Protein actions: principles and modeling
Gespeichert in:
| Hauptverfasser: | , , |
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| Format: | Buch |
| Sprache: | English |
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New York and London
Garland Science
[2017]
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| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xii, 322 Seiten Illustrationen 28 cm |
| ISBN: | 9780815341772 |
Internformat
MARC
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| 020 | |a 9780815341772 |c pbk. : £60.00 |9 978-0-8153-4177-2 | ||
| 035 | |a (OCoLC)984710097 | ||
| 035 | |a (DE-599)BSZ486041956 | ||
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| 245 | 1 | 0 | |a Protein actions |b principles and modeling |c Ivet Bahar, Robert L. Jernigan, Ken A. Dill |
| 264 | 1 | |a New York and London |b Garland Science |c [2017] | |
| 264 | 4 | |c © 2017 | |
| 300 | |a xii, 322 Seiten |b Illustrationen |c 28 cm | ||
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Datensatz im Suchindex
| _version_ | 1804177478012370944 |
|---|---|
| adam_text | VI
TABLE OF CONTENTS
TABLE OF CONTENTS
Chapter 1 Proteins Are Polymers That
Fold into Specific Structures 1
PROTEINS ARE THE MACHINES THAT PERFORM CELLULAR
FUNCTIONS 1
PROTEINS HAVE SEQUENCE-STRUCTURE-FUNCTION
RELATIONSHIPS 2
AMINO ACIDS ARE THE REPEAT UNITS OF PROTEINS 4
Amino Acids Are Chiral Molecules 5
The 20 Amino Acids Have Different Physical Properties 5
In Proteins, Amino Acids Are Linked Together through
Peptide Bonds 7
Peptide Bonds Are Planar 7
The Rotational Freedom around the Backbone Peptide Bond
Is Described Using Ramachandran Maps 8
Side Chains Adopt Preferred Conformations 9
NATIVE PROTEINS HAVE COMPACT WELL-DEFINED
3D STRUCTURES 10
Proteins Come in Different Sizes and Shapes 10
Native Protein Chains Are Balled Up and Tightly Packed 11
Proteins Have Hydrophobic Cores 12
The Amino Acids in Native Proteins Are Hydrogen-Bonded
to Each Other 13
Cysteines Can Form Disulfide Bonds 13
PROTEINS HAVE HIERARCHIES OF STRUCTURE 1 3
The Secondary Structures of Proteins Are Helices and Sheets 14
Supersecondary Structures, Also Called Structural Motifs,
Are Common Combinations of Secondary Structures 1 8
Some Substructures of Proteins Are Compact Functional
Domains 18
Native Protein Topologies Are Described Using Contact Maps 19
How Can You Classify Protein Tertiary Structures? 20
Proteins Are Classified by Structural and Evolutionary
Properties in the CATH Database 22
Quaternary Structures: Higher-Order Structures Result from
Noncovalent Assemblies of Multiple Chains 22
Domain Swapping Is Another Way That Proteins Can Form
Quaternary Structures 23
SOME PROTEINS ARE STABLE AND FUNCTION IN THE
MEMBRANE ENVIRONMENT 23
SOME PROTEINS HAVE FIBROUS STRUCTURES 24
Fibrous Proteins Include Colled Coils and (3-Helices 25
NATIVE PROTEINS ARE CONFORMATIONAL ENSEMBLES 26
Proteins Fluctuate around Their Native Structures 26
A Protein Can Sample Multiple Substates under Native
Conditions 26
Some Proteins Are Intrinsically Disordered 27
SUMMARY 27
REFERENCES 27
SUGGESTED READING 28
Chapter 2 Proteins Perform Cellular
Functions 29
PROTEINS CARRY OUT MANY ACTIVITIES IN THE CELL 29
A PROTEIN’S FUNCTIONALITY IS ENCODED IN ITS
STRUCTURE AND DYNAMICS 30
PROTEINS ARE BORN 31
Ribosomes Manufacture Proteins 31
Molecular Chaperones Are Proteins That Help Other
Proteins to Fold Correctly 32
PROTEINS WORK FOR A LIVING 33
Some Proteins Are Biochemical Catalysts, Called Enzymes 33
Some Proteins, Called Motors, Convert Energy into Motion 36
Some Proteins Help Move Molecules or Ions across
Membranes 40
Some Proteins Have Signaling and Regulatory Actions 43
Some Proteins Are Structural and Protective Materials 48
PROTEINS ARE HEALTHY OR SICK OR DIE 49
Proteins Can Misfold, Sometimes in Association with
Disease 49
Proteasomes Digest Proteins 49
SUMMARY 50
REFERENCES SO
SUGGESTED READING 5 1
Chapter 3 Proteins Have Stable
Equilibrium Conformations
NATIVE AND DENATURED STATES ARE STABLE STATES OF
PROTEINS S3
Anfinsen’s Hypothesis: Native States Are
Thermodynamically Stable 54
The Basic Experiment of Protein Stability Is Equilibrium
Dénaturation 55
Stabilities and Structures Give Insights about the Driving
Forces of Folding 57
STATISTICAL MECHANICS IS THE LANGUAGE FOR
DESCRIBING PROTEIN STABILITIES 61
Why Do Proteins Fold and Unfold? The HP Model 62
Proteins Have a Folding Code 64
The Collapse of the Chain Helps Stabilize Secondary
Structures 65
Protein Folding Energy Landscapes Are Funnel-Shaped 66
SIMPLE PROTEINS DENATURE WITH TWO-STATE
THERMODYNAMICS 67
Protein Stability Depends Linearly on Dénaturant
Concentration 69
Protein Stability Is a Nonlinear Function of Temperature 70
PROTEINS TEND TO UNFOLD IN ACIDIC OR BASIC SOLUTIONS 71
A DENATURED STATE IS A DISTRIBUTION OF
CONFORMATIONS 73
The Radius of the Denatured Chain Grows as №՝6 74
Confinement or Crowding Can Increase a Protein’s Folding
Stability 75
SUMMARY 76
APPENDIX 3A: A SIMPLE ELECTROSTATIC MODEL OF
DENATURATION BY ACIDS AND BASES 76
REFERENCES 80
SUGGESTED READING 80
TABLE OF CONTENTS
Chapter 4 Protein Binding Leads to
Biological Actions 81
INTRODUCTION SI
BINDING CAN BE MODELED USING BINDING POLYNOMIALS 82
Binding Polynomials Are Used to Compute Binding Curves 82
One Ligand Can Bind to, and Saturate, One Site 83
Binding Polynomials Can Be Constructed Using the Rules of
Probability 84
Michaelis-Menten Kinetics Arises from an Underlying
Binding Step 85
Ligand Binding Rates May Follow Two-State Kinetics, or Be
More Complex 87
IN ALLOSTERY, BINDING IS COUPLED TO CONFORMATIONAL
CHANGE 88
Binding Can Be Cooperative between Two Binding Sites 89
The Hill Model Describes a Type of Cooperative Binding 89
Oxygen Binding to Hemoglobin Is a Cooperative Process 90
The MWC Model Describes Allostery 90
Binding Affinities Have Their Molecular Basis in Free Energies 94
INHIBITORS AND ACTIVATORS CAN MODULATE OTHER
BINDING ACTIONS 95
Some Effector Molecules Are Competitive 95
Prebinding: Ligand /Binds Only If XAlso Binds 97
Some Effectors Are Noncompetitive 97
COUPLED BINDING IS KEY TO REGULATION, SIGNALING,
AND ENERGY TRANSDUCTION 98
Biochemical Engines Harness Energetically Downhill
Processes to Drive Uphill Processes 98
BROWNIAN RATCHETS PRODUCE DIRECTED MOTION FROM
COUPLED BINDING EVENTS 1 00
SUMMARY 104
APPENDIX 4A: TYPICAL DISSOCIATION CONSTANTS FOR
PROTEINS 104
REFERENCES 105
SUGGESTED READING 105
Chapter 5 Folding and Aggregation Are
Cooperative Transitions 107
PROTEINS CAN UNDERGO SHARP TRANSITIONS IN THEIR
STRUCTURES OR PROPERTIES 1 07
Stable and Unstable States Are Represented on Energy
Landscapes 109
PROTEINS AND PEPTIDES CAN UNDERGO A COOPERATIVE
HELIX-COIL TRANSITION 110
The Schellman Model Describes the Helix-Coil Transition
as Nucleation Followed by Growth 11 3
Helices Can Be Denatured by Heating 11 5
PROTEIN FOLDING COOPERATIVITY ARISES FROM
SECONDARY AND TERTIARY INTERACTIONS 1 1 6
Helix-Helix Interactions Contribute to Folding
Cooperativity in Helix-Bundle Proteins 11 7
PROTEINS CAN ASSEMBLE COOPERATIVELY INTO
AGGREGATES, FIBRILS, OR CRYSTALS 1 20
Attractive Interactions Can Drive Proteins to Aggregate 121
Amyloid Peptides Can Assemble to Form Fibrils 121
SUMMARY 124
APPENDIX 5A: ADVANCED HELIX-COIL THEORIES 1 24
APPENDIX 5B: AMYLOID AGGREGATION THEORY 125
Most Fibrils Are Relatively Short I 25
Fibril Lengths Increase Sharply with Peptide Concentration 1 26
vii
Increasing the Concentration of Peptides Increases the
Fibril Concentration 126
REFERENCES 127
SUGGESTED READING 127
Chapter 6 The Principles of Protein
Folding Kinetics 129
THE LEVINTHAL PARADOX MOTIVATED THE SEARCH FOR A
PROTEIN FOLDING MECHANISM 1 29
FOLDING RATE EXPERIMENTS ARE CAPTURED BY
MASS-ACTION MODELS 130
Small Proteins Fold Rapidly through Two-State Kinetics 1 31
Single-Exponential Kinetics Is Characterized through the
Concept of a Transition State l 33
Large Proteins Typically Fold via Multi-exponential Kinetics 1 35
What Is the Difference between a Kinetic Intermediate State
and a Transition State? 1 36
RATE MEASUREMENTS GIVE INSIGHTS INTO THE PATHWAYS
OF PROTEIN FOLDING 137
Mutational Studies Can Probe Folding Pathways 139
Dénaturants Can Change Folding Rates: The Chevron Plot 140
Whole Secondary Structures Often Fold as a Unit 142
Ultrafast Folders Shed Light on the Speed Limits to Protein
Folding 142
HOW DO PROTEINS FOLD SO FAST? THEY FOLD ON
FUNNEL-SHAPED ENERGY LANDSCAPES 143
What Do You Learn from Folding Funnels? 144
DIFFERENT PROTEINS CAN FOLD AT VERY DIFFERENT RATES 146
SUMMARY 148
APPENDIX 6A: MASTER EQUATIONS DESCRIBE DYNAMICS 148
APPENDIX 6B: THE ZWANZIG-SZABO-BAGCHI MODEL
SHOWS HOW FUNNELS ACCELERATE FOLDING 1 53
The Equilibrium Properties of the ZSB Funnel Landscape
Model 153
The ZSB Model Relates Landscape Shape to Folding Speed 1 54
The ZSB Model Explains the Unusual Temperature
Dependences of Ultrafast Folders 1 56
The Foldon Assembly Model Is a Folding Mechanism
Variant of the ZSB Model 1 57
APPENDIX 6C: PROTEIN FOLDING FUNNELS CAN BE BUMPY:
THE SPIN-GLASS MODEL 157
REFERENCES 159
SUGGESTED READING 1 59
Chapter 7 Proteins Evolve 161
PROTEINS CHANGE THROUGH EVOLUTIONARY PROCESSES 1 61
What Are the Mechanisms of Evolutionary Change? 162
Homologs Are Proteins with Similar Functions and a
Common Ancestor 163
When Two Protein Sequences Are Similar, Their Structures
and Functions Are Usually Similar Too 165
There Are Three Types of Homolog Relationships:
Orthologs, Paralogs, and Xenologs 165
Gene Duplication Can Explain the High Symmetries in
Protein Structures 166
Evolutionary Processes Can Be Convergent or Divergent 167
Phylogenetic Trees Show Evolutionary Relationships
between Organisms or Sequences 169
MANY DIFFERENT SEQUENCES FOLD INTO THE SAME
NATIVE STRUCTURE 1 70
The HP Model Gives Insight into Sequence-Structure
Mapping 171
viii TABLE OF CONTENTS
Protein Structures Are Tolerant of Single Amino Acid Changes 1 72
Sequence Space Is Filled with Sequences That Collapse to
Compact Protein-Like Folds 173
Proteins and Other Polymers Can Be Designed to Fold to
Stable Structures 174
EVOLUTION IS NOT AN ABSTRACTION. IT’S REAL.
IT’S HAPPENING NOW 175
Drug Resistance Is an Example of Evolution In Action 1 75
Molecular Clocks: Some Evolutionary Changes Proceed at a
Constant Rate 176
Directed Evolution Is a Way to Improve Proteins In the
Laboratory 177
SUMMARY 179
REFERENCES 179
SUGGESTED READING 180
Chapter 8 Bioinformatics: Insights from
Protein Sequences 181
COMPARING AMINO ACID SEQUENCES GIVES INSIGHT INTO
PROTEIN STRUCTURE AND FUNCTION 1 81
Sequences Change through Evolutionary Mutations 1 82
Proteins Having Similar Sequences Usually Have Similar
Structures and Functions 182
HOW DO YOU DETERMINE THE RELATEDNESS BETWEEN
SEQUENCES? 183
Some Amino Acids Swap More Often Than Others 184
TO COMPARE SEQUENCES, YOU START WITH GOOD
ALIGNMENTS 186
How Do You Align One Sequence with Another? 186
BLAST Uses a Query Sequence to Search a Database for
Related Sequences 189
Aligning Multiple Sequences Gives More Insight Than
Aligning Two Sequences 190
You Can Improve Sequence Alignments by Structure
Matching 191
HOW DO YOU CONSTRUCT A PHYLOGENETIC TREE? 1 91
EVOLUTION CONSERVES SOME AMINO ACIDS AND
CHANGES OTHERS 1 93
You Can Express the Degree of Residue Conservation Using
Sequence Entropy 193
Physical and Biological Factors Affect the Evolutionary
Conservation of Amino Acids 195
Evolutionary Variations Are Sometimes Correlated in the
Sequence 196
Mutual Information (Ml) Measures the Tendencies of Pairs
of Amino Acids to Coevolve 197
SUMMARY 197
APPENDIX 8A: EXAMPLE OF A BLAST RUN 1 98
APPENDIX 8B: ESTIMATING EVOLUTIONARY RATES USING A
MARKOV MODEL FOR RESIDUE SUBSTITUTIONS 1 98
REFERENCES 200
SUGGESTED READING 200
Chapter 9 Protein Geometries and
Energetics 201
YOU CAN REPRESENT A PROTEIN STRUCTURE BY ITS
ATOMIC COORDINATES 201
From the Coordinates, You Can Compute the Radius of
Gyration 205
How Similar Are Two Protein Structures? Compute the
RMSD between Them 207
TO SIMULATE PROTEIN PHYSICS ON A COMPUTER, YOU
NEED A MODEL OF INTERATOMIC ENERGIES 207
Molecular Energetics Can Be Described by Atomistic Force
Fields 207
Bond Lengths and Bond Angles Are Treated Using
Spring-Like Forces 209
Van der Waals Interactions Are Short-Ranged Attractions
and Repulsions 210
Charge Interactions Are Modeled Using Coulomb’s Law 210
Solvent Interactions Are a Major Determinant of Protein
Conformations 211
Explicit-water models Represent Waters as Individual
Molecules 212
Implicit-water models Treat Water as a Continuous Medium 212
SUMMARY 215
APPENDIX 9A: HOWTO COMPUTE CARTESIAN
COORDINATES FROM INTERNAL COORDINATES 21 5
APPENDIX 9B: HOW TO OPTIMALLY SUPERIMPOSE TWO
STRUCTURES 216
APPENDIX 90 THE POISSON-BOLTZMANN EQUATION
TREATS ELECTROSTATIC INTERACTIONS 217
REFERENCES 219
SUGGESTED READING 220
Chapter 10 Molecular Simulations and
Conformational Sampling 221
YOU CAN FIND STATES OF LOW ENERGY BY ENERGY
MINIMIZATION 222
To Compute Forces, Take the Derivative of the Potential
Energy 222
Following Gradients Downhill Leads to States of Low Energy 225
MOLECULAR DYNAMICS SIMULATIONS SOLVE NEWTON’S
EQUATIONS OF MOTION ITERATIVELY 225
How Do You Compute a Molecular Dynamics Trajectory? 226
What Time Step At Should You Use for MD Simulations? 227
What Is the Computational Time for MD Simulations? 228
How Do You Analyze Trajectories? 228
Accelerated Sampling Methods Can Explore Larger Actions
and Longer Timescales 231
Stochastic Dynamics Entails Averaging over Solvent
Fluctuations 233
METROPOLIS MONTE CARLO SIMULATION IS A STOCHASTIC
METHOD OF SAMPLING CONFORMATIONS 234
Here’s How to Estimate Averages by Uniform Sampling 234
MMC Is an Efficient Method of Sampling Populated States 235
ADDITIONAL PRINCIPLES LEAD TO IMPROVED
COMPUTATIONAL SAMPLING 239
Sampling at Higher Temperatures Allows Broader
Exploration of Configuration Space 240
The Replica-Exchange Method (REM) Is an Efficient
Sampling Method 240
Using Chemical Alchemy, You Can Compute Changes in
Free Energy 242
SUMMARY 243
APPENDIX 1 OA: THE VERLET AND LEAPFROG ALGORITHMS
GENERATE MD TRAJECTORIES 244
The Verlet Algorithm 244
The Leapfrog Algorithm 244
APPENDIX 1 OB: PERIODIC BOUNDARY CONDITIONS ARE
USED IN MD SIMULATIONS 245
APPENDIX 1 OC: SOME METHODS FOR ENHANCED SAMPLING 245
Histogram-Reweighting Methods Let You Predict a
Distribution at One Temperature from Another 245
TABLE OF CONTENTS
IX
Changes in Free Energy Can Be Computed by Umbrella
Sampling, Free-Energy Perturbation, or Thermodynamic
Integration Methods 246
REFERENCES 248
SUGGESTED READING 249
Chapter 11 Predicting Protein Structures
from Sequences 251
SOME PROTEINS HAVE COMPUTABLE NATIVE STRUCTURES 251
COMPARATIVE MODELING IS A MAIN TOOL FOR STRUCTURE
PREDICTION 252
Here Is How Homology Modeling Works 252
A First Step in Modeling a Protein Is Often Determining Its
Secondary Structures 254
You Can Assemble Protein Structures from Fragments
Instead of Secondary Structures 255
Threading or Fold Recognition Can Help You Find Local
Structures within Your Target Protein 256
Sequence Co-evolution Analysis Can Help You Predict
Protein Structures 256
STATISTICAL POTENTIALS ARE “ENERGY-LIKE” SCORING
FUNCTIONS FOR SELECTING NATIVE-LIKE PROTEIN
STRUCTURES 257
Contact Potentials Express the Noncovalent Pairing
Preferences of Amino Acids in Native Structures 259
OTHER COMPUTATIONAL TOOLS CAN ALSO HELP YOU
PREDICT NATIVE STRUCTURES 261
Clustering Algorithms Can Separate Similar Conformations
from Different Ones 261
Databases of Decoys Can Help You Develop Energy-Like
Scoring Functions 262
To Compute Protein Structures, You Need Accurate
Conformations of Side Chains and Loops 262
Full Protocols Are Available for Predicting Structures 263
CASP: A COMMUNITY-WIDE EVENT EVALUATES
STRUCTURE-PREDICTION METHODS 263
ATOMISTIC PHYSICAL SIMULATIONS CAN PREDICT THE
STRUCTURES OF SOME SMALL PROTEINS 264
METHODS ARE AVAILABLE FOR PREDICTING THE
STRUCTURES OF PROTEIN COMPLEXES, MULTIMERS, AND
ASSEMBLIES 265
SUMMARY 265
APPENDIX 1 1A: THE MIYAZAWA-JERNIGAN
CONTACT-POTENTIAL MATRIX 267
REFERENCES 267
SUGGESTED READING 268
Chapter 12 Biological Actions Arise from
Protein Motions 269
NATIVE PROTEINS HAVE CORRELATED MOTIONS 269
ELASTIC NETWORK MODELS USE BEADS AND SPRINGS TO
DESCRIBE PROTEIN MOTIONS 270
Some Motions of Different Parts of a Structure Are
Correlated with Each Other 272
PROTEIN MOTIONS CAN BE OBSERVED IN EXPERIMENTS
AND PREDICTED BY THE GNM 273
Protein Fluctuations Can Be Observed in NMR Experiments 274
Protein Breathing Motions Can Be Observed in Hydrogen
Exchange (HX) Experiments 275
PROTEIN MOTIONS ARE RELEVANT TO MECHANISMS OF
ACTION 278
Protein Motions Can Be Decomposed into a Spectrum of
Normal Modes 278
Directions of Motion Can Be Found Using the Anisotropic
Network Model 280
MULTIPROTEIN ASSEMBLIES CAN BE STUDIED BY ELASTIC
NETWORK MODELS 284
SUMMARY 287
APPENDIX 12A: HERE’S HOW TO EXPRESS THE ELASTIC FREE
ENERGY IN TERMS OF THE ADJACENCY MATRIX 287
APPENDIX 1 2B: HOW IS Г RELATED TO LOCAL PACKING
DENSITIES? 288
APPENDIX 1 20. HOW DO YOU DETERMINE THE GNM MODES? 289
APPENDIX 1 2D: NORMAL MODE ANALYSIS 290
Normal Mode Analysis Describes the Motions Near
Equilibrium 290
There Is an Inverse Relationship between Covariance and
Stiffness 292
You Can Find the Collective Modes Using NMA 292
APPENDIX 12E: MEAN-SQUARE FLUCTUATIONS IN INTERNAL
DISTANCES DEPEND ON THE NETWORK CONNECTIVITY 294
APPENDIX 1 2F: HOW CAN YOU COMPARE ONE MOTION
WITH ANOTHER? 294
REFERENCES 295
SUGGESTED READING 296
Chapter 13 Molecular Modeling for Drug
Di ccovery 297
DRUGS OFTEN ACT BY BINDING TO PROTEINS 297
PHARMACEUTICAL DISCOVERY IS A MULTISTAGE PIPELINE
PROCESS 298
DESIGNING A DRUG REQUIRES OPTIMIZING MULTIPLE
PROPERTIES 300
What Properties of a Molecule Are Drug-Like? 300
What Are the Properties of a “Druggable Protein? 301
LIGAND-BASED DISCOVERY USES KNOWN LIGANDS TO
DESIGN NEW ONES 303
If You Know the Biological Activities of Some Ligands, You
Can Estimate the Activities of Others Using QSAR Methods 303
Similarity Searching Seeks Compounds Similar to the Good
Ones You Know Already 304
A Pharmacophore Is a 3D Arrangement of Properties of
Some Atoms 305
Some Drugs Are Developed by Linking Fragments Together 305
TARGET-BASED DISCOVERY DESIGNS DRUGS BY USING THE
STRUCTURE OF A TARGET PROTEIN 305
Docking Is a Fast Way to Find Ligands That Bind to a Given
Protein Structure 306
Including Protein Flexibility Can Improve the Modeling of
Ligand Binding 308
Molecular Dynamics Simulations Can Calculate Binding
Free Energies 308
Structure-Based Methods Have Helped to Discover New
Drugs 309
A MAJOR CLASS OF DRUGS IS THE BIOLOGICS 310
CHALLENGES AND RECENT DEVELOPMENTS IN DRUG
DISCOVERY 311
Drug Resistance Can Be Caused by a Protein Mutation Near
a Drug-Binding Site 311
Quantitative Systems Pharmacology Goes beyond “One
Target-One Mechanism 312
Peptides and Macrocycles Can Interfere with
Protein-Protein Interactions 313
Sometimes Biological Activity Correlates Better with
Ligand-Protein Off-Rates Than with Binding Affinities 314
You Can Sometimes Repurpose an Old Drug for a New
Medical Indication 314
SUMMARY 315
REFERENCES 316
SUGGESTED READING 316
INDEX 317
Protein Actions: Principles and Modeling describes the basic principles of protein
molecules - their structures; their folding, binding and aggregation; their dynamics and
mechanisms; and their evolution - as well as the methods of modeling them, including
bioinformatics, physics-based computer simulations, and the tools of drug discovery,
it is intended for a one-semester course for biological scientists learning quantitative
foundations and for physical scientists learning the biology and chemistry. This text is
ideal for graduates, advanced undergraduates, and any professional who seeks an
introduction to the biological, chemical, and physical properties of proteins.
Ivet Bahar is John K. Vries Chair and Distinguished Professor in the Department of
Computational Systems Biology at the University of Pittsburgh, School of Medicine.
She co-founded the Joint PhD Program in Computational Biology between the University
of Pittsburgh and Carnegie Mellon University.
Robert L Jernigan is Professor of Biochemistry, Biophysics, and Molecular Biology at
Iowa State University and former Director of the Baker Center for Bioinformatics and
Biological Statistics.
Ken A Dill is Distinguished Professor in the Departments of Chemistry and Physics and
Astronomy at Stony Brook University, and the Louis Beatrice Laufer Endowed Chair of
Physical Biology. He is the founding Director of the Laufer Center for Physical and
Quantitative Biology.
|
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| author | Bahar, Ivet Jernigan, Robert L. Dill, Ken Austin 1947- |
| author_GND | (DE-588)1135154961 (DE-588)1135155267 (DE-588)108916193X |
| author_facet | Bahar, Ivet Jernigan, Robert L. Dill, Ken Austin 1947- |
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| author_sort | Bahar, Ivet |
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| building | Verbundindex |
| bvnumber | BV044284760 |
| classification_rvk | WD 5100 |
| ctrlnum | (OCoLC)984710097 (DE-599)BSZ486041956 |
| discipline | Biologie |
| format | Book |
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| id | DE-604.BV044284760 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:48:42Z |
| institution | BVB |
| isbn | 9780815341772 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029689041 |
| oclc_num | 984710097 |
| open_access_boolean | |
| owner | DE-11 DE-355 DE-BY-UBR DE-29T DE-20 DE-634 |
| owner_facet | DE-11 DE-355 DE-BY-UBR DE-29T DE-20 DE-634 |
| physical | xii, 322 Seiten Illustrationen 28 cm |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Garland Science |
| record_format | marc |
| spelling | Bahar, Ivet Verfasser (DE-588)1135154961 aut Protein actions principles and modeling Ivet Bahar, Robert L. Jernigan, Ken A. Dill New York and London Garland Science [2017] © 2017 xii, 322 Seiten Illustrationen 28 cm txt rdacontent n rdamedia nc rdacarrier Proteins Proteine (DE-588)4076388-2 gnd rswk-swf Proteine (DE-588)4076388-2 s DE-604 Jernigan, Robert L. Verfasser (DE-588)1135155267 aut Dill, Ken Austin 1947- Verfasser (DE-588)108916193X aut Erscheint auch als Online-Ausgabe 978-1-315-21221-0 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029689041&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029689041&sequence=000002&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Bahar, Ivet Jernigan, Robert L. Dill, Ken Austin 1947- Protein actions principles and modeling Proteins Proteine (DE-588)4076388-2 gnd |
| subject_GND | (DE-588)4076388-2 |
| title | Protein actions principles and modeling |
| title_auth | Protein actions principles and modeling |
| title_exact_search | Protein actions principles and modeling |
| title_full | Protein actions principles and modeling Ivet Bahar, Robert L. Jernigan, Ken A. Dill |
| title_fullStr | Protein actions principles and modeling Ivet Bahar, Robert L. Jernigan, Ken A. Dill |
| title_full_unstemmed | Protein actions principles and modeling Ivet Bahar, Robert L. Jernigan, Ken A. Dill |
| title_short | Protein actions |
| title_sort | protein actions principles and modeling |
| title_sub | principles and modeling |
| topic | Proteins Proteine (DE-588)4076388-2 gnd |
| topic_facet | Proteins Proteine |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029689041&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029689041&sequence=000002&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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