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Computational

Molecular Biology

Alexander P. Goultiaev

Erwin M. Bakker

Organization

Contact:

Alexander P. Goultiaev

Erwin M. Bakker,

email: erwin@liacs.nl

Website:

For agenda, materials, assignments, references, links, etc.

http://liacs.leidenuniv.nl/~bakkerem2/cmb2016/

Course (6EC):

Lectures

Lecture-Assignments

Assignments (40% of the grade):• lab assignments (25% of the grade)

• final assignment (15% of the grade)

Exam (60% of the grade; grade >5; closed book exam)

Final grade = 0.4 x grade assignments + 0.6 x grade exam

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Overview

General Introduction

Sequence Alignment

Introduction to Molecular Biology

Sequence Alignment and Database

Search

Lab: Sequence Alignment

Multiple Sequence Alignment

Lab: Multiple Sequence Alignment

Sequence Alignment Lab Review

Overview

Classical Profiles

Hidden Markov Models

Multiple Sequence Alignment Lab Review

Gene Finding

RNA Structure Prediction

Lab: Structure Prediction

Protein Structure Prediction

BWT and Next Generation Sequencing

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The Structure of DNARosalind Franklin, James D. Watson, Francis Crick

(1953)

Nucleotides (bases)

• Adenine (A)

• Cytosine (C)

• Guanine (G)

• Thymine (T)

Complementary

Binding:

• T – A

• A – T

• C – G

• G - C

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Genes

Gene: Contiguous subparts of single

strand DNA that are templates for

producing proteins. Genes can

appear in either of the DNA strand.

Chromosomes: compact chains of

coiled DNA

Genome: The set of all genes in a

given organism.

Noncoding part: The function of DNA

material between genes is largely

unknown. Certain intergenic regions

of DNA are known to play a major

role in cell regulation (controls the

production of proteins and their

possible interactions with DNA).Source: www.mtsinai.on.ca/pdmg/Genetics/basic.htm

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Central Dogma of Molecular BiologyFrom Sequence to Function

DNA

Transcription

and Splicing

RNA

Translation

Protein

{A,C,T,G}*

{A,C,U,G}*

{20 symbols}*

Code is Structure is Function

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Molecule of the Month

www.pdb.org

Animated gifs from: proteinexplorer.org

March 2008:

Cadherin

• Adhesive Proteins

• Selective Stickiness:

The red tyrosine

amino acid will

bind to Cadherins

on neighbouring

cells

Molecule of the Month

www.pdb.org

November 2015:

Glutamate-gated Chloride

Receptors

• Receptors of Chloride Ion

channels in nerve systems

of parasites like worms

• Targets for antibiotics,

because our own cells don't

use them

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The Cell

Evolutie: The Tree of Life

Phylogenetic trees:

• Traditioneel:

morfologie

• Nieuwe inzichten door

bestudering van

biologische

sequenties, genomen,

etc.

From: wikipedia

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From: Mark Ragan, Phylogenetics without multiple sequence alignment,

IPAM Workshop on Multiple Sequence Alignment UCLA, 13 January 2015

Tikfouten:

Dit is een gen.

Dit is geen gen.

Dit is een gen.

Dit is een zen.

Dit is een den.q w e r t

a s d f g

z x c v b

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Biologische (Sequentie) Databases

Efficiente algoritmen voor het zoeken in steeds

groter wordende biodatabases:

Zoek patroon P in een text T.

(Knuth-Morris-Pratt, 1974/1977).

BLAST, FASTA

SPOT FINDING IN MICROARRAYS

DROSOPHILA: DYNAMIC GENE EXPRESSION PATTERNS

FLUORESCENT TRANSGENIC MOUSE

BUILDING 3D ATLASZEBRA FISHTAGGING INDIVIDUAL CHROMOSOMES

FISH

TAGGING TELOMERSPROTEIN GELS

GO ANNOTATION

KEGG PATHWAYS

ENSEMBLE CONTIG VIEW

CL

US

TE

R V

IE

W

~1012 bp

Overview

String Alignment

t h i s _ i s _ a _ t e s t _ s t r i n g

x x x x x x x x x x x x x x x

t h o s _ i s a _ t e x t _ s t r o n g

t h i s _ i s _ a _ t e s t _ s t r i n g

t d c c

t h o s _ i s a _ t e x t _ s t r o n g

t = typo; c = change leading to different semantics; d = deletion;

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Overview

String Alignment

A G T C A A G T C A A G T - C A

s d i

A G A C A A G - C A A G T A C A

K S Q E T K S Q E T K S Q E - T

s d i

K V Q E T K - Q E T K S Q E V T

s = substitution; d = deletion; i = insertion;

DNA sequences.

Protein sequences: Peptide or amino acid sequences.

Overview

Alignment and Database Search

(symmetric)

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Overview

Alignment and Database Search

BLAST Queries:

Overview

Multiple Alignment, Profiles, Gene Finding

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Overview Phylogeny

Overview Phylogeny

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Overview Phylogeny

Overview

Physical Mapping (PQ Trees), Sequencing

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Overview

Physical Mapping (PQ Trees), Sequencing

Overview Hidden Markov Models

MODEL

- OBSERVER -

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Overview

Applications of HMM

Speech Recognition

Music Recognition

Weather Prediction

…

Molecular Biology

CpG Island Detection

Protein Profile Alignment

Gene Finding

Copy Number Detection

…

Overview

RNA Structure

RNA Folding Simulations

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Overview

Structure Prediction

DNA

sequence 3D structure protein functions

DNA (gene) →→→ pre-RNA →→→ RNA →→→ Protein

RNA-polymerase Spliceosome Ribosome

ACCGACCAAGCGGCGTTCACC

ATGAGGCTGCTGACCCTCCTG

GGCCTTCTG…

TDQAAFDTNIVTLTRFVMEQG

RKARGTGEMTQLLNSLCTAVK

AISTAVRKAGIAHLYGIAGST

NVTGDQVKKLDVLSNDLVINV

LKSSFATCVLVTEEDKNAIIV

EPEKRGKYVVCFDPLDGSSNI

DCLVSIGTIFGIYRKNSTDEP

SEKDALQPGRNLVAAGYALYG

SATML

Overview

Structure Prediction

Primary structure, i.e., the sequence of

amino acids is given/determined

Secondary structure prediction

algorithms consider the residues in a

polypeptide chain to be in:

Three (helix, strand, coil) states

Four (helix, strand, coil, turn) states or even

Eight states and try to predict the location of

these states.

…

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Overview

Structure Prediction

Tertiary structure Prediction three general strategies

1. Comparative (homology) modeling. If there is a clear sequence homology between the target and one or more known structures, an algorithm tries to obtain the most accurate structural model for the target, consistent with the known set.

2. Fold recognition: approaches that try to recognize a known fold in a domain within the target protein. Alternative sequence structure alignments are scored using some kind of conformational energy calculation, based on statistics of known structures.

3. Ab initio methods. Modeling of structures using potential energy calculations.

Sequencing

1953 Watson and Crick: the structure of the DNA molecule DNA carrier of the genetic information, the challenge of

reading the DNA sequence became central to biological research.

methods for DNA sequencing were extremely inefficient, laborious and costly.

1965 Holley: reliably sequencing the yeast gene for tRNAAla required the equivalent of a full year's work per person per base pair (bp) sequenced (1bp/person year).

1970 Two classical methods for sequencing DNA fragments by Sanger and Gilbert.

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Sanger sequencing (sketch):

a. The polymerase extends the labeled primer, randomly incorporating either:

a normal dCTP base, or

a modified ddCTP base

At every position where a ddCTP is inserted, polymerization terminates ! => a population of fragments, where the length of each fragment is a function of the relative distance from the modified base to the primer.

b. electrophoretic separation of the products of each of the four reaction tubes (ddG, ddA, ddT, and ddC), run in individual lanes. The bands on the gel represent the respective fragments (Labelled Strands) shown to the right => the complement of the original template (bottom to top)

Next Generation Sequencing Technologies

(1) A modified nucleotide is added to the complementary DNA strand by a DNA polymerase enzyme.

(2) A laser is used to obtain a read of the nucleotide just added.

(3) The full sequence of a fragment thus determined through successive iterations of the process.

(4) A visualization of the matrix where fragment clusters are attached to flow cells.

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Next Generation Sequencing

Picture from: A. Gritsenko, Scaffolding of next-generation sequencing assemblies using diverse information sources, MSc Thesis, Leiden, 2011

OverviewExact Pattern Matching, Aho-Corasick, Suffix Trees

Exact Matching

Search a pattern P in text T,

where P and T are strings

Knuth Morris Pratt

Preprocess pattern P

Aho Corasick

Pattern P is set of strings {P1,…,Pr}

Suffix Trees

Preprocess text T, where T can be a set of texts,

i.e., a database of texts

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Molecular Biology Databases

UniProt / SWISS-PROT

Genbank (NCBI, America)

EMBL (Europe)

DDBJ (Japan)

ENTREZ (NCBI)

Others

Sequence Databases

First molecular biology database:

Atlas of Protein Sequences (1965)

As a result of novel efficient DNA sequencing techniques:

Early 80’s three major nucleotide sequence databases

EMBL (European Molecular Biology Laboratory)

Genbank (America, National Institute of Health)

DDBJ (DNA DataBank of Japan)

See www.oxfordjournals.org/nar/database/c/ for an overview of a collection of 1552 Molecular Biology Databases

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Sequence Databases

The three major nucleotide sequence databases

EMBL (European)

Genbank (America)

DDBJ (Japan)

In 1988 a common (flat) file format was defined

Currently all sequences are exchanged on a daily basis between these three databases

Submission of a sequence entry at one of the databases

The database that ‘owns’ the submission will update the sequence in the remaining two databases

More than 300 000 organisms

Genbank Flat-file Format

an elementary unit of a sequence database

represents a single sequence entry

used to exchange data between the

databases

GenBank, EMBL, and DDBJ use flat-files

that have only small syntactic differences:

EMBL flat files have line-type prefixes at

every line

Etc. …

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Genbank Flat-file Format

The flat-file has the following global structure:

Header

Description of the record

Features

Annotations on the record

Sequence

The nucleotide sequence itself

Genbank Flat-file Format

Header

The description of the sequence such as names of organism, gene name, sequence length, keywords, literature reference data

Identifiers for searching Accession number ( www.ncbi.nlm.nih.gov/Sequin/acc.html )

• Combination of 1 letter and 5 or 2 letters and 6 digits (U12345, AF123456), indicating the ‘owner’ of nucleotide sequence.

• Does not change, even if the data in the record changes for example due to a sequence correction, etc.

• Version number is used in those cases U12345.1; U12345.2

GI number (GenInfo number)• Identifier unique for every sequence

• If a sequence changes a new GI-number is assigned

ORF numbers• For every open reading frame (ORF) encoded by a nucleotide

sequence a separate number is used.

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Genbank Flat-file Format

Features

Contain the relevant biological annotations of the sequence

Encoded proteins

RNA sequences

Signals such as binding sites

protein binding sites

mRNA

Exons

introns, etc.

Genbank Flat-file Format

Data fields linkage

Many data fields provide links to other databases such as

Pubmed literature database

Protein databases

Etc.

These are hard links between entries from different sequence databases that share the same data fields of the ENTREZ retrieval system

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ENTREZ Retrieval System

Maintained by the American National Center for Biotechnology Information (NCBI)

ENTREZ provides unified access to various databases and resources

Access to the primary database of nucleotide sequences

Access to curated databases RefSeq non-redundant reference sequences

ENTREZ Gene, gene-centered information

Etc.

NCBI ENTREZ (GQuery)http://www.ncbi.nlm.nih.gov/gquery/

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NCBIhttp://www.ncbi.nlm.nih.gov/

EMBLhttp://www.embl.org/

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EMBL-EBIhttp://www.ebi.ac.uk/

EMBL-EBIhttp://www.ebi.ac.uk/services

Programmatic access for processing pipelines.

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GenBank

http://www.ncbi.nlm.nih.gov/genbank/

Genbank Growthhttp://www.ncbi.nlm.nih.gov/genbank/statistics

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Dec. 2015:

203 939 111 071

DDBJ http://www.ddbj.nig.ac.jp/index-e.html

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DDBJ

SWISS-PROT (UniProtKB)

http://www.uniprot.org/

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SWISS-PROT

By University of Geneva and EMBL

Created in 1987, one of the oldest and most used protein sequence database

Access via:

Entrez (NCBI)

SRS (European Bioinformatics Institute (EBI))

It is a secondary database

Manually curated

Adds value to data available in primary nucleotide sequence database

Only very small portion of data is directly submitted to SWISS-PROT

SWISS-PROT

Data

Core Data

Sequence

Bibliography

Taxonomic information

Annotation

Protein function

Domains

Specific sites

Etc.

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SWISS-PROT

ENTREZ

ENTREZ Protein entries from

SWISS-PROT

Translations from coding regions in

GenBank, RefSeq

Hard links to relevant entries in the

other databases

Links to similar, neighboring protein

sequences

NCBI Example: Lactococcus lactis

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http://www.ncbi.nlm.nih.gov/gquery

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NCBI

Microbial Genomes

NCBI

Microbial Genomes

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NCBI

Microbial Genomes

Lactococcus lactis subsp. lactis KF147

Lactococcus lactis. This microbe is a member of the lactic acid bacteria and produces lactic acid from sugars. It is found in many environments including plant and animal habitats. Lactococcus lactis is used as a starter culture for the production of cheese products(such as cheddar) and in milk fermentations and, as such, is one of the most important microbes in the food industry. Many of the important functions for fermentation are encoded on the many conjugative plasmids these bacteria contain. The degradation of casein, acidification by lactic acid, and production of flavor compounds, processes that are caused by the bacteria, contribute to the final product.

Lactococcus lactis subsp. lactis KF147. This strain will be sequence for comparataive genome analysis.

Project ID: 42831

NCBI

Microbial Genomes

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NCBI

Microbial Genomes

NCBI

Microbial Genomes

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Ensembl http://www.ensembl.org/index.html

Ensembl

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Ensembl

Gorilla

http://www.uni-giessen.de/ecoli/IECA/index.php

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HOME WORK

Visit each of the following sites and get

an impression of the available tools and

data sources:

UniProt / SWISS-PROT

Genbank (NCBI, America)

EMBL (Europe)

DDBJ (Japan)

ENTREZ (NCBI)