Applying discriminant and cluster analysis to separate allergenic from non-allergenic proteins

As a result of increased healthcare requirements and the introduction of genetically modified foods, the problem of allergies is becoming a growing health problem. The concept of allergies has prompted the use of new methods such as genomics and proteomics to uncover the nature of allergies. In the present study, a selection of 1400 food proteins was analysed by PLS-DA (Partial Least Square-based Discriminant Analysis) after suitable transformation of structural parameters into uniform vectors. Then, the resulting strings of different length were converted into vectors with equal length by Auto and Cross-Covariance (ACC) analysis. Hierarchical and non-hierarchical (K-means) Cluster Analysis (CA) was also performed in order to reach a certain level of separation within a small training set of plant proteins (16 allergenic and 16 non-allergenic) using a new three-dimensional descriptor based on surface protein properties in combination with amino acid hydrophobicity scales. The novelty of the approach in protein differentiation into allergenic and non-allergenic classes is described in the article. The general goal of the present study was to show the effectiveness of a traditional chemometric method for classification (PLS-DA) and the options of Cluster Analysis (CA) to separate by multivariate statistical methods allergenic from non-allergenic proteins

Unveiling the Effect of Low pH on the SARS-CoV-2 Main Protease by Molecular Dynamics Simulations

(1) Background: Main Protease (Mpro) is an attractive therapeutic target that acts in the replication and transcription of the SARS-CoV-2 coronavirus. Mpro is rich in residues exposed to protonation/deprotonation changes which could affect its enzymatic function. This work aimed to explore the effect of the protonation/deprotonation states of Mpro at different pHs using computa- tional techniques. (2) Methods: The different distribution charges were obtained in all the evaluated pHs by the Semi-Grand Canonical Monte Carlo (SGCMC) method. A set of Molecular Dynamics (MD) simulations was performed to consider the different protonation/deprotonation during 250 ns, verifying the structural stability of Mpro at different pHs. (3) Results: The present findings demon- strate that active site residues and residues that allow Mpro dimerisation was not affected by pH changes. However, Mpro substrate-binding residues were altered at low pHs, allowing the increased pocket volume. Additionally, the results of the solvent distribution around Sγ, Hγ, Nδ1 and Hδ1 atoms of the catalytic residues Cys145 and His41 showed a low and high-water affinity at acidic pH, respectively. It which could be crucial in the catalytic mechanism of SARS-CoV-2 Mpro at low pHs. Moreover, we analysed the docking interactions of PF-00835231 from Pfizer in the preclinical phase, which shows excellent affinity with the Mpro at different pHs. (4) Conclusion: Overall, these findings indicate that SARS-CoV-2 Mpro is highly stable at acidic pH conditions, and this inhibitor could have a desirable function at this condition

Vibrational Analysis of Manganese(II) Oxalates Hydrates: An In Silico Statistical Approach

The experimental and computational vibrational study for three different manganese(II) oxalates hydrates was explored. The elucidation of IR and Raman spectra were discussed based on their structural singularity; in the same way, they establish some interesting relations between them in the field of computational and statistical approaches. The density functional theory (DFT) computational approach was conducted for accurate prediction and interpretation of the intermolecular effects based on experimental and calculated IR and Raman spectra in the solid-state data in combination with multivariate statistical technique. The proposed computational scheme was also explored for the case of the isolated-molecule model. The goals of the study were to access the accuracy of the proposed procedure for solid-state calculations along with electron calculations for the isolated molecules and to reveal the similarities within the groups of objects by the cluster analysis (CA) techniques and two-way CA for the data. The presented simulation procedure should be very valuable for exploring and to classify other oxalate compounds

N-terminal acetylation shields proteins from degradation and promotes age-dependent motility and longevity

Most eukaryotic proteins are N-terminally acetylated, but the functional impact on a global scale has remained obscure. Using genome-wide CRISPR knockout screens in human cells, we reveal a strong genetic dependency between a major N-terminal acetyltransferase and specific ubiquitin ligases. Biochemical analyses uncover that both the ubiquitin ligase complex UBR4-KCMF1 and the acetyltransferase NatC recognize proteins bearing an unacetylated N-terminal methionine followed by a hydrophobic residue. NatC KO-induced protein degradation and phenotypes are reversed by UBR knockdown, demonstrating the central cellular role of this interplay. We reveal that loss of Drosophila NatC is associated with male sterility, reduced longevity, and age-dependent loss of motility due to developmental muscle defects. Remarkably, muscle-specific overexpression of UbcE2M, one of the proteins targeted for NatC KO-mediated degradation, suppresses defects of NatC deletion. In conclusion, NatC-mediated N-terminal acetylation acts as a protective mechanism against protein degradation, which is relevant for increased longevity and motility. The most common protein modification in eukaryotes is N-terminal acetylation, but its functional impact has remained enigmatic. Here, the authors find that a key role for N-terminal acetylation is shielding proteins from ubiquitin ligase-mediated degradation, mediating motility and longevity.Association Francaise contre les Myopathies 261981, Canadian Institutes of Health Research (CIHR) 249843, United States Department of Health & Human Services National Institutes of Health (NIH) - USA F-12540, Portuguese national funding through Fundaco para a Ciencia e a Tecnologia (FCT) 171752-PR-2009-0222, National Funds through Fundaco para a Ciencia e a Tecnologia (FCT) G008018N, G002721N, University of Bergen MC_UU_00028/6, FDN-143264, FDN-143265, PJT-180285, PJT-463531, R01HG005853, R01HG005084, DL 57/2016/CP1361/CT0019, 2022.01782.PTDC,PTDC/BIA-BID/28441/2017,PTDC/BIA-BID/1606/2020, ALG-01-0145-FEDER-028441, PPBI-POCI-01-0145-FEDER-022122, LISBOA-01-0145-FEDER-022170info:eu-repo/semantics/publishedVersio

Retention and diffusion of radioactive and toxic species on cementitious systems: Main outcome of the CEBAMA project

Cement-based materials are key components in radioactive waste repository barrier systems. To improve the available knowledge base, the European CEBAMA (Cement-based materials) project aimed to provide insight on general processes and phenomena that can be easily transferred to different applications. A bottom up approach was used to study radionuclide retention by cementitious materials, encompassing both individual cement mineral phases and hardened cement pastes. Solubility experiments were conducted with Be, Mo and Se under high pH conditions to provide realistic solubility limits and radionuclide speciation schemes as a prerequisite for meaningful adsorption studies. A number of retention mechanisms were addressed including adsorption, solid solution formation and precipitation of radionuclides within new solid phases formed during cement hydration and evolution. Sorption/desorption experiments were carried out on several anionic radionuclides and/or toxic elements which have received less attention to date, namely: Be, Mo, Tc, I, Se, Cl, Ra and 14C. Solid solution formation between radionuclides in a range of oxidation states (Se, I and Mo) with the main aqueous components (OH−, SO4 −2, Cl−) of cementitious systems on AFm phases were also investigated

Contemporary Challenges and Solutions

CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.publishersversionpublishe

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies

NO reduction by CO over gold catalysts based on ceria supports prepared by mechanochemical activation modified by Me3+ (Me=Al or lanthanides): Effect of water in the feed gas

Cataloged from PDF version of article.The reduction of NO by CO was studied over gold catalysts supported on ceria modified by Me3+ ions (Me = Al, La, Sm, Gd and Yb). The ceria supports were prepared by mechanochemical activation. The samples were characterized using XRD, TPR, XPS and Raman spectroscopy. According to the XPS data the concentration of the oxidized gold species was higher than that of metallic gold in the fresh samples modified by lanthanides. On the fresh samples modified by Al only a small part of metallic gold existed in oxidized state. After the catalytic test, only metallic gold was found on the lanthanide-containing catalysts while on the M-modified catalyst a small amount of oxidized Au species in addition to metallic Au was detected. No substantial differences in the average particle sizes of gold, the lattice parameters and the average size of ceria particles were observed. The nature of the modifier and the applied method of ceria supports preparation and gold deposition determined most likely the differences observed in the Raman and TPR data, as well as the catalytic activity results. The catalytic tests were performed under two different conditions: (i) in the presence of H-2 in the gas feed and (ii) adding also water to the gas feed. The lowest activity was observed over the Al-containing catalyst under dry feed, which correlates with the TPR results. The addition of water to the feed led to a significant improvement of the NO and CO conversions over all of the samples studied. At 200 degrees C, Yb-containing gold catalyst exhibited the highest NO and CO conversions. Very promising results for the selectivity toward N-2 were achieved using the lanthanides as dopants. In contrast to the gold supported on Al-doped ceria, no NH3 formation was observed within the whole temperature interval up to 400 degrees C over gold catalysts supported on ceria modified by La, Sm, Gd or Yb. (C) 2009 Elsevier B.V. All rights reserved

Applying Discriminant and Cluster Analyses to Separate Allergenic from Non-allergenic Proteins

As a result of increased healthcare requirements and the introduction of genetically modified foods, the problem of allergies is becoming a growing health problem. The concept of allergies has prompted the use of new methods such as genomics and proteomics to uncover the nature of allergies. In the present study, a selection of 1400 food proteins was analysed by PLS-DA (Partial Least Square-based Discriminant Analysis) after suitable transformation of structural parameters into uniform vectors. Then, the resulting strings of different length were converted into vectors with equal length by Auto and Cross-Covariance (ACC) analysis. Hierarchical and non-hierarchical (K-means) Cluster Analysis (CA) was also performed in order to reach a certain level of separation within a small training set of plant proteins (16 allergenic and 16 non-allergenic) using a new three-dimensional descriptor based on surface protein properties in combination with amino acid hydrophobicity scales. The novelty of the approach in protein differentiation into allergenic and non-allergenic classes is described in the article

Mechanistic investigations of the promoting role of Rh on the NSR performance of NOx storage BaO-based catalysts

To determine the promoting effect of Rh on the overall NOx storage and reduction (NSR) performance, the studies in the current work were directed toward investigating the storage and release ability over Rh NOx storage BaO-based catalysts compared to Pt. In terms of the metal surface dispersion and the ability of the noble metals to release oxygen at lower temperatures, the synthesized catalysts were characterized by means of dynamic CO chemisorption (RT) and N2O dissociation (RI - 773 K). The NOx storage capacity and the thermal stability of the NOx adsorbed species formed on the surface were analyzed via NOx storage tests and temperature programmed desorption (TPD) without and in the presence of CO2 and H2O. In addition, experiments with lean and rich cycling were conducted at 473,573 and 673 K. The results from the N2O dissociation experiments showed the superior ability of Rh/Al and Rh/Ba/Al catalysts compared to Pt toward O-2 release from the catalytic surface at lower temperatures. In this work, we show that the presence of Rh into the BaO/gamma-Al2O3 system has a considerable effect on the spill-over process of NOx to the precious metal, controlling the subsequent desorption of NOx to occur at lower temperatures in comparison with that of the Pt catalysts. It is suggested a mechanism of NOx desorption where the lower temperature of O-2 release from the surface of Rh catalysts could leave a significant number of noble metal sites accessible for adsorption. Thus this could facilitate the rate of spill-over of NOx from the storage site (the surface sites on gamma-Al2O3 and those on BaO) to the noble metal and their desorption at lower temperatures. The limited NOx storage ability of the Rh-based BaO/gamma-Al2O3 catalysts under lean-burn conditions was found to originate from both low NO oxidation activity and NOx reduction activity, while the main limiting factor for the low NSR performance of the Pt-based catalysts was the limited regeneration ability during rich period