Machine learning-based protein crystal detection for monitoring of crystallization processes enabled with large-scale synthetic data sets of photorealistic images

Since preparative chromatography is a sustainability challenge due to large amounts of consumables used in downstream processing of biomolecules, protein crystallization offers a promising alternative as a purification method. While the limited crystallizability of proteins often restricts a broad a...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Analytical and bioanalytical chemistry Ročník 414; číslo 21; s. 6379 - 6391
Hlavní autoři:	Bischoff, Daniel, Walla, Brigitte, Weuster-Botz, Dirk
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022 Springer Springer Nature B.V
Témata:	Algorithms Analysis Analytical Chemistry automation Biochemistry Biomolecules Biotechnology Characterization and Evaluation of Materials Chemistry Chemistry and Materials Science chromatography Cognitive tasks Computer applications Crystallization Crystalloids (Botany) Crystals data collection Datasets Deep learning Food Science Laboratory Medicine Learning algorithms Machine learning Methods Micrography Molecular biology Monitoring Monitoring/Environmental Analysis Photography Photomicrographs Protein purification Proteins Purification purification methods Ray tracing Research Paper Single crystals Structure Sustainability in (Bio-)Analytical Chemistry Synthetic data Germany Deep learning Synthetic data sets Protein crystallization Automated image analysis
ISSN:	1618-2642, 1618-2650, 1618-2650
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	Since preparative chromatography is a sustainability challenge due to large amounts of consumables used in downstream processing of biomolecules, protein crystallization offers a promising alternative as a purification method. While the limited crystallizability of proteins often restricts a broad application of crystallization as a purification method, advances in molecular biology, as well as computational methods are pushing the applicability towards integration in biotechnological downstream processes. However, in industrial and academic settings, monitoring protein crystallization processes non-invasively by microscopic photography and automated image evaluation remains a challenging problem. Recently, the identification of single crystal objects using deep learning has been the subject of increased attention for various model systems. However, the advancement of crystal detection using deep learning for biotechnological applications is limited: robust models obtained through supervised machine learning tasks require large-scale and high-quality data sets usually obtained in large projects through extensive manual labeling, an approach that is highly error-prone for dense systems of transparent crystals. For the first time, recent trends involving the use of synthetic data sets for supervised learning are transferred, thus generating photorealistic images of virtual protein crystals in suspension (PCS) through the use of ray tracing algorithms, accompanied by specialized data augmentations modelling experimental noise. Further, it is demonstrated that state-of-the-art models trained with the large-scale synthetic PCS data set outperform similar fine-tuned models based on the average precision metric on a validation data set, followed by experimental validation using high-resolution photomicrographs from stirred tank protein crystallization processes.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1618-2642 1618-2650 1618-2650
DOI:	10.1007/s00216-022-04101-8