| 1. |
- Brunius, Carl, 1974, et al.
(författare)
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Prediction and modeling of pre-analytical sampling errors as a strategy to improve plasma NMR metabolomics data
- 2017
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1460-2059 .- 1367-4811. ; 33:22, s. 3567-3574
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Tidskriftsartikel (refereegranskat)abstract
- Biobanks are important infrastructures for life science research. Optimal sample handling regarding e.g. collection and processing of biological samples is highly complex, with many variables that could alter sample integrity and even more complex when considering multiple study centers or using legacy samples with limited documentation on sample management. Novel means to understand and take into account such variability would enable high-quality research on archived samples. This study investigated whether pre-analytical sample variability could be predicted and reduced by modeling alterations in the plasma metabolome, measured by NMR, as a function of pre-centrifugation conditions (1-36 h pre-centrifugation delay time at 4 A degrees C and 22 A degrees C) in 16 individuals. Pre-centrifugation temperature and delay times were predicted using random forest modeling and performance was validated on independent samples. Alterations in the metabolome were modeled at each temperature using a cluster-based approach, revealing reproducible effects of delay time on energy metabolism intermediates at both temperatures, but more pronounced at 22 A degrees C. Moreover, pre-centrifugation delay at 4 A degrees C resulted in large, specific variability at 3 h, predominantly of lipids. Pre-analytical sample handling error correction resulted in significant improvement of data quality, particularly at 22 A degrees C. This approach offers the possibility to predict pre-centrifugation delay temperature and time in biobanked samples before use in costly downstream applications. Moreover, the results suggest potential to decrease the impact of undesired, delay-induced variability. However, these findings need to be validated in multiple, large sample sets and with analytical techniques covering a wider range of the metabolome, such as LC-MS.
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| 2. |
- Michel, Mirco, et al.
(författare)
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Large-scale structure prediction by improved contact predictions and model quality assessment
- 2017
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 33:14, s. 123-129
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Accurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known. Results: We present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these 415 have not been reported before. Availability: Datasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net. All programs used here are freely available.
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| 3. |
- Michel, Mirco, et al.
(författare)
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PconsC4: fast, accurate and hassle-free contact predictions
- 2019
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 35:15, s. 2677-2679
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Tidskriftsartikel (refereegranskat)abstract
- MotivationResidue contact prediction was revolutionized recently by the introduction of direct coupling analysis (DCA). Further improvements, in particular for small families, have been obtained by the combination of DCA and deep learning methods. However, existing deep learning contact prediction methods often rely on a number of external programs and are therefore computationally expensive.ResultsHere, we introduce a novel contact predictor, PconsC4, which performs on par with state of the art methods. PconsC4 is heavily optimized, does not use any external programs and therefore is significantly faster and easier to use than other methods.Availability and implementationPconsC4 is freely available under the GPL license from https://github.com/ElofssonLab/PconsC4. Installation is easy using the pip command and works on any system with Python 3.5 or later and a GCC compiler. It does not require a GPU nor special hardware.Supplementary informationSupplementary data are available at Bioinformatics online.
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| 4. |
- Mirabello, Claudio, et al.
(författare)
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Topology independent structural matching discovers novel templates for protein interfaces
- 2018
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Ingår i: Bioinformatics. - : OXFORD UNIV PRESS. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 34:17, s. 787-794
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Protein-protein interactions (PPI) are essential for the function of the cellular machinery. The rapid growth of protein-protein complexes with known 3D structures offers a unique opportunity to study PPI to gain crucial insights into protein function and the causes of many diseases. In particular, it would be extremely useful to compare interaction surfaces of monomers, as this would enable the pinpointing of potential interaction surfaces based solely on the monomer structure, without the need to predict the complete complex structure. While there are many structural alignment algorithms for individual proteins, very few have been developed for protein interfaces, and none that can align only the interface residues to other interfaces or surfaces of interacting monomer subunits in a topology independent (non-sequential) manner. Results: We present InterComp, a method for topology and sequence-order independent structural comparisons. The method is general and can be applied to various structural comparison applications. By representing residues as independent points in space rather than as a sequence of residues, InterComp can be applied to a wide range of problems including interface-surface comparisons and interface-interface comparisons. We demonstrate a use-case by applying InterComp to find similar protein interfaces on the surface of proteins. We show that InterComp pinpoints the correct interface for almost half of the targets (283 of 586) when considering the top 10 hits, and for 24% of the top 1, even when no templates can be found with regular sequence-order dependent structural alignment methods.
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| 5. |
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| 6. |
- Karlsson, Edvin, et al.
(författare)
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Clonality of erythromycin resistance in Francisella tularensis
- 2016
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Ingår i: Journal of Antimicrobial Chemotherapy. - : Oxford University Press (OUP). - 0305-7453 .- 1460-2091. ; 71:10, s. 2815-2823
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: We analysed diverse strains of Francisella tularensis subsp. holarctica to assess if its division into biovars I and II is associated with specific mutations previously linked to erythromycin resistance and to determine the distribution of this resistance trait across this subspecies. Methods:Three-hundred and fourteen F. tularensis subsp. holarctica strains were tested for erythromycin susceptibility and whole-genome sequences for these strains were examined for SNPs in genes previously associated with erythromycin resistance. Each strain was assigned to a global phylogenetic framework using genome-wide canonical SNPs. The contribution of a specific SNP to erythromycin resistance was examined using allelic exchange. The geographical distribution of erythromycin-resistant F. tularensis strains was further investigated by literature search. Results:There was a perfect correlation between biovar II strains (erythromycin resistance) and the phylogenetic group B.12. Only B.12 strains had an AaEuroS -> aEuroSC SNP at position 2059 in the three copies of the rrl gene. Introducing 2059C into an rrl gene of an erythromycin-susceptible F. tularensis strain resulted in resistance. An additional 1144 erythromycin-resistant strains were identified from the scientific literature, all of them from Eurasia. Conclusions:Erythromycin resistance in F. tularensis is caused by an A2059C rrl gene mutation, which exhibits a strictly clonal inheritance pattern found only in phylogenetic group B.12. This group is an extremely successful clone, representing the most common type of F. tularensis throughout Eurasia.
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