Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning.
Ontology highlight
ABSTRACT:
SUBMITTER: Capecchi A
PROVIDER: S-EPMC8524952 | biostudies-literature | 2021 Oct
REPOSITORIES: biostudies-literature
Publications
Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning.
Capecchi Alice A Reymond Jean-Louis JL
Journal of cheminformatics 20211018 1
Natural products (NPs) represent one of the most important resources for discovering new drugs. Here we asked whether NP origin can be assigned from their molecular structure in a subset of 60,171 NPs in the recently reported Collection of Open Natural Products (COCONUT) database assigned to plants, fungi, or bacteria. Visualizing this subset in an interactive tree-map (TMAP) calculated using MAP4 (MinHashed atom pair fingerprint) clustered NPs according to their assigned origin ( https://tm.gdb ...[more]
Similar Datasets
Project description:Climbing plants require an external support to grow vertically and enhance light acquisition. Climbers that find a suitable support demonstrate greater performance and fitness than those that remain prostrate. Support search is characterized by oscillatory movements (i.e., circumnutation), in which plants rotate around a central axis during their growth. Numerous studies have elucidated the mechanistic details of circumnutation, but how this phenomenon is controlled during support searching remains unclear. To fill this gap, here we tested whether simulation-based machine learning methods can capture differences in movement patterns nested in actual kinematical data. We compared machine learning classifiers with the aim of generating models that learn to discriminate between circumnutation patterns related to the presence/absence of a support in the environment. Results indicate that there is a difference in the pattern of circumnutation, depending on the presence of a support, that can be learned and classified rather accurately. We also identify distinctive kinematic features at the level of the junction underneath the tendrils that seems to be a superior indicator for discerning the presence/absence of the support by the plant. Overall, machine learning approaches appear to be powerful tools for understanding the movement of plants.
| S-EPMC9965265 | biostudies-literature
Project description:Background and objectiveSmoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states.MethodsTo test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision.ResultsThe outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications.ConclusionsIn conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions.
| S-EPMC5289882 | biostudies-literature
Project description:The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classification of infant feeding status from clinical notes using Medical Subject Headings (MeSH) terms. Annotation of notes was completed using TeamTat to uniquely classify clinical notes according to infant feeding status. We trained 6 machine learning models to classify infant feeding status: logistic regression, random forest, XGBoost gradient descent, k-nearest neighbors, and support-vector classifier. Model comparison was evaluated based on overall accuracy, precision, recall, and F1 score. Our modeling corpus included an even number of clinical notes that was a balanced sample across each class. We manually reviewed 999 notes that represented 746 mother-infant dyads with a mean gestational age of 38.9 weeks and a mean maternal age of 26.6 years. The most frequent feeding status classification present for this study was exclusive breastfeeding [n = 183 (18.3%)], followed by exclusive formula bottle feeding [n = 146 (14.6%)], and exclusive feeding of expressed mother's milk [n = 102 (10.2%)], with mixed feeding being the least frequent [n = 23 (2.3%)]. Our final analysis evaluated the classification of clinical notes as breast, formula/bottle, and missing. The machine learning models were trained on these three classes after performing balancing and down sampling. The XGBoost model outperformed all others by achieving an accuracy of 90.1%, a macro-averaged precision of 90.3%, a macro-averaged recall of 90.1%, and a macro-averaged F1 score of 90.1%. Our results demonstrate that natural language processing can be applied to clinical notes stored in the electronic health records to classify infant feeding status. Early identification of breastfeeding status using NLP on unstructured electronic health records data can be used to inform precision public health interventions focused on improving lactation support for postpartum patients.
| S-EPMC10991582 | biostudies-literature
Project description:Human infectious diseases caused by various microbial pathogens, in general, impact a large population of individuals every year. These microbial diseases that spread quickly remain to be a big issue in various health-related domains and to withstand the negative drug impacts, the antimicrobial-resistant pathogenic microbial organisms (pathogenic bacteria and pathogenic fungi) have developed a variety of resistance processes against many antimicrobial drug classes. During the COVID-19 outbreak, there seems to be an upsurge in drug and multidrug resistant-associated pathogenic microbial species. The preponderance of existing antimicrobials isn't completely effective, which limits their application in clinical settings. Several naturally occurring chemicals produced from bacteria, plants, animals, marine species, and other sources are now being studied for antimicrobial characteristics. These natural antimicrobial compounds extracted from different sources have been demonstrated to be effective against a variety of diseases, although plants remain the most abundant source. These compounds have shown promise in reducing the microbial diseases linked to the development of drug tolerance and resistance. This paper offers a detailed review of some of the most vital and promising natural compounds and their derivatives against various human infectious microbial organisms. The inhibitory action of different natural antimicrobial compounds, and their possible mechanism of antimicrobial action against a range of pathogenic fungal and bacterial organisms, is provided. The review will be useful in refining current antimicrobial (antifungal and antibacterial) medicines as well as establishing new treatment strategies to tackle the rising number of human bacterial and fungal-associated infections.
| S-EPMC9290337 | biostudies-literature
Project description:Protein kinases are key actors of signaling networks and important drug targets. They cycle between active and inactive conformations, distinguished by a few elements within the catalytic domain. One is the activation loop, whose conserved DFG motif can occupy DFG-in, DFG-out, and some rarer conformations. Annotation and classification of the structural kinome are important, as different conformations can be targeted by different inhibitors and activators. Valuable resources exist; however, large-scale applications will benefit from increased automation and interpretability of structural annotation. Interpretable machine learning models are described for this purpose, based on ensembles of decision trees. To train them, a set of catalytic domain sequences and structures was collected, somewhat larger and more diverse than existing resources. The structures were clustered based on the DFG conformation and manually annotated. They were then used as training input. Two main models were constructed, which distinguished active/inactive and in/out/other DFG conformations. They considered initially 1692 structural variables, spanning the whole catalytic domain, then identified ("learned") a small subset that sufficed for accurate classification. The first model correctly labeled all but 3 of 3289 structures as active or inactive, while the second assigned the correct DFG label to all but 17 of 8826 structures. The most potent classifying variables were all related to well-known structural elements in or near the activation loop and their ranking gives insights into the conformational preferences. The models were used to automatically annotate 3850 kinase structures predicted recently with the Alphafold2 tool, showing that Alphafold2 reproduced the active/inactive but not the DFG-in proportions seen in the Protein Data Bank. We expect the models will be useful for understanding and engineering kinases.
| S-EPMC10962494 | biostudies-literature
Project description:Isolates of a variety of fungal plant pathogens (Alternaria radicina ICMP 5619, Cercospora beticola ICMP 15907, Dactylonectria macrodidyma ICMP 16789, D. torresensis ICMP 20542, Ilyonectria europaea ICMP 16794, and I. liriodendra ICMP 16795) were screened for antimicrobial activity against the human pathogenic bacteria Acinetobacter baumannii, Pseudomonas aeruginosa, Escherichia coli, Mycobacterium abscessus, and M. marinum and were found to have some activity. Investigation of the secondary metabolites of these fungal isolates led to the isolation of ten natural products (1-10) of which one was novel, (E)-4,7-dihydroxyoct-2-enoic acid (1). Structure elucidation of all natural products was achieved by a combination of NMR spectroscopy and mass spectrometry. We also investigated the antimicrobial activity of a number of the isolated natural products. While we did not find (E)-4,7-dihydroxyoct-2-enoic acid (1) to have any activity against the bacteria and fungi in our assays, we did find that cercosporin (7) exhibited potent activity against Methicillin resistant Staphylococcus aureus (MRSA), dehydro-curvularin (6) and radicicol (10) exhibited antimycobacterial activity against M. marinum, and brefeldin A (8) and radicicol (10) exhibited antifungal activity against Candida albicans. Investigation of the cytotoxicity and haemolytic activities of these natural products (6-8 and 10) found that only one of the four active compounds, radicicol (10), was non-cytotoxic and non-haemolytic.
| S-EPMC9920077 | biostudies-literature
Project description:Within the natural products field there is an increasing emphasis on the study of compounds from microbial sources. This has been fuelled by interest in the central role that microorganisms play in mediating both interspecies interactions and host-microbe relationships. To support the study of natural products chemistry produced by microorganisms we released the Natural Products Atlas, a database of known microbial natural products structures, in 2019. This paper reports the release of a new version of the database which includes a full RESTful application programming interface (API), a new website framework, and an expanded database that includes 8128 new compounds, bringing the total to 32 552. In addition to these structural and content changes we have added full taxonomic descriptions for all microbial taxa and have added chemical ontology terms from both NP Classifier and ClassyFire. We have also performed manual curation to review all entries with incomplete configurational assignments and have integrated data from external resources, including CyanoMetDB. Finally, we have improved the user experience by updating the Overview dashboard and creating a dashboard for taxonomic origin. The database can be accessed via the new interactive website at https://www.npatlas.org.
| S-EPMC8728154 | biostudies-literature
Project description:BackgroundSignaling proteins such as protein kinases adopt a diverse array of conformations to respond to regulatory signals in signaling pathways. Perhaps the most fundamental conformational change of a kinase is the transition between active and inactive states, and defining the conformational features associated with kinase activation is critical for selectively targeting abnormally regulated kinases in diseases. While manual examination of crystal structures have led to the identification of key structural features associated with kinase activation, the large number of kinase crystal structures (~3,500) and extensive conformational diversity displayed by the protein kinase superfamily poses unique challenges in fully defining the conformational features associated with kinase activation. Although some computational approaches have been proposed, they are typically based on a small subset of crystal structures using measurements biased towards the active site geometry.ResultsWe utilize an unbiased informatics based machine learning approach to classify all eukaryotic protein kinase conformations deposited in the PDB. We show that the orientation of the activation segment, measured by φ, ψ, χ1, and pseudo-dihedral angles more accurately classify kinase crystal conformations than existing methods. We show that the formation of the K-E salt bridge is statistically dependent upon the activation segment orientation and identify evolutionary differences between the activation segment conformation of tyrosine and serine/threonine kinases. We provide evidence that our method can identify conformational changes associated with the binding of allosteric regulatory proteins, and show that the greatest variation in inactive structures comes from kinase group and family specific side chain orientations.ConclusionWe have provided the first comprehensive machine learning based classification of protein kinase active/inactive conformations, taking into account more structures and measurements than any previous classification effort. Further, our unbiased classification of inactive structures reveals residues associated with kinase functional specificity. To enable classification of new crystal structures, we have made our classifier publicly accessible through a stand-alone program housed at https://github.com/esbg/kinconform [DOI: 10.5281/zenodo.249090 ].
| S-EPMC5290640 | biostudies-literature
Project description:We conducted a screening campaign to investigate fungi as a source for new antimalarial compounds. A subset of our fungal collection comprising Chinese mangrove endophytes provided over 5000 lipophilic extracts. We developed an accelerated discovery program based on small-scale cultivation for crude extract screening and a high-throughput malaria assay. Criteria for hits were developed and high priority hits were subjected to scale-up cultivation. Extracts from large scale cultivation were fractionated and these fractions subjected to both in vitro malaria and cytotoxicity screening. Criteria for advancing fractions to purification were developed, including the introduction of a selectivity index and by dereplication of known metabolites. From the Chinese mangrove endophytes, four new compounds (14-16, 18) were isolated including a new dimeric tetrahydroxanthone, dicerandrol D (14), which was found to display the most favorable bioactivity profile.
| S-EPMC3877901 | biostudies-literature
Project description:Social innovation is widely defined as technological and non-technological new products, services or models that simultaneously meet social needs and create new social relationships or collaborations. Despite a significant interest in the concept, the lack of reliable and comprehensive data is a barrier for social science research. We created the European Social Innovation Database (ESID) to address this gap. ESID is based on the idea of large-scale collection of unstructured web site text to classify and characterise social innovation projects from around the world. We use advanced machine learning techniques to extract features such as social innovation dimensions, project locations, summaries, and topics, among others. Our models perform as high as 0.90 F1. ESID currently includes 11,468 projects from 159 countries. ESID data is available freely and also presented in a web-based app. Our future workplan includes expansion (i.e., increasing the number of projects), extension (i.e., adding new variables) and dynamic retrieval (i.e., retrieving and extracting information in regular intervals).
| S-EPMC9653489 | biostudies-literature