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...
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| Published in: | Analytical and bioanalytical chemistry Vol. 414; no. 21; pp. 6379 - 6391 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.09.2022
Springer Springer Nature B.V |
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| ISSN: | 1618-2642, 1618-2650, 1618-2650 |
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| Abstract | 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. |
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| AbstractList | 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.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. 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. |
| Audience | Academic |
| Author | Walla, Brigitte Weuster-Botz, Dirk Bischoff, Daniel |
| Author_xml | – sequence: 1 givenname: Daniel orcidid: 0000-0003-1780-1683 surname: Bischoff fullname: Bischoff, Daniel organization: Technical University of Munich, Institute of Biochemical Engineering – sequence: 2 givenname: Brigitte orcidid: 0000-0002-9289-5539 surname: Walla fullname: Walla, Brigitte organization: Technical University of Munich, Institute of Biochemical Engineering – sequence: 3 givenname: Dirk orcidid: 0000-0002-1171-4194 surname: Weuster-Botz fullname: Weuster-Botz, Dirk email: dirk.weuster-botz@tum.de organization: Technical University of Munich, Institute of Biochemical Engineering |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35661232$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3389_fbioe_2024_1397465 crossref_primary_10_1007_s00216_022_04211_3 crossref_primary_10_1016_j_earscirev_2023_104430 crossref_primary_10_1107_S2053273323009300 crossref_primary_10_1107_S2059798324009276 crossref_primary_10_1002_cite_202255156 crossref_primary_10_3390_cryst14121009 crossref_primary_10_1080_17460441_2023_2246881 crossref_primary_10_3390_cryst13050773 |
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| Keywords | Deep learning Synthetic data sets Protein crystallization Automated image analysis |
| Language | English |
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| PublicationTitle | Analytical and bioanalytical chemistry |
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