Protein Crystallization for X-ray Crystallography

Abstract X-ray crystallography is the major approach for determining atomic-level protein structures. Because not all proteins can be easily crystallized, accurate prediction of protein crystallization propensity provides critical help in guiding experimental design and improving the success rate of X-ray crystallography experiments. This study has developed a new machine-learning-based pipeline that uses a newly developed deep-cascade forest (DCF) model with multiple types of sequence-based features to predict protein crystallization propensity. Based on the developed pipeline, two new protein crystallization propensity predictors, denoted as DCFCrystal and MDCFCrystal, have been implemented. DCFCrystal is a multistage predictor that can estimate the success propensities of the three individual steps (production of protein material, purification and production of crystals) in the protein crystallization process. MDCFCrystal is a single-stage predictor that aims to estimate the probability that a protein will pass through the entire crystallization process. Moreover, DCFCrystal is designed for general proteins, whereas MDCFCrystal is specially designed for membrane proteins, which are notoriously difficult to crystalize. DCFCrystal and MDCFCrystal were separately tested on two benchmark datasets consisting of 12 289 and 950 proteins, respectively, with known crystallization results from various experimental records. The experimental results demonstrated that DCFCrystal and MDCFCrystal increased the value of Matthew’s correlation coefficient by 199.7% and 77.8%, respectively, compared to the best of other state-of-the-art protein crystallization propensity predictors. Detailed analyses show that the major advantages of DCFCrystal and MDCFCrystal lie in the efficiency of the DCF model and the sensitivity of the sequence-based features used, especially the newly designed pseudo-predicted hybrid solvent accessibility (PsePHSA) feature, which improves crystallization recognition by incorporating sequence-order information with solvent accessibility of residues. Meanwhile, the new crystal-dataset constructions help to train the models with more comprehensive crystallization knowledge.

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Combining `dry' co-crystallization andin situdiffraction to facilitate ligand screening by X-ray crystallography

Acta Crystallographica Section D Biological Crystallography ◽

10.1107/s1399004715010342 ◽

2015 ◽

Vol 71 (8) ◽

pp. 1777-1787 ◽

Cited By ~ 29

Author(s):

Muriel Gelin ◽

Vanessa Delfosse ◽

Frédéric Allemand ◽

François Hoh ◽

Yoann Sallaz-Damaz ◽

...

Keyword(s):

Protein Crystallization ◽

X Ray Diffraction ◽

X Ray ◽

X Ray Crystallography ◽

Ligand Screening ◽

Alternative Approach ◽

Manual Handling ◽

Stock Solutions ◽

Established Technique

X-ray crystallography is an established technique for ligand screening in fragment-based drug-design projects, but the required manual handling steps – soaking crystals with ligand and the subsequent harvesting – are tedious and limit the throughput of the process. Here, an alternative approach is reported: crystallization plates are pre-coated with potential binders prior to protein crystallization and X-ray diffraction is performed directly `in situ' (or in-plate). Its performance is demonstrated on distinct and relevant therapeutic targets currently being studied for ligand screening by X-ray crystallography using either a bending-magnet beamline or a rotating-anode generator. The possibility of using DMSO stock solutions of the ligands to be coated opens up a route to screening most chemical libraries.

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Magnetic Particles used in a New Approach for Designed Protein Crystallization

CrystEngComm ◽

10.1039/d0ce01529f ◽

2021 ◽

Author(s):

Raquel dos Santos ◽

Maria João Romão ◽

Ana C A Roque ◽

Ana Luisa Moreira Carvalho

Keyword(s):

Structure Determination ◽

Magnetic Particles ◽

Protein Crystallization ◽

Protein Structures ◽

3D Structure ◽

New Approach ◽

X Ray ◽

X Ray Crystallography ◽

3D Structure Determination

After more than one hundred and thirty thousand protein structures determined by X-ray crystallography, the challenge of protein crystallization for 3D structure determination remains. In the quest for additives for...

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Protein crystallization: Eluding the bottleneck of X-ray crystallography

AIMS Biophysics ◽

10.3934/biophy.2017.4.557 ◽

2017 ◽

Vol 4 (4) ◽

pp. 557-575 ◽

Cited By ~ 12

Author(s):

Joshua Holcomb ◽

◽

Nicholas Spellmon ◽

Yingxue Zhang ◽

Maysaa Doughan ◽

...

Keyword(s):

Protein Crystallization ◽

X Ray ◽

X Ray Crystallography

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DHS-Crystallize: Deep-Hybrid-Sequence based method for predicting protein Crystallization

10.1101/2020.11.13.381301 ◽

2020 ◽

Author(s):

Azadeh Alavi ◽

David B. Ascher

Keyword(s):

Protein Crystallization ◽

Attrition Rate ◽

Gradient Boosting ◽

Trial And Error ◽

Crystallization Property ◽

X Ray ◽

X Ray Crystallography ◽

Gradient Boosting Machine ◽

Test Sets ◽

High Attrition Rate

AbstractThe key method for determining the structure of a protein to date is X-ray crystallography, which is a very expensive technique that suffers from high attrition rate. On the contrary, a sequence-based predictor that is capable of accurately determining protein crystallization property, would not only overcome such limitations, but also would reduce the trial-and-error settings required to perform crystallization. In this work, to predict protein crystallizability, we have developed a novel sequence-based hybrid method that employs two separate, yet fully automated, concepts for extracting features from protein sequences. Specifically, we use a deep convolutional neural network on a publicly available dataset to extract descriptive features directly from the sequences, then fuse such feature with structural-and-physio-chemical driven features (such as amino-acid composition or AAIndex-based physicochemical properties). Dimentionality reduction is then performed on the resulting features and the output vectors are applied to train optimized gradient boosting machine (XGBoostt). We evaluate our method through three publicly available test sets, and show that our proposed DHS-Crystallize algorithm outperforms state-of-the-art methods, and achieves higher performance compared to using DCNN-deriven features, or structural-and-physio-chemical driven features alone.

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A fully automated crystallization apparatus for small protein quantities

Acta Crystallographica Section F Structural Biology Communications ◽

10.1107/s2053230x20015514 ◽

2021 ◽

Vol 77 (1) ◽

Author(s):

Ryuichi Kato ◽

Masahiko Hiraki ◽

Yusuke Yamada ◽

Mikio Tanabe ◽

Toshiya Senda

Keyword(s):

Protein Crystallization ◽

Diffusion Method ◽

Cubic Phase ◽

Web Browser ◽

X Ray ◽

X Ray Crystallography ◽

Membrane Protein Crystallization ◽

Crystallization Conditions ◽

Set Up ◽

Very High

In 2003, a fully automated protein crystallization and monitoring system (PXS) was developed to support the structural genomics projects that were initiated in the early 2000s. In PXS, crystallization plates were automatically set up using the vapor-diffusion method, transferred to incubators and automatically observed according to a pre-set schedule. The captured images of each crystallization drop could be monitored through the internet using a web browser. While the screening throughput of PXS was very high, the demands of users have gradually changed over the ensuing years. To study difficult proteins, it has become important to screen crystallization conditions using small amounts of proteins. Moreover, membrane proteins have become one of the main targets for X-ray crystallography. Therefore, to meet the evolving demands of users, PXS was upgraded to PXS2. In PXS2, the minimum volume of the dispenser is reduced to 0.1 µl to minimize the amount of sample, and the resolution of the captured images is increased to five million pixels in order to observe small crystallization drops in detail. In addition to the 20°C incubators, a 4°C incubator was installed in PXS2 because crystallization results may vary with temperature. To support membrane-protein crystallization, PXS2 includes a procedure for the bicelle method. In addition, the system supports a lipidic cubic phase (LCP) method that uses a film sandwich plate and that was specifically designed for PXS2. These improvements expand the applicability of PXS2, reducing the bottleneck of X-ray protein crystallography.

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Room-temperature serial crystallography using a kinetically optimized microfluidic device for protein crystallization and on-chip X-ray diffraction

IUCrJ ◽

10.1107/s2052252514016960 ◽

2014 ◽

Vol 1 (5) ◽

pp. 349-360 ◽

Cited By ~ 74

Author(s):

Michael Heymann ◽

Achini Opthalage ◽

Jennifer L. Wierman ◽

Sathish Akella ◽

Doletha M. E. Szebenyi ◽

...

Keyword(s):

Room Temperature ◽

Protein Crystallization ◽

Glucose Isomerase ◽

Feedback Mechanism ◽

X Ray Diffraction ◽

Data Set ◽

X Ray ◽

X Ray Crystallography ◽

Negative Feedback Mechanism ◽

On Chip

An emulsion-based serial crystallographic technology has been developed, in which nanolitre-sized droplets of protein solution are encapsulated in oil and stabilized by surfactant. Once the first crystal in a drop is nucleated, the small volume generates a negative feedback mechanism that lowers the supersaturation. This mechanism is exploited to produce one crystal per drop. Diffraction data are measured, one crystal at a time, from a series of room-temperature crystals stored on an X-ray semi-transparent microfluidic chip, and a 93% complete data set is obtained by merging single diffraction frames taken from different unoriented crystals. As proof of concept, the structure of glucose isomerase was solved to 2.1 Å, demonstrating the feasibility of high-throughput serial X-ray crystallography using synchrotron radiation.

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Life in the fast lane for protein crystallization and X-ray crystallography

Progress in Biophysics and Molecular Biology ◽

10.1016/j.pbiomolbio.2004.07.011 ◽

2005 ◽

Vol 88 (3) ◽

pp. 359-386 ◽

Cited By ~ 54

Author(s):

M PUSEY ◽

Z LIU ◽

W TEMPEL ◽

J PRAISSMAN ◽

D LIN ◽

...

Keyword(s):

Protein Crystallization ◽

X Ray ◽

X Ray Crystallography ◽

Fast Lane

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Potential use of ultrasound to promote protein crystallization

Journal of Applied Crystallography ◽

10.1107/s0021889810040951 ◽

2010 ◽

Vol 43 (6) ◽

pp. 1419-1425 ◽

Cited By ~ 29

Author(s):

Rosa Crespo ◽

Pedro M. Martins ◽

Luís Gales ◽

Fernando Rocha ◽

Ana M. Damas

Keyword(s):

Protein Crystallization ◽

Crystal Formation ◽

Data Sets ◽

Resolution Limit ◽

X Ray Diffraction ◽

Promoting Effect ◽

X Ray ◽

X Ray Crystallography ◽

Egg White Lysozyme ◽

Crystallization Experiments

This work shows promising applications of ultrasound in promoting protein crystallization, which is important for structure determination by X-ray crystallography. It was observed that ultrasound can be used as a nucleation promoter as it decreases the energy barrier for crystal formation. Crystallization experiments on egg-white lysozyme were carried out with and without ultrasonic irradiation using commercial crystallization plates placed in temperature-controlled water baths. The nucleation-promoting effect introduced by ultrasound is illustrated by the reduction of the metastable zone width, as measured by the isothermal microbatch technique. The same effect was confirmed by the increased number of conditions leading to the formation of crystals when vapour diffusion techniques were carried out in the presence of ultrasound. By inducing faster nucleation, ultrasound leads to protein crystals grown at low supersaturation levels, which are known to have better diffraction properties. In fact, X-ray diffraction data sets collected using 13 lysozyme crystals (seven grown with ultrasound and six without) show an average 0.1 Å improvement in the resolution limit when ultrasound was used (p< 0.10). Besides the immediate application of ultrasound in nucleation promotion, the preliminary diffraction results also suggest a promising application in crystal quality enhancement.

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