Wesley Schaal

Characterization of a set of HIV-1 protease inhibitors using binding kinetics data from a biosensor-based screen

The interaction between 290 structurally diverse human immunodeficiency virus type 1 (HIV-1) protease inhibitors and the immobilized enzyme was analyzed with an optical biosensor. Although only a single concentration of inhibitor was used, information about the kinetics of the interaction could be obtained by extracting binding signals at discrete time points. The statistical correlation between the biosensor binding data, inhibition of enzyme activity (K;), and viral replication (EC50) revealed that the association and dissociation rates for the interaction could be resolved and that they were characteristic for the compounds. The most potent inhibitors, with respect to K; and EC50 values, including the clinically used drugs, all exhibited fast association and slow dissociation rates. Selective or partially selective binders for HIV-1 protease could be distinguished from compounds that showed a general protein-binding tendency by using three reference target proteins. This biosensor-based direct binding assay revealed a capacity to efficiently provide high-resolution information on the interaction kinetics and specificity of the interaction of a set of compounds with several targets simultaneously.

Improved CoMFA Modeling by Optimization of Settings

The possibility of improving the predictive ability of comparative molecular field analysis (CoMFA) by settings optimization has been evaluated to show that CoMFA predictive ability can be improved. Ten different CoMFA settings are evaluated, producing a total of 6120 models. This method has been applied to nine different data sets, including the widely used benchmark steroid data set, as well as eight other data sets proposed as QSAR benchmarking data sets by Sutherland et al. (J. Med. Chem. 2004, 47, 5541-5554). All data sets have been studied using training and test sets to allow for both internal (q(2)) and external (r(2)(pred)) predictive ability assessment. CoMFA settings optimization was successful in developing models with improved q(2) and r(2)(pred) as compared to default CoMFA modeling. Optimized CoMFA is compared with comparative molecular similarity indices analysis (CoMSIA) and holographic quantitative structure-activity relationship (HQSAR) models and found to consistently produce models with improved or equivalent q(2) and r(2)(pred). The ability of settings optimization to improve model predictive ability has been validated using both internal and external predictions, and the risk of chance correlation has been evaluated using response variable randomization tests.

Toward a benchmarking data set able to evaluate ligand- and structure-based virtual screening using public HTS data.

Virtual screening has the potential to accelerate and reduce costs of probe development and drug discovery. To develop and benchmark virtual screening methods, validation data sets are commonly used. Over the years, such data sets have been constructed to overcome the problems of analogue bias and artificial enrichment. With the rapid growth of public domain databases containing high-throughput screening data, such as the PubChem BioAssay database, there is an increased possibility to use such data for validation. In this study, we identify PubChem data sets suitable for validation of both structure- and ligand-based virtual screening methods. To achieve this, high-throughput screening data for which a crystal structure of the bioassay target was available in the PDB were identified. Thereafter, the data sets were inspected to identify structures and data suitable for use in validation studies. In this work, we present seven data sets (MMP13, DUSP3, PTPN22, EPHX2, CTDSP1, MAPK10, and CDK5) compiled using this method. In the seven data sets, the number of active compounds varies between 19 and 369 and the number of inactive compounds between 59 405 and 337 634. This gives a higher ratio of the number of inactive to active compounds than what is found in most benchmark data sets. We have also evaluated the screening performance using docking and 3D shape similarity with default settings. To characterize the data sets, we used physicochemical similarity and 2D fingerprint searches. We envision that these data sets can be a useful complement to current data sets used for method evaluation.

3-Substituted pyrazoles and 4-substituted triazoles as inhibitors of human 15-lipoxygenase-1

Investigation of 1N-substituted pyrazole-3-carboxanilides as 15-lipoxygenase-1 (15-LOX-1) inhibitors demonstrated that the 1N-substituent was not essential for activity or selectivity. Additional halogen substituents on the pyrazole ring, however, increased activity. Further development led to triazole-4-carboxanilides and 2-(3-pyrazolyl) benzoxazoles, which are potent and selective 15-LOX-1 inhibitors.

N-Substituted pyrazole-3-carboxamides as inhibitors of human 15-lipoxygenase.

High-throughput screening was used to find selective inhibitors of human 15-lipoxygenase-1 (15-LOX-1). One hit, a 1-benzoyl substituted pyrazole-3-carboxanilide (1a), was used as a starting point in a program to develop potent and selective 15-LOX-1 inhibitors.

Docking of Macrocycles: Comparing Rigid and Flexible Docking in Glide

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.

Automated QuantMap for rapid quantitative molecular network topology analysis

Summary: The previously disclosed QuantMap method for grouping chemicals by biological activity used online services for much of the data gathering and some of the numerical analysis. The present work attempts to streamline this process by using local copies of the databases and in-house analysis. Using computational methods similar or identical to those used in the previous work, a qualitatively equivalent result was found in just a few seconds on the same dataset (collection of 18 drugs). We use the user-friendly Galaxy framework to enable users to analyze their own datasets. Hopefully, this will make the QuantMap method more practical and accessible and help achieve its goals to provide substantial assistance to drug repositioning, pharmacology evaluation and toxicology risk assessment. Availability: http://galaxy.predpharmtox.org Contact: mats.gustafsson@medsci.uu.se or ola.spjuth@farmbio.uu.se Supplementary information: Supplementary data are available at Bioinformatics online.

High frequency of silver resistance genes in invasive isolates of Enterobacter and Klebsiella species

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.

Efficient iterative virtual screening with Apache Spark and conformal prediction.

BackgroundDocking 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.ContributionIn 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.ResultsWe 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.

Tracking the NGS revolution: managing life science research on shared high-performance computing clusters

Abstract Background Next-generation sequencing (NGS) has transformed the life sciences, and many research groups are newly dependent upon computer clusters to store and analyze large datasets. This creates challenges for e-infrastructures accustomed to hosting computationally mature research in other sciences. Using data gathered from our own clusters at UPPMAX computing center at Uppsala University, Sweden, where core hour usage of ∼800 NGS and ∼200 non-NGS projects is now similar, we compare and contrast the growth, administrative burden, and cluster usage of NGS projects with projects from other sciences. Results The number of NGS projects has grown rapidly since 2010, with growth driven by entry of new research groups. Storage used by NGS projects has grown more rapidly since 2013 and is now limited by disk capacity. NGS users submit nearly twice as many support tickets per user, and 11 more tools are installed each month for NGS projects than for non-NGS projects. We developed usage and efficiency metrics and show that computing jobs for NGS projects use more RAM than non-NGS projects, are more variable in core usage, and rarely span multiple nodes. NGS jobs use booked resources less efficiently for a variety of reasons. Active monitoring can improve this somewhat. Conclusions Hosting NGS projects imposes a large administrative burden at UPPMAX due to large numbers of inexperienced users and diverse and rapidly evolving research areas. We provide a set of recommendations for e-infrastructures that host NGS research projects. We provide anonymized versions of our storage, job, and efficiency databases.