proteins and their 3 d structure - bioinformatics laboratory · sms and protein dossier –drug...

Goran Neshichhttp://www.cbi.cnptia.embrapa.br

Proteins and their 3 D Structure

Goran Neshich

Embrapa Informática Agropecuária

Cidade Universitária - UNICAMP

Campinas, SP

Structural BioInformatics Laboratory: SBI


•Gene Anotation

•Gene Comparison

•Structure

Descriptors

•Function

Descriptors

•Gene Expression

Networks

•Proteomics

Sequence

Blast

Lexical

Structure

STING

Sintactic

Function

SMS

Semantic

Role

Microarray

Image

Analysing

Pragmatic

http://www.cbi.cnpia.embrapa.br

Bringing Genome Into Three Dimensions

Old protein map

Parallels that help us to see the problem better


Structure/function descriptors in JPD


Data/information deluge

and

flavors of Bioinformatics


Datalibrary – 2003 (february)

23.950.735 nucleotide sequences,37.486.732.136 bp

112 Published-complete genomes

590 Genomes being done

830.525 Protein Sequences

20.417 Protein Structures

5.300 Plasmodium falciparum genes, 23.000.000 bp

35.000 Genes in Homo sapiens,3.164.000.000 bp,

27936 genes in Xyllela fastidiosa,

2.519.802 Bases, 2775 proteins

10.000.000 Publications in PubMedline


Datalibrary – 2003 (October)

29,189,427 nucleotide sequences (~40 x 109 bp)

Published-complete genomes:

Virus: 1421; Archaea:16; Bacteria:135;

Eucariots: 9 +4 vertebrates+7 plants

590 Genomes being done

1,139,154 Protein Sequences

22,700 Protein Structures (PDB)

480 genes in Mycoplasma genitalium: 580,000 bp

35,000 Genes in Homo sapiens (3.164 x 109 bp)

27,936 genes in Xyllela fastidiosa,

2.519.802 Bases,

>10,000,000 Publications in PubMedline


The “high throughputs...”


Structural Bioinformatics

Ancient Chinese

Hindu

Babylonian

Egyptian

Maya

Roman

Modern Arabic


MCMLVI

1956


Onde atuamos?

Descritores de estruturaanotação

Sequenciamento

de GenomasGenômica

Estrutural

Interação proteína-ligante

(matching DB)

Mutational and

dynamic studiesDocking

Structural DB

Estrutura-Funcão

Livro da vida

Busca por novos efetores Drug Discovery

SMS and Protein Dossier – Drug Target DB


Final goal: complement Genome Track

Small molecules

Database

Fingerprint

Local PDB files

Fingerprint

Complete Genome

Sequence

Homology Modeling

Protein/Ligand interaction

(matching DB)

Mutational and


Protein-binding site 2-D

information (for search)

2D Contour map surface

matching

Ligand-binding site 2-D

information (for search )




1. Sequence similarity search

2. Sequence alignments

3. Structure alignment

4. Secondary structure prediction

5. Structure modeling (homology modeling)

6. Structure prediction (threding)

7. Characterization of structure

8. Relationship: sequence-structure-function

9. Function modifiers

10.Compiling the list of pairs: structure and its function

modifier




Sequence similarity search

Sequence alignment




AKWHGGAFWPPH

WAAGAHWPHAQD

http://www.cbi.cnpia.embrapa.br

Bringing Genome Into

Three Dimensions

How well function can be

inherited from similar

sequences?

Functional Genomics Milestone:

From sequence to function: desires and problems


Data/information deluge

and

flavors of Bioinformatics


1. Genomic sequencing2. Protein crystalization3. Synchrotron crystallography4. NMR5. Mass spectrometry6. Mutageneses experiments7. Screening8. Chemical synthesis

High Throughputs help increase a picture resolution:


1.What do we get?

2.A big puzzle with great many peaces!!!

High Throughputs help increase a picture resolution:


• Transcriptomics involves large-scale analysis of messenger RNAs (molecules

that are transcribed from active genes) to follow when, where, and under what conditions genes are expressed.

• Proteomics the study of protein expression and function—can bring

researchers closer than gene expression studies to what’s actually happening in

the cell.

• Structural genomics initiatives are being launched worldwide to generate the

3-D structures of one or more proteins from each protein family, thus offering clues to function and biological targets for drug design.

• Knockout studies are one experimental method for understanding the function

of DNA sequences and the proteins they encode. Researchers inactivate genes

in living organisms and monitor any changes that could reveal the function of specific genes.

• Comparative genomics—analyzing DNA sequence patterns of humans and

well-studied model organisms side-by-side—has become one of the most

powerful strategies for identifying human genes and interpreting their function.

Next Step in Genomics




From gene to functional protein




Sequence alignment


Scoring Matrices

T G A C

T 1 0 0 0

G 0 1 0 0

A 0 0 1 0

C 0 0 0 1

For DNA/RNA match=1, mismatch = 0

Instead of using points at match/mismatch, we may use

“scoring matrix”

“dotplot” is now converted into diagram of numbers and

best alignment corresponds to this diagonal with greatest

numerical value


A R N D C Q E G H I L K M F P S T W Y V

A 5 -2 -1 -2 -1 -1 -1 0 -2 -1 -2 -1 -1 -3 -1 1 0 -3 -2 0

R -2 7 -1 -2 -4 1 0 -3 0 -4 -3 3 -2 -3 -3 -1 -1 -3 -1 -3

N -1 -1 7 2 -2 0 0 0 1 -3 -4 0 -2 -4 -2 1 0 -4 -2 -3

D -2 -2 2 8 -4 0 2 -1 -1 -4 -4 -1 -4 -5 -1 0 -1 -5 -3 -4

C -1 -4 -2 -4 13 -3 -3 -3 -3 -2 -2 -3 -2 -2 -4 -1 -1 -5 -3 -4

Q -1 1 0 0 -3 7 2 -2 1 -3 -2 2 0 -4 -1 0 -1 -1 -1 -3

E -1 0 0 2 -3 2 6 -3 0 -4 -3 1 -2 -3 -1 -1 -1 -3 -2 -3

G 0 -3 0 -1 -3 -2 -3 8 -2 -4 -4 -2 -3 -4 -2 0 -2 -3 -3 -4

H -2 0 1 -1 -3 1 0 -2 10 -4 -3 0 -1 -1 -2 -1 -2 -3 2 -4

I -1 -4 -3 -4 -2 -3 -4 -4 -4 5 2 -3 2 0 -3 -3 -1 -3 -1 4

L -2 -3 -4 -4 -2 -2 -3 -4 -3 2 5 -3 3 1 -4 -3 -1 -2 -1 1

K -1 3 0 -1 -3 2 1 -2 0 -3 -3 6 -2 -4 -1 0 -1 -3 -2 -3

M -1 -2 -2 -4 -2 0 -2 -3 -1 2 3 -2 7 0 -3 -2 -1 -1 0 1

F -3 -3 -4 -5 -2 -4 -3 -4 -1 0 1 -4 0 8 -4 -3 -2 1 4 -1

P -1 -3 -2 -1 -4 -1 -1 -2 -2 -3 -4 -1 -3 -4 10 -1 -1 -4 -3 -3

S 1 -1 1 0 -1 0 -1 0 -1 -3 -3 0 -2 -3 -1 5 2 -4 -2 -2

T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 2 5 -3 -2 0

W -3 -3 -4 -5 -5 -1 -3 -3 -3 -3 -2 -3 -1 1 -4 -4 -3 15 2 -3

Y -2 -1 -2 -3 -3 -1 -2 -3 2 -1 -1 -2 0 4 -3 -2 -2 2 8 -1

V 0 -3 -3 -4 -1 -3 -3 -4 -4 4 1 -3 1 -1 -3 -2 0 -3 -1 5

A R N D C Q E G H I L K M F P S T W Y V

A 5 -2 -1 -2 -1 -1 -1 0 -2 -1 -2 -1 -1 -3 -1 1 0 -3 -2 0

R -2 7 -1 -2 -4 1 0 -3 0 -4 -3 3 -2 -3 -3 -1 -1 -3 -1 -3

N -1 -1 7 2 -2 0 0 0 1 -3 -4 0 -2 -4 -2 1 0 -4 -2 -3

D -2 -2 2 8 -4 0 2 -1 -1 -4 -4 -1 -4 -5 -1 0 -1 -5 -3 -4

C -1 -4 -2 -4 13 -3 -3 -3 -3 -2 -2 -3 -2 -2 -4 -1 -1 -5 -3 -4

Q -1 1 0 0 -3 7 2 -2 1 -3 -2 2 0 -4 -1 0 -1 -1 -1 -3

E -1 0 0 2 -3 2 6 -3 0 -4 -3 1 -2 -3 -1 -1 -1 -3 -2 -3

G 0 -3 0 -1 -3 -2 -3 8 -2 -4 -4 -2 -3 -4 -2 0 -2 -3 -3 -4

H -2 0 1 -1 -3 1 0 -2 10 -4 -3 0 -1 -1 -2 -1 -2 -3 2 -4

I -1 -4 -3 -4 -2 -3 -4 -4 -4 5 2 -3 2 0 -3 -3 -1 -3 -1 4

L -2 -3 -4 -4 -2 -2 -3 -4 -3 2 5 -3 3 1 -4 -3 -1 -2 -1 1

K -1 3 0 -1 -3 2 1 -2 0 -3 -3 6 -2 -4 -1 0 -1 -3 -2 -3

M -1 -2 -2 -4 -2 0 -2 -3 -1 2 3 -2 7 0 -3 -2 -1 -1 0 1

F -3 -3 -4 -5 -2 -4 -3 -4 -1 0 1 -4 0 8 -4 -3 -2 1 4 -1

P -1 -3 -2 -1 -4 -1 -1 -2 -2 -3 -4 -1 -3 -4 10 -1 -1 -4 -3 -3

S 1 -1 1 0 -1 0 -1 0 -1 -3 -3 0 -2 -3 -1 5 2 -4 -2 -2

T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 2 5 -3 -2 0

W -3 -3 -4 -5 -5 -1 -3 -3 -3 -3 -2 -3 -1 1 -4 -4 -3 15 2 -3

Y -2 -1 -2 -3 -3 -1 -2 -3 2 -1 -1 -2 0 4 -3 -2 -2 2 8 -1

V 0 -3 -3 -4 -1 -3 -3 -4 -4 4 1 -3 1 -1 -3 -2 0 -3 -1 5


Dotplot with scores

Two proteins aligned produce “score dotplot” from which

one can calculate optimal alignment

H E A G A W G H E E

P

A

W

H

E

A

E

H E A G A W G H E E

P -2 -1 -1 -2 -1 -4 -2 -2 -1 -1

A -2 -1 5 0 5 -3 0 -2 -1 -1

W -3 -3 -3 -3 -3 15 -3 -3 -3 -3

H 10 0 -2 -2 -2 -3 -2 10 0 0

E 0 6 -1 -3 -1 -3 -3 0 6 6

A -2 -1 5 0 5 -3 0 -2 -1 -1

E 0 6 -1 -3 -1 -3 -3 0 6 6


Simple alignment

Graphical presentation of alignment

CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC

|| | |

CGAAATCGCATCAGCATACGATCGCATGC


| | | | | | | |



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Alignment with “gaps”

Simple alignment does not always function

well:


|| | | | | | | | |

CGAAATCGCATCACGCATACGATCGCATGC


| | |



| | ||||| | | |



|| | | || ||



| ||| | | || ||



|| | || |



|| || | |



| | |



|| ||||||||||||||||



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In many cases where two sequences do not

“coincide/align” perfectly, it is necessary to

introduce “gaps”.

CGCTTCGGACGAAATCGCATCA-GCATACGATCGCATGCCGGGCGGGATAA

||||||||||||| ||||||||||||||||





Structure elements


STING Millennium Suite:

Analysing structure of proteins and their complexes -

What do we know about structure

and its relationship with function?

What are the building blocks of

microfactories, better known as

PROTEINS?

What is the structural hierarchi in

proteins?




Secondary

structure elements:

Helix

Turn

Sheet

Coil




Peptide bond and

other types of

“intimate” amino acid

contacts


Analysing structure of

proteins and their

complexes -

STING Millennium

Suite:



Analysing

structure of

proteins and

their

complexes -

“Proper”

structural

parameters:

dihedral angles and

Ramachandran plot




proteins and their complexes

Types of “intimate” amino acid

contacts: Hydrogen Bonds


Diamond STING Suite:



Types of “intimate” amino acid

contacts: Hydrogen Bonds





“Proper” structural parameters:

dihedral angles and Ramachandran

plot


Alpha Helix

Analysing structure of proteins and their complexes - SMS way


Ramachandran Plot


Collagen Helix



1. antiparallela

C. Struttura a foglietto ripiegatoExtended sheet - antiparallel


Goran Neshichhttp://www.cbi.cnptia.embrapa.brExtended sheet - parallel



Beta Turn



Type II turn





Protein types


1. mioglobin

2. flavodoxin 3. immunoglobulin lgG: domain CH2



Structural Proteins

1. 3-D presentation

2. Front view

C. Silk fiber

α-helix

superhelix

1. protofilamentA. α-Cheratin

3nm

10nm

1,5 nm

1. Triple Helix

2. Typical Sequence

3. Triple Helix (view from above)

Collagen


1. monomer: cartoon

2. monomer: van der Waals presentation

C. Tertiary structure

1. dimer

2. complex Zn2+ hexamer

D. Quaternary Structure

Globular Proteins


Membrane protein secondary structure prediction

Integral membrane proteinsCitoplasmic side

External

protein

phpspholipidglycoprotein

glycolipid Extracellular cell

side


Table 2. Hydrophobicity scale

by Kyte & Doolittle (1982)

(K-D) and by Goldman,

Engelman & Steitz

(Engelman et al., 1986)

(GES).

Residuo K-D GES

Ile 4.5 3.1

Val 4.2 2.6

Leu 3.8 2.8

Phe 2.8 3.7

Cys 2.5 2.0

Met 1.9 3.4

Ala 1,8 1.6

Tyr 1.3 -0.7

Gly -0.4 1.0

Thr -0.7 1.2

Ser -0.8 0.6

Trp -0.9 1.9

Pro -1.6 -0.2

His -3.2 -3.0

Asp -3.5 -9.2

Glu -3.5 -8.2

Asn -3.5 -4.8

Gln -3.5 -4.1

Lys -3.9 -8.8

Arg -4.5 -12.3




Structure modelling


Sequence-based fold

Recognition

50%

Probably non-globular

Protein

15%

As yet unobserved folds

20%

Full threading methods

15%

Figure 5 Hypothentical applicability of diferent categories of fold-recognition methods to the open

Reading Frames of small bacterial genomes. At present sequance-based fold recognition (e.g.

GenTHREADER) is successful for aroud 50% of the ORFs. Structures of a further 15% of ORFs can

probably be assigned. By full threading methods such as THREADER, and the reamaining 35%

cannot currently be recognized either because the fold has not yet observed, or because the ORF

encodes a non-globular protein (e.g. aTransmembrane protein).


Unannotated regionsPDB match region

Transmembrane or

Low complexity

region

Pie Chart of structural assignments to the proteome of the bacterium Mycoplasma genitalium. Almost

half of the amino acids (49%) in the Mycoplasma genitalium proteins have a structural annotation. In

this case, the structural anotation was taken from the SUPERFAMILY database(version 1.59,

September 2002), described in Section 11.3.2.Roughty one fifth of the proteome is predicted to be a

transmembrane helix or low complexity region by therelevant computer programs. The remaining 30%

of the proteome is unassigned.




Structure alignment




Function modifiers: drugs


Molecular Geometry and 3D

Matching•Formato PDB

•Definições de Superfície Molecular

•Pockets e Cavities

•Fingerprints

•Matching

•Docking


Final goal: complement Genome Track

Small molecules

Database

Fingerprint

Local PDB files

Fingerprint

Complete Genome

Sequence

Homology Modeling

Protein/Ligand interaction

(matching DB)

Mutational and


Protein-binding site 2-D

information (for search)

2D Contour map surface

matching

Ligand-binding site 2-D

information (for search )



Hundreds of targets

millions of compounds

Now…..


Leaving Surface: below the hood...



Intermediary sequence - problem solved!

AKWHGGAFWPPH

WAAGAHWPHAQD

ARWHGGWPHAQE

proteins and their 3 d structure - bioinformatics laboratory · sms and protein dossier –drug...

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