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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
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
•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
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structure/function descriptors in JPD
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Data/information deluge
and
flavors of Bioinformatics
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
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
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
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
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
The “high throughputs...”
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Ancient Chinese
Hindu
Babylonian
Egyptian
Maya
Roman
Modern Arabic
Parallels that help us to see the problem better
MCMLVI
1956
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
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
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
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
dynamic studiesDocking
Protein-binding site 2-D
information (for search)
2D Contour map surface
matching
Ligand-binding site 2-D
information (for search )
SMS and Protein Dossier – Drug Target DB
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
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
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Parallels that help us to see the problem better
Sequence similarity search
Sequence alignment
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Parallels that help us to see the problem better
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
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Data/information deluge
and
flavors of Bioinformatics
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
1. Genomic sequencing2. Protein crystalization3. Synchrotron crystallography4. NMR5. Mass spectrometry6. Mutageneses experiments7. Screening8. Chemical synthesis
High Throughputs help increase a picture resolution:
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
1.What do we get?
2.A big puzzle with great many peaces!!!
High Throughputs help increase a picture resolution:
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
• 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
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Parallels that help us to see the problem better
From gene to functional protein
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Parallels that help us to see the problem better
Sequence alignment
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
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
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
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
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
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
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Simple alignment
Graphical presentation of alignment
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
|| | |
CGAAATCGCATCAGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
| | | | | | | |
CGAAATCGCATCAGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
| | |
CGAAATCGCATCAGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
|| | |||| | | |
CGAAATCGCATCAGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
| ||| | || ||
CGAAATCGCATCAGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
|| | | || ||
CGAAATCGCATCAGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
|| || || |
CGAAATCGCATCAGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
| | | |
CGAAATCGCATCAGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
||
CGAAATCGCATCAGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
|||||||||||||||||||||||||||||
CGAAATCGCATCAGCATACGATCGCATGC
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Alignment with “gaps”
Simple alignment does not always function
well:
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
|| | | | | | | | |
CGAAATCGCATCACGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
| | |
CGAAATCGCATCACGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
| | ||||| | | |
CGAAATCGCATCACGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
|| | | || ||
CGAAATCGCATCACGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
| ||| | | || ||
CGAAATCGCATCACGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
|| | || |
CGAAATCGCATCACGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
|| || | |
CGAAATCGCATCACGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
| | |
CGAAATCGCATCACGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
|| ||||||||||||||||
CGAAATCGCATCACGCATACGATCGCATGC
CGCTTCGGACGAAATCGCATCAGCATACGATCGCATGCCGGGCGGGATAAC
||||||||||||| |
CGAAATCGCATCACGCATACGATCGCATGC
In many cases where two sequences do not
“coincide/align” perfectly, it is necessary to
introduce “gaps”.
CGCTTCGGACGAAATCGCATCA-GCATACGATCGCATGCCGGGCGGGATAA
||||||||||||| ||||||||||||||||
CGAAATCGCATCACGCATACGATCGCATGC
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Parallels that help us to see the problem better
Structure elements
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
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?
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
STING Millennium Suite:
Analysing structure of proteins and their complexes -
Secondary
structure elements:
Helix
Turn
Sheet
Coil
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
STING Millennium Suite:
Analysing structure of proteins and their complexes -
Peptide bond and
other types of
“intimate” amino acid
contacts
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Analysing structure of
proteins and their
complexes -
STING Millennium
Suite:
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
STING Millennium Suite:
Analysing
structure of
proteins and
their
complexes -
“Proper”
structural
parameters:
dihedral angles and
Ramachandran plot
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
STING Millennium Suite:
Analysing structure of
proteins and their complexes
Types of “intimate” amino acid
contacts: Hydrogen Bonds
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Diamond STING Suite:
Analysing structure of
proteins and their complexes
Types of “intimate” amino acid
contacts: Hydrogen Bonds
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
STING Millennium Suite:
Analysing structure of
proteins and their complexes
“Proper” structural parameters:
dihedral angles and Ramachandran
plot
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Alpha Helix
Analysing structure of proteins and their complexes - SMS way
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Ramachandran Plot
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Collagen Helix
Analysing structure of proteins and their complexes - SMS way
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
1. antiparallela
C. Struttura a foglietto ripiegatoExtended sheet - antiparallel
Analysing structure of proteins and their complexes - SMS way
Goran Neshichhttp://www.cbi.cnptia.embrapa.brExtended sheet - parallel
Analysing structure of proteins and their complexes - SMS way
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Beta Turn
Analysing structure of proteins and their complexes - SMS way
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Type II turn
Analysing structure of proteins and their complexes - SMS way
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Parallels that help us to see the problem better
Protein types
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
1. mioglobin
2. flavodoxin 3. immunoglobulin lgG: domain CH2
Analysing structure of proteins and their complexes - SMS way
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
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
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
1. monomer: cartoon
2. monomer: van der Waals presentation
C. Tertiary structure
1. dimer
2. complex Zn2+ hexamer
D. Quaternary Structure
Globular Proteins
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Membrane protein secondary structure prediction
Integral membrane proteinsCitoplasmic side
External
protein
phpspholipidglycoprotein
glycolipid Extracellular cell
side
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
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
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Parallels that help us to see the problem better
Structure modelling
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
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).
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
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.
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Parallels that help us to see the problem better
Structure alignment
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Parallels that help us to see the problem better
Function modifiers: drugs
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Molecular Geometry and 3D
Matching•Formato PDB
•Definições de Superfície Molecular
•Pockets e Cavities
•Fingerprints
•Matching
•Docking
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
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
dynamic studiesDocking
Protein-binding site 2-D
information (for search)
2D Contour map surface
matching
Ligand-binding site 2-D
information (for search )
SMS and Protein Dossier – Drug Target DB
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Hundreds of targets
millions of compounds
Now…..
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Leaving Surface: below the hood...
Goran Neshichhttp://www.cbi.cnptia.embrapa.br
Structural Bioinformatics
Intermediary sequence - problem solved!
AKWHGGAFWPPH
WAAGAHWPHAQD
ARWHGGWPHAQE
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