In silico screening of potential agonists of a glucagon-like peptide-1 receptor among female sex hormone derivatives.

Glucagon-like peptide-1 (GLP-1) is an intestinal hormone that exerts its pleiotropic effects through a specific GLP-1 receptor (GLP-1R). The hormone-receptor complex might regulate glucose-dependent insulin secretion, and energy homeostasis; moreover, it could decrease inflammation and provide cardio- and neuroprotection. Additionally, the beneficial influence of GLP-1 on obesity in women might lead to improvement of their ovarian function. The links between metabolism and reproduction are tightly connected, and it is not surprising that different estrogen derivatives, estrogen-receptor modulator (SERM) and progestins used for gonadal and oncological disorders might influence carbohydrate and lipid metabolism. However, their possible influence on the GLP-1R has not been studied. The docking scores and top-ranked poses of raloxifene were much higher than those observed for other investigated SERMs and estradiol per se. Among different studied progestins, drospirenone showed slightly higher affinity to GLP-1R. Herein, the same data set of the drugs is evaluated by molecular dynamics (MD) simulations and compared with the obtained docking result. Notably, it is demonstrated that the used docking protocol and the applied MD calculations ranked the same ligand (raloxifene) as the best one. In the present study, raloxifene might exert an allosteric influence on GLP-1R signaling, which might contribute to potential beneficial effects on metabolism and weight regulation. However, further experimental and clinical studies are needed to reveal if the GLP-1R modulation has a real biological impact.Communicated by Ramaswamy H. Sarma.

A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

Revealing the Formation Dynamics of Janus Polymer Particles: Insights from Experiments and Molecular Dynamics.

Seeded emulsion polymerization is one of the best-known methods for preparing polymer particles with a controlled size, composition, and shape. It first requires the preparation of seed particles, which are then swollen with additional monomer (the same as the one used for the seed or a different one), to either increase the seed's size or change its morphology. The use of surfactants plays a central role in guaranteeing the required colloidal stability and contributing to the final shape and structure of the particles by lowering the interfacial energy between the polymer of the seed and the added monomer. We here study the polymerization of methyl methacrylate in the presence of polystyrene seed particles at various surfactant concentrations in the presence and absence of a surfactant (sodium dodecyl sulfate). We first show experimentally that the morphology of the colloidal particles can be tuned from Janus to core-shell, depending on the presence or absence of surfactant on the seeds particles' surface. Furthermore, using classical molecular dynamics simulations, we investigate the mechanism and behavior of the surfactants during the first stages of the polymerization process. We use a newly developed approach based on contact statistical analysis to confirm the critical role played by the organization of surfactant molecules on the surface of the seed particles in dictating the final particle morphology.

Machine Learning-Based Screening for Potential Singlet Fission Chromophores: The Challenge of Imbalanced Data Sets.

Excitation with one photon of a singlet fission (SF) material generates two triplet excitons, thus doubling the solar cell efficiency. Therefore, the SF molecules are regarded as new generation organic photovoltaics, but it is hard to identify them. Recently, it was demonstrated that molecules of low-to-intermediate diradical character (DRC) are potential SF chromophores. This prompts a low-cost strategy for finding new SF candidates by computational high-throughput workflows. We propose a machine learning aided screening for SF entrants based on their DRC. Our data set comprises 469 784 compounds extracted from the PubChem database, structurally rich but inherently imbalanced regarding DRC values. We developed well performing classification models that can retrieve potential SF chromophores. The latter (∼4%) were analyzed by K-means clustering to reveal qualitative structure-property relationships and to extract strategies for molecular design. The developed screening procedure and data set can be easily adapted for applications of diradicaloids in photonics and spintronics.

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

Collection of Partition Coefficients in Hexadecyltrimethylammonium Bromide, Sodium Cholate, and Lithium Perfluorooctanesulfonate Micellar Solutions: Experimental Determination and Computational Predictions.

This study focuses on determining the partition coefficients (logP) of a diverse set of 63 molecules in three distinct micellar systems: hexadecyltrimethylammonium bromide (HTAB), sodium cholate (SC), and lithium perfluorooctanesulfonate (LPFOS). The experimental log p values were obtained through micellar electrokinetic chromatography (MEKC) experiments, conducted under controlled pH conditions. Then, Quantum Mechanics (QM) and machine learning approaches are proposed for the prediction of the partition coefficients in these three micellar systems. In the applied QM approach, the experimentally obtained partition coefficients were correlated with the calculated values for the case of the 15 solvent mixtures. Using Density Function Theory (DFT) with the B3LYP functional, we calculated the solvation free energies of 63 molecules in these 16 solvents. The combined data from the experimental partition coefficients in the three micellar formulations showed that the 1-propanol/water combination demonstrated the best agreement with the experimental partition coefficients for the SC and HTAB micelles. Moreover, we employed the SVM approach and k-means clustering based on the generation of the chemical descriptor space. The analysis revealed distinct partitioning patterns associated with specific characteristic features within each identified class. These results indicate the utility of the combined techniques when we want an efficient and quicker model for predicting partition coefficients in diverse micelles.

Computer-Aided Drug Design and Synthesis of Rhenium Clotrimazole Antimicrobial Agents.

In the context of the global health issue caused by the growing occurrence of antimicrobial resistance (AMR), the need for novel antimicrobial agents is becoming alarming. Inorganic and organometallic complexes represent a relatively untapped source of antibiotics. Here, we report a computer-aided drug design (CADD) based on a 'scaffold-hopping' approach for the synthesis and antibacterial evaluation of fac-Re(I) tricarbonyl complexes bearing clotrimazole (ctz) as a monodentate ligand. The prepared molecules were selected following a pre-screening in silico analysis according to modification of the 2,2'-bipyridine (bpy) ligand in the coordination sphere of the complexes. CADD pointed to chiral 4,5-pinene and 5,6-pinene bipyridine derivatives as the most promising candidates. The corresponding complexes were synthesized, tested toward methicillin-sensitive and -resistant S. aureus strains, and the obtained results evaluated with regard to their binding affinity with a homology model of the S. aureus MurG enzyme. Overall, the title species revealed very similar minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values as those of the reference compound used as the scaffold in our approach. The obtained docking scores advocate the viability of 'scaffold-hopping' for de novo design, a potential strategy for more cost- and time-efficient discovery of new antibiotics.

Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach.

This Article proposes a novel chemometric approach to understanding and exploring the allergenic nature of food proteins. Using machine learning methods (supervised and unsupervised), this work aims to predict the allergenicity of plant proteins. The strategy is based on scoring descriptors and testing their classification performance. Partitioning was based on support vector machines (SVM), and a k-nearest neighbor (KNN) classifier was applied. A fivefold cross-validation approach was used to validate the KNN classifier in the variable selection step as well as the final classifier. To overcome the problem of food allergies, a robust and efficient method for protein classification is needed.

Developing an Intelligent Data Analysis Approach for Marine Sediments.

(1) Background: As the chemical and physicochemical properties of marine sediments are closely related to natural and anthropogenic events, it is a real challenge to use their specific assessment as an indicator of environmental pollution discharges. (2) Methods: It is addressed in this study that collection with intelligent data analysis methods, such as cluster analysis, principal component analysis, and source apportionment modeling, are applied for the assessment of the quality of marine sediment and for the identification of the contribution of pollution sources to the formation of the total concentration of polluting species. A study of sediment samples was carried out on 174 samples from three different areas along the coast of the Varna Gulf, Bulgaria. This was performed to determine the effects of pollution. As chemical descriptors, 34 indicators (toxic metals, polyaromatic hydrocarbons, polychlorinated biphenyls, nutrient components, humidity, and ignition loss) were used. The major goal of the present study was to assess the sediment quality in three different areas along the Gulf of Varna, Bulgaria by the source apportionment method. (3) Results: There is a general pattern for identifying three types of pollution sources in each area of the coastline with varying degrees of variation between zone A (industrially impacted zones), zone B (recreational areas), and zone C (anthropogenic and industrial wastes). (4) Conclusions: The quantitative apportionment procedure made it possible to determine the contribution of each identified pollution source for each zone in forming the total pollutant concentrations.

Antimicrobial Activity of Rhenium Di- and Tricarbonyl Diimine Complexes: Insights on Membrane-Bound S. aureus Protein Binding.

Antimicrobial resistance is one of the major human health threats, with significant impacts on the global economy. Antibiotics are becoming increasingly ineffective as drug-resistance spreads, imposing an urgent need for new and innovative antimicrobial agents. Metal complexes are an untapped source of antimicrobial potential. Rhenium complexes, amongst others, are particularly attractive due to their low in vivo toxicity and high antimicrobial activity, but little is known about their targets and mechanism of action. In this study, a series of rhenium di- and tricarbonyl diimine complexes were prepared and evaluated for their antimicrobial potential against eight different microorganisms comprising Gram-negative and -positive bacteria. Our data showed that none of the Re dicarbonyl or neutral tricarbonyl species have either bactericidal or bacteriostatic potential. In order to identify possible targets of the molecules, and thus possibly understand the observed differences in the antimicrobial efficacy of the molecules, we computationally evaluated the binding affinity of active and inactive complexes against structurally characterized membrane-bound S. aureus proteins. The computational analysis indicates two possible major targets for this class of compounds, namely lipoteichoic acids flippase (LtaA) and lipoprotein signal peptidase II (LspA). Our results, consistent with the published in vitro studies, will be useful for the future design of rhenium tricarbonyl diimine-based antibiotics.

A multilevel approach for screening natural compounds as an antiviral agent for COVID-19.

The COVID-19 has a worldwide spread, which has prompted concerted efforts to find successful drug treatments. Drug design focused on finding antiviral therapeutic agents from plant-derived compounds which may disrupt the attachment of SARS-CoV-2 to host cells is with a pivotal need and role in the last year. Herein, we provide an approach based on drug design methods combined with machine learning approaches to classify and discover inhibitors for COVID-19 from natural products. The spike receptor-binding domain (RBD) was docked with database of 125 ligands. The docking protocol based on several steps was performed within Autodock Vina to identify the high-affinity binding mode and to reveal more insights into interaction between the phytochemicals and the RBD domain. A protein-ligand interaction analyzer has been developed. The drug-likeness properties of explored inhibitors are analyzed in the frame of exploratory data analyses. The developed computational protocol yielded a comprehensive pipeline for predicting the inhibitors to prevent the entry RBD region.

Biomedical Applications of Iron Oxide Nanoparticles: Current Insights Progress and Perspectives.

The enormous development of nanomaterials technology and the immediate response of many areas of science, research, and practice to their possible application has led to the publication of thousands of scientific papers, books, and reports. This vast amount of information requires careful classification and order, especially for specifically targeted practical needs. Therefore, the present review aims to summarize to some extent the role of iron oxide nanoparticles in biomedical research. Summarizing the fundamental properties of the magnetic iron oxide nanoparticles, the review's next focus was to classify research studies related to applying these particles for cancer diagnostics and therapy (similar to photothermal therapy, hyperthermia), in nano theranostics, multimodal therapy. Special attention is paid to research studies dealing with the opportunities of combining different nanomaterials to achieve optimal systems for biomedical application. In this regard, original data about the synthesis and characterization of nanolipidic magnetic hybrid systems are included as an example. The last section of the review is dedicated to the capacities of magnetite-based magnetic nanoparticles for the management of oncological diseases.

Persistent organic pollutants (POPs) - QSPR classification models by means of Machine learning strategies.

Persistent Organic pollutants (POPs) are toxic chemicals with a shallow degradation rate and global negative impact. Their physicochemical is combined with the complex effects of long-term POPs accumulation in the environment and transport function through the food chain. That is why POPs have been linked to adverse effects on human health and animals. They circulate globally via different environmental pathways, and could be detected in regions far from their source of origin. The primary goal of the present study is to carry out classification of various representatives of POPs using different theoretical descriptors (molecular, structural) to develop quantitative structure-properties relationship (QSPR) models for predicting important properties POPs. Multivariate statistical methods such as hierarchical cluster analysis, principal components analysis and self-organizing maps were applied to reach excellent partitioning of 149 representatives of POPs into 4 classes using ten most appropriate descriptors (out of 63) defined by variable reduction procedure. The predictive capabilities of the defined classes could be applied as a pattern recognition for new and unidentified POPs, based only on structural properties that similar molecules may have. The additional self-organizing maps technique made it possible to visualize the feature-space and investigate possible patterns and similarities between POPs molecules. It contributes to confirmation of the proper classification into four classes. Based on SOM results, the effect of each variable and pattern formation has been presented.

Inhibition Ability of Natural Compounds on Receptor-Binding Domain of SARS-CoV2: An In Silico Approach.

The lack of medication to treat COVID-19 is still an obstacle that needs to be addressed by all possible scientific approaches. It is essential to design newer drugs with varied approaches. A receptor-binding domain (RBD) is a key part of SARS-CoV-2 virus, located on its surface, that allows it to dock to ACE2 receptors present on human cells, which is followed by admission of virus into cells, and thus infection is triggered. Specific receptor-binding domains on the spike protein play a pivotal role in binding to the receptor. In this regard, the in silico method plays an important role, as it is more rapid and cost effective than the trial and error methods using experimental studies. A combination of virtual screening, molecular docking, molecular simulations and machine learning techniques are applied on a library of natural compounds to identify ligands that show significant binding affinity at the hydrophobic pocket of the RBD. A list of ligands with high binding affinity was obtained using molecular docking and molecular dynamics (MD) simulations for protein-ligand complexes. Machine learning (ML) classification schemes have been applied to obtain features of ligands and important descriptors, which help in identification of better binding ligands. A plethora of descriptors were used for training the self-organizing map algorithm. The model brings out descriptors important for protein-ligand interactions.

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 computational 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 demonstrate 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.

Preparation and Machine-Learning Methods of Nacre-like Composites from the Self-Assembly of Magnetic Colloids Exposed to Rotating Magnetic Fields.

Composite materials designed by nature, such as nacre, can display unique mechanical properties and have therefore been often mimicked by scientists. In this work, we prepared composite materials mimicking the nacre structure in two steps. First, we synthesized a silica gel skeleton with a layered structure using a bottom-up approach by modifying a sol-gel synthesis. Magnetic colloids were added to the sol solution, and a rotating magnetic field was applied during the sol-gel transition. When exposed to a rotating magnetic field, magnetic colloids organize in layers parallel to the plane of rotation of the field and template the growing silica phase, resulting in a layered anisotropic silica network mimicking the nacre's inorganic phase. Heat treatment has been applied to further harden the silica monoliths. The final nacre-inspired composite is created by filling the porous structure with a monomer, leading to a soft elastomer upon polymerization. Compression tests of the platelet-structured composite show that the mechanical properties of the nacre-like composite material far exceed those of nonstructured composite materials with an identical chemical composition. Increased toughness and a nearly 10-fold increase in Young's modulus were achieved. The natural brittleness and low elastic deformation of silica monoliths could be overcome by mimicking the natural architecture of nacre. Pattern recognition obtained with a classification of machine learning algorithms was applied to achieve a better understanding of the physical and chemical parameters that have the highest impact on the mechanical properties of the monoliths. Multivariate statistical analysis was performed to show that the structural control and the heat treatment have a very strong influence on the mechanical properties of the monoliths.

Tracing the movement of persistent organic pollutants at a high-mountain sampling site by chemometric assessment.

The main objective of the present study was to determine and differentiate the concentration levels, to define the probable sources of persistent organic pollutants (POPs) pollution in the atmospheric air and their seasonal variations in Bulgaria, on the high mountain peak Moussala, Rila Mountain. The study was based on the obtained results from the passive monitoring of POPs in 2014-2017. During this period, the measurements of POPs were performed with passive samplers, advanced instrumental methods analytically determined the concentrations of PAHs, and the analysis of the obtained data was performed by the multivariate statistical analysis (cluster, factor and time-series analysis). It is shown that the POPs species could be correctly classified according to their chemical nature into several patterns of similarity and their concentration profile depends on the annual season.

Multivariate Statistical Approach for Nephrines in Women with Obesity.

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2.

A new original procedure based on k-means clustering is designed to find the most appropriate clinical variables able to efficiently separate into groups similar patients diagnosed with diabetes mellitus type 2 (DMT2) and underlying diseases (arterial hypertonia (AH), ischemic heart disease (CHD), diabetic polyneuropathy (DPNP), and diabetic microangiopathy (DMA)). Clustering is a machine learning tool for discovering structures in datasets. Clustering has been proven to be efficient for pattern recognition based on clinical records. The considered combinatorial k-means procedure explores all possible k-means clustering with a determined number of descriptors and groups. The predetermined conditions for the partitioning were as follows: every single group of patients included patients with DMT2 and one of the underlying diseases; each subgroup formed in such a way was subject to partitioning into three patterns (good health status, medium health status, and degenerated health status); optimal descriptors for each disease and groups. The selection of the best clustering is obtained through the parameter called global variance, defined as the sum of all variance values of all clinical variables of all the clusters. The best clinical parameters are found by minimizing this global variance. This methodology has to identify a set of variables that are assumed to separate each underlying disease efficiently in three different subgroups of patients. The hierarchical clustering obtained for these four underlying diseases could be used to build groups of patients with correlated clinical data. The proposed methodology gives surmised results from complex data based on a relationship with the health status of the group and draws a picture of the prediction rate of the ongoing health status.

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.