Protein crystal quality oriented disulfide bond engineering

Pu, Mingliang; Xu, Zhijie; Peng, Yao; Hou, Yaguang; Li, Dongsheng; Wang, Yang; Liu, Haiguang; Song, Gaojie; Liu, Zhijie

doi:10.1007/s13238-017-0482-7

Cited by 15 publications

(13 citation statements)

References 14 publications

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“…The performance on the prediction was further validated using two recently studied proteins that are frequently used as fusion partners in GPCR protein crystallization: Bril protein with 106 amino acids (PDB ID 1M6T), and Flavodoxin with 147 amino acids (PDB ID 1J8Q). The experiments were carried out in an independent study 13 , so the data can be considered as a double-blind test for the SSbondPre algorithm. For Bril, 10 mutants were tested in the experiment; 6 mutants formed disulfide bonds.…”

Section: Prediction Performance On Engineered Disulfide Bondsmentioning

confidence: 99%

“…Using these 13 experimentally tested protein mutants as an independent testing data, the performance of the present method is evaluated and yields an accuracy of approximately 70% (9/13) ( Table 4). The algorithm based on the support vector machine method with designed geometry features achieved 60% accuracy for the prediction of Bril disulfide bonds 13 . The multi-agent based method MaestroWeb correctly predicted 8 out of 13 engineered disulfide sites, and the DbD2 only correctly predicted 4 engineered sites for this dataset (see Table 4 for details).…”

Section: Prediction Performance On Engineered Disulfide Bondsmentioning

confidence: 99%

“…Salam et al applied both empirical knowledge about the disulfide bond geometry and physical interactions between neighboring atoms to predict disulfide bonds in mutant proteins based on wild type structures 12 . A disulfide bond prediction algorithm that aims to improve crystal quality has been developed to locate the potential disulfide bonds that lead to lower entropy of the protein molecules by using geometry restraints and support vector machine method 13 .…”

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confidence: 99%

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Prediction of disulfide bond engineering sites using a machine learning method

Gao

Dong

et al. 2020

Sci Rep

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Disulfide bonds are covalently bonded sulfur atoms from cysteine pairs in protein structures. Due to the importance of disulfide bonds in protein folding and structural stability, artificial disulfide bonds are often engineered by cysteine mutation to enhance protein structural stability. To facilitate the experimental design, we implemented a method based on neural networks to predict amino acid pairs for cysteine mutations to form engineered disulfide bonds. The designed neural network was trained with high-resolution structures curated from the Protein Data Bank. The testing results reveal that the proposed method recognizes 99% of natural disulfide bonds. In the test with engineered disulfide bonds, the algorithm achieves similar accuracy levels with other state-of-the-art algorithms in published dataset and better performance for two comprehensively studied proteins with 70% accuracy, demonstrating potential applications in protein engineering. The neural network framework allows exploiting the full features in distance space, and therefore improves accuracy of the disulfide bond engineering site prediction. The source code and a web server are available at http://liulab.csrc. ac.cn/ssbondpre.

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Section: Prediction Performance On Engineered Disulfide Bondsmentioning

confidence: 99%

Section: Prediction Performance On Engineered Disulfide Bondsmentioning

confidence: 99%

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confidence: 99%

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Prediction of disulfide bond engineering sites using a machine learning method

Gao

Dong

et al. 2020

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“…Disulfides in proteins produced in the E. coli cytoplasm are not uncommon: Numerous proteins with intact disulfide can be found in the PDB or have been studied with other methods (e.g., thioredoxin, 3DIE, Garcia-Pino et al, 2009 ; artificial disulfide peptides, Pu et al, 2017 ; human Zn-Cu superoxide dismutase, Mercatelli et al, 2016 ). The fraction of oxidized versus reduced disulfides in the cytoplasm of E. coli and other cells depends on a combination of the actual reduction potential of the disulfide in question, the average reduction potential of the cytoplasm ( E. coli BL21: −260 mV; range: −235–305 mV; Zhang et al, 2014 ), the ratio of redox mediators (mostly from the GSH/GSSG couple; E°′ ≈-250 ± 20 mV; see Gorin et al, 1975 and references therein) and the levels of formation of cysteine sulfenic acid in proteins by endogenous H 2 O 2 and cytoplasmic thiol peroxidases ( Flohe, 2013 ; Mercatelli et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

Persulfide Dioxygenase From Acidithiobacillus caldus: Variable Roles of Cysteine Residues and Hydrogen Bond Networks of the Active Site

et al. 2018

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Persulfide dioxygenases (PDOs) are abundant in Bacteria and also crucial for H2S detoxification in mitochondria. One of the two pdo-genes of the acidophilic bacterium Acidithiobacillus caldus was expressed in Escherichia coli. The protein (AcPDO) had 0.77 ± 0.1 Fe/subunit and an average specific sulfite formation activity of 111.5 U/mg protein (Vmax) at 40°C and pH 7.5 with sulfur and GSH following Michaelis–Menten kinetics. KM for GSH and Kcat were 0.5 mM and 181 s−1, respectively. Glutathione persulfide (GSSH) as substrate gave a sigmoidal curve with a Vmax of 122.3 U/mg protein, a Kcat of 198 s−1 and a Hill coefficient of 2.3 ± 0.22 suggesting positive cooperativity. Gel permeation chromatography and non-denaturing gels showed mostly tetramers. The temperature optimum was 40–45°C, the melting point 63 ± 1.3°C in thermal unfolding experiments, whereas low activity was measurable up to 95°C. Site-directed mutagenesis showed that residues located in the predicted GSH/GSSH binding site and in the central hydrogen bond networks including the iron ligands are essential for activity. Among these, the R139A, D141A, and H171A variants were inactive concomitant to a decrease of their melting points by 3–8 K. Other variants were inactivated without significant melting point change. Two out of five cysteines are likewise essential, both of which lie presumably in close proximity at the surface of the protein (C87 and C224). MalPEG labeling experiments suggests that they form a disulfide bridge. The reducing agent Tris(2-carboxyethyl)phosphine was inhibitory besides N-ethylmaleimide and iodoacetamide suggesting an involvement of cysteines and the disulfide in catalysis and/or protein stabilization. Mass spectrometry revealed modification of C87, C137, and C224 by 305 mass units equivalent to GSH after incubation with GSSH and with GSH in case of the C87A and C224A variants. The results of this study suggest that disulfide formation between the two essential surface-exposed cysteines and Cys-S-glutathionylation serve as a protective mechanism against uncontrolled thiol oxidation and the associated loss of enzyme activity.

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“…The principle for mutagenesis design was to strengthen inter-helical interaction patterns by predicting hydrogen bonds (salt bridges), hydrophobic interactions and disulfide bonds, or to strengthen ligand-receptor interaction patterns by covalent bonds or other interactions. Besides manual prediction based on modeling, the prediction of disulfide bonds was also assisted by a disulfide prediction algorithm (Pu et al, 2018). In each round beneficial mutations were passed on to the next round after consideration of monodispersity (the percentage of monomeric fraction in total fractions in SEC), protein yield (the height of monomeric fraction in SEC) and thermal stability (the melting temperature).…”

Section: Mutation Overviewmentioning

confidence: 99%