scholarly journals CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Reny Pratiwi ◽  
Aijaz Ahmad Malik ◽  
Nalini Schaduangrat ◽  
Virapong Prachayasittikul ◽  
Jarl E. S. Wikberg ◽  
...  
Keyword(s):  
Amino Acids ◽  
Physicochemical Properties ◽  
Predictive Model ◽  
High Performance ◽  
Web Server ◽  
Principal Component ◽  
Large Set ◽  
Data Set ◽  
Diverse Range ◽  
Ice Binding

Antifreeze protein (AFP) is an ice-binding protein that protects organisms from freezing in extremely cold environments. AFPs are found across a diverse range of species and, therefore, significantly differ in their structures. As there are no consensus sequences available for determining the ice-binding domain of AFPs, thus the prediction and characterization of AFPs from their sequence is a challenging task. This study addresses this issue by predicting AFPs directly from sequence on a large set of 478 AFPs and 9,139 non-AFPs using machine learning (e.g., random forest) as a function of interpretable features (e.g., amino acid composition, dipeptide composition, and physicochemical properties). Furthermore, AFPs were characterized using propensity scores and important physicochemical properties via statistical and principal component analysis. The predictive model afforded high performance with an accuracy of 88.28% and results revealed that AFPs are likely to be composed of hydrophobic amino acids as well as amino acids with hydroxyl and sulfhydryl side chains. The predictive model is provided as a free publicly available web server called CryoProtect for classifying query protein sequence as being either AFP or non-AFP. The data set and source code are for reproducing the results which are provided on GitHub.

scholarly journals Serum Metabolic Profiles of Chinese Women With Perimenopausal Obesity Explored by the Untargeted Metabolomics Approach

2021 ◽  
Vol 12 ◽  
Author(s):  
Shanshan Ding ◽  
Mingyi Chen ◽  
Ying Liao ◽  
Qiliang Chen ◽  
Xuejuan Lin ◽  
...  
Keyword(s):  
Amino Acids ◽  
Metabolic Pathways ◽  
High Performance ◽  
Indoleacetic Acid ◽  
Chinese Women ◽  
Principal Component ◽  
Untargeted Metabolomics ◽  
Negative Ion ◽  
Metabolic Profiles ◽  
Metabolic Characteristics

By far, no study has focused on observing the metabolomic profiles in perimenopause-related obesity. This study attempted to identify the metabolic characteristics of subjects with perimenopause obesity (PO). Thirty-nine perimenopausal Chinese women, 21 with PO and 18 without obesity (PN), were recruited in this study. A conventional ultra-high-performance liquid chromatography-quadrupole time-of-flight/mass spectrometry (UHPLC-QTOF/MS) followed by principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used as untargeted metabolomics approaches to explore the serum metabolic profiles. Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaboAnalyst were used to identify the related metabolic pathways. A total of 46 differential metabolites, along with seven metabolic pathways relevant to PO were identified, which belonged to lipid, amino acids, carbohydrates, and organic acids. As for amino acids, we found a significant increase in l-arginine and d-ornithine in the positive ion (POS) mode and l-leucine, l-valine, l-tyrosine, and N-acetyl-l-tyrosine in the negative ion (NEG) mode and a significant decrease in l-proline in the POS mode of the PO group. We also found phosphatidylcholine (PC) (16:0/16:0), palmitic acid, and myristic acid, which are associated with the significant upregulation of lipid metabolism. Moreover, the serum indole lactic acid and indoleacetic acid were upregulated in the NEG mode. With respect to the metabolic pathways, the d-arginine and d-ornithine metabolisms and the arginine and proline metabolism pathways in POS mode were the most dominant PO-related pathways. The changes of metabolisms of lipid, amino acids, and indoleacetic acid provided a pathophysiological scenario for Chinese women with PO. We believe that the findings of this study are helpful for clinicians to take measures to prevent the women with PO from developing severe incurable obesity-related complications, such as cardiovascular disease and stroke.

scholarly journals Leri: a web-server for identifying protein functional networks from evolutionary couplings

2020 ◽  
Author(s):  
Ngaam J. Cheung ◽  
Arun T. John Peter ◽  
Benoit Kornmann
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Protein Function ◽  
Structure Prediction ◽  
High Performance ◽  
Web Server ◽  
Functional Networks ◽  
Computational Results ◽  
High Quality ◽  
Recent Developments

ABSTRACTInformation on the co-evolution of amino acid pairs in a protein can be used for endeavors such as protein engineering, mutation design, and structure prediction. Here we report a method that captures significant determinants of proteins using estimated co-evolution information to identify networks of residues, termed “residue communities”, relevant to protein function. By taking advantage of recent developments in high-performance and parallel computing, we constructed a web-server, Leri, that identifies relevant residue communities to allow researchers to investigate how a protein evolves and folds for function(s). All the data of the computational results including high-quality images can be downloaded and presented for publication. This web-server, written in C++, is sufficiently rapid to enable the studies on proteins of up to 400 amino acids.

scholarly journals Amino Acid Profiles to Differentiate White Wines from Three Autochtonous Galician Varieties

Foods ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 114 ◽  
Author(s):  
José Manuel Mirás-Avalos ◽  
Yolanda Bouzas-Cid ◽  
Emiliano Trigo-Córdoba ◽  
Ignacio Orriols ◽  
Elena Falqué
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
High Performance ◽  
Principal Component ◽  
Wine Quality ◽  
Photodiode Array Detection ◽  
Relevant Role ◽  
Amino Acid Profiles ◽  
Primary Amino ◽  
Amino Acid Contents

Amino acids play a relevant role in wine quality and can allow for classifying wines according to the variety. In this work, the amino acid contents of Albariño, Godello, and Treixadura wines, three autochthonous varieties from Galicia (NW Spain), were determined. During three consecutive vintages, these varieties were grown on the same vineyard and were harvested at optimum maturity, and the wines were elaborated following the same enological protocol. The identification and quantification of the primary amino acids were carried out by high-performance liquid chromatography with photodiode array detection, after a derivatization. Amino acid contents in these white varieties were within the range of values reported for other European wines, but Treixadura wines showed the highest concentrations, while wines from the Albariño variety showed the lowest contents. Apart from proline, whose concentrations were caused by yeast release, the most abundant amino acids were aspartic acid, glutamic acid, lysine, arginine, asparagine, alanine, and histidine. Principal component analysis separated wines by variety according to their amino acid contents.

scholarly journals BEAGLE 3: Improved Performance, Scaling, and Usability for a High-Performance Computing Library for Statistical Phylogenetics

2019 ◽  
Vol 68 (6) ◽  
pp. 1052-1061 ◽  
Author(s):  
Daniel L Ayres ◽  
Michael P Cummings ◽  
Guy Baele ◽  
Aaron E Darling ◽  
Paul O Lewis ◽  
...  
Keyword(s):  
High Performance ◽  
Evolutionary Model ◽  
Data Partitioning ◽  
Partial Likelihood ◽  
Processing Unit ◽  
Data Set ◽  
Diverse Range ◽  
Central Processing ◽  
Automated Method ◽  
Software Packages

Abstract BEAGLE is a high-performance likelihood-calculation library for phylogenetic inference. The BEAGLE library defines a simple, but flexible, application programming interface (API), and includes a collection of efficient implementations for calculation under a variety of evolutionary models on different hardware devices. The library has been integrated into recent versions of popular phylogenetics software packages including BEAST and MrBayes and has been widely used across a diverse range of evolutionary studies. Here, we present BEAGLE 3 with new parallel implementations, increased performance for challenging data sets, improved scalability, and better usability. We have added new OpenCL and central processing unit-threaded implementations to the library, allowing the effective utilization of a wider range of modern hardware. Further, we have extended the API and library to support concurrent computation of independent partial likelihood arrays, for increased performance of nucleotide-model analyses with greater flexibility of data partitioning. For better scalability and usability, we have improved how phylogenetic software packages use BEAGLE in multi-GPU (graphics processing unit) and cluster environments, and introduced an automated method to select the fastest device given the data set, evolutionary model, and hardware. For application developers who wish to integrate the library, we also have developed an online tutorial. To evaluate the effect of the improvements, we ran a variety of benchmarks on state-of-the-art hardware. For a partitioned exemplar analysis, we observe run-time performance improvements as high as 5.9-fold over our previous GPU implementation. BEAGLE 3 is free, open-source software licensed under the Lesser GPL and available at https://beagle-dev.github.io.

scholarly journals Assessment of Paddy Soil Quality Index Based on Differences in the Productivity of Paddy in Wonogiri Regency

2017 ◽  
Vol 5 (2) ◽  
pp. 181
Author(s):  
Supriyadi Darsowiyono ◽  
Retno Rosariastuti ◽  
Pranoto Pranoto ◽  
Fitria Okvitasari
Keyword(s):  
Soil Quality ◽  
Paddy Field ◽  
Principal Component ◽  
Soil Improvement ◽  
Soil Quality Index ◽  
Data Set ◽  
Diverse Range ◽  
Available N ◽  
Cultivation Practices ◽  
Using Data

Rice or paddy is a staple food in some countries. The need for food is huge as the population growth increase, in Wonogiri regency majority of the land area is used as paddy field and land intensification is done to improve productivity. Land intensification raises the issues of having no definitive measure about the status of soil improvement after the cultivation practices of Paddy (Oryza sativa) has been done, in addition, the diversity of age utilization of paddy field by farmers resulting the differences in productivity that will affect the quality difference of the paddy field soil. The assessment is aimed to determine the soil quality function whether it’s good or bad after doing such land cultivation practices for a certain period. The assessment of soil quality is using data analysis method from the influential indicators based on the nature of at least the Minimum Data Set (MDS) that is obtained based on calculations of Principal Component Analysis (PCA). Indicators that affect, such as, C-Organic,Bulk Density, K-Available, N-Total, and average soil quality were obtained at all points of the sample with a diverse range of values. With the known SQI values, in the future the efforts to maintain and improve the quality of the soil can be done by considering several important factors.

scholarly journals Plasma Amino Acids May Improve Prediction Accuracy of Cerebral Vasospasm after Aneurysmal Subarachnoid Haemorrhage

2022 ◽  
Vol 11 (2) ◽  
pp. 380
Author(s):  
Ernest Jan Bobeff ◽  
Malgorzata Bukowiecka-Matusiak ◽  
Konrad Stawiski ◽  
Karol Wiśniewski ◽  
Izabela Burzynska-Pedziwiatr ◽  
...  
Keyword(s):  
Amino Acids ◽  
Cerebral Vasospasm ◽  
High Performance ◽  
Principal Component ◽  
Predictive Performance ◽  
Aneurysmal Subarachnoid Haemorrhage ◽  
Plasma Amino Acids ◽  
Performance Error ◽  
Leave One Out ◽  
Great Burden

Aneurysmal subarachnoid haemorrhages (aSAH) account for 5% of strokes and continues to place a great burden on patients and their families. Cerebral vasospasm (CVS) is one of the main causes of death after aSAH, and is usually diagnosed between day 3 and 14 after bleeding. Its pathogenesis remains poorly understood. To verify whether plasma concentration of amino acids have prognostic value in predicting CVS, we analysed data from 35 patients after aSAH (median age 55 years, IQR 39–62; 20 females, 57.1%), and 37 healthy volunteers (median age 50 years, IQR 38–56; 19 females, 51.4%). Fasting peripheral blood samples were collected on postoperative day one and seven. High performance liquid chromatography-mass spectrometry (HPLC-MS) analysis was performed. The results showed that plasma from patients after aSAH featured a distinctive amino acids concentration which was presented in both principal component analysis and direct comparison. No significant differences were noted between postoperative day one and seven. A total of 18 patients from the study group (51.4%) developed CVS. Hydroxyproline (AUC = 0.7042, 95%CI 0.5259–0.8826, p = 0.0248) and phenylalanine (AUC = 0.6944, 95%CI 0.5119–0.877, p = 0.0368) presented significant CVS prediction potential. Combining the Hunt-Hess Scale and plasma levels of hydroxyproline and phenylalanine provided the model with the best predictive performance and the lowest leave-one-out cross-validation of performance error. Our results suggest that plasma amino acids may improve sensitivity and specificity of Hunt-Hess scale in predicting CVS.

scholarly journals Numerical simulation, clustering, and prediction of multicomponent polymer precipitation

2020 ◽  
Vol 1 ◽  
Author(s):  
Pavan Inguva ◽  
Lachlan R. Mason ◽  
Indranil Pan ◽  
Miselle Hengardi ◽  
Omar K. Matar
Keyword(s):  
Polymer Blends ◽  
High Performance ◽  
Molar Fraction ◽  
Principal Component ◽  
Engineering Practice ◽  
Data Set ◽  
Pattern Clustering ◽  
Polymer Precipitation ◽  
Multicomponent Polymer ◽  
Process Classification

Abstract Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy.

scholarly journals Multidimensional Comparative Analysis of Bioactive Phenolic Compounds of Honeys of Various Origin

Antioxidants ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 530
Author(s):  
Michał Gośliński ◽  
Dariusz Nowak ◽  
Artur Szwengiel
Keyword(s):  
Comparative Analysis ◽  
Phenolic Compounds ◽  
Antioxidant Capacity ◽  
High Performance ◽  
Antioxidant Properties ◽  
Principal Component ◽  
Total Content ◽  
Multidimensional Analysis ◽  
Manuka Honey ◽  
Data Set

Honey is a natural product which owes its health benefits to its numerous bioactive compounds. The composition of honey is highly diverse and depends on the type of honey and its origin. Antioxidant capacity arises mainly from the total content of polyphenols and their composition. The aim of this study was to perform a multidimensional comparative analysis of phenolic compounds of honeys of various origins. Honeydew, buckwheat, manuka, Malaysian and goldenrod honeys had the highest antioxidant capacity (above 400 mg Trolox equivalents kg−1). These honeys were also characterized by the highest total polyphenol content (about 2500 mg gallic acid equivalents (GAE) kg−1) and the highest total flavonoid content (1400–1800 mg catechin equivalents (CAE) kg−1). Other honeys had much lower antioxidant properties. A multidimensional analysis of the profiles of phenolic compounds showed that honeys constitute a non-homogeneous data set and manuka honey was in contrast to other samples. Principal component analysis (PCA) (based on 18 phenolic compounds) distinguished honeys into five groups. Manuka, Malaysian and honeydew honeys created their own separate groups and the location of other honeys was variable. Ultra-high-performance liquid chromatography (UHPLC) analysis demonstrated that profiles of polyphenols in honeys were highly varied. Caffeic acid, datiscetin and rhamnetin were characteristic compounds for manuka honey. Quercetin, kaempferol and apigenin were present in all honeys except Malaysian honey. The antioxidant properties and the profiles of bioactive phenolic compounds of honeys were miscellaneous. The richest sources of polyphenols were local buckwheat and honeydew honeys, alongside exotic manuka and Malaysian honeys. These honeys could provide valuable ingredients to the human diet, helping to prevent diseases.

Extraction, characterization and antioxidant properties of phenolic compounds in açaí juçara (Euterpe edulis Mart.) from Atlantic Forest

2021 ◽  
Vol 24 ◽  
Author(s):  
Leticia Dangui da Silva ◽  
Rafael Sari ◽  
Camila Diedrich ◽  
Celeide Pereira ◽  
Vanderlei Aparecido de Lima ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Phenolic Compounds ◽  
Atlantic Forest ◽  
High Performance ◽  
Antioxidant Activities ◽  
Antioxidant Properties ◽  
Principal Component ◽  
Data Set ◽  
Antioxidant Power ◽  
Euterpe Edulis

Abstract The açaí is a popular Brazilian fruit, however, already part of the world's healthy eating habits owing to its antioxidant properties. The study aimed to determine the effect of solvent in extracting phenolic compounds with antioxidant potential in açaí juçara (Euterpe edulis Mart.) using a Completely Randomized Design (CRD). The phenolic compound profile was quantified by High-Performance Liquid Chromatography (HPLC), and the data set was analyzed by Principal Component Analysis (PCA). The PCA was applied to evidence the relationships between the concentration of phenolic compounds and the solvents. Furthermore, the antioxidant activity was also determined by 2,2’-diphenyl-1-picrylhydrazyl (DPPH), 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and Ferric Reducing Antioxidant Power (FRAP) methods. The solvent ethanol: water 70% was more efficient in extracting phenolic compounds with high antioxidant activity. In this extract, salicylic acid was found in high concentrations as well as catechin, epicatechin, and coumaric acid. Based on a consensus that phenolic compounds are associated with the most powerful antioxidant activities of fruits, the “açaí juçara” from the Atlantic Forest is a potential source of polyphenols. They could be used as natural antioxidants for application in the food and pharmaceutical industry in order to substitute the synthetic antioxidants.

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