Optimizing Associative Experimental Design for Protein Crystallization Screening

Dinc, Imren; Pusey, Marc L.; Aygün, Ramazan

doi:10.1109/tnb.2016.2536030

Cited by 14 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prior to IL optimization tests the proteins were subjected to two initial rounds of crystallization screening. The first round used the Hampton Research High Throughput screen (Hampton Research, Aliso Viejo, CA, USA, cat.# HR2-130), the JCSG+ screen (Molecular Dimensions, Maumee, OH, USA, cat.# MD1-40), the MCSG-3 screen (Anatrace, Maumee, OH, USA, cat.# 50-109-1514), and a 96-condition screen (Screen 4a) under development in-house to complement the above 3 screens [24]. Crystallization screening was carried out using Corning CrystalEX sitting drop plates (Hampton Research, Aliso Viejo, CA, USA, Cat.# HR8-140), with a reservoir consisting of 50 µL of precipitant solution and protein:precipitant drop ratios of 1:1, 2:1, and 4:1.…”

Section: Methodsmentioning

confidence: 99%

Ionic Liquids as Protein Crystallization Additives

2021

Self Cite

View full text Add to dashboard Cite

Among its attributes, the mythical philosopher’s stone is supposedly capable of turning base metals to gold or silver. In an analogous fashion, we are finding that protein crystallization optimization using ionic liquids (ILs) often results in the conversion of base protein precipitate to crystals. Recombinant inorganic pyrophosphatases (8 of the 11 proteins) from pathogenic bacteria as well as several other proteins were tested for optimization by 23 ILs, plus a dH2O control, at IL concentrations of 0.1, 0.2, and 0.4 M. The ILs were used as additives, and all proteins were crystallized in the presence of at least one IL. For 9 of the 11 proteins, precipitation conditions were converted to crystals with at least one IL. The ILs could be ranked in order of effectiveness, and it was found that ~83% of the precipitation-derived crystallization conditions could be obtained with a suite of just eight ILs, with the top two ILs accounting for ~50% of the hits. Structural trends were found in the effectiveness of the ILs, with shorter-alkyl-chain ILs being more effective. The two top ILs, accounting for ~50% of the unique crystallization results, were choline dihydrogen phosphate and 1-butyl-3-methylimidazolium tetrafluoroborate. Curiously, however, a butyl group was present on the cation of four of the top eight ILs.

show abstract

Section: Methodsmentioning

confidence: 99%

Ionic Liquids as Protein Crystallization Additives

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are many commercially available crystallization screening kits from companies such as Hampton Research (USA), Molecular Dimensions (UK), Rigaku and Emerald BioSystems (USA), Jena Bioscience (Germany), Qiagen (Germany). The chemical components are usually designed in 8 × 3 × m ( m = 2 n , n = 0, 1, 2, 3 …) conditions so as to meet the requirement of automation and/or be compatible with commercial crystallization plates . In most cases, a standard screening kit contains 24, 48, or 96 conditions.…”

Section: Overview Of Commercial Screening Kitsmentioning

confidence: 99%

“…The results of trials illustrated that based on new solutions generated by AED, there were four, two, and one successful crystallization outputs for nucleoside diphosphate kinase, HAD superfamily hydrolase, and nucleoside kinase, respectively. Further experiments showed that AED following an optimization treatment could dramatically lead to occurrence of crystals at 4, 3, and 20 times in various conditions for holo Human transferrin, archaeal exosome protein, and nucleoside diphosphate kinase, respectively …”

Section: New Progress In Crystallization Condition Screening Practicementioning

confidence: 99%

An Analysis on Commercial Screening Kits and Chemical Components in Biomacromolecular Crystallization Screening

Qin

Xie

Zhang

et al. 2019

Crystal Research and Technology

View full text Add to dashboard Cite

Obtaining diffraction-quality crystals is still a bottleneck for biomacromolecular structure determination using X-ray diffraction. In practice, the bottleneck mainly comprises two aspects: difficulties in screening and in optimization, and both of them need screening kits. The paper aims at an investigation of the chemical components employed in 86 common commercial screening kits, and comparison with those in the Biological Macromolecules Crystallization Database (BMCD). First, the 86 screening kits are systematically analyzed in terms of design methods, classification, applicable scope, and manufacturing company. Second, the chemical components applied in these screening kits are investigated in detail in terms of the classification and utilization frequency, which are compared with those in BMCD. Third, four main chemical reagents of screening kits including salts, buffers, precipitants, and additives are analyzed in detail. The top ten utilized chemical components are listed. Last, the paper summarizes new progress and further innovation in crystallization condition screening practice and screening kits design. The analysis result would be a valuable reference for rational selection and design of screening kits, and even for personalized screening of a target biomacromolecule, which would provide new ideas to solve the bottleneck problem of biomacromolecular structure determination.

show abstract

“…[1] A combination of all these factors suitable for the protein being crystallized is critical for the formation of crystals, and the prediction of these parameters is quite challenging since there is no prior information about the protein solubility [2, 3]. Therefore, thousands of experimental trials may be required for successful crystallization.…”

Section: Introductionmentioning

confidence: 99%

“…Protein crystallization is a highly empirical process that depends on numerous factors such as pH and temperature of the environment, protein concentration, the type of precipitant, ionic strength of the solution, gravity, the crystallization methods, etc. [ 1 ] A combination of all these factors suitable for the protein being crystallized is critical for the formation of crystals, and the prediction of these parameters is quite challenging since there is no prior information about the protein solubility [ 2 , 3 ]. Therefore, thousands of experimental trials may be required for successful crystallization.…”

Section: Introductionmentioning

confidence: 99%

Feature analysis for classification of trace fluorescent labeled protein crystallization images

Sigdel¹,

Dinc²,

Sigdel³

et al. 2017

BioData Mining

Self Cite

View full text Add to dashboard Cite

BackgroundLarge number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these features make utilization of automated classification tools on stand-alone computing systems inconvenient due to the required time to complete the classification tasks. Combinations of image feature sets, feature reduction and classification techniques for crystallization images benefiting from trace fluorescence labeling are investigated.ResultsFeatures are categorized into intensity, graph, histogram, texture, shape adaptive, and region features (using binarized images generated by Otsu’s, green percentile, and morphological thresholding). The effects of normalization, feature reduction with principle components analysis (PCA), and feature selection using random forest classifier are also analyzed. The time required to extract feature categories is computed and an estimated time of extraction is provided for feature category combinations. We have conducted around 8624 experiments (different combinations of feature categories, binarization methods, feature reduction/selection, normalization, and crystal categories). The best experimental results are obtained using combinations of intensity features, region features using Otsu’s thresholding, region features using green percentile G 90 thresholding, region features using green percentile G 99 thresholding, graph features, and histogram features. Using this feature set combination, 96% accuracy (without misclassifying crystals as non-crystals) was achieved for the first level of classification to determine presence of crystals. Since missing a crystal is not desired, our algorithm is adjusted to achieve a high sensitivity rate. In the second level classification, 74.2% accuracy for (5-class) crystal sub-category classification. Best classification rates were achieved using random forest classifier.ContributionsThe feature extraction and classification could be completed in about 2 s per image on a stand-alone computing system, which is suitable for real time analysis. These results enable research groups to select features according to their hardware setups for real-time analysis.

show abstract

Optimizing Associative Experimental Design for Protein Crystallization Screening

Cited by 14 publications

References 35 publications

Ionic Liquids as Protein Crystallization Additives

Ionic Liquids as Protein Crystallization Additives

An Analysis on Commercial Screening Kits and Chemical Components in Biomacromolecular Crystallization Screening

Feature analysis for classification of trace fluorescent labeled protein crystallization images

Contact Info

Product

Resources

About