| 1. |
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
- Rahmani, V., et al.
(författare)
-
Data reduction for X-ray serial crystallography using machine learning
- 2023
-
Ingår i: Journal of applied crystallography. - 0021-8898 .- 1600-5767. ; 56, s. 200-213
-
Tidskriftsartikel (refereegranskat)abstract
- Serial crystallography experiments produce massive amounts of experimental data. Yet in spite of these large-scale data sets, only a small percentage of the data are useful for downstream analysis. Thus, it is essential to differentiate reliably between acceptable data (hits) and unacceptable data (misses). To this end, a novel pipeline is proposed to categorize the data, which extracts features from the images, summarizes these features with the 'bag of visual words' method and then classifies the images using machine learning. In addition, a novel study of various feature extractors and machine learning classifiers is presented, with the aim of finding the best feature extractor and machine learning classifier for serial crystallography data. The study reveals that the oriented FAST and rotated BRIEF (ORB) feature extractor with a multilayer perceptron classifier gives the best results. Finally, the ORB feature extractor with multilayer perceptron is evaluated on various data sets including both synthetic and experimental data, demonstrating superior performance compared with other feature extractors and classifiers.
|
|
| 6. |
- Coombes, D., et al.
(författare)
-
The basis for non-canonical ROK family function in the N-acetylmannosamine kinase from the pathogen Staphylococcus aureus
- 2020
-
Ingår i: Journal of Biological Chemistry. - 0021-9258. ; 295:10, s. 3301-3315
-
Tidskriftsartikel (refereegranskat)abstract
- In environments where glucose is limited, some pathogenic bacteria metabolize host-derived sialic acid as a nutrient source. N-Acetylmannosamine kinase (NanK) is the second enzyme of the bacterial sialic acid import and degradation pathway and adds phosphate to N-acetylmannosamine using ATP to prime the molecule for future pathway reactions. Sequence alignments reveal that Gram-positive NanK enzymes belong to the Repressor, ORF, Kinase (ROK) family, but many lack the canonical Zn-binding motif expected for this function, and the sugar-binding EXGH motif is altered to EXGY. As a result, it is unclear how they perform this important reaction. Here, we study the Staphylococcus aureus NanK (SaNanK), which is the first characterization of a Gram-positive NanK. We report the kinetic activity of SaNanK along with the ligand?free, N-acetylmannosamine?bound and substrate analog GlcNAc?bound crystal structures (2.33, 2.20, and 2.20 ? resolution, respectively). These demonstrate, in combination with small-angle X-ray scattering, that SaNanK is a dimer that adopts a closed conformation upon substrate binding. Analysis of the EXGY motif reveals that the tyrosine binds to the N-acetyl group to select for the ?boat? conformation of N-acetylmannosamine. Moreover, SaNanK has a stacked arginine pair coordinated by negative residues critical for thermal stability and catalysis. These combined elements serve to constrain the active site and orient the substrate in lieu of Zn binding, representing a significant departure from canonical NanK binding. This characterization provides insight into differences in the ROK family and highlights a novel area for antimicrobial discovery to fight Gram-positive and S. aureus infections.
|
|
| 7. |
- Nawaz, S., et al.
(författare)
-
Explainable machine learning for diffraction patterns
- 2023
-
Ingår i: Journal of applied crystallography. - : International Union of Crystallography (IUCr). - 0021-8898 .- 1600-5767. ; 56:5, s. 1494-1504
-
Tidskriftsartikel (refereegranskat)abstract
- Serial crystallography experiments at X-ray free-electron laser facilities produce massive amounts of data but only a fraction of these data are useful for downstream analysis. Thus, it is essential to differentiate between acceptable and unacceptable data, generally known as 'hit' and 'miss', respectively. Image classification methods from artificial intelligence, or more specifically convolutional neural networks (CNNs), classify the data into hit and miss categories in order to achieve data reduction. The quantitative performance established in previous work indicates that CNNs successfully classify serial crystallography data into desired categories [Ke, Brewster, Yu, Ushizima, Yang & Sauter (2018). J. Synchrotron Rad. 25, 655-670], but no qualitative evidence on the internal workings of these networks has been provided. For example, there are no visualization methods that highlight the features contributing to a specific prediction while classifying data in serial crystallography experiments. Therefore, existing deep learning methods, including CNNs classifying serial crystallography data, are like a 'black box'. To this end, presented here is a qualitative study to unpack the internal workings of CNNs with the aim of visualizing information in the fundamental blocks of a standard network with serial crystallography data. The region(s) or part(s) of an image that mostly contribute to a hit or miss prediction are visualized.
|
|
| 8. |
- Bairy, Sneha, et al.
(författare)
-
Automation aided optimization of cloning, expression and purification of enzymes of the bacterial sialic acid catabolic and sialylation pathways enzymes for structural studies.
- 2018
-
Ingår i: Microbial biotechnology. - : Wiley. - 1751-7915. ; 11:2, s. 420-428
-
Tidskriftsartikel (refereegranskat)abstract
- The process of obtaining a well-expressing, soluble and correctly folded constructs can be made easier and quicker by automating the optimization of cloning, expression and purification. While there are many semiautomated pipelines available for cloning, expression and purification, there is hardly any pipeline that involves complete automation. Here, we achieve complete automation of all the steps involved in cloning and invivo expression screening. This is demonstrated using 18 genes involved in sialic acid catabolism and the surface sialylation pathway. Our main objective was to clone these genes into a His-tagged Gateway vector, followed by their small-scale expression optimization invivo. The constructs that showed best soluble expression were then selected for purification studies and scaled up for crystallization studies. Our technique allowed us to quickly find conditions for producing significant quantities of soluble proteins in Escherichia coli, their large-scale purification and successful crystallization of a number of these proteins. The method can be implemented in other cases where one needs to screen a large number of constructs, clones and expression vectors for successful recombinant production of functional proteins.
|
|
| 9. |
- Caing Carlsson, Rhawnie, et al.
(författare)
-
Crystal structure of N-acetylmannosamine kinase from Fusobacterium nucleatum
- 2017
-
Ingår i: Acta crystallographica. Section F, Structural biology communications. - 2053-230X. ; 73:6, s. 356-362
-
Tidskriftsartikel (refereegranskat)abstract
- Sialic acids comprise a varied group of nine-carbon amino sugars that are widely distributed among mammals and higher metazoans. Some human commensals and bacterial pathogens can scavenge sialic acids from their environment and degrade them for use as a carbon and nitrogen source. The enzyme N-acetylmannosamine kinase (NanK; EC 2.7.1.60) belongs to the transcriptional repressors, uncharacterized open reading frames and sugar kinases (ROK) superfamily. NanK catalyzes the second step of the sialic acid catabolic pathway, transferring a phosphate group from adenosine 5′-triphosphate to the C6 position of N-acetylmannosamine to generate N-acetylmannosamine 6-phosphate. The structure of NanK from Fusobacterium nucleatum was determined to 2.23 Å resolution by X-ray crystallography. Unlike other NanK enzymes and ROK family members, F. nucleatum NanK does not have a conserved zinc-binding site. In spite of the absence of the zinc-binding site, all of the major structural features of enzymatic activity are conserved.
|
|
| 10. |
|
|
