scholarly journals Residue–Residue Contact Can Be a Potential Feature for the Prediction of Lysine Crotonylation Sites

2022 ◽  
Vol 12 ◽  
Author(s):  
Rulan Wang ◽  
Zhuo Wang ◽  
Zhongyan Li ◽  
Tzong-Yi Lee
Keyword(s):  
Amino Acid ◽  
Target Site ◽  
Residue Contact ◽  
Pair Composition ◽  
Feature Encoding ◽  
Auc Value ◽  
New Feature ◽  
First Time ◽  
Prediction Sequence ◽  
Residue Composition

Lysine crotonylation (Kcr) is involved in plenty of activities in the human body. Various technologies have been developed for Kcr prediction. Sequence-based features are typically adopted in existing methods, in which only linearly neighboring amino acid composition was considered. However, modified Kcr sites are neighbored by not only the linear-neighboring amino acid but also those spatially surrounding residues around the target site. In this paper, we have used residue–residue contact as a new feature for Kcr prediction, in which features encoded with not only linearly surrounding residues but also those spatially nearby the target site. Then, the spatial-surrounding residue was used as a new scheme for feature encoding for the first time, named residue–residue composition (RRC) and residue–residue pair composition (RRPC), which were used in supervised learning classification for Kcr prediction. As the result suggests, RRC and RRPC have achieved the best performance of RRC at an accuracy of 0.77 and an area under curve (AUC) value of 0.78, RRPC at an accuracy of 0.74, and an AUC value of 0.80. In order to show that the spatial feature is of a competitively high significance as other sequence-based features, feature selection was carried on those sequence-based features together with feature RRPC. In addition, different ranges of the surrounding amino acid compositions’ radii were used for comparison of the performance. After result assessment, RRC and RRPC features have shown competitively outstanding performance as others or in some cases even around 0.20 higher in accuracy or 0.3 higher in AUC values compared with sequence-based features.

scholarly journals The First Report of Target-Site Resistance to Glyphosate in Sweet Summer Grass (Moorochloa eruciformis)

Plants ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1885
Author(s):  
Romesh Salgotra ◽  
Bhagirath Singh Chauhan
Keyword(s):  
Amino Acid ◽  
Management Strategies ◽  
Glyphosate Resistance ◽  
Target Site ◽  
Single Amino Acid ◽  
Invasive Weeds ◽  
Epsps Gene ◽  
Target Site Resistance ◽  
Multiple Amino Acid ◽  
First Time

Sweet summer grass is a problematic weed in the central Queensland region of Australia. This study found glyphosate resistance in two biotypes (R1 and R2) of sweet summer grass. The level of resistance in these biotypes was greater than 8-fold. The glyphosate dose required to reduce dry matter by 50% (GR50) for the resistant populations varied from 1993 to 2100 g ha−1. A novel glyphosate resistance double point mutation in the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene was identified for the first time in sweet summer grass. Multiple mutations, including multiple amino acid changes at the glyphosate target site, as well as mutations involving two nucleotide changes at a single amino acid codon, were observed. Both resistant biotypes exhibited a nucleotide change of CAA to ACA in codon 106, which predicts an amino acid change of proline to a threonine (Pro-106-Thr). In addition, the R1 biotype also possessed a mutation at codon 100, where a nucleotide substitution of T for G occurred (GCT to TCT), resulting in a substitution of serine for alanine (Ala-100-Ser). Understanding the molecular mechanism of glyphosate resistance will help to design effective management strategies to control invasive weeds.

Single Amino Acid Based Self-Assemblies of Cysteine and Methionine

2018 ◽  
Author(s):  
Nidhi Gour ◽  
Bharti Koshti ◽  
Chandra Kanth P. ◽  
Dhruvi Shah ◽  
Vivek Shinh Kshatriya ◽  
...  
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Self Assembly ◽  
Aromatic Amino Acids ◽  
Neutral Condition ◽  
Single Amino Acid ◽  
Binding Assays ◽  
Human Neuroblastoma ◽  
Cytotoxicity Assays ◽  
First Time

We report for the very first time self-assembly of Cysteine and Methionine to discrenible strucutres under neutral condition. To get insights into the structure formation, thioflavin T and Congo red binding assays were done which revealed that aggregates may not have amyloid like characteristics. The nature of interactions which lead to such self-assemblies was purported by coincubating assemblies in urea and mercaptoethanol. Further interaction of aggregates with short amyloidogenic dipeptide diphenylalanine (FF) was assessed. While cysteine aggregates completely disrupted FF fibres, methionine albeit triggered fibrillation. The cytotoxicity assays of cysteine and methionine structures were performed on Human Neuroblastoma IMR-32 cells which suggested that aggregates are not cytotoxic in nature and thus, may not have amyloid like etiology. The results presented in the manuscript are striking, since to the best of our knowledge,this is the first report which demonstrates that even non-aromatic amino acids (cysteine and methionine) can undergo spontaneous self-assembly to form ordered aggregates.

Bioluminescent proteins prediction with voting strategy.

2020 ◽  
Vol 15 ◽  
Author(s):  
Shulin Zhao ◽  
Ying Ju ◽  
Xiucai Ye ◽  
Jun Zhang ◽  
Shuguang Han
Keyword(s):  
Amino Acid ◽  
Model Building ◽  
Prediction Method ◽  
Pair Composition ◽  
Proposed Model ◽  
Counting Rules ◽  
Significant Phenomenon ◽  
Voting Strategy ◽  
Improved Accuracy ◽  
Selection Of

Background: Bioluminescence is a unique and significant phenomenon in nature. Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical research, including for gene expression analysis and bioluminescence imaging technology.In recent years, researchers have identified a number of methods for predicting bioluminescent proteins (BLPs), which have increased in accuracy, but could be further improved. Method: In this paper, we propose a new bioluminescent proteins prediction method based on a voting algorithm. We used four methods of feature extraction based on the amino acid sequence. We extracted 314 dimensional features in total from amino acid composition, physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest MCC value to establish the optimal prediction model, then used a voting algorithm to build the model.To create the best performing model, we discuss the selection of base classifiers and vote counting rules. Results: Our proposed model achieved 93.4% accuracy, 93.4% sensitivity and 91.7% specificity in the test set, which was better than any other method. We also improved a previous prediction of bioluminescent proteins in three lineages using our model building method, resulting in greatly improved accuracy.

Access to 2-Arylquinazolines via Catabolism/Reconstruction of Amino Acids with Insertion of Dimethyl Sulfoxide

2021 ◽  
Author(s):  
Jin-Tian Ma ◽  
Li-Sheng Wang ◽  
Zhi Chai ◽  
Xin-Feng Chen ◽  
Bo-Cheng Tang ◽  
...  
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Dimethyl Sulfoxide ◽  
Nitrogen Source ◽  
Carbon And Nitrogen ◽  
First Time

Quinazoline skeletons are synthesized by amino acids catabolism/reconstruction combined with dimethyl sulfoxide insertion/cyclization for the first time. The amino acid acts as a carbon and nitrogen source through HI-mediated catabolism...

scholarly journals Molecular characterization of Prunus necrotic ringspot virus isolated from rose in Brazil

2015 ◽  
Vol 45 (12) ◽  
pp. 2197-2200 ◽  
Author(s):  
Thor Vinícius Martins Fajardo ◽  
Monique Bezerra Nascimento ◽  
Marcelo Eiras ◽  
Osmar Nickel ◽  
Gilvan Pio-Ribeiro
Keyword(s):  
Amino Acid ◽  
Molecular Characterization ◽  
Molecular Analysis ◽  
Nucleotide Sequences ◽  
Amino Acid Sequences ◽  
Causal Agent ◽  
Ringspot Virus ◽  
Prunus Necrotic Ringspot Virus ◽  
First Time

ABSTRACT: There is no molecular characterization of Brazilian isolates of Prunus necrotic ringspot virus (PNRSV), except for those infecting peach. In this research, the causal agent of rose mosaic was determined and the movement (MP) and coat (CP) protein genes of a PNRSV isolate from rose were molecularly characterized for the first time in Brazil. The nucleotide and deduced amino acid sequences of MP and CP complete genes were aligned and compared with other isolates. Molecular analysis of the MP and CP nucleotide sequences of a Brazilian PNRSV isolate from rose and others from this same host showed highest identities of 96.7% and 98.6%, respectively, and Rose-Br isolate was classified in PV32 group.

scholarly journals Self-Dividing Micelles: A Mechanistic Look with Evolutionary and Clinical Implications

2021 ◽  
pp. 8-20
Keyword(s):  
Fatty Acid ◽  
Amino Acid ◽  
Laboratory Testing ◽  
Prebiotic Chemistry ◽  
Clinical Implications ◽  
Proof Of Concept ◽  
Mass Spec ◽  
First Time ◽  
Division Mechanism ◽  
Viable Treatment

Micellar therapy has become a usefully viable treatment arm in various fields, ranging from oncology to bioimaging. As such, research leading to any improvements or adaptations in administration and techniques can have far-reaching consequences. Potential aspects of prebiotic chemistry may also be explored in such research as well. To that end, proof-of-concept experiments were performed to elucidate a possible mechanism of action for prebiotic protocell division. Representative potentially prebiotically plausible biomolecules, i.e., a fatty acid, amino acid, and nucleotide were mixed and heated in water and subjected to microscopic examination for observation of possible self-division and laboratory testing for the presence of polypeptides and polynucleotides (Biuret, MALDI mass-spec, etc.) with and without the presence of nucleotide. The results are presented for the first time here and a mechanism is proposed that best fits the data obtained. The evolutionary, e.g., prebiotic biomolecular cooperativity, and clinical, e.g., potential antineoplastic micellar/vesicular therapy, ramifications are discussed as well. Keywords: Micelle; Liposome; Protocell; MRNA; Self-division; Mechanism; Solid tumors

scholarly journals Dynamics of intravital concentration of amino acid metabolites in human brain in post-traumatic period

2019 ◽  
Vol 484 (2) ◽  
pp. 238-242
Author(s):  
N. A. Semenova ◽  
P. E. Menshchikov ◽  
A. V. Manzhurtsev ◽  
M. V. Ublinskiy ◽  
T. A. Akhadov ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Amino Acid ◽  
Human Brain ◽  
Leading Role ◽  
Acid Metabolites ◽  
Post Traumatic ◽  
First Time

Intracellular concentrations of N acetyaspartate (NAA), aspartate (Asp) and glutamate (Glu) were determined for the first time in human brain in vivo, and the effect of severe traumatic brain injury on NAA synthesis in acute and late post-traumatic period was investigated. In MRI‑negative frontal lobes one day after injury Asp and Glu levels were found to decrease by 45 and 35%, respectively, while NAA level decreased by only 16%. A negative correlation between NAA concentration and the ratio of Asp/Glu concentrations was found. In the long-term period, Glu level returned to normal, Asp level remained below normal by 60%, NAA level was reduced by 65% relative to normal, and Asp/Glu ratio significantly decreased. The obtained results revealed leading role of the neuronal aspartate-malate shuttle in violation of NAA synthesis.

On the Possible Spatial Structures of the β – Amyloid.

2022 ◽  
Vol 7 (1) ◽  
pp. 0-0
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Spatial Structure ◽  
Spatial Models ◽  
Globular Proteins ◽  
Spatial Structures ◽  
Higher Dimensional ◽  
Protein Molecules ◽  
Β Amyloid ◽  
First Time

Spatial models of the β - structures of protein molecules, forming layers of amino acids, in principle, of unlimited length for both antiparallel and parallel conformation have been constructed. It is shown that the simplified flat Pauling models do not reflect the spatial structure of these layers. Using the recently developed theory of higher-dimensional polytopic prismahedrons, models of the volumetric filling of space with amino acid molecules are constructed. The constructed models for the first time mathematically describe the native structures of globular proteins.

scholarly journals Amino Acid k-mer Feature Extraction for Quantitative Antimicrobial Resistance (AMR) Prediction by Machine Learning and Model Interpretation for Biological Insights

Biology ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 365
Author(s):  
Taha ValizadehAslani ◽  
Zhengqiao Zhao ◽  
Bahrad A. Sokhansanj ◽  
Gail L. Rosen
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Amino Acid ◽  
Computational Complexity ◽  
Antimicrobial Resistance ◽  
Learning Algorithms ◽  
Extraction Methods ◽  
Machine Learning Algorithms ◽  
Model Interpretation ◽  
New Feature

Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide k-mers have been proposed for presenting DNA sequences to the model. However, there are trade-offs between interpretability, computational complexity and accuracy for different feature extraction methods. In this study, we have proposed a new feature extraction method, counting amino acid k-mers or oligopeptides, which provides easier model interpretation compared to counting nucleotide k-mers and reaches the same or even better accuracy in comparison with different methods. Additionally, we have trained machine learning algorithms using different feature extraction methods and compared the results in terms of accuracy, model interpretability and computational complexity. We have built a new feature selection pipeline for extraction of important features so that new AMR determinants can be discovered by analyzing these features. This pipeline allows the construction of models that only use a small number of features and can predict resistance accurately.

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