Drug repositioning for enzyme modulator based on human metabolite-likeness

Background

Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme’s metabolites and drugs.

Methods

We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden’s index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness.

Results

In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for their corresponding metabolites. In addition, we showed that drug repositioning results of 10 enzymes were concordant with the literature evidence.

Conclusions

This study introduced a method to predict the repositioning of known drugs to possible modulators of disease associated enzymes using human metabolite-likeness. We demonstrated that this approach works correctly with known antimetabolite drugs and showed that the proposed method has better performance compared to other drug target prediction methods in terms of enzyme modulators prediction. This study as a proof-of-concept showed how to apply metabolite-likeness to drug repositioning as well as potential in further expansion as we acquire more disease associated metabolite-target protein relations.

     

Understanding the Jasmine phenotype of rice through metabolite profiling and sensory evaluation

Introduction

Aromatic rices are culturally and economically important for many countries in Asia. Investigation of the volatile compounds emitted by rice during cooking is the key to understanding the flavour of elite aromatic rice varieties.

Objectives

The objectives of this study were to compare Jasmine-type aromatic rices from the Greater Mekong Subregion and Australia in terms of their metabolomics and sensory profiles and to draw out associations between the volatile organic compounds and human sensory perception of rice aroma.

Methods

A set of aromatic rice varieties from South East Asia and Australia, along with non-aromatic controls, was grown in tropical and temperate areas of Australia. Untargeted metabolite profiling of volatile compounds, from the heated rice flour, by static headspace extraction and separation by two dimensional gas chromatography time-of-flight mass spectrometry was performed. Volatile compounds were also assayed in the standard references used in the sensory evaluation and compared to the compounds detected in the headspace of rice.

Results

While 2-acetyl-1-pyrroline (2-AP) was a discriminating compound, we identified several of its structural homologues, and a number of other metabolites that were consistently detected in fragrant Jasmine rice. 2-AP producing rice varieties have different sensory properties and these variations were defined by the discriminating compounds identified in each rice type.

Conclusions

The results of this study are valuable in understanding the aspects of aromatic rice that are important to consumers, and in the identification of compounds that breeding programs can use to select for pleasant aromas, enabling breeding programs to target markets with greater accuracy.

     

Understanding the structural characteristics of compstatin by conformational space annealing

The structural characteristics of the 13-residue compstatin molecule are investigated using the conformational space annealing (CSA) method with CHARMM force field and the GBSA continuum solvent model. In order to sample conformations in the energy range of the minimized NMR structures, we have used the stopping criterion to the CSA search when a conformation whose energy is less than -490 kcal/mol is found. With this stopping criterion, a great variety of conformations are generated around experimentally known structures. Twenty independent CSA runs starting from random states find 1000 representative conformations in the energy landscape of the compstatin, which are classified into thirty-one structural families. The majority of the conformations (94.4%) are in the coil state. Other conformers containing a 3(10)-helix, a pi-helix, a beta-hairpin, and an alpha-helix are also found.     

Principles of classifying and naming enzymes

Common principles of enzyme classification, nomenclature and code recommended by the Committee on Enzyme of International Union of Biochemistry are described.     

Riddled with holes: Understanding air space formation in plant leaves

Plants use energy from sunlight to transform carbon dioxide from the air into complex organic molecules, ultimately producing much of the food we eat. To make this complex chemistry more efficient, plant leaves are intricately constructed in 3 dimensions: They are flat to maximise light capture and contain extensive internal air spaces to increase gas exchange for photosynthesis. Many years of work has built up an understanding of how leaves form flat blades, but the molecular mechanisms that control air space formation are poorly understood. Here, I review our current understanding of air space formation and outline how recent advances can be harnessed to answer key questions and take the field forward. Increasing our understanding of plant air spaces will not only allow us to understand a fundamental aspect of plant development, but also unlock the potential to engineer the internal structure of crops to make them more efficient at photosynthesis with lower water requirements and more resilient in the face of a changing environment.

Leaves are interwoven with large air spaces to increase the efficiency of photosynthesis; however, how these air spaces form and how different patterns have evolved is almost unknown. This Unsolved Mystery article discusses the existing evidence and poses new avenues of research to answer this question.     

Identifying and classifying biomedical perturbations in text

Molecular perturbations provide a powerful toolset for biomedical researchers to scrutinize the contributions of individual molecules in biological systems. Perturbations qualify the context of experimental results and, despite their diversity, share properties in different dimensions in ways that can be formalized. We propose a formal framework to describe and classify perturbations that allows accumulation of knowledge in order to inform the process of biomedical scientific experimentation and target analysis. We apply this framework to develop a novel algorithm for automatic detection and characterization of perturbations in text and show its relevance in the study of gene–phenotype associations and protein–protein interactions in diabetes and cancer. Analyzing perturbations introduces a novel view of the multivariate landscape of biological systems.     

On classifying interactions between populations

Summary The classification of interspecific interactions can have an important impact on ecologists' world views. Previous classifications have often been incomplete, have suffered from ambiguously defined categories, and/or have wrongly equated categories of population level effects with particular mechanisms of interaction. I use several simple mathematical models to argue that effects on short-term population growth rate, long term population size, and short term relative fitness of interactants may differ qualitatively. Equating all (--) effects with competition and all (+-) effects with predation may have caused ecologists to ignore a variety of potentially important interaction mechanisms. Failure to define the type of effect used in classifying interactions has led to confusion about the nature of interactions; several controversies regarding competition have apparently been caused or exaccerbated by problems with definition or clasification. In applying classification schemes, ecologists should realize that the classification of an interaction between two populations may change with the sizes of those populations or of other populations with which they interact.     

Mining, compressing and classifying with extensible motifs

Background  

Motif patterns of maximal saturation emerged originally in contexts of pattern discovery in biomolecular sequences and have recently proven a valuable notion also in the design of data compression schemes. Informally, a motif is a string of intermittently solid and wild characters that recurs more or less frequently in an input sequence or family of sequences. Motif discovery techniques and tools tend to be computationally imposing, however, special classes of "rigid" motifs have been identified of which the discovery is affordable in low polynomial time.     

Intra- and interobserver concordance in classifying dental morphology

A sample of 50 Kodiak Island Eskimo dental casts were observed, using standard reference plaques, three times by one observer and once by a second observer, for 47 graded and discrete morphological characters of the dentition and jaws. Although the frequency of both intra- and interobserver scoring differences between observation sessions are relatively high for some characters, statistical analysis reveals that differences are largely random in direction and cancel out. Most dental morphological variants can be observed in a ranked fashion with adequate within- and between-observer reliability. However, four traits present both within- and between-observer difficulties: the tuberculum dentale, the canine distal accessory ridge, marginal accessory cusps of the upper first premolar, and the anterior fovea on the lower first molar.     

Corn hybrids display lower metabolite variability and complex metabolite inheritance patterns

We conducted a comparative analysis of the root metabolome of six parental maize inbred lines and their 14 corresponding hybrids showing fresh weight heterosis. We demonstrated that the metabolic profiles not only exhibit distinct features for each hybrid line compared with its parental lines, but also separate reciprocal hybrids. Reconstructed metabolic networks, based on robust correlations between metabolic profiles, display a higher network density in most hybrids as compared with the corresponding inbred lines. With respect to metabolite level inheritance, additive, dominant and overdominant patterns are observed with no specific overrepresentation. Despite the observed complexity of the inheritance pattern, for the majority of metabolites the variance observed in all 14 hybrids is lower compared with inbred lines. Deviations of metabolite levels from the average levels of the hybrids correlate negatively with biomass, which could be applied for developing predictors of hybrid performance based on characteristics of metabolite patterns.     

Methods of classifying nemerteans: an assessment

Phenetic, cladistic and phyletic methods of classifying animals are discussed with particular reference to nemerteans. It is concluded that phenetic (numerical) taxonomy is particularly inapplicable to any group of invertebrates for which well defined character differences are relatively few, whilst both the phenetic and cladistic methods fail through their fundamental assumption that convergent evolution is a rare occurrence. Terrestrial and freshwater nemerteans especially demonstrate convergent evolution in many ways; cladistic classifications proposed for these animals are therefore untenable. Convergence is shown to be a common occurrence in other nemerteans also. It is concluded that because the traditional phyletic approach does not implicitly assume that resemblances between organisms are more likely to be due to common ancestry than to convergence, it is far more likely to reveal true evolutionary relationships between taxa.     

An automated phylogenetic key for classifying homeoboxes

When novel gene sequences are discovered, they are usually identified, classified, and annotated based on aggregate measures of sequence similarity. This method is prone to errors, however. Phylogenetic analysis is a more accurate basis for gene classification and ortholog identification, but it is relatively labor-intensive and computationally demanding. Here we report and demonstrate a rapid new method for gene classification based on phylogenetic principles. Given the phylogeny of a minimal sample of gene family members, our method automatically identifies amino acids that are phylogenetically characteristic of each class of sequences in the family; it then classifies a novel sequence based on the presence of these characteristic attributes in its sequence. Using a subset of homeobox protein sequences as a test case, we show that our method approximates classification based on full-scale phylogenetic analysis with very high accuracy in a tiny fraction of the time.