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E-raamat: Data Analytics for Protein Crystallization

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  • Formaat: EPUB+DRM
  • Sari: Computational Biology 25
  • Ilmumisaeg: 27-Nov-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319589374
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Data Analytics for Protein CrystallizationSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: Computational Biology 25
  • Ilmumisaeg: 27-Nov-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319589374
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This unique text/reference presents an overview of the computational aspects of protein crystallization, describing how to build robotic high-throughput and crystallization analysis systems. The coverage encompasses the complete data analysis cycle, including the set-up of screens by analyzing prior crystallization trials, the classification of crystallization trial images by effective feature extraction, the analysis of crystal growth in time series images, the segmentation of crystal regions in images, the application of focal stacking methods for crystallization images, and the visualization of trials.

Topics and features: describes the fundamentals of protein crystallization, and the scoring and categorization of crystallization image trials; introduces a selection of computational methods for protein crystallization screening, and the hardware and software architecture for a basic high-throughput system; presents an overview of the image features used in protein crystallization classification, and a spatio-temporal analysis of protein crystal growth; examines focal stacking techniques to avoid blurred crystallization images, and different thresholding methods for binarization or segmentation; discusses visualization methods and software for protein crystallization analysis, and reviews alternative methods to X-ray diffraction for obtaining structural information; provides an overview of the current challenges and potential future trends in protein crystallization.









This interdisciplinary work serves as an essential reference on the computational and data analytics components of protein crystallization for the structural biology community, in addition to computer scientists wishing to enter the field of protein crystallization.
1 Introduction to Protein Crystallization
1(20)
1.1 Introduction
1(1)
1.1.1 The Protein Molecule
1(1)
1.2 The Phase Diagram
2(3)
1.3 The Second Virial Coefficient
5(2)
1.3.1 Second Virial Coefficient Thought Experiments
6(1)
1.3.2 But the Protein Still Does Not Crystallize!
7(1)
1.4 Practical Considerations When Crystallizing Proteins
7(1)
1.4.1 Other Factors Affecting Protein Crystallization
7(1)
1.4.2 The Importance of the Protein
8(1)
1.5 The Protein Crystallization Screening Process
8(5)
1.5.1 Screening Methods
10(1)
1.5.2 Experimental Design in Introducing the Protein to Precipitant
10(1)
1.5.3 Screening Data Analysis
11(2)
1.6 Introducing the Protein to the Precipitant---How to Do It?
13(2)
1.6.1 Dialysis
13(1)
1.6.2 Liquid-Liquid Diffusion
13(1)
1.6.3 Vapor Diffusion
14(1)
1.6.4 Batch Method
15(1)
1.7 Following the Crystallization Experiment
15(1)
1.7.1 Methods for Viewing the Crystallization Screening Results
16(1)
1.8 Results Interpretation
16(1)
1.9 Crystallization of Complexes
17(1)
1.10 Crystallization of Integral Membrane Proteins
17(1)
1.11 Summary
18(3)
References
18(3)
2 Scoring and Phases of Crystallization
21(12)
2.1 Introduction
21(1)
2.2 Why Score Crystallization Drop Results?
22(1)
2.3 Our Scoring Scale
22(1)
2.4 Our Scoring Procedure
22(8)
2.4.1 What You See Is Not Always Simply Classified
24(4)
2.4.2 Hierarchical Categories
28(2)
2.5 Even if You Are Not Going to Process Your Scored Data
30(1)
2.6 Summary
31(2)
References
31(2)
3 Computational Methods for Protein Crystallization Screening
33(24)
3.1 Introduction
33(1)
3.2 Overview of Experimental Design Methods for Screening
34(1)
3.3 Using Neural Networks for Experimental Design
35(2)
3.4 Genetic Algorithm for Protein Crystallization Screening
37(2)
3.5 Associative Experimental Design
39(2)
3.6 Optimization of Cocktails
41(5)
3.6.1 Elimination of Prohibited Combinations
42(1)
3.6.2 Prioritization of Reagents
43(1)
3.6.3 Ranking of Prioritized Conditions
43(2)
3.6.4 Optimizing Concentration Values
45(1)
3.7 Experiments and Evaluation
46(6)
3.7.1 Proteins for Preliminary Experiments
46(1)
3.7.2 Results for Preliminary Data
47(2)
3.7.3 Expanded Screen Analysis
49(2)
3.7.4 Evaluation of Ranked Results
51(1)
3.8 Summary
52(5)
References
55(2)
4 Robotic Image Acquisition
57(26)
4.1 Introduction
57(4)
4.2 Components of a Robotic Setup
61(3)
4.2.1 Well Plates
61(1)
4.2.2 Fluorescence Microscopy
61(3)
4.3 Image Acquisition
64(1)
4.4 Image Processing and Segmentation
64(6)
4.4.1 Image Preprocessing
65(2)
4.4.2 Segmentation
67(3)
4.5 Feature Extraction
70(5)
4.5.1 Intensity Features
70(1)
4.5.2 Region Features
71(4)
4.6 Accuracy and Timing Analysis
75(4)
4.6.1 Multilayer Perceptron Neural Network (MLP)
76(1)
4.6.2 Max-Class Ensemble Method
76(3)
4.6.3 Computation Time
79(1)
4.7 Summary
79(4)
References
80(3)
5 Classification of Crystallization Trial Images
83(42)
5.1 Introduction
83(9)
5.1.1 Challenges of Protein Crystallization Classification
84(2)
5.1.2 Factors for Classification
86(2)
5.1.3 Feature Analysis for Building Real-Time Classifiers
88(4)
5.2 Data Preprocessing
92(2)
5.2.1 Feature Normalization
92(1)
5.2.2 Dimensionality Reduction and Feature Selection
92(1)
5.2.3 Image Processing
93(1)
5.3 Classifiers
94(2)
5.4 Feature Sets
96(6)
5.4.1 Intensity Features
96(1)
5.4.2 Histogram Features
96(2)
5.4.3 Texture Features
98(1)
5.4.4 Region Features
99(2)
5.4.5 Graph Features
101(1)
5.4.6 Shape-Adaptive Features
101(1)
5.5 Analysis of Feature Sets
102(10)
5.5.1 Data
103(2)
5.5.2 Evaluating Features for Hierarchical Classification
105(1)
5.5.3 First-Level (3-Class) Classification
105(4)
5.5.4 Second-Level Classification
109(3)
5.6 Timing Analysis for Classification
112(3)
5.7 Deep Learning for Protein Crystallization Images
115(1)
5.8 Discussion
116(3)
5.9 Summary
119(6)
References
120(5)
6 Crystal Growth Analysis
125(26)
6.1 Introduction
125(2)
6.2 Is it a Protein---Rule of Thumb
127(1)
6.2.1 Protein---Get it While it is Fresh
128(1)
6.3 Temporal Analysis of Time Series Images
128(4)
6.3.1 Stages of Temporal Analysis
129(2)
6.3.2 Sample Dataset and Experimental Setup
131(1)
6.4 Identifying Trials for Spatiotemporal Analysis
132(3)
6.4.1 Image Thresholding
132(1)
6.4.2 Canny Edge Detection
133(1)
6.4.3 Merging Results of Thresholding and Canny Edge Detection
134(1)
6.4.4 Evaluation
135(1)
6.5 Spatiotemporal Analysis of Protein Crystal Growth
135(6)
6.5.1 Identifying Crystallographically Important Regions
136(2)
6.5.2 Image Registration and Alignment
138(1)
6.5.3 Spatiotemporal Features
138(3)
6.6 Determining Crystal Growth
141(1)
6.7 Detection of New Crystals
142(2)
6.8 Detection of Crystal Size Increase
144(1)
6.9 Discussion
145(2)
6.9.1 Trace Fluorescent Labeling
145(1)
6.9.2 Spatiotemporal Analysis
146(1)
6.10 Summary
147(4)
References
148(3)
7 Focal Stacking for Crystallization Microscopy
151(26)
7.1 Introduction
151(1)
7.2 Typical Viewing Area ~ 2 mm in Diameter
152(2)
7.2.1 Objective Characteristics
153(1)
7.2.2 Depth of Field
153(1)
7.2.3 Drop Depth and Your Crystal Probably Isn't Where You Are Looking
154(1)
7.3 Take Multiple Images to See Through the Drop
154(1)
7.4 Auto-Focusing
155(1)
7.4.1 Active Auto-Focusing
155(1)
7.4.2 Passive Auto-Focusing
155(1)
7.5 Focal Stacking
156(3)
7.5.1 Pixel-Based Focal Stacking (PBFS)
158(1)
7.5.2 Neighborhood-Based Focal Stacking (NBFS)
158(1)
7.5.3 Transformation-Based Focal Stacking
158(1)
7.6 Focal Stacking for Trace Fluorescently Labeling Microscopy
159(5)
7.6.1 Modification of Harris Corner Response Measure (HCRM)
159(2)
7.6.2 Calculating Representative HCRM Value
161(1)
7.6.3 Generating Focused Image
162(2)
7.7 Handling High-Resolution Images
164(1)
7.8 Handling Varying Illumination
165(3)
7.9 Evaluation of Focal Stacking Methods
168(7)
7.9.1 Low-Resolution Image
169(3)
7.9.2 High-Resolution Image
172(1)
7.9.3 Varying Illumination Images
173(1)
7.9.4 Comparison of Different Methods
173(2)
7.10 Summary
175(2)
References
176(1)
8 Crystal Image Region Segmentation
177(22)
8.1 Introduction
177(1)
8.2 Image Binarization Methods and Limitations
178(2)
8.3 Supervised Thresholding
180(5)
8.3.1 Building the Training Set
181(1)
8.3.2 Correctness Measurement
181(1)
8.3.3 Feature Extraction
182(3)
8.4 Framework of Super-Thresholding
185(1)
8.5 Priori Approach
186(1)
8.6 Posteriori Approach
187(1)
8.7 Evaluation of Super-Thresholding
188(6)
8.7.1 Results
189(4)
8.7.2 Discussion
193(1)
8.8 Summary
194(5)
References
195(4)
9 Visualization
199(12)
9.1 Introduction
199(1)
9.2 Plate Visualization
200(4)
9.3 Well View
204(1)
9.4 Scoring Crystallization Trials
205(1)
9.5 Multiple Crystallization Trial Analysis
206(1)
9.5.1 Time Course Analysis
206(1)
9.5.2 Support for Sequential View
206(1)
9.5.3 Multiple Light Source Support
206(1)
9.6 Chemical Space Mapping
207(1)
9.7 Summary
208(3)
References
209(2)
10 Other Structure Determination Methods
211(12)
10.1 Introduction
211(1)
10.2 Neutron Diffraction (ND)
212(1)
10.3 Nuclear Magnetic Resonance (NMR)
213(1)
10.4 Cryogenic Electron Microscopy (Cryo-EM)
214(1)
10.5 X-Ray Free Electron Laser (XFEL)
215(2)
10.6 Other Approaches
217(3)
10.6.1 Chemical Cross linking
217(1)
10.6.2 Fluorescence Resonance Energy Transfer
218(1)
10.6.3 Circular Dichroism (CD)
219(1)
10.7 Summary
220(3)
References
220(3)
11 Future of Computational Protein Crystallization
223(4)
11.1 Introduction
223(1)
11.2 Challenges and Future Directions
224(2)
11.3 Summary
226(1)
Index 227
Dr. Marc L. Pusey is a Research Scientist at iXpressGenes, Inc., Huntsville, AL, USA. Dr. Ramazan S. Aygün is an Associate Professor in the Computer Science Department of the University of Alabama in Huntsville, USA.
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