Magnetic Particles used in a New Approach for Designed Protein Crystallization

CrystEngComm ◽  
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...

Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features

2020 ◽  
Author(s):  
Yi-Heng Zhu ◽  
Jun Hu ◽  
Fang Ge ◽  
Fuyi Li ◽  
Jiangning Song ◽  
...  
Keyword(s):  
Crystallization Process ◽  
Solvent Accessibility ◽  
Protein Crystallization ◽  
Protein Structures ◽  
X Ray ◽  
X Ray Crystallography ◽  
Benchmark Datasets ◽  
Pass Through ◽  
Matthew’S Correlation Coefficient ◽  
Dcf Model

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.

scholarly journals Structure Determination by Single-Particle Cryo-Electron Microscopy: Only the Sky (and Intrinsic Disorder) is the Limit

2019 ◽  
Vol 20 (17) ◽  
pp. 4186 ◽  
Author(s):  
Emeka Nwanochie ◽  
Vladimir N. Uversky
Keyword(s):  
Electron Microscopy ◽  
Nmr Spectroscopy ◽  
Structure Determination ◽  
Single Particle ◽  
Protein Structures ◽  
X Ray ◽  
X Ray Crystallography ◽  
Intrinsically Disordered ◽  
Small Proteins ◽  
Cryo Electron Microscopy

Traditionally, X-ray crystallography and NMR spectroscopy represent major workhorses of structural biologists, with the lion share of protein structures reported in protein data bank (PDB) being generated by these powerful techniques. Despite their wide utilization in protein structure determination, these two techniques have logical limitations, with X-ray crystallography being unsuitable for the analysis of highly dynamic structures and with NMR spectroscopy being restricted to the analysis of relatively small proteins. In recent years, we have witnessed an explosive development of the techniques based on Cryo-electron microscopy (Cryo-EM) for structural characterization of biological molecules. In fact, single-particle Cryo-EM is a special niche as it is a technique of choice for the structural analysis of large, structurally heterogeneous, and dynamic complexes. Here, sub-nanometer atomic resolution can be achieved (i.e., resolution below 10 Å) via single-particle imaging of non-crystalline specimens, with accurate 3D reconstruction being generated based on the computational averaging of multiple 2D projection images of the same particle that was frozen rapidly in solution. We provide here a brief overview of single-particle Cryo-EM and show how Cryo-EM has revolutionized structural investigations of membrane proteins. We also show that the presence of intrinsically disordered or flexible regions in a target protein represents one of the major limitations of this promising technique.

scholarly journals PROBABILISTIC ENSEMBLES FOR IMPROVED INFERENCE IN PROTEIN-STRUCTURE DETERMINATION

2012 ◽  
Vol 10 (01) ◽  
pp. 1240009 ◽  
Author(s):  
AMEET SONI ◽  
JUDE SHAVLIK
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
Protein Structures ◽  
Three Dimensional ◽  
Protein Structure Determination ◽  
Complex Problem ◽  
Approximate Inference ◽  
X Ray ◽  
X Ray Crystallography ◽  
Inference Methods

Protein X-ray crystallography — the most popular method for determining protein structures — remains a laborious process requiring a great deal of manual crystallographer effort to interpret low-quality protein images. Automating this process is critical in creating a high-throughput protein-structure determination pipeline. Previously, our group developed ACMI, a probabilistic framework for producing protein-structure models from electron-density maps produced via X-ray crystallography. ACMI uses a Markov Random Field to model the three-dimensional (3D) location of each non-hydrogen atom in a protein. Calculating the best structure in this model is intractable, so ACMI uses approximate inference methods to estimate the optimal structure. While previous results have shown ACMI to be the state-of-the-art method on this task, its approximate inference algorithm remains computationally expensive and susceptible to errors. In this work, we develop Probabilistic Ensembles in ACMI (PEA), a framework for leveraging multiple, independent runs of approximate inference to produce estimates of protein structures. Our results show statistically significant improvements in the accuracy of inference resulting in more complete and accurate protein structures. In addition, PEA provides a general framework for advanced approximate inference methods in complex problem domains.

Structure Determination by X-Ray Crystallography

1978 ◽  
Vol 29 (1) ◽  
pp. 24-25
Author(s):  
A M Glazer
Keyword(s):  
Structure Determination ◽  
X Ray ◽  
X Ray Crystallography

scholarly journals 3D structure determination of native mammalian cells using cryo-FIB and cryo-electron tomography

2012 ◽  
Vol 180 (2) ◽  
pp. 318-326 ◽  
Author(s):  
Ke Wang ◽  
Korrinn Strunk ◽  
Gongpu Zhao ◽  
Jennifer L. Gray ◽  
Peijun Zhang
Keyword(s):  
Structure Determination ◽  
Mammalian Cells ◽  
Electron Tomography ◽  
3D Structure ◽  
Cryo Electron Tomography ◽  
3D Structure Determination

Synthesis of (1,2-benzenediolato(2-)-O,O')oxobis(1-piperidinolato(1-)-O,N)molybdenum(VI), [MoO(C5H10NO)2(C6H4O2)], and structure determination by correlated proton-carbon-13 two-dimensional NMR spectroscopy and x-ray crystallography

1985 ◽  
Vol 24 (24) ◽  
pp. 4070-4077 ◽  
Author(s):  
Stuart Bristow ◽  
John H. Enemark ◽  
C. David Garner ◽  
Martin Minelli ◽  
Gareth A. Morris ◽  
...  
Keyword(s):  
Nmr Spectroscopy ◽  
Structure Determination ◽  
Two Dimensional ◽  
X Ray ◽  
X Ray Crystallography
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