Understanding bioinformatics:
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| Format: | Buch |
| Sprache: | English |
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New York [u.a.]
Garland Science [u.a.]
2008
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXIII, 772 S. Ill., graph. Darst. |
| ISBN: | 9780815340249 0815340249 |
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| 100 | 1 | |a Zvelebil, Marketa J. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Understanding bioinformatics |c Marketa Zvelebil & Jeremy O. Baum |
| 264 | 1 | |a New York [u.a.] |b Garland Science [u.a.] |c 2008 | |
| 300 | |a XXIII, 772 S. |b Ill., graph. Darst. | ||
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Datensatz im Suchindex
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| adam_text | CONTENTS IN BRIEF
PART 1 Background Basics
Chapter 1: The Nucleic Acid World 3
Chapter 2: Protein Structure 25
Chapter 3: Dealing With Databases 45
PART 2 Sequence Alignments
Chapter 4: Producing and Analyzing Sequence Alignments Applications Chapter 71
Chapter 5: Pairwise Sequence Alignment and Database Searching Theory Chapter 115
Chapter 6: Patterns, Profiles, and Multiple Alignments Theory Chapter 165
PART 3 Evolutionary Processes
Chapter 7: Recovering Evolutionary History Applications Chapter 223
Chapter 8: Building Phylogenetic Trees Theory Chapter 267
PART 4 Genome Characteristics
Chapter 9: Revealing Genome Features Applications Chapter 317
Chapter 10: Gene Detection and Genome Annotation Theory Chapter 357
PART 5 Secondary Structures
Chapter 11: Obtaining Secondary Structure from Sequence Applications Chapter 411
Chapter 12: Predicting Secondary Structures Theory Chapter 461
PART 6 Tertiary Structures
Chapter 13: Modeling Protein Structure Applications Chapter 521
Chapter 14: Analyzing Structure-Function Relationships Applications Chapter 567
PART 7 Cells and Organisms
Chapter 15: Proteome and Gene Expression Analysis 599
Chapter 16: Clustering Methods and Statistics 625
Chapter 17: Systems Biology 667
APPENDICES Background Theory
Appendix A: Probability, Information, and Bayesian Analysis 695
Appendix B: Molecular Energy Functions 700
Appendix C: Function Optimization 709
xiii
CONTENTS
Preface v
A Note to the Reader vii
List of Reviewers xii
Contents in Brief xiii
Part 1 Background Basics
Chapter 1 The Nucleic Acid World
1.1 The Structure of DNA and RNA 5
DNA is a linear polymer of only four different bases 5
IWo complementary DNA strands interact by
base pairing to form a double helix 7
RNA molecules are mostly single stranded but
can also have base-pair structures 9
1.2 DNA, RNA, and Protein: The Central Dogma 10
DNA is the information store, but RNA is
the messenger 11
Messenger RNA is translated into protein
according to the genetic code 12
Translation involves transfer RNAs and
RNA-containing ribosomes 13
1.3 Gene Structure and Control 14
RNA polymerase binds to specific sequences that
position it and identify where to begin transcription 15
The signals initiating transcription in eukaryotes
are generally more complex than those in bacteria 17
Eukaryotic mRNA transcripts undergo several
modifications prior to their use in translation 18
The control of translation 19
1.4 The Tree of Life and Evolution 20
A brief survey of the basic characteristics of the
major forms of life 21
Nucleic acid sequences can change as a result of
mutation 22
Summary 23
Further Reading 24
Chapter 2 Protein Structure
2.1 Primary and Secondary Structure 25
Protein structure can be considered on several
different levels 26
Amino acids are the building blocks of proteins 27
The differing chemical and physical properties of
amino acids are due to their side chains 28
xiv
Amino acids are covalently linked together in the
protein chain by peptide bonds 29
Secondary structure of proteins is made up of
a-helices and p-strands 33
Several different types of (3-sheet are found
in protein structures 35
Turns, hairpins and loops connect helices
and strands 36
2.2 Implication for Bioinformatics 37
Certain amino acids prefer a particular
structural unit 37
Evolution has aided sequence analysis 38
Visualization and computer manipulation
of protein structures 38
2.3 Proteins Fold to Form Compact Structures 40
The tertiary structure of a protein is defined
by the path of the polypeptide chain 41
The stable folded state of a protein represents
a state of low energy 41
Many proteins are formed of multiple subunits 42
Summary 43
Further Reading 44
Chapter 3 Dealing with Databases
3.1 The Structure of Databases 46
Flat-file databases store data as text files 48
Relational databases are widely used for storing
biological information 49
XML has the flexibility to define bespoke data
classifications 50
Many other database structures are used
for biological data 51
Databases can be accessed locally or online
and often link to each other 52
3.2 Types of Database 52
There s more to databases than just data 53
Primary and derived data 53
How we define and connect things is very
important: Ontologies 54
3.3 Looking for Databases 55
Sequence databases 55
Microarray databases 58
Protein interaction databases 58
Structural databases 59
3.4 Data Quality 61
Nonredundancy is especially important for some
applications of sequence databases 62
Automated methods can be used to check for data
consistency 63
Initial analysis and annotation is usually
automated 64
Human intervention is often required to produce
the highest quality annotation 65
The importance of updating databases and entry
identifier and version numbers 65
Summary 66
Further Reading 67
Part 2 Sequence Alignments
APPLICATIONS CHAPTER
Chapter 4 Producing and Analyzing Sequence
Alignments
4.1 Principles of Sequence Alignment 72
Alignment is the task of locating equivalent
regions of two or more sequences to maximize
their similarity 73
Alignment can reveal homology between sequences 74
It is easier to detect homology when comparing
protein sequences than when comparing nucleic
acid sequences 75
4.2 Scoring Alignments 76
The quality of an alignment is measured by giving
it a quantitative score 76
The simplest way of quantifying similarity
between two sequences is percentage identity 76
The dot-plot gives a visual assessment of similarity
based on identity 77
Genuine matches do not have to be identical 79
There is a minimum percentage identity that can
be accepted as significant 81
There are many different ways of scoring an
alignment 81
4.3 Substitution Matrices 81
Substitution matrices are used to assign individual
scores to aligned sequence positions 81
The RAM substitution matrices use substitution
frequencies derived from sets of closely related
protein sequences 82
The BLOSUM substitution matrices use mutation
data from highly conserved local regions of
sequence 84
The choice of substitution matrix depends on the
problem to be solved 84
Contents
4.4 Inserting Gaps 85
Gaps inserted in a sequence to maximize similarity
require a scoring penalty 85
Dynamic programming algorithms can determine
the optimal introduction of gaps 86
4.5 Types of Alignment 87
Different kinds of alignments are useful in
different circumstances 87
Multiple sequence alignments enable the
simultaneous comparison of a set of similar
sequences 90
Multiple alignments can be constructed by
several different techniques 90
Multiple alignments can improve the accuracy of
alignment for sequences of low similarity 91
ClustalW can make global multiple alignments
of both DNA and protein sequences 92
Multiple alignments can be made by combining
a series of local alignments 92
Alignment can be improved by incorporating
additional information 93
4.6 Searching Databases 93
Fast yet accurate search algorithms have been
developed 94
FASTA is a fast database-search method based on
matching short identical segments 95
BLAST is based on finding very similar short segments 95
Different versions of BLAST and FASTA are used
for different problems 95
PSI-BLAST enables profile-based database searches 96
SSEARCH is a rigorous alignment method 97
4.7 Searching with Nucleic Acid or Protein Sequences 97
DNA or RNA sequences can be used either
directly or after translation 97
The quality of a database match has to be tested
to ensure that it could not have arisen by chance 97
Choosing an appropriate E-value threshold helps
to limit a database search 98
Low-complexity regions can complicate
homology searches 100
Different databases can be used to solve
particular problems 102
4.8 Protein Sequence Motifs or Patterns 103
Creation of pattern databases requires expert
knowledge 104
The BLOCKS database contains automatically
compiled short blocks of conserved multiply
aligned protein sequences 105
4.9 Searching Using Motifs and Patterns 107
The PROSITE database can be searched for
protein motifs and patterns 107
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Contents
The pattern-based program PHI-BLAST searches
for both homology and matching motifs 108
Patterns can be generated from multiple
sequences using PRATT 108
The PRINTS database consists of fingerprints
representing sets of conserved motifs that
describe a protein family 109
The Pfam database defines profiles of protein
families 109
4.10 Patterns and Protein Function 109
Searches can be made for particular functional
sites in proteins 109
Sequence comparison is not the only way of
analyzing protein sequences 110
Summary 111
Further Reading 112
THEORY CHAPTER
Chapter 5 Pairwise Sequence Alignment and
Database Searching
5.1 Substitution Matrices and Scoring 117
Alignment scores attempt to measure the
likelihood of a common evolutionary ancestor 117
The PAM (MDM) substitution scoring matrices
were designed to trace the evolutionary origins
of proteins 119
The BLOSUM matrices were designed to find
conserved regions of proteins 122
Scoring matrices for nucleotide sequence
alignment can be derived in similar ways 125
The substitution scoring matrix used must be
appropriate to the specific alignment problem 126
Gaps are scored in a much more heuristic way
than substitutions 126
5.2 Dynamic Programming Algorithms 127
Optimal global alignments are produced using
efficient variations of the Needleman-Wunsch
algorithm 129
Local and suboptimal alignments can be produced
by making small modifications to the dynamic
programming algorithm 135
Time can be saved with a loss of rigor by not
calculating the whole matrix 139
5.3 Indexing Techniques and Algorithmic
Approximations 141
Suffix trees locate the positions of repeats and
unique sequences 141
Hashing is an indexing technique that lists the
starting positions of all k-tuples 143
The FASTA algorithm uses hashing and chaining
for fast database searching 144
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The BLAST algorithm makes use of finite-state
automata 147
Comparing a nucleotide sequence directly with a
protein sequence requires special modifications
to the BLAST and FASTA algorithms 150
5.4 Alignment Score Significance 153
The statistics of gapped local alignments can be
approximated by the same theory 156
5.5 Aligning Complete Genome Sequences 156
Indexing and scanning whole genome sequences
efficiently is crucial for the sequence alignment
of higher organisms 157
The complex evolutionary relationships between
the genomes of even closely related organisms
require novel alignment algorithms 159
Summary 159
Further Reading 161
THEORY CHAPTER
Chapter 6 Patterns, Profiles, and Multiple
Alignments
6.1 Profiles and Sequence Logos 167
Position-specific scoring matrices are an
extension of substitution scoring matrices 168
Methods for overcoming a lack of data in deriving
the values for a PSSM 171
PSI-BLAST is a sequence database searching
program 176
Representing a profile as a logo 177
6.2 Profile Hidden Markov Models 179
The basic structure of HMMs used in sequence
alignment to profiles 180
Estimating HMM parameters using aligned
sequences 185
Scoring a sequence against a profile HMM:
The most probable path and the sum over
all paths 187
Estimating HMM parameters using unaligned
sequences 190
6.3 Aligning Profiles 193
Comparing two PSSMs by alignment 193
Aligning profile HMMs I95
6.4 Multiple Sequence Alignments by Gradual
Sequence Addition !96
The order in which sequences are added is chosen
based on the estimated likelihood of incorporating
errors in the alignment I98
Many different scoring schemes have been used
in constructing multiple alignments 200
The multiple alignment is built using the guide
tree and profile methods and may be further
refined 204
6.5 Other Ways of Obtaining Multiple Alignments 207
The multiple sequence alignment program
DIALIGN aligns ungapped blocks 207
The SAGA method of multiple alignment uses
a genetic algorithm 209
6.6 Sequence Pattern Discovery 211
Discovering patterns in a multiple alignment:
eMOTIFandAACC 213
Probabilistic searching for common patterns in
sequences: Gibbs and MEME 215
Searching for more general sequence patterns 217
Summary 218
Further Reading 219
Part 3 Evolutionary Processes
APPLICATIONS CHAPTER
Chapter 7 Recovering Evolutionary History
7.1 The Structure and Interpretation of
Phylogenetic Trees 225
Phylogenetic trees reconstruct evolutionary
relationships 225
Tree topology can be described in several ways 230
Consensus and condensed trees report the
results of comparing tree topologies 232
7.2 Molecular Evolution and its Consequences 235
Most related sequences have many positions
that have mutated several times 236
The rate of accepted mutation is usually not the
same for all types of base substitution 236
Different codon positions have different
mutation rates 238
Only orthologous genes should be used to
construct species phylogenetic trees 239
Major changes affecting large regions of the
genome are surprisingly common 247
7.3 Phylogenetic Tree Reconstruction 248
Small ribosomal subunit rRNA sequences are well
suited to reconstructing the evolution of species 249
The choice of the method for tree reconstruction
depends to some extent on the size and quality of
the dataset 249
A model of evolution must be chosen to use with
the method 251
All phylogenetic analyses must start with an
accurate multiple alignment 255
Contents
Phylogenetic analyses of a small dataset of
16S RNA sequence data 255
Building a gene tree for a family of enzymes can
help to identify how enzymatic functions evolved 259
Summary 264
Further Reading 265
THEORY CHAPTER
Chapter 8 Building Phylogenetic Trees
8.1 Evolutionary Models and the Calculation
of Evolutionary Distance 268
A simple but inaccurate measure of evolutionary
distance is the p-distance 268
The Poisson distance correction takes account of
multiple mutations at the same site 270
The Gamma distance correction takes account of
mutation rate variation at different sequence
positions 270
The Jukes-Cantor model reproduces some basic
features of the evolution of nucleotide sequences 271
More complex models distinguish between the
relative frequencies of different types of mutation 272
There is a nucleotide bias in DNA sequences 275
Models of protein-sequence evolution are closely
related to the substitution matrices used for
sequence alignment 276
8.2 Generating Single Phylogenetic Trees 276
Clustering methods produce a phylogenetic tree
based on evolutionary distances 276
The UPGMA method assumes a constant
molecular clock and produces an ultrametric tree 278
The Fitch-Margoliash method produces an
unrooted additive tree 279
The neighbor-joining method is related to the
concept of minimum evolution 282
Stepwise addition and star-decomposition
methods are usually used to generate starting
trees for further exploration, not the final tree 285
8.3 Generating Multiple Tree Topologies 286
The branch-and-bound method greatly improves
the efficiency of exploring tree topology 288
Optimization of tree topology can be achieved
by making a series of small changes to an existing
tree 288
Finding the root gives a phylogenetic tree a
direction in time 291
8.4 Evaluating Tree Topologies 293
Functions based on evolutionary distances can
be used to evaluate trees 293
Unweighted parsimony methods look for the trees
with the smallest number of mutations 297
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Contents
Mutations can be weighted in different ways
in the parsimony method 300
Trees can be evaluated using the maximum
likelihood method 302
The quartet-puzzling method also involves maximum
likelihood in the standard implementation 305
Bayesian methods can also be used to reconstruct
phylogenetic trees 306
8.5 Assessing the Reliability of Tree Features
and Comparing Trees 307
The long-branch attraction problem can arise
even with perfect data and methodology 308
Tree topology can be tested by examining the
interior branches 309
Tests have been proposed for comparing two
or more alternative trees 310
Summary 311
Further Reading 312
Part 4 Genome Characteristics
APPLICATIONS CHAPTER
Chapter 9 Revealing Genome Features
9.1 Preliminary Examination of Genome Sequence 318
Whole genome sequences can be split up to
simplify gene searches 319
Structural RNA genes and repeat sequences
can be excluded from further analysis 319
Homology can be used to identify genes in both
prokaryotic and eukaryotic genomes 322
9.2 Gene Prediction in Prokaryotic Genomes 322
9.3 Gene Prediction in Eukaryotic Genomes 323
Programs for predicting exons and introns use
a variety of approaches 323
Gene predictions must preserve the correct
reading frame 324
Some programs search for exons using only
the query sequence and a model for exons 327
Some programs search for genes using only
the query sequence and a gene model 332
Genes can be predicted using a gene model
and sequence similarity 334
Genomes of related organisms can be used
to improve gene prediction 336
9.4 Splice Site Detection 337
Splice sites can be detected independently by
specialized programs 338
9.5 Prediction of Promoter Regions 338
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Prokaryotic promoter regions contain relatively
well-defined motifs 339
Eukaryotic promoter regions are typically more
complex than prokaryotic promoters 340
A variety of promoter-prediction methods are
available online 340
Promoter prediction results are not very clear-cut 341
9.6 Confirming Predictions 342
There are various methods for calculating the
accuracy of gene-prediction programs 342
Translating predicted exons can confirm the
correctness of the prediction 343
Constructing the protein and identifying homologs 343
9.7 Genome Annotation 346
Genome annotation is the final step in genome
analysis 347
Gene ontology provides a standard vocabulary
for gene annotation 348
9.8 Large Genome Comparisons 353
Summary 354
Further Reading 355
THEORY CHAPTER
Chapter 10 Gene Detection and Genome
Annotation
10.1 Detection of Functional RNA Molecules Using
Decision Trees 361
Detection of tRNA genes using the tRNAscan
algorithm 361
Detection of tRNA genes in eukaryotic genomes 362
10.2 Features Useful for Gene Detection in Prokaryotes 364
10.3 Algorithms for Gene Detection in Prokaryotes 368
GeneMark uses inhomogeneous Markov chains
and dicodon statistics 368
GLIMMER uses interpolated Markov models of
coding potential 371
ORPHEUS uses homology, codon statistics, and
ribosome-binding sites 372
GeneMark.hmm uses explicit state duration
hidden Markov models 373
EcoParse is an HMM gene model 376
10.4 Features Used in Eukaryotic Gene Detection 377
Differences between prokaryotic and
eukaryotic genes 377
Introns, exons, and splice sites 379
Promoter sequences and binding sites for
transcription factors 381
10.5 Predicting Eukaryotic Gene Signals 381
Detection of core promoter binding signals is
a key element of some eukaryotic gene-
prediction methods 381
A set of models has been designed to locate
the site of core promoter sequence signals 383
Predicting promoter regions from general
sequence properties can reduce the numbers
of false-positive results 387
Predicting eukaryotic transcription and
translation start sites 389
Translation and transcription stop signals
complete the gene definition 389
10.6 Predicting Exon/Intron Structure 389
Exons can be identified using general sequence
properties 390
Splice-site prediction 392
Splice sites can be predicted by sequence patterns
combined with base statistics 393
GenScan uses a combination of weight matrices
and decision trees to locate splice sites 394
GeneSplicer predicts splice sites using first-order
Markov chains 394
NetPlantGene uses neural networks with
intron and exon predictions to predict splice sites 395
Other splicing features may yet be exploited for
splice-site prediction 396
Specific methods exist to identify initial and
terminal exons 396
Exons can be defined by searching databases for
homologous regions 397
10.7 Complete Eukaryotic Gene Models 397
10.8 Beyond the Prediction of Individual Genes 399
Functional annotation 400
Comparison of related genomes can help resolve
uncertain predictions 403
Evaluation and reevaluation of gene-detection
methods 405
Summary 405
Further Reading 406
Part 5 Secondary Structures
APPLICATIONS CHAPTER
Chapter 11 Obtaining Secondary Structure
from Sequence
11.1 Types of Prediction Methods 413
Statistical methods are based on rules that give
the probability that a residue will form part of a
particular secondary structure 414
Nearest-neighbor methods are statistical methods
Contents
that incorporate additional information about
protein structure 414
Machine-learning approaches to secondary
structure prediction mainly make use of neural
networks and HMM methods 415
11.2 Training and Test Databases 416
There are several ways to define protein
secondary structures 417
11.3 Assessing the Accuracy of Prediction
Programs 417
Q3 measures the accuracy of individual residue
assignments 417
Secondary structure predictions should not be
expected to reach 100% residue accuracy 418
The Sov value measures the prediction accuracy
for whole elements 419
CAFASP/CASP: Unbiased and readily available
protein prediction assessments 419
11.4 Statistical and Knowledge-Based Methods 421
The GOR method uses an information theory
approach 422
The program Zpred includes multiple alignment
of homologous sequences and residue
conservation information 425
There is an overall increase in prediction accuracy
using multiple sequence information 426
The nearest-neighbor method: The use of multiple
nonhomologous sequences 428
PREDATOR is a combined statistical and
knowledge-based program that includes the
nearest-neighbor approach 428
11.5 Neural Network Methods of Secondary Structure
Prediction 430
Assessing the reliability of neural net predictions 432
Several examples of Web-based neural network
secondary structure prediction programs 432
PROF: Protein forecasting 434
PSIPRED 434
Jnet: Using several alternative representations
of the sequence alignment 434
11.6 Some Secondary Structures Require Specialized
Prediction Methods 435
Transmembrane proteins 436
Quantifying the preference for a membrane
environment 437
11.7 Prediction of Transmembrane Protein Structure 438
Multi-helix membrane proteins 439
A selection of prediction programs to predict
transmernbrane helices 441
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Contents
Statistical methods 443
Knowledge-based prediction 443
Evolutionary information from protein families
improves the prediction 444
Neural nets in transmembrane prediction 445
Predicting transmembrane helices with
hidden Markov models 446
Comparing the results: What to choose 447
What happens if a non-transmembrane protein is
submitted to transmembrane prediction programs 448
Prediction of transmembrane structure
containing (3-strands 448
11.8 Coiled-coil Structures 451
The COILS prediction program 452
PAIRCOIL and MULTICOIL are an extension
of the COILS algorithm 453
Zipping the Leucine zipper: A specialized
coiled coil 453
11.9 RNA Secondary Structure Prediction 455
Summary 458
Further Reading 459
THEORY CHAPTER
Chapter 12 Predicting Secondary Structures
12.1 Defining Secondary Structure and Prediction
Accuracy 463
The definitions used for automatic protein secondary
structure assignment do not give identical results 464
There are several different measures of the
accuracy of secondary structure prediction 469
12.2 Secondary Structure Prediction Based on
Residue Propensities 472
Each structural state has an amino acid preference
which can be assigned as a residue propensity 473
The simplest prediction methods are based on die
average residue propensity over a sequence window 476
Residue propensities are modulated by nearby
sequence 479
Predictions can be significantly improved by
including information from homologous sequences 484
12.3 The Nearest-Neighbor Methods are Based on
Sequence Segment Similarity 485
Short segments of similar sequence are found
to have similar structure 487
Several sequence similarity measures have been
used to identify nearest-neighbor segments 488
A weighted average of the nearest-neighbor
segment structures is used to make the prediction 490
A nearest-neighbor method has been developed to
predict regions with a high potential to misfold 491
xx
12.4 Neural Networks Have Been Employed
Successfully for Secondary Structure Prediction 492
Layered feed-forward neural networks can
transform a sequence into a structural prediction 494
Inclusion of information on homologous
sequences improves neural network accuracy 502
More complex neural nets have been applied to
predict secondary and other structural features 503
12.5 Hidden Markov Models Have Been Applied to
Structure Prediction 504
HMM methods have been found especially
effective for transmembrane proteins 506
Nonmembrane protein secondary structures can
also be successfully predicted with HMMs 509
12.6 General Data Classification Techniques Can
Predict Structural Features 510
Support vector machines have been successfully
used for protein structure prediction 511
Discriminants, SOMs, and other methods have
also been used 512
Summary 514
Further Reading 515
Part 6 Tertiary Structures
APPLICATIONS CHAPTER
Chapter 13 Modeling Protein Structure
13.1 Potential Energy Functions and Force Fields 524
The conformation of a protein can be visualized
in terms of a potential energy surface 525
Conformational energies can be described by
simple mathematical functions 525
Similar force fields can be used to represent
conformational energies in the presence of
averaged environments 526
Potential energy functions can be used to assess
a modeled structure 527
Energy minimization can be used to refine a modeled
structure and identify local energy minima 527
Molecular dynamics and simulated annealing
are used to find global energy minima 528
13.2 Obtaining a Structure by Threading 529
The prediction of protein folds in the absence of
known structural homologs 531
Libraries or databases of nonredundant protein
folds are used in threading 531
Two distinct types of scoring schemes have been
used in threading methods 531
Dynamic programming methods can identify
optimal alignments of target sequences and
structural folds 533
Several methods are available to assess the
confidence to be put on the fold prediction 534
The C2-like domain from the Dictyostelia:
A practical example of threading 535
13.3 Principles of Homology Modeling 537
Closely related target and template sequences give
better models 539
Significant sequence identity depends on the
length of the sequence 540
Homology modeling has been automated to deal with
the numbers of sequences that can now be modeled 541
Model building is based on a number of
assumptions 541
13.4 Steps in Homology Modeling 542
Structural homologs to the target protein are
found in the PDB 543
Accurate alignment of target and template
sequences is essential for successful modeling 543
The structurally conserved regions of a protein
are modeled first 544
The modeled core is checked for misfits before
proceeding to the next stage 545
Sequence realignment and remodeling may
improve the structure 545
Insertions and deletions are usually modeled
as loops 545
Nonidentical amino acid side chains are modeled
mainly by using rotamer libraries 547
Energy minimization is used to relieve
structural errors 548
Molecular dynamics can be used to explore
possible conformations for mobile loops 548
Models need to be checked for accuracy 549
How far can homology models be trusted? 551
13.5 Automated Homology Modeling 552
The program MODELLER models by satisfying
protein structure constraints 553
COMPOSER uses fragment-based modeling to
automatically generate a model 553
Automated methods available on the Web for
comparative modeling 554
Assessment of structure prediction 554
13.6 Homology ModeUng of PI3 Kinase pi 10a 557
Swiss-Pdb Viewer can be used for manual
or semi-manual modeling 557
Alignment, core modeling, and side-chain
modeling are carried out all in one 558
The loops are modeled from a database of
possible structures 559
Energy minimization and quality inspection
can be carried out within Swiss-Pdb Viewer 559
Contents
MolIDE is a downloadable semi-automatic
modeling package 560
Automated modeling on the Web illustrated with
pllOakinase 561
Modeling a functionally related but sequentially
dissimilar protein: mTOR 563
Generating a multidomain three-dimensional
structure from sequence 564
Summary 564
Further Reading 565
APPLICATIONS CHAPTER
Chapter 14 Analyzing Structure-Function
Relationships
14.1 Functional Conservation 568
Functional regions are usually structurally
conserved 569
Similar biochemical function can be found
in proteins with different folds 570
Fold libraries identify structurally similar proteins
regardless of function 571
14.2 Structure Comparison Methods 574
Finding domains in proteins aids structure
comparison 574
Structural comparisons can reveal conserved
functional elements not discernible from a
sequence comparison 576
The CE method builds up a structural alignment
from pairs of aligned protein segments 576
The Vector Alignment Search Tool (VAST) aligns
secondary structural elements 577
DALI identifies structure superposition without
maintaining segment order 578
FATCAT introduces rotations between rigid
segments 579
14.3 Finding Binding Sites 580
Highly conserved, strongly charged, or hydrophobic
surface areas may indicate interaction sites 582
Searching for protein-protein interactions
using surface properties 584
Surface calculations highlight clefts or holes
in a protein that may serve as binding sites 585
Looking at residue conservation can identify
binding sites 586
14.4 Docking Methods and Programs 587
Simple docking procedures can be used when
the structure of a homologous protein bound
to a ligand analog is known 588
Specialized docking programs will automatically
dock a ligand to a structure 588
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Contents
Scoring functions are used to identify the most
likely docked ligand 590
The DOCK program is a semirigid-body method
that analyzes shape and chemical
complementarity of ligand and binding site 590
Fragment docking identifies potential substrates
by predicting types of atoms and functional
groups in the binding area 591
GOLD is a flexible docking program, which
utilizes a genetic algorithm 591
The water molecules in binding sites should also
be considered 592
Summary 593
Further Reading 594
Part 7 Cells and Organisms
Chapter 15 Proteome and Gene Expression Analysis
15.1 Analysis of Large-scale Gene Expression 601
The expression of large numbers of different
genes can be measured simultaneously by DNA
microarrays 602
Gene expression microarrays are mainly used
to detect differences in gene expression in
different conditions 602
Serial analysis of gene expression (SAGE) is also
used to study global patterns of gene expression 604
Digital differential display uses bioinformatics
and statistics to detect differential gene
expression in different tissues 605
Facilitating the integration of data from different
places and experiments 606
The simplest method of analyzing gene expression
microarray data is hierarchical cluster analysis 606
Techniques based on self-organizing maps
can be used for analyzing microarray data 608
Self-organizing tree algorithms (SOTAs) cluster
from the top down by successive subdivision
of clusters 610
Clustered gene expression data can be used as
a tool for further research 610
15.2 Analysis of Large-scale Protein Expression 612
Two-dimensional gel electrophoresis is a method
for separating the individual proteins in a cell 613
Measuring the expression levels shown in 2D gels 614
Differences in protein expression levels between
different samples can be detected by 2D gels 615
Clustering methods are used to identify protein
spots with similar expression patterns 615
Principal component analysis (PCA) is an
alternative to clustering for analyzing microarray
and 2D gel data 618
xxii
The changes in a set of protein spots can be
tracked over a number of different samples 618
Databases and online tools are available to aid
the interpretation of 2D gel data 620
Protein microarrays allow the simultaneous
detection of the presence or activity of large
numbers of different proteins 621
Mass spectrometry can be used to identify the
proteins separated and purified by 2D gel
electrophoresis or other means 621
Protein-identification programs for mass
spectrometry are freely available on the Web 622
Mass spectrometry can be used to measure
protein concentration 623
Summary 623
Further Reading 624
Chapter 16 Clustering Methods and Statistics
16.1 Expression Data Require Preparation Prior
to Analysis 626
Data normalization is designed to remove
systematic experimental errors 627
Expression levels are often analyzed as ratios
and are usually transformed by taking logarithms 628
Sometimes further normalization is useful after
the data transformation 630
Principal component analysis is a method for
combining the properties of an object 631
16.2 Cluster Analysis Requires Distances to be Defined
Between all Data Points 633
Euclidean distance is the measure used in
everyday life 634
The Pearson correlation coefficient measures
distance in terms of the shape of the expression
response 635
The Mahalanobis distance takes account of the
variation and correlation of expression responses 636
16.3 Clustering Methods Identify Similar and Distinct
Expression Patterns 637
Hierarchical clustering produces a related set of
alternative partitions of the data 639
fc-means clustering groups data into several
clusters but does not determine a relationship
between clusters 641
Self-organizing maps (SOMs) use neural network
methods to cluster data into a predetermined
number of clusters 644
Evolutionary clustering algorithms use selection,
recombination, and mutation to find the best
possible solution to a problem 646
The self-organizing tree algorithm (SOTA)
determines the number of clusters required 648
Biclustering identifies a subset of similar
expression level patterns occurring in a subset
of the samples 649
The validity of clusters is determined by
independent methods 650
16.4 Statistical Analysis can Quantify the Significance
of Observed Differential Expression 651
Mests can be used to estimate the significance
of the difference between two expression levels 654
Nonparametric tests are used to avoid making
assumptions about the data sampling 656
Multiple testing of differential expression requires
special techniques to control error rates 657
16.5 Gene and Protein Expression Data Can be Used
to Classify Samples 659
Many alternative methods have been proposed
that can classify samples 660
Support vector machines are another form of
supervised learning algorithms that can produce
classifiers 661
Summary 662
Further Reading 664
Chapter 17 Systems Biology
17.1 What is a System? 669
A system is more than the sum of its parts 669
A biological system is a living network 670
Databases are useful starting points in
constructing a network 671
To construct a model more information is
needed than a network 672
There are three possible approaches to
constructing a model 674
Kinetic models are not the only way in
systems biology 678
17.2 Structure of the Model 679
Control circuits are an essential part of any
biological system 680
The interactions in networks can be represented
as simple differential equations 680
17.3 Robustness of Biological Systems 683
Robustness is a distinct feature of complexity
in biology 684
Modularity plays an important part in robustness 685
Redundancy in the system can provide robustness 686
Living systems can switch from one state to
another by means of bistable switches 688
Contents
17.4 Storing and Running System Models 689
Specialized programs make simulating
systems easier 691
Standardized system descriptions aid their
storage and reuse 692
Summary 692
Further Reading 693
APPENDICES Background Theory
Appendix A: Probability, Information, and
Bayesian Analysis
Probability Theory, Entropy, and Information 695
Mutually exclusive events 695
Occurrence of two events 696
Occurrence of two random variables 696
Bayesian Analysis 697
Bayes theorem 697
Inference of parameter values 698
Further Reading 699
Appendix B: Molecular Energy Functions
Force Fields for Calculating Intra- and Intermolecular
Interaction Energies 701
Bonding terms 702
Nonbonding terms 704
Potentials used in Threading 706
Potentials of mean force 706
Potential terms relating to solvent effects 707
Further Reading 708
Appendix C: Function Optimization
Full Search Methods 710
Dynamic programming and branch-and-bound 710
Local Optimization 710
The downhill simplex method 711
The steepest descent method 711
The conjugate gradient method 714
Methods using second derivatives 714
Thermodynamic Simulation and Global Optimization 715
Monte Carlo and genetic algorithms 716
Molecular dynamics 718
Simulated annealing 719
Summary 719
Further Reading 719
List of Symbols 721
Glossary 734
Index 751
xxiii
|
| adam_txt |
CONTENTS IN BRIEF
PART 1 Background Basics
Chapter 1: The Nucleic Acid World 3
Chapter 2: Protein Structure 25
Chapter 3: Dealing With Databases 45
PART 2 Sequence Alignments
Chapter 4: Producing and Analyzing Sequence Alignments Applications Chapter 71
Chapter 5: Pairwise Sequence Alignment and Database Searching Theory Chapter 115
Chapter 6: Patterns, Profiles, and Multiple Alignments Theory Chapter 165
PART 3 Evolutionary Processes
Chapter 7: Recovering Evolutionary History Applications Chapter 223
Chapter 8: Building Phylogenetic Trees Theory Chapter 267
PART 4 Genome Characteristics
Chapter 9: Revealing Genome Features Applications Chapter 317
Chapter 10: Gene Detection and Genome Annotation Theory Chapter 357
PART 5 Secondary Structures
Chapter 11: Obtaining Secondary Structure from Sequence Applications Chapter 411
Chapter 12: Predicting Secondary Structures Theory Chapter 461
PART 6 Tertiary Structures
Chapter 13: Modeling Protein Structure Applications Chapter 521
Chapter 14: Analyzing Structure-Function Relationships Applications Chapter 567
PART 7 Cells and Organisms
Chapter 15: Proteome and Gene Expression Analysis 599
Chapter 16: Clustering Methods and Statistics 625
Chapter 17: Systems Biology 667
APPENDICES Background Theory
Appendix A: Probability, Information, and Bayesian Analysis 695
Appendix B: Molecular Energy Functions 700
Appendix C: Function Optimization 709
xiii
CONTENTS
Preface v
A Note to the Reader vii
List of Reviewers xii
Contents in Brief xiii
Part 1 Background Basics
Chapter 1 The Nucleic Acid World
1.1 The Structure of DNA and RNA 5
DNA is a linear polymer of only four different bases 5
IWo complementary DNA strands interact by
base pairing to form a double helix 7
RNA molecules are mostly single stranded but
can also have base-pair structures 9
1.2 DNA, RNA, and Protein: The Central Dogma 10
DNA is the information store, but RNA is
the messenger 11
Messenger RNA is translated into protein
according to the genetic code 12
Translation involves transfer RNAs and
RNA-containing ribosomes 13
1.3 Gene Structure and Control 14
RNA polymerase binds to specific sequences that
position it and identify where to begin transcription 15
The signals initiating transcription in eukaryotes
are generally more complex than those in bacteria 17
Eukaryotic mRNA transcripts undergo several
modifications prior to their use in translation 18
The control of translation 19
1.4 The Tree of Life and Evolution 20
A brief survey of the basic characteristics of the
major forms of life 21
Nucleic acid sequences can change as a result of
mutation 22
Summary 23
Further Reading 24
Chapter 2 Protein Structure
2.1 Primary and Secondary Structure 25
Protein structure can be considered on several
different levels 26
Amino acids are the building blocks of proteins 27
The differing chemical and physical properties of
amino acids are due to their side chains 28
xiv
Amino acids are covalently linked together in the
protein chain by peptide bonds 29
Secondary structure of proteins is made up of
a-helices and p-strands 33
Several different types of (3-sheet are found
in protein structures 35
Turns, hairpins and loops connect helices
and strands 36
2.2 Implication for Bioinformatics 37
Certain amino acids prefer a particular
structural unit 37
Evolution has aided sequence analysis 38
Visualization and computer manipulation
of protein structures 38
2.3 Proteins Fold to Form Compact Structures 40
The tertiary structure of a protein is defined
by the path of the polypeptide chain 41
The stable folded state of a protein represents
a state of low energy 41
Many proteins are formed of multiple subunits 42
Summary 43
Further Reading 44
Chapter 3 Dealing with Databases
3.1 The Structure of Databases 46
Flat-file databases store data as text files 48
Relational databases are widely used for storing
biological information 49
XML has the flexibility to define bespoke data
classifications 50
Many other database structures are used
for biological data 51
Databases can be accessed locally or online
and often link to each other 52
3.2 Types of Database 52
There's more to databases than just data 53
Primary and derived data 53
How we define and connect things is very
important: Ontologies 54
3.3 Looking for Databases 55
Sequence databases 55
Microarray databases 58
Protein interaction databases 58
Structural databases 59
3.4 Data Quality 61
Nonredundancy is especially important for some
applications of sequence databases 62
Automated methods can be used to check for data
consistency 63
Initial analysis and annotation is usually
automated 64
Human intervention is often required to produce
the highest quality annotation 65
The importance of updating databases and entry
identifier and version numbers 65
Summary 66
Further Reading 67
Part 2 Sequence Alignments
APPLICATIONS CHAPTER
Chapter 4 Producing and Analyzing Sequence
Alignments
4.1 Principles of Sequence Alignment 72
Alignment is the task of locating equivalent
regions of two or more sequences to maximize
their similarity 73
Alignment can reveal homology between sequences 74
It is easier to detect homology when comparing
protein sequences than when comparing nucleic
acid sequences 75
4.2 Scoring Alignments 76
The quality of an alignment is measured by giving
it a quantitative score 76
The simplest way of quantifying similarity
between two sequences is percentage identity 76
The dot-plot gives a visual assessment of similarity
based on identity 77
Genuine matches do not have to be identical 79
There is a minimum percentage identity that can
be accepted as significant 81
There are many different ways of scoring an
alignment 81
4.3 Substitution Matrices 81
Substitution matrices are used to assign individual
scores to aligned sequence positions 81
The RAM substitution matrices use substitution
frequencies derived from sets of closely related
protein sequences 82
The BLOSUM substitution matrices use mutation
data from highly conserved local regions of
sequence 84
The choice of substitution matrix depends on the
problem to be solved 84
Contents
4.4 Inserting Gaps 85
Gaps inserted in a sequence to maximize similarity
require a scoring penalty 85
Dynamic programming algorithms can determine
the optimal introduction of gaps 86
4.5 Types of Alignment 87
Different kinds of alignments are useful in
different circumstances 87
Multiple sequence alignments enable the
simultaneous comparison of a set of similar
sequences 90
Multiple alignments can be constructed by
several different techniques 90
Multiple alignments can improve the accuracy of
alignment for sequences of low similarity 91
ClustalW can make global multiple alignments
of both DNA and protein sequences 92
Multiple alignments can be made by combining
a series of local alignments 92
Alignment can be improved by incorporating
additional information 93
4.6 Searching Databases 93
Fast yet accurate search algorithms have been
developed 94
FASTA is a fast database-search method based on
matching short identical segments 95
BLAST is based on finding very similar short segments 95
Different versions of BLAST and FASTA are used
for different problems 95
PSI-BLAST enables profile-based database searches 96
SSEARCH is a rigorous alignment method 97
4.7 Searching with Nucleic Acid or Protein Sequences 97
DNA or RNA sequences can be used either
directly or after translation 97
The quality of a database match has to be tested
to ensure that it could not have arisen by chance 97
Choosing an appropriate E-value threshold helps
to limit a database search 98
Low-complexity regions can complicate
homology searches 100
Different databases can be used to solve
particular problems 102
4.8 Protein Sequence Motifs or Patterns 103
Creation of pattern databases requires expert
knowledge 104
The BLOCKS database contains automatically
compiled short blocks of conserved multiply
aligned protein sequences 105
4.9 Searching Using Motifs and Patterns 107
The PROSITE database can be searched for
protein motifs and patterns 107
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Contents
The pattern-based program PHI-BLAST searches
for both homology and matching motifs 108
Patterns can be generated from multiple
sequences using PRATT 108
The PRINTS database consists of fingerprints
representing sets of conserved motifs that
describe a protein family 109
The Pfam database defines profiles of protein
families 109
4.10 Patterns and Protein Function 109
Searches can be made for particular functional
sites in proteins 109
Sequence comparison is not the only way of
analyzing protein sequences 110
Summary 111
Further Reading 112
THEORY CHAPTER
Chapter 5 Pairwise Sequence Alignment and
Database Searching
5.1 Substitution Matrices and Scoring 117
Alignment scores attempt to measure the
likelihood of a common evolutionary ancestor 117
The PAM (MDM) substitution scoring matrices
were designed to trace the evolutionary origins
of proteins 119
The BLOSUM matrices were designed to find
conserved regions of proteins 122
Scoring matrices for nucleotide sequence
alignment can be derived in similar ways 125
The substitution scoring matrix used must be
appropriate to the specific alignment problem 126
Gaps are scored in a much more heuristic way
than substitutions 126
5.2 Dynamic Programming Algorithms 127
Optimal global alignments are produced using
efficient variations of the Needleman-Wunsch
algorithm 129
Local and suboptimal alignments can be produced
by making small modifications to the dynamic
programming algorithm 135
Time can be saved with a loss of rigor by not
calculating the whole matrix 139
5.3 Indexing Techniques and Algorithmic
Approximations 141
Suffix trees locate the positions of repeats and
unique sequences 141
Hashing is an indexing technique that lists the
starting positions of all k-tuples 143
The FASTA algorithm uses hashing and chaining
for fast database searching 144
xvi
The BLAST algorithm makes use of finite-state
automata 147
Comparing a nucleotide sequence directly with a
protein sequence requires special modifications
to the BLAST and FASTA algorithms 150
5.4 Alignment Score Significance 153
The statistics of gapped local alignments can be
approximated by the same theory 156
5.5 Aligning Complete Genome Sequences 156
Indexing and scanning whole genome sequences
efficiently is crucial for the sequence alignment
of higher organisms 157
The complex evolutionary relationships between
the genomes of even closely related organisms
require novel alignment algorithms 159
Summary 159
Further Reading 161
THEORY CHAPTER
Chapter 6 Patterns, Profiles, and Multiple
Alignments
6.1 Profiles and Sequence Logos 167
Position-specific scoring matrices are an
extension of substitution scoring matrices 168
Methods for overcoming a lack of data in deriving
the values for a PSSM 171
PSI-BLAST is a sequence database searching
program 176
Representing a profile as a logo 177
6.2 Profile Hidden Markov Models 179
The basic structure of HMMs used in sequence
alignment to profiles 180
Estimating HMM parameters using aligned
sequences 185
Scoring a sequence against a profile HMM:
The most probable path and the sum over
all paths 187
Estimating HMM parameters using unaligned
sequences 190
6.3 Aligning Profiles 193
Comparing two PSSMs by alignment 193
Aligning profile HMMs I95
6.4 Multiple Sequence Alignments by Gradual
Sequence Addition !96
The order in which sequences are added is chosen
based on the estimated likelihood of incorporating
errors in the alignment I98
Many different scoring schemes have been used
in constructing multiple alignments 200
The multiple alignment is built using the guide
tree and profile methods and may be further
refined 204
6.5 Other Ways of Obtaining Multiple Alignments 207
The multiple sequence alignment program
DIALIGN aligns ungapped blocks 207
The SAGA method of multiple alignment uses
a genetic algorithm 209
6.6 Sequence Pattern Discovery 211
Discovering patterns in a multiple alignment:
eMOTIFandAACC 213
Probabilistic searching for common patterns in
sequences: Gibbs and MEME 215
Searching for more general sequence patterns 217
Summary 218
Further Reading 219
Part 3 Evolutionary Processes
APPLICATIONS CHAPTER
Chapter 7 Recovering Evolutionary History
7.1 The Structure and Interpretation of
Phylogenetic Trees 225
Phylogenetic trees reconstruct evolutionary
relationships 225
Tree topology can be described in several ways 230
Consensus and condensed trees report the
results of comparing tree topologies 232
7.2 Molecular Evolution and its Consequences 235
Most related sequences have many positions
that have mutated several times 236
The rate of accepted mutation is usually not the
same for all types of base substitution 236
Different codon positions have different
mutation rates 238
Only orthologous genes should be used to
construct species phylogenetic trees 239
Major changes affecting large regions of the
genome are surprisingly common 247
7.3 Phylogenetic Tree Reconstruction 248
Small ribosomal subunit rRNA sequences are well
suited to reconstructing the evolution of species 249
The choice of the method for tree reconstruction
depends to some extent on the size and quality of
the dataset 249
A model of evolution must be chosen to use with
the method 251
All phylogenetic analyses must start with an
accurate multiple alignment 255
Contents
Phylogenetic analyses of a small dataset of
16S RNA sequence data 255
Building a gene tree for a family of enzymes can
help to identify how enzymatic functions evolved 259
Summary 264
Further Reading 265
THEORY CHAPTER
Chapter 8 Building Phylogenetic Trees
8.1 Evolutionary Models and the Calculation
of Evolutionary Distance 268
A simple but inaccurate measure of evolutionary
distance is the p-distance 268
The Poisson distance correction takes account of
multiple mutations at the same site 270
The Gamma distance correction takes account of
mutation rate variation at different sequence
positions 270
The Jukes-Cantor model reproduces some basic
features of the evolution of nucleotide sequences 271
More complex models distinguish between the
relative frequencies of different types of mutation 272
There is a nucleotide bias in DNA sequences 275
Models of protein-sequence evolution are closely
related to the substitution matrices used for
sequence alignment 276
8.2 Generating Single Phylogenetic Trees 276
Clustering methods produce a phylogenetic tree
based on evolutionary distances 276
The UPGMA method assumes a constant
molecular clock and produces an ultrametric tree 278
The Fitch-Margoliash method produces an
unrooted additive tree 279
The neighbor-joining method is related to the
concept of minimum evolution 282
Stepwise addition and star-decomposition
methods are usually used to generate starting
trees for further exploration, not the final tree 285
8.3 Generating Multiple Tree Topologies 286
The branch-and-bound method greatly improves
the efficiency of exploring tree topology 288
Optimization of tree topology can be achieved
by making a series of small changes to an existing
tree 288
Finding the root gives a phylogenetic tree a
direction in time 291
8.4 Evaluating Tree Topologies 293
Functions based on evolutionary distances can
be used to evaluate trees 293
Unweighted parsimony methods look for the trees
with the smallest number of mutations 297
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Contents
Mutations can be weighted in different ways
in the parsimony method 300
Trees can be evaluated using the maximum
likelihood method 302
The quartet-puzzling method also involves maximum
likelihood in the standard implementation 305
Bayesian methods can also be used to reconstruct
phylogenetic trees 306
8.5 Assessing the Reliability of Tree Features
and Comparing Trees 307
The long-branch attraction problem can arise
even with perfect data and methodology 308
Tree topology can be tested by examining the
interior branches 309
Tests have been proposed for comparing two
or more alternative trees 310
Summary 311
Further Reading 312
Part 4 Genome Characteristics
APPLICATIONS CHAPTER
Chapter 9 Revealing Genome Features
9.1 Preliminary Examination of Genome Sequence 318
Whole genome sequences can be split up to
simplify gene searches 319
Structural RNA genes and repeat sequences
can be excluded from further analysis 319
Homology can be used to identify genes in both
prokaryotic and eukaryotic genomes 322
9.2 Gene Prediction in Prokaryotic Genomes 322
9.3 Gene Prediction in Eukaryotic Genomes 323
Programs for predicting exons and introns use
a variety of approaches 323
Gene predictions must preserve the correct
reading frame 324
Some programs search for exons using only
the query sequence and a model for exons 327
Some programs search for genes using only
the query sequence and a gene model 332
Genes can be predicted using a gene model
and sequence similarity 334
Genomes of related organisms can be used
to improve gene prediction 336
9.4 Splice Site Detection 337
Splice sites can be detected independently by
specialized programs 338
9.5 Prediction of Promoter Regions 338
xviii
Prokaryotic promoter regions contain relatively
well-defined motifs 339
Eukaryotic promoter regions are typically more
complex than prokaryotic promoters 340
A variety of promoter-prediction methods are
available online 340
Promoter prediction results are not very clear-cut 341
9.6 Confirming Predictions 342
There are various methods for calculating the
accuracy of gene-prediction programs 342
Translating predicted exons can confirm the
correctness of the prediction 343
Constructing the protein and identifying homologs 343
9.7 Genome Annotation 346
Genome annotation is the final step in genome
analysis 347
Gene ontology provides a standard vocabulary
for gene annotation 348
9.8 Large Genome Comparisons 353
Summary 354
Further Reading 355
THEORY CHAPTER
Chapter 10 Gene Detection and Genome
Annotation
10.1 Detection of Functional RNA Molecules Using
Decision Trees 361
Detection of tRNA genes using the tRNAscan
algorithm 361
Detection of tRNA genes in eukaryotic genomes 362
10.2 Features Useful for Gene Detection in Prokaryotes 364
10.3 Algorithms for Gene Detection in Prokaryotes 368
GeneMark uses inhomogeneous Markov chains
and dicodon statistics 368
GLIMMER uses interpolated Markov models of
coding potential 371
ORPHEUS uses homology, codon statistics, and
ribosome-binding sites 372
GeneMark.hmm uses explicit state duration
hidden Markov models 373
EcoParse is an HMM gene model 376
10.4 Features Used in Eukaryotic Gene Detection 377
Differences between prokaryotic and
eukaryotic genes 377
Introns, exons, and splice sites 379
Promoter sequences and binding sites for
transcription factors 381
10.5 Predicting Eukaryotic Gene Signals 381
Detection of core promoter binding signals is
a key element of some eukaryotic gene-
prediction methods 381
A set of models has been designed to locate
the site of core promoter sequence signals 383
Predicting promoter regions from general
sequence properties can reduce the numbers
of false-positive results 387
Predicting eukaryotic transcription and
translation start sites 389
Translation and transcription stop signals
complete the gene definition 389
10.6 Predicting Exon/Intron Structure 389
Exons can be identified using general sequence
properties 390
Splice-site prediction 392
Splice sites can be predicted by sequence patterns
combined with base statistics 393
GenScan uses a combination of weight matrices
and decision trees to locate splice sites 394
GeneSplicer predicts splice sites using first-order
Markov chains 394
NetPlantGene uses neural networks with
intron and exon predictions to predict splice sites 395
Other splicing features may yet be exploited for
splice-site prediction 396
Specific methods exist to identify initial and
terminal exons 396
Exons can be defined by searching databases for
homologous regions 397
10.7 Complete Eukaryotic Gene Models 397
10.8 Beyond the Prediction of Individual Genes 399
Functional annotation 400
Comparison of related genomes can help resolve
uncertain predictions 403
Evaluation and reevaluation of gene-detection
methods 405
Summary 405
Further Reading 406
Part 5 Secondary Structures
APPLICATIONS CHAPTER
Chapter 11 Obtaining Secondary Structure
from Sequence
11.1 Types of Prediction Methods 413
Statistical methods are based on rules that give
the probability that a residue will form part of a
particular secondary structure 414
Nearest-neighbor methods are statistical methods
Contents
that incorporate additional information about
protein structure 414
Machine-learning approaches to secondary
structure prediction mainly make use of neural
networks and HMM methods 415
11.2 Training and Test Databases 416
There are several ways to define protein
secondary structures 417
11.3 Assessing the Accuracy of Prediction
Programs 417
Q3 measures the accuracy of individual residue
assignments 417
Secondary structure predictions should not be
expected to reach 100% residue accuracy 418
The Sov value measures the prediction accuracy
for whole elements 419
CAFASP/CASP: Unbiased and readily available
protein prediction assessments 419
11.4 Statistical and Knowledge-Based Methods 421
The GOR method uses an information theory
approach 422
The program Zpred includes multiple alignment
of homologous sequences and residue
conservation information 425
There is an overall increase in prediction accuracy
using multiple sequence information 426
The nearest-neighbor method: The use of multiple
nonhomologous sequences 428
PREDATOR is a combined statistical and
knowledge-based program that includes the
nearest-neighbor approach 428
11.5 Neural Network Methods of Secondary Structure
Prediction 430
Assessing the reliability of neural net predictions 432
Several examples of Web-based neural network
secondary structure prediction programs 432
PROF: Protein forecasting 434
PSIPRED 434
Jnet: Using several alternative representations
of the sequence alignment 434
11.6 Some Secondary Structures Require Specialized
Prediction Methods 435
Transmembrane proteins 436
Quantifying the preference for a membrane
environment 437
11.7 Prediction of Transmembrane Protein Structure 438
Multi-helix membrane proteins 439
A selection of prediction programs to predict
transmernbrane helices 441
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Contents
Statistical methods 443
Knowledge-based prediction 443
Evolutionary information from protein families
improves the prediction 444
Neural nets in transmembrane prediction 445
Predicting transmembrane helices with
hidden Markov models 446
Comparing the results: What to choose 447
What happens if a non-transmembrane protein is
submitted to transmembrane prediction programs 448
Prediction of transmembrane structure
containing (3-strands 448
11.8 Coiled-coil Structures 451
The COILS prediction program 452
PAIRCOIL and MULTICOIL are an extension
of the COILS algorithm 453
Zipping the Leucine zipper: A specialized
coiled coil 453
11.9 RNA Secondary Structure Prediction 455
Summary 458
Further Reading 459
THEORY CHAPTER
Chapter 12 Predicting Secondary Structures
12.1 Defining Secondary Structure and Prediction
Accuracy 463
The definitions used for automatic protein secondary
structure assignment do not give identical results 464
There are several different measures of the
accuracy of secondary structure prediction 469
12.2 Secondary Structure Prediction Based on
Residue Propensities 472
Each structural state has an amino acid preference
which can be assigned as a residue propensity 473
The simplest prediction methods are based on die
average residue propensity over a sequence window 476
Residue propensities are modulated by nearby
sequence 479
Predictions can be significantly improved by
including information from homologous sequences 484
12.3 The Nearest-Neighbor Methods are Based on
Sequence Segment Similarity 485
Short segments of similar sequence are found
to have similar structure 487
Several sequence similarity measures have been
used to identify nearest-neighbor segments 488
A weighted average of the nearest-neighbor
segment structures is used to make the prediction 490
A nearest-neighbor method has been developed to
predict regions with a high potential to misfold 491
xx
12.4 Neural Networks Have Been Employed
Successfully for Secondary Structure Prediction 492
Layered feed-forward neural networks can
transform a sequence into a structural prediction 494
Inclusion of information on homologous
sequences improves neural network accuracy 502
More complex neural nets have been applied to
predict secondary and other structural features 503
12.5 Hidden Markov Models Have Been Applied to
Structure Prediction 504
HMM methods have been found especially
effective for transmembrane proteins 506
Nonmembrane protein secondary structures can
also be successfully predicted with HMMs 509
12.6 General Data Classification Techniques Can
Predict Structural Features 510
Support vector machines have been successfully
used for protein structure prediction 511
Discriminants, SOMs, and other methods have
also been used 512
Summary 514
Further Reading 515
Part 6 Tertiary Structures
APPLICATIONS CHAPTER
Chapter 13 Modeling Protein Structure
13.1 Potential Energy Functions and Force Fields 524
The conformation of a protein can be visualized
in terms of a potential energy surface 525
Conformational energies can be described by
simple mathematical functions 525
Similar force fields can be used to represent
conformational energies in the presence of
averaged environments 526
Potential energy functions can be used to assess
a modeled structure 527
Energy minimization can be used to refine a modeled
structure and identify local energy minima 527
Molecular dynamics and simulated annealing
are used to find global energy minima 528
13.2 Obtaining a Structure by Threading 529
The prediction of protein folds in the absence of
known structural homologs 531
Libraries or databases of nonredundant protein
folds are used in threading 531
Two distinct types of scoring schemes have been
used in threading methods 531
Dynamic programming methods can identify
optimal alignments of target sequences and
structural folds 533
Several methods are available to assess the
confidence to be put on the fold prediction 534
The C2-like domain from the Dictyostelia:
A practical example of threading 535
13.3 Principles of Homology Modeling 537
Closely related target and template sequences give
better models 539
Significant sequence identity depends on the
length of the sequence 540
Homology modeling has been automated to deal with
the numbers of sequences that can now be modeled 541
Model building is based on a number of
assumptions 541
13.4 Steps in Homology Modeling 542
Structural homologs to the target protein are
found in the PDB 543
Accurate alignment of target and template
sequences is essential for successful modeling 543
The structurally conserved regions of a protein
are modeled first 544
The modeled core is checked for misfits before
proceeding to the next stage 545
Sequence realignment and remodeling may
improve the structure 545
Insertions and deletions are usually modeled
as loops 545
Nonidentical amino acid side chains are modeled
mainly by using rotamer libraries 547
Energy minimization is used to relieve
structural errors 548
Molecular dynamics can be used to explore
possible conformations for mobile loops 548
Models need to be checked for accuracy 549
How far can homology models be trusted? 551
13.5 Automated Homology Modeling 552
The program MODELLER models by satisfying
protein structure constraints 553
COMPOSER uses fragment-based modeling to
automatically generate a model 553
Automated methods available on the Web for
comparative modeling 554
Assessment of structure prediction 554
13.6 Homology ModeUng of PI3 Kinase pi 10a 557
Swiss-Pdb Viewer can be used for manual
or semi-manual modeling 557
Alignment, core modeling, and side-chain
modeling are carried out all in one 558
The loops are modeled from a database of
possible structures 559
Energy minimization and quality inspection
can be carried out within Swiss-Pdb Viewer 559
Contents
MolIDE is a downloadable semi-automatic
modeling package 560
Automated modeling on the Web illustrated with
pllOakinase 561
Modeling a functionally related but sequentially
dissimilar protein: mTOR 563
Generating a multidomain three-dimensional
structure from sequence 564
Summary 564
Further Reading 565
APPLICATIONS CHAPTER
Chapter 14 Analyzing Structure-Function
Relationships
14.1 Functional Conservation 568
Functional regions are usually structurally
conserved 569
Similar biochemical function can be found
in proteins with different folds 570
Fold libraries identify structurally similar proteins
regardless of function 571
14.2 Structure Comparison Methods 574
Finding domains in proteins aids structure
comparison 574
Structural comparisons can reveal conserved
functional elements not discernible from a
sequence comparison 576
The CE method builds up a structural alignment
from pairs of aligned protein segments 576
The Vector Alignment Search Tool (VAST) aligns
secondary structural elements 577
DALI identifies structure superposition without
maintaining segment order 578
FATCAT introduces rotations between rigid
segments 579
14.3 Finding Binding Sites 580
Highly conserved, strongly charged, or hydrophobic
surface areas may indicate interaction sites 582
Searching for protein-protein interactions
using surface properties 584
Surface calculations highlight clefts or holes
in a protein that may serve as binding sites 585
Looking at residue conservation can identify
binding sites 586
14.4 Docking Methods and Programs 587
Simple docking procedures can be used when
the structure of a homologous protein bound
to a ligand analog is known 588
Specialized docking programs will automatically
dock a ligand to a structure 588
xxi
Contents
Scoring functions are used to identify the most
likely docked ligand 590
The DOCK program is a semirigid-body method
that analyzes shape and chemical
complementarity of ligand and binding site 590
Fragment docking identifies potential substrates
by predicting types of atoms and functional
groups in the binding area 591
GOLD is a flexible docking program, which
utilizes a genetic algorithm 591
The water molecules in binding sites should also
be considered 592
Summary 593
Further Reading 594
Part 7 Cells and Organisms
Chapter 15 Proteome and Gene Expression Analysis
15.1 Analysis of Large-scale Gene Expression 601
The expression of large numbers of different
genes can be measured simultaneously by DNA
microarrays 602
Gene expression microarrays are mainly used
to detect differences in gene expression in
different conditions 602
Serial analysis of gene expression (SAGE) is also
used to study global patterns of gene expression 604
Digital differential display uses bioinformatics
and statistics to detect differential gene
expression in different tissues 605
Facilitating the integration of data from different
places and experiments 606
The simplest method of analyzing gene expression
microarray data is hierarchical cluster analysis 606
Techniques based on self-organizing maps
can be used for analyzing microarray data 608
Self-organizing tree algorithms (SOTAs) cluster
from the top down by successive subdivision
of clusters 610
Clustered gene expression data can be used as
a tool for further research 610
15.2 Analysis of Large-scale Protein Expression 612
Two-dimensional gel electrophoresis is a method
for separating the individual proteins in a cell 613
Measuring the expression levels shown in 2D gels 614
Differences in protein expression levels between
different samples can be detected by 2D gels 615
Clustering methods are used to identify protein
spots with similar expression patterns 615
Principal component analysis (PCA) is an
alternative to clustering for analyzing microarray
and 2D gel data 618
xxii
The changes in a set of protein spots can be
tracked over a number of different samples 618
Databases and online tools are available to aid
the interpretation of 2D gel data 620
Protein microarrays allow the simultaneous
detection of the presence or activity of large
numbers of different proteins 621
Mass spectrometry can be used to identify the
proteins separated and purified by 2D gel
electrophoresis or other means 621
Protein-identification programs for mass
spectrometry are freely available on the Web 622
Mass spectrometry can be used to measure
protein concentration 623
Summary 623
Further Reading 624
Chapter 16 Clustering Methods and Statistics
16.1 Expression Data Require Preparation Prior
to Analysis 626
Data normalization is designed to remove
systematic experimental errors 627
Expression levels are often analyzed as ratios
and are usually transformed by taking logarithms 628
Sometimes further normalization is useful after
the data transformation 630
Principal component analysis is a method for
combining the properties of an object 631
16.2 Cluster Analysis Requires Distances to be Defined
Between all Data Points 633
Euclidean distance is the measure used in
everyday life 634
The Pearson correlation coefficient measures
distance in terms of the shape of the expression
response 635
The Mahalanobis distance takes account of the
variation and correlation of expression responses 636
16.3 Clustering Methods Identify Similar and Distinct
Expression Patterns 637
Hierarchical clustering produces a related set of
alternative partitions of the data 639
fc-means clustering groups data into several
clusters but does not determine a relationship
between clusters 641
Self-organizing maps (SOMs) use neural network
methods to cluster data into a predetermined
number of clusters 644
Evolutionary clustering algorithms use selection,
recombination, and mutation to find the best
possible solution to a problem 646
The self-organizing tree algorithm (SOTA)
determines the number of clusters required 648
Biclustering identifies a subset of similar
expression level patterns occurring in a subset
of the samples 649
The validity of clusters is determined by
independent methods 650
16.4 Statistical Analysis can Quantify the Significance
of Observed Differential Expression 651
Mests can be used to estimate the significance
of the difference between two expression levels 654
Nonparametric tests are used to avoid making
assumptions about the data sampling 656
Multiple testing of differential expression requires
special techniques to control error rates 657
16.5 Gene and Protein Expression Data Can be Used
to Classify Samples 659
Many alternative methods have been proposed
that can classify samples 660
Support vector machines are another form of
supervised learning algorithms that can produce
classifiers 661
Summary 662
Further Reading 664
Chapter 17 Systems Biology
17.1 What is a System? 669
A system is more than the sum of its parts 669
A biological system is a living network 670
Databases are useful starting points in
constructing a network 671
To construct a model more information is
needed than a network 672
There are three possible approaches to
constructing a model 674
Kinetic models are not the only way in
systems biology 678
17.2 Structure of the Model 679
Control circuits are an essential part of any
biological system 680
The interactions in networks can be represented
as simple differential equations 680
17.3 Robustness of Biological Systems 683
Robustness is a distinct feature of complexity
in biology 684
Modularity plays an important part in robustness 685
Redundancy in the system can provide robustness 686
Living systems can switch from one state to
another by means of bistable switches 688
Contents
17.4 Storing and Running System Models 689
Specialized programs make simulating
systems easier 691
Standardized system descriptions aid their
storage and reuse 692
Summary 692
Further Reading 693
APPENDICES Background Theory
Appendix A: Probability, Information, and
Bayesian Analysis
Probability Theory, Entropy, and Information 695
Mutually exclusive events 695
Occurrence of two events 696
Occurrence of two random variables 696
Bayesian Analysis 697
Bayes' theorem 697
Inference of parameter values 698
Further Reading 699
Appendix B: Molecular Energy Functions
Force Fields for Calculating Intra- and Intermolecular
Interaction Energies 701
Bonding terms 702
Nonbonding terms 704
Potentials used in Threading 706
Potentials of mean force 706
Potential terms relating to solvent effects 707
Further Reading 708
Appendix C: Function Optimization
Full Search Methods 710
Dynamic programming and branch-and-bound 710
Local Optimization 710
The downhill simplex method 711
The steepest descent method 711
The conjugate gradient method 714
Methods using second derivatives 714
Thermodynamic Simulation and Global Optimization 715
Monte Carlo and genetic algorithms 716
Molecular dynamics 718
Simulated annealing 719
Summary 719
Further Reading 719
List of Symbols 721
Glossary 734
Index 751
xxiii |
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| illustrated | Illustrated |
| index_date | 2024-07-02T16:39:23Z |
| indexdate | 2024-07-09T20:53:21Z |
| institution | BVB |
| isbn | 9780815340249 0815340249 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015461156 |
| oclc_num | 255514065 |
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| spelling | Zvelebil, Marketa J. Verfasser aut Understanding bioinformatics Marketa Zvelebil & Jeremy O. Baum New York [u.a.] Garland Science [u.a.] 2008 XXIII, 772 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Bioinformatik (DE-588)4611085-9 gnd rswk-swf Bioinformatik (DE-588)4611085-9 s DE-604 Baum, Jeremy O. Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015461156&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zvelebil, Marketa J. Baum, Jeremy O. Understanding bioinformatics Bioinformatik (DE-588)4611085-9 gnd |
| subject_GND | (DE-588)4611085-9 |
| title | Understanding bioinformatics |
| title_auth | Understanding bioinformatics |
| title_exact_search | Understanding bioinformatics |
| title_exact_search_txtP | Understanding bioinformatics |
| title_full | Understanding bioinformatics Marketa Zvelebil & Jeremy O. Baum |
| title_fullStr | Understanding bioinformatics Marketa Zvelebil & Jeremy O. Baum |
| title_full_unstemmed | Understanding bioinformatics Marketa Zvelebil & Jeremy O. Baum |
| title_short | Understanding bioinformatics |
| title_sort | understanding bioinformatics |
| topic | Bioinformatik (DE-588)4611085-9 gnd |
| topic_facet | Bioinformatik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015461156&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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