ◽  
Biological Research ◽  
Sequence Information ◽  
Feature Extraction Method ◽  
Cellular Processes ◽  
High Level

The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.

Selecting DFT methods for use in optimizations of enzyme active sites: applications to ONIOM treatments of DNA glycosylases

2013 ◽  
Vol 91 (7) ◽  
pp. 559-572 ◽  
Author(s):  
Jennifer L. Kellie ◽  
Stacey D. Wetmore
Keyword(s):  
Active Sites ◽  
Noncovalent Interactions ◽  
Single Point ◽  
Enzymatic Reaction ◽  
Basis Set ◽  
Basis Sets ◽  
Dna Glycosylases ◽  
Dft Methods ◽  
Primary Focus ◽  
High Level

When using a hybrid methodology to treat an enzymatic reaction, many factors contribute to selecting the method for the high-level region, which can be complicated by the presence of dispersion-driven interactions such as π–π stacking. In addition, the proper treatment of the reaction center often requires a large number of heavy atoms to be included in the high-level region, precluding the use of ab initio methods such as MP2 as well as large basis sets, in the optimization step. In the present work, popular DFT methods were tested to identify an appropriate functional for treating the high-level region in ONIOM optimizations of reactions catalyzed by nonmetalloenzymes. Eight different DFT methods (B3LYP, B97-2, MPW1K, MPWB1K, BB1K, B1B95, M06-2X, and ωB97X-D) in combination with four double-ζ quality Pople basis sets were tested for their ability to optimize noncovalent interactions (hydrogen bonding and π–π) and characterize reactions (proton transfer, SN2 hydrolysis, and unimolecular cleavage). Although the primary focus of this study is accurate structure determination, energetics were also examined at both the optimization level of theory, and with triple-ζ quality basis set and select (M06-2X or ωB97X-D) methods. If dispersion-driven interactions exist within the active site, then MPWB1K/6-31G(d,p) or M06-2X/6-31+G(d,p) are recommended for the optimization step with subsequent triple-ζ quality single-point energies. However, since dispersion-corrected functionals (M06-2X and ωB97X-D) generally require diffuse functions to yield appropriate geometries, the possible size of the high-level region is greatly limited with these methods. In contrast, if the model is large enough to recover steric constraints on π–π interactions, then B3LYP with a small basis set performs comparatively well for the optimization step and is significantly less computationally expensive. Interestingly, the functionals that afford the best geometries often do not yield the best energetics, which emphasizes the importance of structural benchmark studies.

scholarly journals Glutathione S-transferase Yc cDNA from Syrian hamster kidney

1997 ◽  
Vol 323 (1) ◽  
pp. 147-149
Author(s):  
Frideriki MAGGOUTA ◽  
Sara A. LI ◽  
Jonathan J. LI ◽  
James S. NORRIS
Keyword(s):  
Amino Acid ◽  
Nucleotide Sequence ◽  
Amino Acid Sequence ◽  
Northern Blot ◽  
Syrian Hamster ◽  
Vas Deferens ◽  
Glutathione S Transferase ◽  
Tissue Specific ◽  
High Level

A cDNA encoding alpha-class glutathione S-transferase Yc (GSTYc) has been isolated from a Syrian hamster kidney library, and its nucleotide sequence (968 bp) has been determined. Analysis of the deduced amino acid sequence revealed a high level of identity between Syrian hamster GSTYc, rat GST Yc1 and Yc2 and mouse GSTYc. Northern-blot experiments demonstrated that Syrian hamster GSTYc expression is tissue-specific. A GSTYc mRNA of approx. 1 kb is expressed in liver, kidney, vas deferens and epididymis. Expression of the GSTYc transcript was not detected in testis or uterus.

Bamboo-Shaped Carbon Nanotubes on Coal Fly Ash Cenospheres for Pb(II) Adsorption

2020 ◽  
Vol 20 (8) ◽  
pp. 5089-5095
Author(s):  
Xiaomin Zhang ◽  
Jin Li ◽  
Bo He ◽  
Heng Li ◽  
Chao Qi ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Fly Ash ◽  
Adsorption Capacity ◽  
Active Sites ◽  
Coal Fly Ash ◽  
Chemical Vapor ◽  
Structural Defects ◽  
3D Network ◽  
Supporting Material ◽  
High Level

The structural defects of bamboo-shaped carbon nanotubes (B-CNTs) provide abundant active sites for ion adsorption during wastewater treatment. However, a suitable supporting material for the growth of B-CNTs growth is less reported. In this paper, the catalytic growth of B-CNTs on the cenospheres (CSs) of coal fly ash was studied. The results showed that all CSs were covered by a layer of B-CNTs during the chemical vapor deposition (CVD) process, regardless of the fluctuation of the iron distribution from 0.52 to 2.09 wt%. B-CNTs with a diameter of 30–40 nm shared a similar morphology of compartment structures, which were uniformly scattered on the surfaces of the CSs and formed a 3D network structure. A high level of structural defects was present on the B-CNTs, which was denoted by an ID/IG value of 1.77 via Raman spectrum analysis. Adsorption experiments of the as-prepared CSs@B-CNTs revealed an excellent adsorption capacity for lead ions of 37.32 mg/g (pH 7, initial concentration of 70 mg/L). By excluding the function of CSs, the adsorption capacity of the pure B-CNTs was estimated to be as high as 275.19 mg/g, which has not been previously reported.

scholarly journals Cross-modal object recognition and dynamic weighting of sensory inputs in a fish

2016 ◽  
Vol 113 (27) ◽  
pp. 7638-7643 ◽  
Author(s):  
Sarah Schumacher ◽  
Theresa Burt de Perera ◽  
Johanna Thenert ◽  
Gerhard von der Emde
Keyword(s):  
Object Recognition ◽  
Environmental Changes ◽  
Mammalian Species ◽  
Brain Structures ◽  
Weakly Electric Fish ◽  
Mammalian Brain ◽  
Sensory Inputs ◽  
Level Function ◽  
Dynamic Weighting ◽  
High Level

Most animals use multiple sensory modalities to obtain information about objects in their environment. There is a clear adaptive advantage to being able to recognize objects cross-modally and spontaneously (without prior training with the sense being tested) as this increases the flexibility of a multisensory system, allowing an animal to perceive its world more accurately and react to environmental changes more rapidly. So far, spontaneous cross-modal object recognition has only been shown in a few mammalian species, raising the question as to whether such a high-level function may be associated with complex mammalian brain structures, and therefore absent in animals lacking a cerebral cortex. Here we use an object-discrimination paradigm based on operant conditioning to show, for the first time to our knowledge, that a nonmammalian vertebrate, the weakly electric fish Gnathonemus petersii, is capable of performing spontaneous cross-modal object recognition and that the sensory inputs are weighted dynamically during this task. We found that fish trained to discriminate between two objects with either vision or the active electric sense, were subsequently able to accomplish the task using only the untrained sense. Furthermore we show that cross-modal object recognition is influenced by a dynamic weighting of the sensory inputs. The fish weight object-related sensory inputs according to their reliability, to minimize uncertainty and to enable an optimal integration of the senses. Our results show that spontaneous cross-modal object recognition and dynamic weighting of sensory inputs are present in a nonmammalian vertebrate.

scholarly journals BLVector: Fast BLAST-Like Algorithm for Manycore CPU With Vectorization

2021 ◽  
Vol 12 ◽  
Author(s):  
Sergio Gálvez ◽  
Federico Agostini ◽  
Javier Caselli ◽  
Pilar Hernandez ◽  
Gabriel Dorado
Keyword(s):  
Amino Acids ◽  
Execution Time ◽  
High Performance ◽  
Protein Sequences ◽  
Processing Unit ◽  
Central Processing ◽  
Real Scenario ◽  
High Level ◽  
Performance Computing ◽  
Comprehensive Study

New High-Performance Computing architectures have been recently developed for commercial central processing unit (CPU). Yet, that has not improved the execution time of widely used bioinformatics applications, like BLAST+. This is due to a lack of optimization between the bases of the existing algorithms and the internals of the hardware that allows taking full advantage of the available CPU cores. To optimize the new architectures, algorithms must be revised and redesigned; usually rewritten from scratch. BLVector adapts the high-level concepts of BLAST+ to the x86 architectures with AVX-512, to harness their capabilities. A deep comprehensive study has been carried out to optimize the approach, with a significant reduction in time execution. BLVector reduces the execution time of BLAST+ when aligning up to mid-size protein sequences (∼750 amino acids). The gain in real scenario cases is 3.2-fold. When applied to longer proteins, BLVector consumes more time than BLAST+, but retrieves a much larger set of results. BLVector and BLAST+ are fine-tuned heuristics. Therefore, the relevant results returned by both are the same, although they behave differently specially when performing alignments with low scores. Hence, they can be considered complementary bioinformatics tools.

scholarly journals Local frustration around enzyme active sites

2019 ◽  
Vol 116 (10) ◽  
pp. 4037-4043 ◽  
Author(s):  
Maria I. Freiberger ◽  
A. Brenda Guzovsky ◽  
Peter G. Wolynes ◽  
R. Gonzalo Parra ◽  
Diego U. Ferreiro
Keyword(s):  
Primary Structure ◽  
Active Sites ◽  
Protein Sequences ◽  
Structure And Function ◽  
Energy Landscapes ◽  
Biological Functions ◽  
Complex Energy ◽  
Oligomeric State ◽  
Catalytic Sites ◽  
And Function

Conflicting biological goals often meet in the specification of protein sequences for structure and function. Overall, strong energetic conflicts are minimized in folded native states according to the principle of minimal frustration, so that a sequence can spontaneously fold, but local violations of this principle open up the possibility to encode the complex energy landscapes that are required for active biological functions. We survey the local energetic frustration patterns of all protein enzymes with known structures and experimentally annotated catalytic residues. In agreement with previous hypotheses, the catalytic sites themselves are often highly frustrated regardless of the protein oligomeric state, overall topology, and enzymatic class. At the same time a secondary shell of more weakly frustrated interactions surrounds the catalytic site itself. We evaluate the conservation of these energetic signatures in various family members of major enzyme classes, showing that local frustration is evolutionarily more conserved than the primary structure itself.

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