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E-grāmata: Structural Bioinformatics Tools for Drug Design: Extraction of Biologically Relevant Information from Structural Databases

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Structural Bioinformatics Tools for Drug Design: Extraction of Biologically Relevant Information from Structural DatabasesPalielināt
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The book describes the individual steps necessary for biomacromolecular fragments analysis, as well as a list of essential software tools. For each step, it also shows corresponding web-based tools in detail and provides practical examples of their use.All tools and databases mentioned in the examples are available free of charge, platform-independent, web-based, user-friendly and do not require a prior IT background to be fully used.

1. IntroductionDescription of current situation about biomacromolecular structural data, their amount, growth, availability. Motivation, why it has sense to process and analyze these data, including applications towards drug design. Description of basic steps of their analysis via structural bioinformatics approaches.2. Biomacromolecular fragments3. DatabasesStatus of current structural databases focused on biomacromolecules, ligands, fragments, structural patterns etc. Practical examples based on PDBe and PDBsum.4. Detection & ExtractionLanguages and formalisms for description, detection, extraction and comparison of biomacromolecular fragments and structural patterns. Practical examples based on PatternQuery and SiteBinder.Approaches and software tools for extraction of channels and pores. Practical examples based on MOLE.5. ValidationMethodologies and software tools for validation of biomacromolecules and ligands. Practical examples based on MotiveValidator,

ValidatorDB and PDBe validation reports.6. CharacterizationCalculation of partial atomic charges of biomacromolecules and ligands. Practical examples based on AtomicChargeCalculator.Calculation of channel characteristics (radius, length, lining residues, physico-chemical properties). Practical examples based on MOLE.7. Selected Examples Few biologically relevant examples where all or most of the tools would be used. Small exemplary bioinformatics project. Problem formulation - fragment(s) definition - database search and data extraction - data validation - atomic charges calculation/biomacromolecular channel ^ conclusions.
1 Introduction
1(6)
Jaroslav Koca
Radka Svobodova Varekova
Lukas Pravda
Karel Berka
Stanislav Geidl
David Sehnal
Michal Otyepka
References
4(3)
Part I Patterns, Fragments and Data Sources
2 Biomacromolecular Fragments and Patterns
7(10)
Lukas Pravda
2.1 Pattern Examples
8(2)
2.1.1 Active Site and Their Inhibition -- -- Cyclooxygenase Inhibitors
8(1)
2.1.2 Allosteric Site -- -- Structural Flexibility of HIV Protease
9(1)
2.1.3 Transcription Factor -- -- Zinc Finger Motif
9(1)
2.2 Pattern Prediction
10(7)
2.2.1 Ubiquitin-Binding Domain Prediction
11(1)
2.2.2 Pattern Detection
12(1)
2.2.3 Phosphorylation of Drug Binding Pockets
12(1)
References
13(4)
3 Structural Bioinformatics Databases of General Use
17(14)
Karel Berka
3.1 How a Biomacromolecule Looks Codes What It Does
17(2)
3.2 Worldwide Protein Data Bank (PDB) -- -- Essential Structure Repository
19(4)
3.2.1 Protein Data Bank in Europe (PDBe)
20(2)
3.2.2 RCSB PDB
22(1)
3.3 Other Notable Databases
23(4)
3.3.1 PDBsum -- -- Pictorial View on PDB Database
23(1)
3.3.2 PDB_REDO and WHY_NOT Databases for Curated Structures
23(1)
3.3.3 CATH and Pfam Databases for Classification of Protein Folds and Sequences
23(1)
3.3.4 PDB Flex, Pocketome and PED3 Databases to Analyze Protein Flexibility and Disorder
24(1)
3.3.5 OPM and MemProtMD Databases for Membrane Protein
25(1)
3.3.6 NDB and GFDB Databases for Other Macromolecules
25(1)
3.3.7 UniProt and ChEMBL Databases -- -- Power of Connection
26(1)
3.4 Conclusion
27(1)
3.5 Exercises
27(4)
3.5.1 Use of PDBe
27(1)
3.5.2 Use of RCSB and ChEMBL
28(1)
3.5.3 Use of PDBsum
28(1)
3.5.4 Use of CATH
28(1)
References
29(2)
4 Validation
31(12)
Radka Svobodova Varekova
David Sehnal
Lukas Pravda
Stanislav Geidl
Jaroslav Koca
4.1 Introduction and Motivation
31(1)
4.2 Nipah G Attachment Glycoprotein Validation Example
32(1)
4.3 Objects of Validation
33(1)
4.4 Source Data for Validation
34(1)
4.5 Validation Approaches
34(1)
4.6 Evolution of Validation Tools
35(1)
4.7 How to Handle Structures with Errors
35(1)
4.8 Exercises
36(7)
References
38(5)
Part II Detection and Extraction
5 Detection and Extraction of Fragments
43(16)
Lukas Pravda
David Sehnal
Radka Svobodova Varekova
Jaroslav Koca
5.1 PatternQuery
43(8)
5.1.1 PatternQuery Explained
44(1)
5.1.2 Thinking in PatternQuery
45(1)
5.1.3 Basic Principles of the Language
46(5)
5.2 MetaPocket 2.0
51(1)
5.2.1 Serotonin Receptor Example
51(1)
5.3 Note on Pattern Comparison
52(1)
5.4 Exercises
53(6)
5.4.1 PatternQuery
53(2)
5.4.2 MetaPocket
55(1)
References
56(3)
6 Detection of Channels
59(14)
Lukas Pravda
Karel Berka
David Sehnal
Michal Otyepka
Radka Svobodova Varekova
Jaroslav Koca
6.1 Introduction and Motivation
59(5)
6.1.1 Bunyavirus Polymerase Example
62(1)
6.1.2 Aquaporin Example
63(1)
6.2 MOLE -- Channel Analysis Tool
64(1)
6.3 Identification of Channels Using MOLEonlinc
64(3)
6.3.1 Setup
64(1)
6.3.2 Geometry Properties
65(2)
6.4 Exercises
67(6)
References
67(6)
Part III Characterization
7 Characterization via Charges
73(8)
Radka Svobodova Varekova
David Sehnal
Stanislav Geidl
Jaroslav Koca
7.1 Introduction and Motivation
73(1)
7.2 Dinitrotoluene Example
73(1)
7.3 Charge Calculation Approaches
74(1)
7.4 Charge Visualization
75(2)
7.5 Formats for Saving of Charges
77(1)
7.6 Exercises
77(4)
References
79(2)
8 Channel Characteristics
81(12)
Lukas Pravda
Karel Berka
David Sehnal
Michal Otyepka
Radka Svobodova Varekova
Jaroslav Koca
8.1 Physicochemical Properties
81(3)
8.1.1 Hydropathy
81(1)
8.1.2 Polarity
82(1)
8.1.3 Mutability
82(1)
8.1.4 Charge
83(1)
8.2 Characterization of Channels Using MOLEonline
84(3)
8.2.1 Results Analysis
84(3)
8.3 Common Errors in Channel Calculation and Characterization
87(3)
8.3.1 No Channels Have Been Identified
87(2)
8.3.2 A Lot of Different Channels Are Identified, However None of Them Seems to be Relevant to My Expectations
89(1)
8.4 Exercises
90(3)
References
90(3)
Part IV Complete Process of Data Extraction and Analysis
9 Complete Process of Data Extraction and Analysis
93(18)
Radka Svobodova Varekova
Karel Berka
9.1 Lectin Example (Validation, Extraction, Comparison, Charge Calculation)
93(7)
9.1.1 Step 1: Detection of All Occurrences of the Binding Site
93(2)
9.1.2 Step 2: Validation of the Obtained PDB Entries
95(1)
9.1.3 Step 3: Analysis of Organisms and Proteins, from Which the Obtained Binding Sites Originate
95(1)
9.1.4 Step 4: Analysis of Common Amino Acid Composition
96(1)
9.1.5 Step 5: Analysis of Common 3D Structure Parts
97(1)
9.1.6 Step 6: Analysis of Charge Distribution
98(1)
9.1.7 Methodology of Data Analysis
99(1)
9.2 Cytochrome P450 Example (Database Search, Detection of Channels, Channel Characterization)
100(11)
9.2.1 Database Search
101(1)
9.2.2 Channels Detection
102(1)
9.2.3 Channels Characterization
102(1)
9.2.4 Solution
102(9)
Part V Conclusion
10 Concluding Remarks
111(2)
Jaroslav Koca
Radka Svobodova Varekova
Lukas Pravda
Karel Berka
Stanislav Geidl
David Sehnal
Michal Otyepka
11 Exercises Solution
113(28)
Jaroslav Koca
Radka Svobodova Varekova
Lukas Pravda
Karel Berka
Stanislav Geidl
David Sehnal
Michal Otyepka
11.1 Structural Bioinformatics Databases of General Use
113(8)
11.2 Validation
121(4)
11.3 Detection and Extraction of Fragments
125(8)
11.3.1 PatternQuery
125(4)
11.3.2 MetaPocket
129(4)
11.4 Detection of Channels
133(1)
11.5 Characterization via Charges
134(4)
11.6 Channel Characteristics
138(3)
References
139(2)
Glossary 141(2)
Index 143
Prof. RNDr. Jaroslav Koca, DrSc., is director of the Central European Institute of Technology and Head of the research group Computational Chemistry at Masaryk University in Brno, Czech Republic.





His field of expertise is mathematical and computer assisted chemistry and biochemistry, computer assisted molecular modeling of biologically interesting chemical species, information technology in chemistry, biochemistry, and environmental chemistry. Last years“ projects were mainly on conformational search, molecular docking and molecular dynamics simulations on peptides, proteins, oligonucleotides, nucleotide sugars, and carbohydrates. Methods for analysis conformational potential energy surfaces and energy landscapes of chemical reactions using molecular mechanics and quantum chemistry techniques. For more information see: http://www.chemi.muni.cz/~jkoca/
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97833194738882e.html
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