Transcript Uniikki kuitu

Helsinki University of Technology
DNA, RNA, Protein Structure Prediction
Laura Pombo
Laboratory of Computational Engineering
Helsinki University of Technology
1
Helsinki University of Technology

INTRODUCTION:
Bioinformatics
DNA
RNA
Proteins
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
2
Helsinki University of Technology
BIOINFORMATICS



Bioinformatics involves the integration of computers,
software tools, and databases in an effort to address
biological questions. Bioinformatics approaches are
often used for major initiatives that generate large
data sets.
Two important large-scale activities that use
bioinformatics are genomics and proteomics.
Genomics refers to the analysis of genomes.
– A genome can be thought of as the complete set of DNA sequences
that codes for the hereditary material that is passed on from
generation to generation.
– Thus, genomics refers to the sequencing and analysis of all of these
genomic entities, including genes and transcripts, in an organism.
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
3
Helsinki University of Technology
Bioinformatics, continue …

Proteomics, on the other hand, refers to the analysis of the
complete set of proteins or proteome.

In addition to genomics and proteomics, there are many more
areas of biology where bioinformatics is being applied (i.e.,
metabolomics, transcriptomics). Each of these important
areas in bioinformatics aims to understand complex
biological systems. Many scientists today refer to the next
wave in bioinformatics as systems biology, an approach to tackle
new and complex biological questions. Systems biology involves
the integration of genomics, proteomics, and bioinformatics
information to create a whole system view of a biological entity.
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
4
Helsinki University of Technology
Bioinformatics
http://www.bioinformatics.ubc.ca/
Para ver esta película, debe
disponer de QuickTime™ y de
un descompresor TIFF (sin comprimir).
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
5
Helsinki University of Technology
Central Dogma

DNA

RNA

Protein
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
6
Helsinki University of Technology
DNA to RNA
Portions of DNA Sequence Are Transcribed into RNA
 The first step of a cell is to copy a particular portion of its DNA
nucleotide sequence ( =gene)
 Similarities:
– DNA and RNA is a linear polymer made of four different types of
nucleotide subunits linked together by phosphodiester bonds
– DNA and RNA contains the bases adenine (A), guanine (G) and
cytosine (C)

Differences:
– In RNA the nucleotides are ribonucleotides (=contain the sugar ribose)
– RNA contains uracil (U) instead of the thymine (T)
My summary from the book: Molecular Biology of THE CELL (Bruce Alberts, et al.)
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
7
Helsinki University of Technology
Different RNAs

mRNAs
– (messenger RNAs), code for proteins

rRNAs
– (ribosomal RNAs), form the basic structure of the ribosome and catalyze protein
synthesis

tRNAs
– (transfer RNA), central to protein synthesis as adaptors between mRNA and
amino acids

snRNAs
– (small nuclear RNAs), function in a variety of nuclear processes, including the
splicing of pre-Mrna

snoRNAs
– (small nucleolar RNAs), used to process and chemically modify rRNAs

Other noncoding RNAs
– function in diverse cellular processes, including telomere synthesis, Xchromosome inactivation and the transport of proteins into te ER
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
8
Helsinki University of Technology
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
9
Helsinki University of Technology
RNA structure prediction
Para v er esta pelíc ula, debe
dis poner de Quic kTime™ y de
un desc ompres or TIFF (s in compr imir ).
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
Para ver esta película, debe
disponer de QuickTime™ y de
un descompresor TIFF (sin comprimir).
10
Helsinki University of Technology
http://gibk26.bse.kyutech.ac.jp/jouhou/image/dna-protein/all/N3utr.gif
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
11
Helsinki University of Technology

RNA is transcribed (or synthesized) in cells as single strands
of (ribose) nucleic acids. However, these sequences are not
simply long strands of nucleotides. Rather, intra-strand base
pairing will produce structures.

In RNA, guanine and cytosine pair (GC) by forming a triple
hydrogen bond, and adenine and uracil pair (AU) by a double
hydrogen bond; additionally, guanine and uracil can form a
single hydrogen bond base pair.
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
12
Helsinki University of Technology
RNA structure prediction

Vienna RNA (PackageRNA Secondary Structure Prediction and
Comparison)
http://www.tbi.univie.ac.at/~ivo/RNA/
including a few precompiled binaries for download
http://www.tbi.univie.ac.at/~ivo/RNA/windoze/ [under Windows]

The Vienna RNA Package consists of a C code library and several standalone programs for the prediction and comparison of RNA secondary
structures.
– RNA secondary structure prediction through energy minimization is the most used
function in the package.
– The program provides three kinds of dynamic programming algorithms for structure
prediction:
» the minimum free energy algorithm of (Zuker & Stiegler 1981) which yields a single optimal
structure,
» the partition function algorithm of (McCaskill 1990) which calculates base pair probabilities in the
thermodynamic ensemble, and
» the suboptimal folding algorithm of (Wuchty et.al 1999) which generates all suboptimal structures
within a given energy range of the optimal energy.
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
13
Helsinki University of Technology
RNAFOLD tool

RNAfold reads RNA sequences from stdin and calculates
their minimum free energy (mfe) structure, partition function
(pf) and base pairing probability matrix. It returns the mfe
structure in bracket notation, its energy, the free energy of the
thermodynamic ensemble and the frequency of the mfe
structure in the ensemble to stdout. It also produces PostScript
files with plots of the resulting secondary structure graph and a
"dot plot" of the base pairing matrix. The dot plot shows a
matrix of squares with area proportional to the pairing
probability in the upper half, and one square for each pair in
the minimum free energy structure in the lower half
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
14
Helsinki University of Technology
ALIDOT program

Detecting Conserved RNA Structures The program alidot
is designed to detect conserved RNA secondary structures in
small data sets of related RNA sequences. The method is a
combination of structure prediction and comparative sequence
alignment.
http://www.tbi.univie.ac.at/~ivo/RNA/ALIDOT/
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
15
Helsinki University of Technology
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
16
Helsinki University of Technology
http://images.google.com/imgres?imgurl=http://images.clinicaltools.com/images/gene/dna_versus_rna_reversed.jpg&imgrefurl=http://www.geneticsolutions.c
om/PageReq%3Fid%3D1530:1873&h=461&w=405&sz=135&tbnid=R7LVIZO4g6cJ:&tbnh=125&tbnw=109&hl=en&start=2&prev=/images%3Fq%3DDNA%2B
to%2BRNA%26svnum%3D10%26hl%3Den%26lr%3D%26sa%3DG
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
17
Helsinki University of Technology
DNA structure prediction
Para ver es ta película, debe
disponer de Quic kTime™ y de
un desc ompresor TIFF ( sin comprimir).
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
Para ver esta película, debe
disponer de QuickTime™ y de
un descompresor TIFF (sin comprimir).
18
Helsinki University of Technology
MEME

MEME is a tool for discovering motifs in a group of related
DNA or protein sequences. A motif is a sequence pattern that
occurs repeatedly in a group of related protein or DNA
sequences.
– MEME represents motifs as position-dependent letter-probability matrices
which describe the probability of each possible letter at each position in the
pattern.
– Individual MEME motifs do not contain gaps. Patterns with variable-length gaps
are split by MEME into two or more separate motifs. MEME takes as input a
group of DNA or protein sequences (the training set) and outputs as many
motifs as requested.
– MEME uses statistical modeling techniques to automatically choose the best
width, number of occurrences, and description for each motif.
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
19
Helsinki University of Technology
DNA structure prediction
Other similar programs:
Cassandra
http://www-hto.usc.edu/software/procrustes/cassandra/cass_frm.html
DNA Sequence Translation
GENEID which predicts Gene Structure in Query Sequences
(US)
GRAIL, GenHunt, Censor, Pythia, Entrez, Beauty, etc.
You should have a look in: http://restools.sdsc.edu/biotools/biotools16.html
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
20
Helsinki University of Technology
PROTEIN

Protein: A large molecule composed of one or more chains of amino acids in a
specific order determined by the base sequence of nucleotides in the DNA coding
for the protein.

Proteins are required for the structure, function, and regulation of the body's cells,
tissues, and organs. Each protein has unique functions. Proteins are essential
components of muscles, skin, bones and the body as a whole.

Protein is one of the three types of nutrients used as energy sources by the body,
the other two being carbohydrate and fat. Proteins and carbohydrates each provide
4 calories of energy per gram, while fats produce 9 calories per gram.

The word "protein" was introduced into science by the great Swedish physician
and chemist Jöns Jacob Berzelius (1779-1848) who also determined the atomic and
molecular weights of thousands of substances, discovered several elements
including selenium, first isolated silicon and titanium, and created the present
system of writing chemical symbols and reactions.
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
21
Helsinki University of Technology
Tools for PROTEIN Structure Prediction: ExPASy



The ExPASy (Expert Protein Analysis System) proteomics server from the Swiss
Institute of Bioinformatics (SIB) is dedicated to molecular biology with an emphasis
on data relevant to proteins.It allows you to browse through a number of databases
produced in Geneva, such as Swiss-Prot, PROSITE, SWISS-2DPAGE, SWISS3DIMAGE, ENZYME, as well as other cross-referenced databases (such as
EMBL/GenBank/DDBJ, OMIM, Medline, FlyBase, ProDom, SGD, SubtiList, etc).
It also allows access to many analytical tools for the identification of proteins, the
analysis of their sequence and the prediction of their tertiary structure. ExPASy also
offers you many documents relevant to these field of research and you will find from
the servers, links to most relevant sources of information across the Web.Swiss2DService is a non-profit 2-D PAGE service to the scientific community
ExPASy was created in August 1993, it was one of the first WWW servers for
biological sciences. Since that date it has undergone constant modifications and
improvements.
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
22
Helsinki University of Technology
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
23
Helsinki University of Technology
Para ver esta película, debe
disponer de QuickTime™ y de
un descompresor TIFF (sin comprimir).
Para ver esta película, debe
disponer de QuickTime™ y de
un descompresor TIFF (sin comprimir).
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
24
Helsinki University of Technology
PROSITE Database of protein families and domains

PROSITE is a database of protein families and domains.

It consists of biologically significant sites, patterns and profiles that help to reliably
identify to which known protein family (if any) a new sequence belongs.

It is based on the observation that, while there is a huge number of different
proteins, most of them can be grouped, on the basis of similarities in their
sequences, into a limited number of families.

Proteins or protein domains belonging to a particular family generally share
functional attributes and are derived from a common ancestor.It is apparent, when
studying protein sequence families, that some regions have been better conserved
than others during evolution.
http://au.expasy.org/prosite/prosite_details.html
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
25
Helsinki University of Technology

These regions are generally important for the function of a protein and/or for the
maintenance of its three- dimensional structure. By analyzing the constant and
variable properties of such groups of similar sequences, it is possible to derive a
signature for a protein family or domain, which distinguishes its members from all
other unrelated proteins.

A pertinent analogy is the use of fingerprints by the police for identification
purposes. A fingerprint is generally sufficient to identify a given individual. Similarly,
a protein signature can be used to assign a newly sequenced protein to a specific
family of proteins and thus to formulate hypotheses about its function.

PROSITE currently contains patterns and profiles specific for more than a thousand
protein families or domains. Each of these signatures comes with documentation
providing background information on the structure and function of these proteins.
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
26
Helsinki University of Technology
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
27
Helsinki University of Technology
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
28
Helsinki University of Technology
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
29
Helsinki University of Technology
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
30
Helsinki University of Technology
(Protein) Structure Prediction
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
31
Helsinki University of Technology
Experimental Data





Disulphide bonds
Spectroscopic data
Site directed mutagenesis studies
Knowledge of proteolytic cleavage sites
Etc.
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
32
Helsinki University of Technology
Protein sequence data

Is your protein a transmembrane protein, or does it contain
transmembrane segments? Methods for predicting these
segments:
–
–
–
–
–

TMAP (EMBL)
PredictProtein (EMBL/Columbia)
TMHMM (CBS, Denmark)
TMpred (Baylor College)
DAS (Stockholm)
Does your protein contain coiled-coils? Methods:
– At COILS server, the COILS program

Does your protein contain regions of low complexity?
Methods:
– the program SEG
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
33
Helsinki University of Technology
Sequence database searching 1/2

Comparisons with sequence databases to find homologues, methods:
– the BLAST suite of programs.
– National Center for Biotechnology Information (USA) Searches
– European Bioinformatics Institute (UK) Searches
– BLAST search through SBASE (domain database; ICGEB, Trieste)
– Other methods for comparing a single sequence to a database include:
» The FASTA suite (William Pearson, University of Virginia, USA)
» SCANPS (Geoff Barton, European Bioinformatics Institute, UK)
» BLITZ (Compugen's fast Smith Waterman search)

Multiple sequence information
– building a profile from some kind of multiple sequence alignment.
Methods:
» PSI-BLAST (NCBI, Washington)
» ProfileScan Server (ISREC, Geneva)
» HMMER Hidden Markov Model searching (Sean Eddy, Washington
University)
» Wise package (Ewan Birney, Sanger Centre; this is for protein versus DNA
comparisons)
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
34
Helsinki University of Technology
Sequence database searching 2/2

Incorporating multiple sequence information – a MOTIF.
– describes the key residues that are conserved and define the family.
– Sometimes this is called a "signature". For example, "H-[FW]-x-[LIVM]x-G-x(5)-[LV]-H-x(3)-[DE]" describes a family of DNA binding proteins.
Methods:
» PROSITE, ExPASy
» EBI

Pre-prepared protein alignments, databases:
– SMART (Oxford/EMBL)
– PFAM (Sanger Centre/Wash-U/Karolinska Intitutet)
– COGS (NCBI)
– PRINTS (UCL/Manchester)
– BLOCKS (Fred Hutchinson Cancer Research Centre, Seatle)
– SBASE (ICGEB, Trieste)
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
35
Helsinki University of Technology
Multiple Sequence Alignment

Some methods and tools:
– EBI (UK) Clustalw Server
– IBCP (France) Multalin Server
– IBCP (France) Clustalw Server
– IBCP (France) Combined Multalin/Clustalw
– MSA (USA) Server
– BCM Multiple Sequence Alignment ClustalW Sever (USA)

Alignments can provide:
– Information as to protein domain structure
– The location of residues likely to be involved in protein function
– Information of residues likely to be buried in the protein core or exposed to solvent
– More information than a single sequence for applications like homology modelling and
secondary structure prediction.
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
36
Helsinki University of Technology
Secondary Structure Prediction methods and links

Methods and tools:
– PSI-pred (PSI-BLAST profiles used for prediction; David Jones, Warwick)
– JPRED Consensus prediction (includes many of the methods given below; Cuff & Barton,
EBI)
– DSC King & Sternberg (this server)
– PREDATORFrischman & Argos (EMBL)
– PHD home page Rost & Sander, EMBL, Germany
– ZPRED server Zvelebil et al., Ludwig, U.K.
– nnPredict Cohen et al., UCSF, USA.
– BMERC PSA Server Boston University, USA
– SSP (Nearest-neighbor) Solovyev and Salamov, Baylor College, USA.

If no homologue of known structure from which to make a 3D model
– to predict secondary structure to provide the location of alpha helices, and beta strands
within a protein or protein family.
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
37
Helsinki University of Technology
Fold recognition methods and links 1/2


Methods:
– 3D-pssm (this server)
– TOPITS (EMBL)
– UCLA-DOE Structre Prediction Server (UCLA)
– 123D
– UCSC HMM (UCSC)
– FAS (Burnham Institute)
– THREADER(Warwick)
– ProFIT CAME (Salzburg)
Even with no homologue of known 3D structure, it may be possible to find a suitable fold for
your protein among known 3D structures by way of fold recognition methods. Prediction of
protein 3D structures is not possible at present,
– and a general solution to the protein folding problem is not likely to be found in the near
future.
– However, it has long been recognised that proteins often adopt similar folds despite no
significant sequence or functional similarity
– There are numerous protein structure classifications now available via the WWW:
»
»
»
»
»
»
SCOP (MRC Cambridge)
CATH (University College, London)
FSSP (EBI, Cambridge)
3 Dee (EBI, Cambridge)
HOMSTRAD (Biochemistry, Cambridge)
VAST (NCBI, USA)
DNA, RNA, Protein Structure Prediction 23.11.2005
 [email protected]
The goal of fold recognition
38
Helsinki University of Technology
Fold recognition methods and links 2/2

The goal of fold recognition

the best alignment of sequence on to tertiary
structure is still likely to come from human
intervention.
– detect similarities between protein 3D structure that are not
accompanied by any significant sequence similarity.
– to find folds that are compatible with a particular sequence.
– 3D structure information to predict how well a fold will fit a
sequence.
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
39
Helsinki University of Technology
Analysis of protein folds and alignment of secondary structure
elements

to which fold your protein belongs, methods:
–
–
–
–
–
–

SCOP (MRC Cambridge)
CATH (University College, London)
FSSP (EBI, Cambridge)
3 Dee (EBI, Cambridge)
HOMSTRAD (Biochemistry, Cambridge)
VAST (NCBI, USA)
If there is any functional similarity between your protein and any members
of the fold, then you may be able to back up your prediction of fold
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
40
Helsinki University of Technology
Alignment of sequence to tertiary structure

Starting with the alignment from the fold recognition method,
and considering the alignment of secondary structures.

Proteins having similar three-dimensional structures with little
or no sequence similarity can differ substantial with respect to
the finer details of their structures (i.e. loops, precise
orientation of side chains, orientation of secondary structures,
etc.).
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
41
Helsinki University of Technology
Comparative or Homology Modelling

If significant homology to another protein of known three-dimensional structure,
– model of your protein 3D structure can be obtained via homology modelling.

To build models, if you have found a suitable fold via fold recognition
– to generate models automatically using the very useful SWISSMODEL server;
» WHAT IF (G. Vriend, EMBL, Heidelberg)
» MODELLER (A. Sali, Rockefeller University)
» MODELLER Mirror FTP site

Once you have a three-dimensional model, it is useful to look at protein 3D
structures: methods:
– GRASP Anthony Nicholls, Columbia, USA.
– MolMol Reto Koradi, ETH, Zurrich, C.H.
– Prepi Suhail Islam, ICRF, U.K.
– RasMol Roger Sayle, Glaxo, U.K.
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
42
Helsinki University of Technology
DNA, RNA, Protein Structure Prediction 23.11.2005
[email protected]
43