Transcript Powerpoint slides

Structure and function of nucleic
acids.
DNA structure.
History:
• 1868 Miescher – discovered nuclein
• 1944 Avery – experimental evidence that DNA is
constituent of genes.
• 1953 Watson&Crick – double helical nature of DNA.
• 1980 X-ray structure of more than a full turn of B-DNA.
Five types of bases.
Nucleotides and phosphodiester bond.
Phosphodiester bond
Complementarity of nucleosides – bases
for double stranded helical structure.
Double helical structure of DNA.
A- and B-DNA – right-handed helix,
Z-DNA – left-handed helix
B-DNA – fully hydrated DNA in vivo,
10 base pairs per turn of helix
Sugar-phosphate backbones form ridges
on edges of helix.
Copyright © Ramaswamy H. Sarma 1996
Hydration of B-DNA.
From R. Dickerson, Structure & Expression
Difference between DNA & RNA:
Differences between DNA & RNA:
• T is replaced by U
• Extra –OH group at 2’ pentose sugar
• Sugar is ribose, not deoxyribose
RNA as a structural molecule, information
transfer molecule, information decoding
molecule
rRNA
mRNA
tRNA
Classwork I.
1. Go to http://ndbserver.rutgers.edu/.
2. Select Crystal structure of B-DNA, resolution
>=2 Angstroms.
3. Select Crystal structure of single-stranded RNA
with mismatch base pairing with resolution >= 2
Angstroms.
RNA secondary structure prediction
Assumptions used in predictions:
- The most likely structure is the most stable one.
- The energy associated with a given position
depends only on the local sequence/structure
- The structure is formed w/o knots.
Minimum energy method of RNA
secondary structure prediction.
• Self-complementary regions can be found in a
dot matrix
• The energy of each structure is estimated by the
nearest-neighbor rule
• The most energetically favorable conformations
are predicted by the method similar to dynamic
programming
Minimum energy method of RNA
secondary structure prediction.
Classwork II: Predict secondary structure
for RNA “ACGUGCGU”.
Stacking energies for base pairs
A/U
C/G
G/C
U/A
G/U
U/G
A/U
-0.9
-1.8
-2.3
-1.1
-1.1
-0.8
C/G
-1.7
-2.9
-3.4
-2.3
-2.1
-1.4
G/C
-2.1
-2.0
-2.9
-1.8
-1.9
-1.2
U/A
-0.9
-1.7
-2.1
-0.9
-1.0
-0.5
G/U
-0.5
-1.2
-1.4
-0.8
-0.4
-0.2
U/G
-1.0
-1.9
-2.1
-1.1
-1.5
-0.4
Destabilizing energies for loops
Number of
bases
1
5
10
20
30
Internal
-
5.3
6.6
7.0
7.4
Bulge
3.9
4.8
5.5
6.3
6.7
Hairpin
-
4.4
5.3
6.1
6.5
Prediction of most probable structure.
Probability of forming a base pair:
P  exp( G / kt)
For a double-stranded structure probability =
product of Boltzmann factors for each of stacking
base pairs.
Sequence covariation method.
Some positions from different species can covary because they are
involved in pairing
fm(B1) - frequences in column m;
fn(B2) – frequences in column n;
fm,n(B1,B2) – joint frequences of two nucleotides in two columns.
f m,n ( B1 , B2 ) /( f m ( B1 )  f n ( B2 ))
Seq 1
Seq 2
Seq 3
Seq 4
---G------C-----C------G-----A------C-----A------T---
Ribozymes.
• RNA of self-splicing group I introns, contain 4
sequence elements and form specific secondary
structures
• RNA self-splicing group II introns
• RNA from viral and plant satellite RNAs
• Ribosomal RNAs
Gene prediction.
Gene – DNA sequence encoding protein, rRNA,
tRNA (snRNA, snoRNA)…
Gene concept is complicated:
- Introns/exons
- Alternative splicing
- Genes-in-genes
- Multisubunit proteins
Gene identification
• Homology-based gene prediction
– Similarity Searches (e.g. BLAST, BLAT)
– Genome Browsers
– RNA evidence (ESTs)
• Ab initio gene prediction
– Prokaryotes
• ORF identification
– Eukaryotes
• Promoter prediction
• PolyA-signal prediction
• Splice site, start/stop-codon predictions
Prokaryotic genes – searching for ORFs.
- Small genomes have high gene density
Haemophilus influenza – 85% genic
- No introns
- Operons
One transcript, many genes
- Open reading frames (ORF) –
contiguous set of codons, start with Met-codon, ends with
stop codon.
Prediction of eukaryotic genes.
Ab initio gene prediction.
Predictions are based on the observation that
gene DNA sequence is not random:
- Each species has a characteristic pattern of synonymous
codon usage.
- Every third base tends to be the same.
- Non-coding ORFs are very short.
GeneMark (HMMs), GenScan, Grail II(neural
networks) and GeneParser (DP)
Gene preference score – important
indicator of coding region.
Observation: occurrence of codon pairs in coding regions is
not random.
The probability of exon starting at base 1:
P  a1 / a  Cn1
a1 – the score for an exon starting at base 1;
a – the sum of all scores for base 1, base2 and base 3;
n – the score for noncoding region starting at base 1;
C – the ratio of coding to noncoding bases in the organism.
Confirming gene location using EST
libraries.
• Expressed Sequence Tags (ESTs) – sequenced
short segments of cDNA. They are organized in
the database “UniGene”.
• If region matches ESTs with high statistical
significance, then it is a gene or pseudogene.
Gene prediction accuracy.
Factors which influence the accuracy:
- genetic code of a given genome may differ
from the universal code
- one tissue can splice one mRNA differently
from another
- mRNA can be edited
Gene prediction accuracy.
True positives (TP) – nucleotides, which are
correctly predicted to be within the gene.
Actual positives (AP) – nucleotides, which are
located within the actual gene.
Predicted positives (PP) – nucleotides, which are
predicted in the gene.
Sensitivity = TP / AP
Specificity = TP / PP
Gene prediction accuracy.
GenScan Website
Common difficulties
• First and last exons difficult to annotate because they
contain UTRs.
• Smaller genes are not statistically significant so they are
thrown out.
• Algorithms are trained with sequences from known
genes which biases them against genes about which
nothing is known.
GenBank – an annotated collection of all
publicly available DNA sequences.
Gene prediction: classwork III.
• Go to http://www.ncbi.nlm.nih.gov/mapview/ and
view all hemoglobin genes of H. sapiens
• Find 6 hemoglobin genes on chromosome 11,
view the DNA sequence of this chromosome
region
• Submit this sequence to GenScan server at
http://genes.mit.edu/GENSCAN.html
Genome analysis.
Genome – the sum of genes and intergenic
sequences of haploid cell.
The value of genome sequences lies in their
annotation
• Annotation – Characterizing genomic features
using computational and experimental methods
• Genes: Four levels of annotation
– Gene Prediction – Where are genes?
– What do they look like?
– What do they encode?
– What proteins/pathways involved in?
Koonin & Galperin
Accuracy of genome annotation.
• In most genomes functional predictions has been made
for majority of genes 54-79%.
• The source of errors in annotation:
- overprediction (those hits which are statistically
significant in the database search are not checked)
- multidomain protein (found the similarity to only one
domain, although the annotation is extended to the
whole protein).
The error of the genome annotation can be as big as 25%.
Sample genomes
Species
H.sapiens
Size
Genes
Genes/Mb
3,200Mb
35,000
11
D.melanogaster
137Mb
13.338
97
C.elegans
85.5Mb
18,266
214
A.thaliana
115Mb
25,800
224
S.cerevisiae
15Mb
6,144
410
E.coli
4.6Mb
4,300
934
List of 68 eukaryotes, 141 bacteria, and 17 archaea at
http://www.ncbi.nlm.nih.gov/PMGifs/Genomes/links2a.html
So much DNA – so “few” genes …
s
T
Genic
C
Intergenic
T
Human Genome project.
Comparative genomics - comparison of gene
number, gene content and gene location in
genomes..
Campbell & Heyer “Genomics”
Analysis of gene order (synteny).
Genes with a related function are frequently
clustered on the chromosome.
Ex: E.coli genes responsible for synthesis of Trp
are clustered and order is conserved between
different bacterial species.
Operon: set of genes transcribed simultaneously
with the same direction of transcription
Analysis of gene order (synteny).
Koonin & Galperin “Sequence, Evolution, Function”
Analysis of gene order (synteny).
• The order of genes is not very well conserved if
%identity between prokaryotic genomes is <
50%
• The gene neighborhood can be conserved so
that the all neighboring genes belong to the
same functional class.
• Functional prediction based on gene
neighboring.
COGs – Clusters of Orthologous Genes.
Orthologs – genes in different
species that evolved from a
common ancestral gene by
speciation;
Paralogs – paralogs are genes
related by duplication within a
genome.