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A Física e a Biologia: A Física e a Biologia: Exemplos de InterdisciplinaridadeExemplos de Interdisciplinaridade
Paulo M. BischLaboratório de Física Biológica
Instituto de Biofísica Carlos Chagas FilhoUFRJ Rio de Janeiro
Genomics and Bioinformatics
Introduction
Genômica
Projetos Genoma
mais de 165 genomas já foram publicados, mais de 270 estão em processo de seqüenciamento
NCBI 2004
Para 40% a 60% das seqüencias não é possível identificar as funções celulares e/ou as funções moleculares a partir de análises baseadas em comparações de seqüências.
ANO
EMBL
Haemophilus influenzae Rd (1995)
Bioinformática
PostGenomicsPostGenomics
Functional Functional GenomicsGenomics
ProteomicsProteomics
Structural Structural GenomicsGenomics
DNAMicroarrayDNAMicroarray2D Gel Electrophoresis 2D Gel Electrophoresis
Mass Spectrometry Mass Spectrometry Protein SequencingProtein Sequencing
X RayX RayCrystallography Crystallography
NMRNMR
Sequence Form FunctionSequence Form Function
Genômica Funcional
Projetos Genoma
Projetos Proteoma
Projetos Transcriptoma
Projetos Genoma
Estrutural
Bioinformática
Systems biologyModel-driven research takes the approach that sets up a biological model by combining the knowledge of the system with related data and simulates the behavior of the system in order to understand the biological mechanism of the system. It is simply called, ‘ ‘ Systems Biology’ ’ .
Merits of systems biologyThere are some important features and merits of this approach. One of the aims is to take important knowledge in the form of qualitative biological theories and try to express this as explicitly and quantitatively as possible. Thus implicit knowledge can be transferred to become explicit knowledge and disparate human knowledge can accumulate in an integrated way.
Ultimate goal of systems biologyThe ultimate goal of this approach is to develop a ‘ ‘ Life Simulator’ ’ , which will be attained, step by step, hierarchically from subsystem simulators of subcellular mechanisms, whole cell simulators, cell development simulators, organ simulators, physiological simulators, pathological simulators to body simulators.
T. Yao / Progress in Biophysics & Molecular Biology 80 (2002) 23– 42
Molecular Modeling
Structural Genomics
StructuralStructural Biology:Biology:MethodsMethods
BiochemistryBiochemistryMolecular BiologyMolecular Biology
Cell BiologyCell BiologyMicroscopyMicroscopy
CrystallographyCrystallographyNMRNMR
SpectroscopySpectroscopyNucleic
acid
Carbohydrates
Lipids
Proteins
Vectors Parasites
VirusPrions
Amyloid
Immune SystemDefense
Macromolecular Assemblies
Drug DesignVaccines
BioinformaticsBioinformaticsMolecular Molecular ModelingModeling
Structural Genomics
Molecular ModelingMolecular Modeling
3D Graphical Representation
Structural Homology
Folding Prediction
Molecular Dynamics Simulations
Quantum Mechanics Calculations
DPPC bilayer simulation
Applications
Studying catalytic mechanism
Designing and improving ligands
Docking of macromolecules, prediction of protein partners
Virtual screening and docking of small ligands
Defining antibody epitopes
Supporting sitedirected mutagenesis
Functional relationships from structural similarity
Identifying patches of conserved surface residues
Finding functional sites by 3D motif searching
David Baker and Andrej Sali, Science 2001
CONSTRUCTING MODELSBY HOMOLOGY
TCR/ SUPERANTIGEN/MHC classe II COMPLEX
TCR Cw3/1.1 αChain
TCR Cw3/1.1 β Chain
Superantigen SEC2
MHC classe II complexed with a peptide
❒ Refining
Geometry Optimizationε = 80Cutoff; 20 ÅConvergence; 0,05 Kcal/mol
❒ Validation
PROCHECK program Ramachandran maps
Final Model for βTCR Cw3/1.1/SEC2 Complex
βTCR Cw3/1.1
SEC2
Mutational analysis and molecular modeling of the binding of s. Aureus enterotoxin c2 to a Murine t cell receptor vß10 chain. Shaila C. Rössle, Paulo M. Bisch, YuChun Lone, JeanPierre Abastado,Philippe Kourilsky and Maria BellioEuropean Journal of Immunology,32(8):21728. (2002 Aug)
Características do MHOLline
MHOLdb
Ambiente Computacional
MHOL3D
MHOLbrowser
MHOLline
MHOLdb
• acesso via Internet
• projetos individualizados
• projetos em larga escala (projetos genoma)
• possibilidade de acrescentar outros programas e outros bancos de dados
Interfaces gráficas
Construção do MHOLdb
Fold Recognition – Threader3
Secondary Structure – Predator
Transmembrane Regions – HMMTOP
Multiple sequence alignment – TCoffee
3D structure prediction using Comparative Modelling
MHOLbrowser MHOLline
MHOLdb
Web interface – MHOLbrowser
Program – MHOLline
Database – MHOLdb
MHOL3D project
Structural information about protein sequences
MHOLline
Processador Pentium IV / 1GHzProcessador Pentium IV / 1GHz10 minutos p/sequencia10 minutos p/sequenciaThreader 1HThreader 1H
File de entrada contém milhares File de entrada contém milhares de sequências.genomicas: de sequências.genomicas: Bacteria 5.0000Bacteria 5.0000Protozoario 20.000,Protozoario 20.000,Humano >> 30.000)Humano >> 30.000)
Trabalho pode ser distribuidoTrabalho pode ser distribuidop/programa e/ou p/sequênciap/programa e/ou p/sequência
Cada sequencia gera > 300 MbCada sequencia gera > 300 MbAramzenamento x TransmissãoAramzenamento x Transmissão
Related Projects
• Structural Genomic Workflows supported by Web Services– Maria Cláudia Cavalcanti, Fernanda Baião, Shaila C. Rössle, Paulo M. Bisch,
Rafael Targino, Paulo F. Pires, Maria Luiza Campos e Marta Mattoso
• Parallelism in Bioinformatics Workflows– Luiz A.V.C. Meyer, Shaila C. Rössle, Paulo M. Bisch, e Marta Mattoso
• Data Management via Web Services in Bioinformatics Workflows– Fabricio Teixeira, Fernanda Baião, Maria Cláudia Cavalcanti, Luiz A.V.C. Meyer,
Shaila C. Rössle, Paulo M. Bisch, e Marta Mattoso
COPPE – Sistemas
Molecular Modeling
Molecular Dynamics
Molecular ModelingMolecular ModelingThor Program: Molecular Mechanics Force Field
Molecular Geometry Optimization: Step Descendent and Conjugated Gradients Stochastic Simulated Annealing
Molecular Dynamics: Effective Medium
Interfaces and Membranes
MD Explicit Solvent Simulations (N,V,T) and (N,P,V) Ensembles Periodic Boundary Conditions Stochastic Boundaries
Force FieldForce Field
V({ri}) = V(r1,r2,...,rNat) =
Kb (b b0)2 + Kθ(θ θ0)2 + Kξ(ξ ξ0)2
+ Kϕ [1 + cos(nϕ δ)] + 4 ∈ IJ [(σIJ/r IJ)12 (σIJ /rIJ )6]
+ qiqj/(4πε0εrIJ)
Hydrogen bonds
Molecular Dynamics Simulations miai = FI = ∂V({ri})/∂ri
ai = dvi/dtvi = dri/dt
i = 1, Nat
Summed Verlet (leapfrog) Algorithm :
ri(t + δt) = ri(t) + vi(t + δt/2) δt
vi(t + δt/2) = vi(t δt/2) + ai (t) δt
δt << 1
Temperature: T0
3Nat KBT= <∑i = 1, Nat{mi(vi)2}>Nsteps
vi(t + δt/2) = vi(t δt/2)[(T0/T)1/2]
Total energy :
Etot = Ekin({vi}) + V({ri}) Ekin({vi})=∑i = 1, Nat{(1/2)mi(vi)2}
Molecular Dynamics
Etot = Ecin({vi}) + V({ri}) Ecin({vi})=∑i = 1, Nat{(1/2)mi(vi)2}
Temperature: T0
3Nat KBT= <∑i = 1, Nat{mi(vi)2}>
vi(t + δt/2)=vi(t δt/2)[(T0/T)1/2]
Explicit Representation of the Solvent
Periodic Boundary Conditions
S o necess rios o QuickTime e ヒ ヌum descompressor Cinepak para ver
esta figura.
Simulação de Dinâmica Molecular daInteração do Peptídeo de Fusão doVirus VSV com uma MembranaLipídica:
Sistema Simulado:126 moléculas de DMPS 1 trímero (60 aminoácidos)131 Na+, 5.0000 moléculas de água.Ao todo, 28988 átomos30.0000 passos (passo de tempo de 0,5 fs) = 250 ps
Athlon XP 1500 MHz / 512 MbDuração de processamento = 24 horas
Pedro LoureiroPedro LoureiroPedro G. PascuttiPedro G. PascuttiFabiana A. CarneiroFabiana A. Carneiro Andrea da PoainAndrea da Poain
CISTEINO PROTEINASES
Papain cisteinoproteinase from Carica Papaya latex
Cruzain and cruzipain 2 cisteino proteinase (isoforms) from Tripanosoma cruzi
Falcipain cisteinoproteinase from Plasmodium falciparum (Malaria)
Papain: complex papainleupeptine (PDB: 1POP 2.1 Å resolution)
Cys25 Oxidize (PDB: 9PAP 1.65 Å resolution)
Cruzain: with inhibitors
(1AIM e 2AIM 2.0 Å e 2.2 Å resolution)
Cruzipain 2 and falcipain: homology models
Structures
PAPAIN
Catalytic Triad : Cys25, His159 e Asn175.
Gln19 and Asp158 represented in yellow and orange respectively.
Crystallographic structure by XRay. Resolution 1.65 Å (PDB: 9PAP).
SURFACE ACCESSIBLE to the SOLVENT (SAS)(dielectric constant inside the protein εp = 2 ; solvent dielectric constant εs = 80)
Surface generated according to the Connolly algorithm (5 points/Å2; Spherical probe R = 1.4 Å) Red: usual SAS; Green: modified SAS in the catalytic region.
13.4272.716 (SG)
10.448 (ND1) 28.427
(εp = 2 ; εs = 80 ; I = 0.150 M)
PAPAIN Catalytic Region (9PAP)Electrostatic Potential
Kcal/mol
Laurent E. Dardenne, Araken S. Werneck, Marçal de Oliveira Neto and Paulo M. Bisch, Electrostatic Properties in the Catalytic Site of Papain: A Possible Regulatory Mechanism for the Reactivity of the Ion Pair, PROTEINS: Structure, Function and Genetics, 52 (2), 236253 (2003)
Molecular Dynamics and High Throughput Docking
of Bioactive Molecules (GIGA Application)
FunctionalExperiments
MHOLdbStructure
Function relation
Models for MolecularDynamics Simulations
Molecular Target
Databank
Annotation of biological function by structural
similarity
DOCKTHOR
Ligands Databank
THOR
Drug/VaccinePrototypes
Structural information for protein “folding” studies
Using MHOLdb
A B C D E
F G H I J
KGluconacetobacter diazotrophicus proteins identified by mass spectrometry(Leticia Lery). GenBank sequences were blasted against PDB to look for structure similarity. Initial molecular models were built for : A) ModA B) Nitrogenase Reductase C) NifS D) FixA E) GlnK1 F) Alpha Subunit Dinitrogenase G) Kinase E H) Gluthamine Synthetase I) ModC J) NifN K) FdxN
Francisco J. P. Lopes
Orientadores
Paulo Mascarello Bisch
Fernando de M. C. Vieira
Redes Complexas de Reações Aplicadas ao ao Desenvolvimento Embrionário da
Drosophila melanogaster
Instituto de Biofísica Carlos Chagas Filho UFRJ
Colaboração Carlos E. V. Alonso
John Reinitz, Eric Mjolsness and David H. Charp.
Gradiente de Bicoid: Proteína que regula a expressão dos genes gap.
Início do desenvolvimento embrionário.
Padrão de Expressão
Padrão de expressão: Concentração das proteína dos genes gap: hb, kni, Kr e gt.
Concentração da proteína hunchback.
hbgt
gtKr
kni
hb
Modelo para Sistema de Dois Sítios
BB
h
B1
B2
B12
B11
B22
B112
B122
B2222
Estados Possíveis de Ocupação
Bicoid
DNA
S tios de liga o de Bicoidメ ヘヒna regi o regulat rio do ヒ ラ hb
S tio 1メ S tio 2メ
Ocupação Total.Azul: A1; Vermelho: X1
A1: Dímero; X1: Dímero
][h][B]B[][B][B s tioメ do totalocupa oヘヒ
0
122112111 +++=
Dinâmica de Ocupação
A1: Vazio; X1: Dímero
Resultados da Literatura sobre aAutoregulação do hunchback
(i) O hunchback se autoativa*(ii) A ativação de P1 é independente da
ativação de P2.(iii) P1 e P2 são responsáveis pela faixa
do centro.(iv) A faixa do centro é formada por
autoregulação.
Ausência de autoregulação*
HipóteseExistem, no mínimo, 2 sítios independentes para a auto
regulação.
P1P2
* Margolis et al, Development 121, 1995.
Modelo de Autoregulação para o hunchback
Rede de AutoregulaçãoHipóteseExistem, no mínimo, 2 sítios independentes para a auto
regulação.
H: Molécula da proteína Hunchback h: Gene hunchbackB: Molécula da proteína Bicoid0: Espécie inerte
Análise do Modelo de Autoregulação
B(t)B(t)
H(t)H(t)