Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis.

Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved 'high-coverage' and 'high-accuracy' glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

Pharmacoinformatics and molecular dynamics simulation-based phytochemical screening of neem plant (Azadiractha indica) against human cancer by targeting MCM7 protein.

Minichromosome maintenance complex component 7 (MCM7) belongs to the minichromosome maintenance family that is important for the initiation of eukaryotic DNA replication. Overexpression of the MCM7 protein is relative to cellular proliferation and responsible for aggressive malignancy in various cancers. Mechanistically, inhibition of MCM7 significantly reduces the cellular proliferation associated with cancer. To date, no effective small molecular candidate has been identified that can block the progression of cancer induced by the MCM7 protein. Therefore, the study has been designed to identify small molecular-like natural drug candidates against aggressive malignancy associated with various cancers by targeting MCM7 protein. To identify potential compounds against the targeted protein a comprehensive in silico drug design including molecular docking, ADME (Absorption, Distribution, Metabolism and Excretion), toxicity, and molecular dynamics (MD) simulation approaches has been applied. Seventy phytochemicals isolated from the neem tree (Azadiractha indica) were retrieved and screened against MCM7 protein by using the molecular docking simulation method, where the top four compounds have been chosen for further evaluation based on their binding affinities. Analysis of ADME and toxicity properties reveals the efficacy and safety of the selected four compounds. To validate the stability of the protein-ligand complex structure MD simulations approach has also been performed to the protein-ligand complex structure, which confirmed the stability of the selected three compounds including CAS ID:105377-74-0, CID:12308716 and CID:10505484 to the binding site of the protein. In the study, a comprehensive data screening process has performed based on the docking, ADMET properties, and MD simulation approaches, which found a good value of the selected four compounds against the targeted MCM7 protein and indicates as a promising and effective human anticancer agent.

BigSMARTS: A Topologically Aware Query Language and Substructure Search Algorithm for Polymer Chemical Structures.

Molecular search is important in chemistry, biology, and informatics for identifying molecular structures within large data sets, improving knowledge discovery and innovation, and making chemical data FAIR (findable, accessible, interoperable, reusable). Search algorithms for polymers are significantly less developed than those for small molecules because polymer search relies on searching by polymer name, which can be challenging because polymer naming is overly broad (i.e., polyethylene), complicated for complex chemical structures, and often does not correspond to official IUPAC conventions. Chemical structure search in polymers is limited to substructures, such as monomers, without awareness of connectivity or topology. This work introduces a novel query language and graph traversal search algorithm for polymers that provides the first search method able to fully capture all of the chemical structures present in polymers. The BigSMARTS query language, an extension of the small-molecule SMARTS language, allows users to write queries that localize monomer and functional group searches to different parts of the polymer, like the middle block of a triblock, the side chain of a graft, and the backbone of a repeat unit. The substructure search algorithm is based on the traversal of graph representations of the generating functions for the stochastic graphs of polymers. Operationally, the algorithm first identifies cycles representing the monomers and then the end groups and finally performs a depth-first search to match entire subgraphs. To validate the algorithm, hundreds of queries were searched against hundreds of target chemistries and topologies from the literature, with approximately 440,000 query-target pairs. This tool provides a detailed algorithm that can be implemented in search engines to provide search results with full matching of the monomer connectivity and polymer topology.

Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future.

The dawn of the 21st century has brought with it a surge of research related to computer-guided approaches to catalyst design. In the past two decades, chemoinformatics, the application of informatics to solve problems in chemistry, has increasingly influenced prediction of activity and mechanistic investigations of organic reactions. The advent of advanced statistical and machine learning methods, as well as dramatic increases in computational speed and memory, has contributed to this emerging field of study. This review summarizes strategies to employ quantitative structure-selectivity relationships (QSSR) in asymmetric catalytic reactions. The coverage is structured by initially introducing the basic features of these methods. Subsequent topics are discussed according to increasing complexity of molecular representations. As the most applied subfield of QSSR in enantioselective catalysis, the application of local parametrization approaches and linear free energy relationships (LFERs) along with multivariate modeling techniques is described first. This section is followed by a description of global parametrization methods, the first of which is continuous chirality measures (CCM) because it is a single parameter derived from the global structure of a molecule. Chirality codes, global, multivariate descriptors, are then introduced followed by molecular interaction fields (MIFs), a global descriptor class that typically has the highest dimensionality. To highlight the current reach of QSSR in enantioselective transformations, a comprehensive collection of examples is presented. When combined with traditional experimental approaches, chemoinformatics holds great promise to predict new catalyst structures, rationalize mechanistic behavior, and profoundly change the way chemists discover and optimize reactions.

Recommendations for the Nonpharmacological Treatment of Apathy in Brain Disorders.

Apathy is a common neuropsychiatric syndrome observed across many neurocognitive and psychiatric disorders. Although there are currently no definitive standard therapies for the treatment of apathy, nonpharmacological treatment (NPT) is often considered to be at the forefront of clinical management. However, guidelines on how to select, prescribe, and administer NPT in clinical practice are lacking. Furthermore, although new Information and Communication Technologies (ICT) are beginning to be employed in NPT, their role is still unclear. The objective of the present work is to provide recommendations for the use of NPT for apathy, and to discuss the role of ICT in this domain, based on opinions gathered from experts in the field. The expert panel included 20 researchers and healthcare professionals working on brain disorders and apathy. Following a standard Delphi methodology, experts answered questions via several rounds of web-surveys, and then discussed the results in a plenary meeting. The experts suggested that NPT are useful to consider as therapy for people presenting with different neurocognitive and psychiatric diseases at all stages, with evidence of apathy across domains. The presence of a therapist and/or a caregiver is important in delivering NPT effectively, but parts of the treatment may be performed by the patient alone. NPT can be delivered both in clinical settings and at home. However, while remote treatment delivery may be cost and time-effective, it should be considered with caution, and tailored based on the patient's cognitive and physical profile and living conditions.

Mining Natural Product Biosynthesis in Eukaryotic Algae.

Eukaryotic algae are an extremely diverse category of photosynthetic organisms and some species produce highly potent bioactive compounds poisonous to humans or other animals, most notably observed during harmful algal blooms. These natural products include some of the most poisonous small molecules known and unique cyclic polyethers. However, the diversity and complexity of algal genomes means that sequencing-based research has lagged behind research into more readily sequenced microbes, such as bacteria and fungi. Applying informatics techniques to the algal genomes that are now available reveals new natural product biosynthetic pathways, with different groups of algae containing different types of pathways. There is some evidence for gene clusters and the biosynthetic logic of polyketides enables some prediction of these final products. For other pathways, it is much more challenging to predict the products and there may be many gene clusters that are not identified with the automated tools. These results suggest that there is a great diversity of biosynthetic capacity for natural products encoded in the genomes of algae and suggest areas for future research focus.

An Informatics Framework to Assess Consumer Health Language Complexity Differences: Proof-of-Concept Study.

BACKGROUND: The language gap between health consumers and health professionals has been long recognized as the main hindrance to effective health information comprehension. Although providing health information access in consumer health language (CHL) is widely accepted as the solution to the problem, health consumers are found to have varying health language preferences and proficiencies. To simplify health documents for heterogeneous consumer groups, it is important to quantify how CHLs are different in terms of complexity among various consumer groups. OBJECTIVE: This study aimed to propose an informatics framework (consumer health language complexity [CHELC]) to assess the complexity differences of CHL using syntax-level, text-level, term-level, and semantic-level complexity metrics. Specifically, we identified 8 language complexity metrics validated in previous literature and combined them into a 4-faceted framework. Through a rank-based algorithm, we developed unifying scores (CHELC scores [CHELCS]) to quantify syntax-level, text-level, term-level, semantic-level, and overall CHL complexity. We applied CHELCS to compare posts of each individual on online health forums designed for (1) the general public, (2) deaf and hearing-impaired people, and (3) people with autism spectrum disorder (ASD). METHODS: We examined posts with more than 4 sentences of each user from 3 health forums to understand CHL complexity differences among these groups: 12,560 posts from 3756 users in Yahoo! Answers, 25,545 posts from 1623 users in AllDeaf, and 26,484 posts from 2751 users in Wrong Planet. We calculated CHELCS for each user and compared the scores of 3 user groups (ie, deaf and hearing-impaired people, people with ASD, and the public) through 2-sample Kolmogorov-Smirnov tests and analysis of covariance tests. RESULTS: The results suggest that users in the public forum used more complex CHL, particularly more diverse semantics and more complex health terms compared with users in the ASD and deaf and hearing-impaired user forums. However, between the latter 2 groups, people with ASD used more complex words, and deaf and hearing-impaired users used more complex syntax. CONCLUSIONS: Our results show that the users in 3 online forums had significantly different CHL complexities in different facets. The proposed framework and detailed measurements help to quantify these CHL complexity differences comprehensively. The results emphasize the importance of tailoring health-related content for different consumer groups with varying CHL complexities.

COVID-19 Research: Navigating the European General Data Protection Regulation.

Researchers must collaborate globally to rapidly respond to the COVID-19 pandemic. In Europe, the General Data Protection Regulation (GDPR) regulates the processing of personal data, including health data of value to researchers. Even during a pandemic, research still requires a legal basis for the processing of sensitive data, additional justification for its processing, and a basis for any transfer of data outside Europe. The GDPR does provide legal grounds and derogations that can support research addressing a pandemic, if the data processing activities are proportionate to the aim pursued and accompanied by suitable safeguards. During a pandemic, a public interest basis may be more promising for research than a consent basis, given the high standards set out in the GDPR. However, the GDPR leaves many aspects of the public interest basis to be determined by individual Member States, which have not fully or uniformly made use of all options. The consequence is an inconsistent legal patchwork that displays insufficient clarity and impedes joint approaches. The COVID-19 experience provides lessons for national legislatures. Responsiveness to pandemics requires clear and harmonized laws that consider the related practical challenges and support collaborative global research in the public interest.

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery.

BACKGROUND: Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems. RESULTS: In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, edge2vec significantly outperforms state-of-the-art models on all three tasks. CONCLUSIONS: We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.

Multitask Modeling with Confidence Using Matrix Factorization and Conformal Prediction.

Multitask prediction of bioactivities is often faced with challenges relating to the sparsity of data and imbalance between different labels. We propose class conditional (Mondrian) conformal predictors using underlying Macau models as a novel approach for large scale bioactivity prediction. This approach handles both high degrees of missing data and label imbalances while still producing high quality predictive models. When applied to ten assay end points from PubChem, the models generated valid models with an efficiency of 74.0-80.1% at the 80% confidence level with similar performance both for the minority and majority class. Also when deleting progressively larger portions of the available data (0-80%) the performance of the models remained robust with only minor deterioration (reduction in efficiency between 5 and 10%). Compared to using Macau without conformal prediction the method presented here significantly improves the performance on imbalanced data sets.

Molecule Property Prediction Based on Spatial Graph Embedding.

Accurate prediction of molecular properties is important for new compound design, which is a crucial step in drug discovery. In this paper, molecular graph data is utilized for property prediction based on graph convolution neural networks. In addition, a convolution spatial graph embedding layer (C-SGEL) is introduced to retain the spatial connection information on molecules. And, multiple C-SGELs are stacked to construct a convolution spatial graph embedding network (C-SGEN) for end-to-end representation learning. In order to enhance the robustness of the network, molecular fingerprints are also combined with C-SGEN to build a composite model for predicting molecular properties. Our comparative experiments have shown that our method is accurate and achieves the best results on some open benchmark datasets.

Converging a Knowledge-Based Scoring Function: DrugScore2018.

We present DrugScore2018, a new version of the knowledge-based scoring function DrugScore, which builds upon the same formalism used to derive DrugScore but exploits a training data set of nearly 40 000 X-ray complex structures, a highly diverse and the, by far, largest data set ever used for such an endeavor. About 2.5 times as many pair potentials than before now have a data basis required to yield smooth potentials, and pair potentials could now be derived for eight more atom types, including interactions involving halogen atoms and metal ions highly relevant for medicinal chemistry. Probing for dependence on training data set size and quality, we show that DrugScore2018 potentials are converged. We evaluated DrugScore2018 in comprehensive scoring, ranking, docking, and screening tests on the CASF-2013 data set, allowing for a comparison with >30 other scoring functions. There, DrugScore2018 showed similar or improved performance in all aspects when compared to either DrugScore, DrugScoreCSD, or DSX and was, overall, the scoring function showing the most consistently good performance in scoring, ranking, and docking tests. Applying DrugScore2018 as objective function in AutoDock3 in a large-scale docking trial, using 4056 protein-ligand complexes from PDBbind 2016, reproduced a docked pose to within 2 ŠRMSD to the crystal structure in >75% of all dockings. These results are remarkable as the DrugScore2018 potentials were derived from crystallographic information only, without any further adaptation using binding affinity or docking decoy data. DrugScore2018 should thus be a competitive scoring and objective function for structure-based ligand design purposes.

Evaluation of Cross-Validation Strategies in Sequence-Based Binding Prediction Using Deep Learning.

Binding prediction between targets and drug-like compounds through deep neural networks has generated promising results in recent years, outperforming traditional machine learning-based methods. However, the generalization capability of these classification models is still an issue to be addressed. In this work, we explored how different cross-validation strategies applied to data from different molecular databases affect to the performance of binding prediction proteochemometrics models. These strategies are (1) random splitting, (2) splitting based on K-means clustering (both of actives and inactives), (3) splitting based on source database, and (4) splitting based both in the clustering and in the source database. These schemas are applied to a deep learning proteochemometrics model and to a simple logistic regression model to be used as baseline. Additionally, two different ways of describing molecules in the model are tested: (1) by their SMILES and (2) by three fingerprints. The classification performance of our deep learning-based proteochemometrics model is comparable to the state of the art. Our results show that the lack of generalization of these models is due to a bias in public molecular databases and that a restrictive cross-validation schema based on compound clustering leads to worse but more robust and credible results. Our results also show better performance when representing molecules by their fingerprints.

Conservation and Relevance of Pharmacophore Point Types.

Pharmacophore models in general use a variety of features for distinct chemical characteristics, such as hydrogen-bond properties, lipohilicity, and ionizability. Usually, features have to match onto their identical type. To clarify if this stringent one-to-one assignment is justified, we investigated a set of 581 unique ligands from the BindingDB with known orientation inside the respective binding pockets and conducted a statistical analysis of the likelihood of observed exchanges in between the pharmacophore features, respectively their degree of conservation. To find out if certain features are obsolete, we derived a ranking to determine the most relevant ones. We found that the most conserved one-to-one feature is the negative ionizable (acids), followed by hydrogen-bond donor, positive ionizable (basic nitrogens), hydrogen-bond acceptor, aromatic, nonaromatic π-systems, and other lipophilic characteristics. The most likely exchanges were found between carboxylate groups and hydrogen-bond acceptors and likewise between basic nitrogens and hydrogen-bond donors, which reflects the characteristics of Lewis acids and bases. Exchanges between hydrogen-bond donors and hydrogen-bond acceptors are hardly more likely than by chance. The kind of target (e.g., kinase, phosphatase, protease, phosphodiesterase, nuclear receptor, metal-containing, or transmembrane protein) did not show substantial influence on the degree of conservation. The relevance of the actual pharmacophore features was found to be strongly dependent on the applied ranking scheme. Mutual information ranks all hydrophobic features as least important, whereas the aromatic feature is put into second place by using a geometric series. Both ranking schemes see the negative ionizable feature of higher significance than the positively ionizable feature.

Analysis of Volatile Compounds by Advanced Analytical Techniques and Multivariate Chemometrics.

Smelling is one of the five senses, which plays an important role in our everyday lives. Volatile compounds are, for example, characteristics of food where some of them can be perceivable by humans because of their aroma. They have a great influence on the decision making of consumers when they choose to use a product or not. In the case where a product has an offensive and strong aroma, many consumers might not appreciate it. On the contrary, soft and fresh natural aromas definitely increase the acceptance of a given product. These properties can drastically influence the economy; thus, it has been of great importance to manufacturers that the aroma of their food product is characterized by analytical means to provide a basis for further optimization processes. A lot of research has been devoted to this domain in order to link the quality of, e.g., a food to its aroma. By knowing the aromatic profile of a food, one can understand the nature of a given product leading to developing new products, which are more acceptable by consumers. There are two ways to analyze volatiles: one is to use human senses and/or sensory instruments, and the other is based on advanced analytical techniques. This work focuses on the latter. Although requirements are simple, low-cost technology is an attractive research target in this domain; most of the data are generated with very high-resolution analytical instruments. Such data gathered based on different analytical instruments normally have broad, overlapping sensitivity profiles and require substantial data analysis. In this review, we have addressed not only the question of the application of chemometrics for aroma analysis but also of the use of different analytical instruments in this field, highlighting the research needed for future focus.

Metaproteomic and 16S rRNA Gene Sequencing Analysis of the Infant Fecal Microbiome.

A metaproteomic analysis was conducted on the fecal microbiome of eight infants to characterize global protein and pathway expression. Although mass spectrometry-based proteomics is now a routine tool, analysis of the microbiome presents specific technical challenges, including the complexity and dynamic range of member taxa, the need for well-annotated metagenomic databases, and high inter-protein sequence redundancy and similarity. In this study, an approach was developed for assessment of biological phenotype and metabolic status, as a functional complement to DNA sequence analysis. Fecal samples were prepared and analysed by tandem mass spectrometry and a homology-based meta-clustering strategy was used to combine peptides from multiple species into representative proteins. In total, 15,250 unique peptides were sequenced and assigned to 2154 metaclusters, which were then assigned to pathways and functional groups. Differences were noted in several pathways, consistent with the dominant genera observed in different subjects. Although this study was not powered to draw conclusions from the comparisons, the results obtained demonstrate the applicability of this approach and provide the methods needed for performing semi-quantitative comparisons of human fecal microbiome composition, physiology and metabolism, as well as a more detailed assessment of microbial composition in comparison to 16S rRNA gene sequencing.

[Digital psychiatry]. / Digitale psychiatrie.

BACKGROUND: The digitization of society has an increasing impact on healthcare in general and, therefore, also on psychiatry.
AIM: To provide an overview of digital developments and their influence on the design of future professional psychiatric care.
METHOD: With the help of examples from literature, show how digitization will influence diagnostic procedures as well as psychiatric treatment.
RESULTS: Digitization will have a major impact on psychiatric diagnostics and treatment. For example, psychiatric diagnostics will be strengthened by continuous monitoring of behaviour with digital wearables and the collection of large amounts of personal data. How we deal with these new sources of information needs to be developed in everyday practice. Psychiatric treatments with E-health, online therapies, apps and virtual reality are being developed rapidly. There is increasing evidence concerning the efficacy of these treatments in a variety of patient groups.
CONCLUSION: The digital revolution in psychiatric health services has just begun. To maximise the benefits of digitization for psychiatry, it is necessary to connect technological possibilities with well-founded scientific knowledge, professional expertise, expectations and needs of patients, and clear legal instructions.

Understanding the glycome: an interactive view of glycosylation from glycocompositions to glycoepitopes.

Nowadays, due to the advance of experimental techniques in glycomics, large collections of glycan profiles are regularly published. The rapid growth of available glycan data accentuates the lack of innovative tools for visualizing and exploring large amount of information. Scientists resort to using general-purpose spreadsheet applications to create ad hoc data visualization. Thus, results end up being encoded in publication images and text, while valuable curated data is stored in files as supplementary information. To tackle this problem, we have built an interactive pipeline composed with three tools: Glynsight, EpitopeXtractor and Glydin'. Glycan profile data can be imported in Glynsight, which generates a custom interactive glycan profile. Several profiles can be compared and glycan composition is integrated with structural data stored in databases. Glycan structures of interest can then be sent to EpitopeXtractor to perform a glycoepitope extraction. EpitopeXtractor results can be superimposed on the Glydin' glycoepitope network. The network visualization allows fast detection of clusters of glycoepitopes and discovery of potential new targets. Each of these tools is standalone or can be used in conjunction with the others, depending on the data and the specific interest of the user. All the tools composing this pipeline are part of the Glycomics@ExPASy initiative and are available at https://www.expasy.org/glycomics.

Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty.

Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed to be correct to the selected extent. In this study, we apply conformal regression to model molecular properties and bioactivity values and investigate different ways to scale the resultant prediction intervals to create as efficient (i.e., narrow) regressors as possible. Different algorithms to estimate the prediction uncertainty were used to normalize the prediction ranges, and the different approaches were evaluated on 29 publicly available data sets. Our results show that the most efficient conformal regressors are obtained when using the natural exponential of the ensemble standard deviation from the underlying random forest to scale the prediction intervals, but other approaches were almost as efficient. This approach afforded an average prediction range of 1.65 pIC50 units at the 80% confidence level when applied to bioactivity modeling. The choice of nonconformity function has a pronounced impact on the average prediction range with a difference of close to one log unit in bioactivity between the tightest and widest prediction range. Overall, conformal regression is a robust approach to generate bioactivity predictions with associated confidence.

Influence of Descriptor Implementation on Compound Ranking Based on Multiparameter Assessment.

Most of the common molecular descriptors have numerous different implementations. This can influence the results of compound prioritization based on the multiparameter assessment (MPA) approach that allows a medicinal chemist to simultaneously analyze and achieve the desired balance of the diverse and often conflicting molecular and pharmacological properties. In this study, we analyzed the feasibility of using different implementations of common descriptors (logP, logS, TPSA, logBB, hERG, nHBA) interchangeably in predesigned sets of requirements in the course of multiparameter compound optimization. The influence of methods of descriptor calculation, continuity or discreteness of their values, their applicability domains, as well as of the nature of desirability functions in an MPA profile were examined in terms of the stability of MPA compound ranking. It was shown that the interchangeable use of different methods of descriptor calculation is reliably acceptable only for continuously distributed parameters transformed by a smooth desirability function. If a descriptor in an MPA scheme is discretely distributed, only the implementation that was used for building the scoring profile may be used for assessment. An inconsistency of assessment due to different applicability domains of descriptors was also demonstrated.