By collecting and analyzing diapers, we identified a >6-fold increase in carriage of extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae for healthy preschool children in Sweden ...(p<0.0001). For 6 of the 50 participating preschools, the carriage rate was >40%. We analyzed samples from 334 children and found 56 containing >1 ESBL producer. The prevalence in the study population increased from 2.6% in 2010 to 16.8% in 2016 (p<0.0001), and for 6 of the 50 participating preschools, the carriage rate was >40%. Furthermore, 58% of the ESBL producers were multidrug resistant, and transmission of ESBL-producing and non-ESBL-producing strains was observed at several of the preschools. Toddlers appear to be major carriers of ESBL producers in Sweden.
Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water–octanol distribution coefficient (logD) for ...chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of
Q
2
=
0.973
and with the best performing nonconformity measure having median prediction interval of
±
0.39
log units at 80% confidence and
±
0.60
log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.
Background
Docking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening. The problem is trivially parallelizable, and calculations are ...generally carried out on computer clusters or on large workstations in a brute force manner, by docking and scoring all available ligands.
Contribution
In this study we propose a strategy that is based on iteratively docking a set of ligands to form a training set, training a ligand-based model on this set, and predicting the remainder of the ligands to exclude those predicted as ‘low-scoring’ ligands. Then, another set of ligands are docked, the model is retrained and the process is repeated until a certain model efficiency level is reached. Thereafter, the remaining ligands are docked or excluded based on this model. We use SVM and conformal prediction to deliver valid prediction intervals for ranking the predicted ligands, and Apache Spark to parallelize both the docking and the modeling.
Results
We show on 4 different targets that conformal prediction based virtual screening (CPVS) is able to reduce the number of docked molecules by 62.61% while retaining an accuracy for the top 30 hits of 94% on average and a speedup of 3.7. The implementation is available as open source via GitHub (
https://github.com/laeeq80/spark-cpvs
) and can be run on high-performance computers as well as on cloud resources.
Comparing chemical structures to infer protein targets and functions is a common approach, but basing comparisons on chemical similarity alone can be misleading. Here we present a methodology for ...predicting target protein clusters using deep neural networks. The model is trained on clusters of compounds based on similarities calculated from combined compound-protein and protein-protein interaction data using a network topology approach. We compare several deep learning architectures including both convolutional and recurrent neural networks. The best performing method, the recurrent neural network architecture MolPMoFiT, achieved an F1 score approaching 0.9 on a held-out test set of 8907 compounds. In addition, in-depth analysis on a set of eleven well-studied chemical compounds with known functions showed that predictions were justifiable for all but one of the chemicals. Four of the compounds, similar in their molecular structure but with dissimilarities in their function, revealed advantages of our method compared to using chemical similarity.
Background
Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues.
Contributions
We ...present an open-source, extendable web service for predicting target profiles with confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking scores from a large virtual library. The method uses conformal prediction to produce valid measures of prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical structures to the panel of targets with QuickVina on individual compound basis.
Results
The docking procedure and resulting models were validated by docking well-known inhibitors for each of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking scores against an external validation set. The implementation as publicly available microservices on Kubernetes ensures resilience, scalability, and extensibility.
The interaction between HIV-1 protease and 58 structurally diverse transition-state analogue inhibitors has been analyzed by a surface plasmon resonance based biosensor. Association and dissociation ...rate constants and affinities were determined and displayed as k on−k off−K D maps. It was shown that different classes of inhibitors fall into distinct clusters in these maps. Significant changes in association and dissociation rates were found as a result of modifying the P1/P1‘ or P2/P2‘ side chains of a linear lead compound. Similarly, cyclic urea and cyclic sulfamide inhibitors displayed different kinetic features and the affinities of both classes of cyclic compounds were limited by fast dissociation rates. These results confirm that association and dissociation rates are important features of drug−target interactions and indicate that optimization of inhibitor efficacy may be guided by aiming for high association and low dissociation rates rather than high affinity alone. The present approach thus provides a new tool for structure−interaction kinetic analysis and drug discovery.
In recent years, there has been an increased interest in using macrocyclic compounds for drug discovery and development. For docking of these commonly large and flexible compounds to be addressed, a ...screening and a validation set were assembled from the PDB consisting of 16 and 31 macrocycle-containing protein complexes, respectively. The macrocycles were docked in Glide by rigid docking of pregenerated conformational ensembles produced by the macrocycle conformational sampling method (MCS) in Schrödinger Release 2015-3 or by direct Glide flexible docking after performing ring-templating. The two protocols were compared to rigid docking of pregenerated conformational ensembles produced by an exhaustive Monte Carlo multiple minimum (MCMM) conformational search and a shorter MCMM conformational search (MCMM-short). The docking accuracy was evaluated and expressed as the RMSD between the heavy atoms of the ligand as found in the X-ray structure after refinement and the poses obtained by the docking protocols. The median RMSD values for top-scored poses of the screening set were 0.83, 0.80, 0.88, and 0.58 Å for MCMM, MCMM-short, MCS, and Glide flexible docking, respectively. There was no statistically significant difference in the performance between rigid docking of pregenerated conformations produced by the MCS and direct docking using Glide flexible docking. However, the flexible docking protocol was 2-times faster in docking the screening set compared to that of the MCS protocol. In a final study, the new Prime-MCS method was evaluated in Schrödinger Release 2016-3. This method is faster compared that of to MCS; however, the conformations generated were found to be suboptimal for rigid docking. Therefore, on the basis of timing, accuracy, and ease of set up, standard Glide flexible docking with prior ring-templating is recommended over current gold standard protocols using rigid docking of pregenerated conformational ensembles.
Summary Background Silver-based products have been marketed as an alternative to antibiotics, and their consumption has increased. Bacteria may, however, develop resistance to silver. Aim To study ...the presence of genes encoding silver resistance ( silE , silP , silS ) over time in three clinically important Enterobacteriaceae genera. Methods Using polymerase chain reaction (PCR), 752 bloodstream isolates from the years 1990–2010 were investigated. Age, gender, and ward of patients were registered, and the susceptibility to antibiotics and silver nitrate was tested. Clonality and single nucleotide polymorphism were assessed with repetitive element sequence-based PCR, multi-locus sequence typing, and whole-genome sequencing. Findings Genes encoding silver resistance were detected most frequently in Enterobacter spp. (48%), followed by Klebsiella spp. (41%) and Escherichia coli 4%. Phenotypical resistance to silver nitrate was found in Enterobacter (13%) and Klebsiella (3%) isolates. The lowest carriage rate of sil genes was observed in blood isolates from the neonatology ward (24%), and the highest in blood isolates from the oncology/haematology wards (66%). Presence of sil genes was observed in international high-risk clones. Sequences of the sil and pco clusters indicated that a single mutational event in the silS gene could have caused the phenotypic resistance. Conclusion Despite a restricted consumption of silver-based products in Swedish health care, silver resistance genes are widely represented in clinical isolates of Enterobacter and Klebsiella species. To avoid further selection and spread of silver-resistant bacteria with a high potential for healthcare-associated infections, the use of silver-based products needs to be controlled and the silver resistance monitored.
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