Post on 18-Jul-2020
Biomolecular Modeling, Classical Force-Field,
and Molecular Dynamic Simulations
Pradip K. Biswas
Department of PhysicsTougaloo College
Globalization of Physics!
Subatomicphysics
At. Molecularphysics BiologyChemistry
Physics &Engineering
Mysteries of Early Universe.
Matter-antimatter symmetry breaking and life with matter only.
Mysteries of Early Universe.
Matter-antimatter symmetry breaking and life with matter only.
Amino acids which chain together to form proteins can exist in two isoforms: L-Chiral & D-Chiral.
Interestingly, all natural proteins are made of L-Chiral amino acids only.
D-chiral amino acids are not part of life; we can find them in outer space and in our synthesis of proteins.
Mysteries of Early Universe.
Matter-antimatter symmetry breaking and life with matter only.
Amino acids which chain together to form proteins can exist in two isoforms: L-Chiral & D-Chiral.
Interestingly, all natural proteins are made of L-Chiral amino acids only.
D-chiral amino acids are not part of life; we can find them in outer space and in our synthesis of proteins.
Origin of DNA is still unknown and unexplained.
Mysteries of Early Universe.
Matter-antimatter symmetry breaking and life with matter only.
Amino acids which chain together to form proteins can exist in two isoforms: L-Chiral & D-Chiral.
Interestingly, all natural proteins are made of L-Chiral amino acids only.
D-chiral amino acids are not part of life; we can find them in outer space and in our synthesis of proteins.
Origin of DNA is still unknown and unexplained.
Proteins, the nano-robots and the main work force in our body, still pose a big challenge as we want to understand their various mechanism.
How do they work? Relevance of Protein Conformation
• Like our hand, proteins change their shape to do different work
• Like our hand, each protein can only assume certain shapes - and thus can only do certain works
• Knowing the conformations of a protein is thus important in order to intervene with their reactivity
• Simulations are the major tool to explore protein conformations and find ways to intervene with their reactivity (??? why ???)
Introduction to Biomolecular Modeling and their purpose
• It starts with an atomistic representation of biological supramolecular systems: proteins, lipids, cholesterols, DNA....
• Visualization tools such as VMD, Swiss-PDB-Viwer, Pymol, SyByL, etc to visualize and analyze their structures
• Quantum, Classical, or Semi-Empirical models/rules to represent the interaction between atoms in the molecule
• Computer code to simulate the time evolution of the system according to the models/rules; (Assumption: statistical ensemble averages are equal to time average)
• Predict reactivity, new conformations, new structures, design new molecules.....
What is inside a Protein?How does it look like?
•Amino Acids (building block of protein)
•Peptide Bonds:
•Protein Primary Structure: Chain of amino acids.
•Protein Secondary Structures: α-helix and β-sheet.
•Protein Tertiary Structure: 3-D Folded Protein.
What is inside a Protein?How does it look like?
•Amino Acids (building block of protein)
•Peptide Bonds:
•Protein Primary Structure: Chain of amino acids.
•Protein Secondary Structures: α-helix and β-sheet.
•Protein Tertiary Structure: 3-D Folded Protein.
What is inside a Protein?How does it look like?
•Amino Acids (building block of protein)
•Peptide Bonds:
•Protein Primary Structure: Chain of amino acids.
•Protein Secondary Structures: α-helix and β-sheet.
•Protein Tertiary Structure: 3-D Folded Protein.
What is inside a Protein?How does it look like?
•Amino Acids (building block of protein)
•Peptide Bonds:
•Protein Primary Structure: Chain of amino acids.
•Protein Secondary Structures: α-helix and β-sheet.
•Protein Tertiary Structure: 3-D Folded Protein.
What is inside a Protein?How does it look like?
•Amino Acids (building block of protein)
•Peptide Bonds:
•Protein Primary Structure: Chain of amino acids.
•Protein Secondary Structures: α-helix and β-sheet.
•Protein Tertiary Structure: 3-D Folded Protein.
What is inside a Protein?How does it look like?
•Amino Acids (building block of protein)
•Peptide Bonds:
•Protein Primary Structure: Chain of amino acids.
•Protein Secondary Structures: α-helix and β-sheet.
•Protein Tertiary Structure: 3-D Folded Protein.
Protein structural information using X-ray Crystallography
Important Steps
Protein structural information using X-ray Crystallography
• Purification of proteins (Biochemical Process)
Important Steps
Protein structural information using X-ray Crystallography
• Purification of proteins (Biochemical Process)
• Crystallization of proteins (Biochemical Process)
Important Steps
Protein structural information using X-ray Crystallography
• Purification of proteins (Biochemical Process)
• Crystallization of proteins (Biochemical Process)
• X-ray diffraction from crystallized proteins (X-ray crystallography)
Important Steps
Protein structural information using X-ray Crystallography
• Purification of proteins (Biochemical Process)
• Crystallization of proteins (Biochemical Process)
• X-ray diffraction from crystallized proteins (X-ray crystallography)
• Processing of X-ray diffraction data (X-ray crystallography)
Important Steps
Protein structural information using X-ray Crystallography
• Purification of proteins (Biochemical Process)
• Crystallization of proteins (Biochemical Process)
• X-ray diffraction from crystallized proteins (X-ray crystallography)
• Processing of X-ray diffraction data (X-ray crystallography)
• Structural Refinement (Computational Science)
Important Steps
Protein structural information using X-ray Crystallography
• Purification of proteins (Biochemical Process)
• Crystallization of proteins (Biochemical Process)
• X-ray diffraction from crystallized proteins (X-ray crystallography)
• Processing of X-ray diffraction data (X-ray crystallography)
• Structural Refinement (Computational Science)
• Protein PDB (Protein Data Bank) file (Final Product - a text file)
Important Steps
Protein PDB file SampleCRYST1 54.094 82.217 58.041 90.00 111.33 90.00 P21
ATOM 1 N ALA A 305 36.780 -16.046 0.284 1.00 0.00ATOM 2 CA ALA A 305 36.130 -14.722 0.061 1.00 0.00ATOM 3 C ALA A 305 35.329 -14.305 1.291 1.00 0.00ATOM 4 O ALA A 305 34.145 -13.980 1.190 1.00 0.00ATOM 5 CB ALA A 305 35.193 -14.819 -1.138 1.00 0.00ATOM 6 N LEU A 306 35.983 -14.315 2.450 1.00 0.00ATOM 7 CA LEU A 306 35.333 -13.947 3.703 1.00 0.00ATOM 8 C LEU A 306 34.928 -12.476 3.720 1.00 0.00ATOM 9 O LEU A 306 35.576 -11.633 3.100 1.00 0.00ATOM 10 CB LEU A 306 36.250 -14.246 4.890 1.00 0.00ATOM 11 CG LEU A 306 35.631 -14.089 6.281 1.00 0.00ATOM 12 CD1 LEU A 306 36.229 -15.096 7.252 1.00 0.00ATOM 13 CD2 LEU A 306 35.887 -12.693 6.828 1.00 0.00ATOM 14 N ALA A 307 33.850 -12.177 4.436 1.00 0.00ATOM 15 CA ALA A 307 33.352 -10.813 4.543 1.00 0.00ATOM 16 C ALA A 307 33.782 -10.203 5.868 1.00 0.00ATOM 17 O ALA A 307 33.855 -8.983 6.008 1.00 0.00ATOM 18 CB ALA A 307 31.834 -10.805 4.436 1.00 0.00ATOM 19 N LEU A 308 34.067 -11.061 6.842 1.00 0.00ATOM 20 CA LEU A 308 34.488 -10.598 8.158 1.00 0.00ATOM 21 C LEU A 308 35.903 -10.034 8.105 1.00 0.00ATOM 22 O LEU A 308 36.378 -9.433 9.069 1.00 0.00ATOM 23 CB LEU A 308 34.426 -11.747 9.169 1.00 0.00ATOM 24 CG LEU A 308 33.209 -12.681 9.135 1.00 0.00ATOM 25 CD1 LEU A 308 33.185 -13.508 10.409 1.00 0.00ATOM 26 CD2 LEU A 308 31.918 -11.882 9.003 1.00 0.00ATOM 27 N SER A 309 36.566 -10.226 6.969 1.00 0.00ATOM 28 CA SER A 309 37.933 -9.750 6.781 1.00 0.00
Quantum Models & Simulations
• Wave Function approach:
Hel(x; R) Ψ(x) = E Ψ(x)Approximations to Ψ(x) lead to methods in quantum chemistry : Hartree–Fock, Coupled Cluster, Configuration Interaction, etc.
• Density Functional approach:
E = E[ρ]• Localized Slater/Gaussian vs Plane
Wave basis set
• Born-Oppenheimer / Car-Parrinello scheme for nuclear motion
Quantum Models & Simulations
• Wave Function approach:
Hel(x; R) Ψ(x) = E Ψ(x)Approximations to Ψ(x) lead to methods in quantum chemistry : Hartree–Fock, Coupled Cluster, Configuration Interaction, etc.
• Density Functional approach:
E = E[ρ]• Localized Slater/Gaussian vs Plane
Wave basis set
• Born-Oppenheimer / Car-Parrinello scheme for nuclear motion
Positionsri
Quantum Models & Simulations
• Wave Function approach:
Hel(x; R) Ψ(x) = E Ψ(x)Approximations to Ψ(x) lead to methods in quantum chemistry : Hartree–Fock, Coupled Cluster, Configuration Interaction, etc.
• Density Functional approach:
E = E[ρ]• Localized Slater/Gaussian vs Plane
Wave basis set
• Born-Oppenheimer / Car-Parrinello scheme for nuclear motion
Positionsri
OptimizeΨ(x) or ρ
Quantum Models & Simulations
• Wave Function approach:
Hel(x; R) Ψ(x) = E Ψ(x)Approximations to Ψ(x) lead to methods in quantum chemistry : Hartree–Fock, Coupled Cluster, Configuration Interaction, etc.
• Density Functional approach:
E = E[ρ]• Localized Slater/Gaussian vs Plane
Wave basis set
• Born-Oppenheimer / Car-Parrinello scheme for nuclear motion
FindForces
Positionsri
OptimizeΨ(x) or ρ
Quantum Models & Simulations
• Wave Function approach:
Hel(x; R) Ψ(x) = E Ψ(x)Approximations to Ψ(x) lead to methods in quantum chemistry : Hartree–Fock, Coupled Cluster, Configuration Interaction, etc.
• Density Functional approach:
E = E[ρ]• Localized Slater/Gaussian vs Plane
Wave basis set
• Born-Oppenheimer / Car-Parrinello scheme for nuclear motion
FindAcceleration
& Velocity
FindForces
Positionsri
OptimizeΨ(x) or ρ
Quantum Models & Simulations
• Wave Function approach:
Hel(x; R) Ψ(x) = E Ψ(x)Approximations to Ψ(x) lead to methods in quantum chemistry : Hartree–Fock, Coupled Cluster, Configuration Interaction, etc.
• Density Functional approach:
E = E[ρ]• Localized Slater/Gaussian vs Plane
Wave basis set
• Born-Oppenheimer / Car-Parrinello scheme for nuclear motion
FindAcceleration
& Velocity
FindForces
Positionsri
NewPositions
ri
OptimizeΨ(x) or ρ
Classical Force-Field Based Atomistic Simulation
• Crystal Structures of proteins are used to create a point-like atomistic representation of the protein supra-molecules.
• An Energy functional is used to model the interactions between atoms.
• Forces on the atoms are derived from Energy.
• Accelerations, velocities, and positions are derived from Forces and F = ma
• Atoms are moved to new positions based on the selected time-step of discretization.
• Two different Simulation types are used: i) Molecular Dynamics Simulation. ii) Energy Minimization (Stable config.)
Classical Force-Field Based Atomistic Simulation
• Crystal Structures of proteins are used to create a point-like atomistic representation of the protein supra-molecules.
• An Energy functional is used to model the interactions between atoms.
• Forces on the atoms are derived from Energy.
• Accelerations, velocities, and positions are derived from Forces and F = ma
• Atoms are moved to new positions based on the selected time-step of discretization.
• Two different Simulation types are used: i) Molecular Dynamics Simulation. ii) Energy Minimization (Stable config.)
Positionsri
Classical Force-Field Based Atomistic Simulation
• Crystal Structures of proteins are used to create a point-like atomistic representation of the protein supra-molecules.
• An Energy functional is used to model the interactions between atoms.
• Forces on the atoms are derived from Energy.
• Accelerations, velocities, and positions are derived from Forces and F = ma
• Atoms are moved to new positions based on the selected time-step of discretization.
• Two different Simulation types are used: i) Molecular Dynamics Simulation. ii) Energy Minimization (Stable config.)
EnergyE(ri)
Positionsri
E = EBond + EAng + EDihe + ECoul + E(vdw+Pauli)
Classical Force-Field Based Atomistic Simulation
• Crystal Structures of proteins are used to create a point-like atomistic representation of the protein supra-molecules.
• An Energy functional is used to model the interactions between atoms.
• Forces on the atoms are derived from Energy.
• Accelerations, velocities, and positions are derived from Forces and F = ma
• Atoms are moved to new positions based on the selected time-step of discretization.
• Two different Simulation types are used: i) Molecular Dynamics Simulation. ii) Energy Minimization (Stable config.)
EnergyE(ri)
Forcesfi = !"iE(ri)
Positionsri
E = EBond + EAng + EDihe + ECoul + E(vdw+Pauli)
Classical Force-Field Based Atomistic Simulation
• Crystal Structures of proteins are used to create a point-like atomistic representation of the protein supra-molecules.
• An Energy functional is used to model the interactions between atoms.
• Forces on the atoms are derived from Energy.
• Accelerations, velocities, and positions are derived from Forces and F = ma
• Atoms are moved to new positions based on the selected time-step of discretization.
• Two different Simulation types are used: i) Molecular Dynamics Simulation. ii) Energy Minimization (Stable config.)
Acceleration& Velocityare Found
EnergyE(ri)
Forcesfi = !"iE(ri)
Positionsri
E = EBond + EAng + EDihe + ECoul + E(vdw+Pauli)
Classical Force-Field Based Atomistic Simulation
• Crystal Structures of proteins are used to create a point-like atomistic representation of the protein supra-molecules.
• An Energy functional is used to model the interactions between atoms.
• Forces on the atoms are derived from Energy.
• Accelerations, velocities, and positions are derived from Forces and F = ma
• Atoms are moved to new positions based on the selected time-step of discretization.
• Two different Simulation types are used: i) Molecular Dynamics Simulation. ii) Energy Minimization (Stable config.)
Acceleration& Velocityare Found
EnergyE(ri)
Forcesfi = !"iE(ri)
Positionsri
NewPositions
ri
E = EBond + EAng + EDihe + ECoul + E(vdw+Pauli)
Classical Force-Field Energy Functional
Classical Force-Field Energy Functional
EBond = kb(b! b0)2
Classical Force-Field Energy Functional
EBond = kb(b! b0)2 EAng = k!(! ! !0)2
Classical Force-Field Energy Functional
EBond = kb(b! b0)2 EAng = k!(! ! !0)2 EDihe = k![1 + cos(n!! ")]
Classical Force-Field Energy Functional
EBond = kb(b! b0)2 EAng = k!(! ! !0)2 EDihe = k![1 + cos(n!! ")]
qi qjrij
ECoul = qiqj/rij
Classical Force-Field Energy Functional
EBond = kb(b! b0)2 EAng = k!(! ! !0)2 EDihe = k![1 + cos(n!! ")]
qi qjrij
ECoul = qiqj/rij
qi qjrij
E(vdw+Pauli) ! ELJ = !ij
!"
"Cij
rij
#6
+"
Cij
rij
#12$
Classical Force-Field Energy Functional
EBond = kb(b! b0)2 EAng = k!(! ! !0)2 EDihe = k![1 + cos(n!! ")]
E = EBond + EAng + EDihe + ECoul + E(vdw+Pauli)
qi qjrij
ECoul = qiqj/rij
qi qjrij
E(vdw+Pauli) ! ELJ = !ij
!"
"Cij
rij
#6
+"
Cij
rij
#12$
Classical Force-Field Energy Functional
EBond = kb(b! b0)2 EAng = k!(! ! !0)2 EDihe = k![1 + cos(n!! ")]
E = EBond + EAng + EDihe + ECoul + E(vdw+Pauli)
qi qjrij
ECoul = qiqj/rij
qi qjrij
E(vdw+Pauli) ! ELJ = !ij
!"
"Cij
rij
#6
+"
Cij
rij
#12$
Force-Field Parameters: kb, b0; k!, !0; k", n, "; qi, qj ; #ij , Cij
Application to Surface Technologyfor Medical Implants
Peptide adsorption on polymers
Application to Surface Technologyfor Medical Implants
Peptide adsorption on polymers
• Take a polymer slab, a peptide and solvate them in water.
Application to Surface Technologyfor Medical Implants
Peptide adsorption on polymers
• Take a polymer slab, a peptide and solvate them in water.
• Energy minimize, equilibrate, and perform Umbrella Sampling MD simulation.
Application to Surface Technologyfor Medical Implants
Peptide adsorption on polymers
• Take a polymer slab, a peptide and solvate them in water.
• Energy minimize, equilibrate, and perform Umbrella Sampling MD simulation.
• Evaluate the free energy of adsorption of the peptide on the polymer surface.
Application to Surface Technologyfor Medical Implants
Peptide adsorption on polymers
• Take a polymer slab, a peptide and solvate them in water.
• Energy minimize, equilibrate, and perform Umbrella Sampling MD simulation.
• Evaluate the free energy of adsorption of the peptide on the polymer surface.
• Take surface with a different functional group. Repeat.
Protein adsorption on Polymer Surfaces-What are the Challenges?
Peptide adsorption on polymers
Protein adsorption on Polymer Surfaces-What are the Challenges?
Peptide adsorption on polymers• Force-Field parameters optimized for
liquid-phase proteins - do not hold good for solid-phase polymers.
• Addressing the polarization of solvent and peptide in the presence of polymers.
Problems:
Protein adsorption on Polymer Surfaces-What are the Challenges?
Peptide adsorption on polymers• Force-Field parameters optimized for
liquid-phase proteins - do not hold good for solid-phase polymers.
• Addressing the polarization of solvent and peptide in the presence of polymers.
Problems:
• Using a polarizable force-field suitable for both liquid and solid phases.
• Using dedicated/tuned FF for liquid-phase, solid-phase, and solid-liquid interphase.
• Latter scheme added to CHARMM.
Solutions:
Estrogen Receptor Proteins & Drug Designing for Breast Cancer
• Estrogens are Hormones - simply, a particular family of molecules having specific biological roles.
• They bind to certain proteins called Estrogen Receptors (ER) - present in humans, animals, and aquatic animals.
• Estrogen binding allows ER to perform gene transcription.
• 70% of breast cancers has Estrogen related gene transcription signatures.
Estrogen Receptor Proteins & Drug Designing for Breast Cancer
• Estrogens are Hormones - simply, a particular family of molecules having specific biological roles.
• They bind to certain proteins called Estrogen Receptors (ER) - present in humans, animals, and aquatic animals.
• Estrogen binding allows ER to perform gene transcription.
• 70% of breast cancers has Estrogen related gene transcription signatures.
Estriol
Estradiol
Estrone
Estrogen Receptor Proteins & Drug Designing for Breast Cancer
• Estrogens are Hormones - simply, a particular family of molecules having specific biological roles.
• They bind to certain proteins called Estrogen Receptors (ER) - present in humans, animals, and aquatic animals.
• Estrogen binding allows ER to perform gene transcription.
• 70% of breast cancers has Estrogen related gene transcription signatures.
Estrogen Receptors and their Biological Activity
• Estrogen Receptors (ER) are activated by estrogens.
• ER exist in two isoforms ERα and ERβ -have similar domains with different lengths. Both have 12 α-helices and 2 β-sheets.
• Depending on the size and chemistry of the ligand, their binding can cause large conformational changes in ER.
• Triggers ER to dimerize: ERαα, ERββ, ERαβ
• ER-dimer binds to specific DNA sequence and activates gene transcription thorough co-activator proteins.
• Calcium Binding Proteins are considered to be the essential co-activator proteins that mediate ER signal to the RNA.
Estrogen Receptors and their Biological Activity
• Estrogen Receptors (ER) are activated by estrogens.
• ER exist in two isoforms ERα and ERβ -have similar domains with different lengths. Both have 12 α-helices and 2 β-sheets.
• Depending on the size and chemistry of the ligand, their binding can cause large conformational changes in ER.
• Triggers ER to dimerize: ERαα, ERββ, ERαβ
• ER-dimer binds to specific DNA sequence and activates gene transcription thorough co-activator proteins.
• Calcium Binding Proteins are considered to be the essential co-activator proteins that mediate ER signal to the RNA.
595
AF-1 DBD Hinge LBD AF-2
185 5503052601
ERα
AF-1 DBD Hinge LBD AF-2
145 505255225 5301
ERβ
Estrogen Receptors and their Biological Activity
• Estrogen Receptors (ER) are activated by estrogens.
• ER exist in two isoforms ERα and ERβ -have similar domains with different lengths. Both have 12 α-helices and 2 β-sheets.
• Depending on the size and chemistry of the ligand, their binding can cause large conformational changes in ER.
• Triggers ER to dimerize: ERαα, ERββ, ERαβ
• ER-dimer binds to specific DNA sequence and activates gene transcription thorough co-activator proteins.
• Calcium Binding Proteins are considered to be the essential co-activator proteins that mediate ER signal to the RNA.
595
AF-1 DBD Hinge LBD AF-2
185 5503052601
ERα
AF-1 DBD Hinge LBD AF-2
145 505255225 5301
ERβ
Estrogen Receptors and their Biological Activity
• Estrogen Receptors (ER) are activated by estrogens.
• ER exist in two isoforms ERα and ERβ -have similar domains with different lengths. Both have 12 α-helices and 2 β-sheets.
• Depending on the size and chemistry of the ligand, their binding can cause large conformational changes in ER.
• Triggers ER to dimerize: ERαα, ERββ, ERαβ
• ER-dimer binds to specific DNA sequence and activates gene transcription thorough co-activator proteins.
• Calcium Binding Proteins are considered to be the essential co-activator proteins that mediate ER signal to the RNA.
595
AF-1 DBD Hinge LBD AF-2
185 5503052601
ERα
AF-1 DBD Hinge LBD AF-2
145 505255225 5301
ERβ
Estrogen Receptors and their Biological Activity
• Estrogen Receptors (ER) are activated by estrogens.
• ER exist in two isoforms ERα and ERβ -have similar domains with different lengths. Both have 12 α-helices and 2 β-sheets.
• Depending on the size and chemistry of the ligand, their binding can cause large conformational changes in ER.
• Triggers ER to dimerize: ERαα, ERββ, ERαβ
• ER-dimer binds to specific DNA sequence and activates gene transcription thorough co-activator proteins.
• Calcium Binding Proteins are considered to be the essential co-activator proteins that mediate ER signal to the RNA.
595
AF-1 DBD Hinge LBD AF-2
185 5503052601
ERα
AF-1 DBD Hinge LBD AF-2
145 505255225 5301
ERβ
Estrogen Receptors and their Biological Activity
• Estrogen Receptors (ER) are activated by estrogens.
• ER exist in two isoforms ERα and ERβ -have similar domains with different lengths. Both have 12 α-helices and 2 β-sheets.
• Depending on the size and chemistry of the ligand, their binding can cause large conformational changes in ER.
• Triggers ER to dimerize: ERαα, ERββ, ERαβ
• ER-dimer binds to specific DNA sequence and activates gene transcription thorough co-activator proteins.
• Calcium Binding Proteins are considered to be the essential co-activator proteins that mediate ER signal to the RNA.
595
AF-1 DBD Hinge LBD AF-2
185 5503052601
ERα
AF-1 DBD Hinge LBD AF-2
145 505255225 5301
ERβ
Simulation Studies on Breast Cancer
• Objective: Studying Estrogen Receptors’ biological activity of gene-transcription and designing ER inhibitors.
• Methodology: Classical force-field based Atomistic Simulation using GROMACS pkg and OPLS force-field.
• Key Elements: Estrogen Receptors (ER), Hormones and ER binding Ligands, Calcium Binding Proteins (CaM), and CaM segments.
Atomistic Simulation to Explore Ligand Binding Pocket
Atomistic Simulation to Explore Ligand Binding Pocket
Atomistic Simulation to Explore Ligand Binding Pocket
GLU-353--DES: 2.25AARG-394--DES: 2.24AHIS-524--DES: (3.58A)*
Crystal Structure: 3ERD
Atomistic Simulation to Explore Ligand Binding Pocket
GLU-353--DES: 2.25AARG-394--DES: 2.24AHIS-524--DES: (3.58A)*
Crystal Structure: 3ERD
GLU-353--DES: 1.65AARG-394--DES: 2.78AHIS-524--DES: 2.76A*
Energy Minimized: 3ERD
Atomistic Simulation ofSolvated ERα-DES Complex
GLU-353
ARG-394
LEU-391
HIS-524
LEU-525
Atomistic Simulation ofSolvated ERα-DES Complex
GLU-353
ARG-394
LEU-391
HIS-524
LEU-525
Atomistic Simulation ofSolvated ERα-DES Complex
GLU-353
ARG-394
LEU-391
HIS-524
LEU-525
Atomistic Simulation ofSolvated ERα-DES Complex
GLU-353--DES: 1.52AARG-394--DES: 2.25AHIS-524--DES: 3.31A*
Energy Minimized: 3ERD-DES-SOL
LEU-391--DES: 3.14AGLY-521--DES: 2.80ALEU-525--DES: 2.73A
HOH--DES: 1.69A
GLU-353
ARG-394
LEU-391
HIS-524
LEU-525
ERαα-DimerIdentification of dimerization domain using atomistic simulations
ERαα-DimerIdentification of dimerization domain using atomistic simulations
ERαα-DimerIdentification of dimerization domain using atomistic simulations
ERαα-DimerIdentification of dimerization domain using atomistic simulations
ASP-480---GLN-506: 1.58AASP-484---GLN-502: 1.87AASP-484---HIS-501: 1.63A
ASP-480---GLN-506: 1.59AASP-480---GLN-502: 2.70AASP-484---GLN-502: 1.77A
ASP-484
HIS-503
GLN-439
GLN-435
ASP-480
ERαα-DimerIdentification of dimerization domain using atomistic simulations
ASP-480---GLN-506: 1.58AASP-484---GLN-502: 1.87AASP-484---HIS-501: 1.63A
ASP-480---GLN-506: 1.59AASP-480---GLN-502: 2.70AASP-484---GLN-502: 1.77A
ASP-484
HIS-503
GLN-439
GLN-435
ASP-480
Need to send (I-Helix) troops to bind to ER and
block Dimerization??
Atomistic Simulation ofERα-CaM Complex
Atomistic Simulation ofERα-CaM Complex
Atomistic Simulation ofERα-CaM Complex
Atomistic Simulation ofERα-CaM Complex
Atomistic Simulation ofERα--CaM-segment
THR-496, LEU-497(3), GLN-500, HIS-501(3), LEU-504(3), ALA-505(2), LEU-508
Remarks
• Atomistic Simulation could effectively identify the binding pockets and specific H-bonding between interacting molecules.
• One can easily identify any peptide segment of known co-activator proteins and study its binding affinity to design inhibitor nano-peptides.
• Cautions need to be made to draw conclusions - Effect of solvent should be taken into account.
• Cautions need to be taken to eliminate other unwanted activities of the designed nano-peptide.
Inadequacy of Classical MD -Role of Multiscale QM/MM
• Classical MD simulations are fast, fairly deterministic but lack important necessary features
• Quantum MD simulations are computationally expensive and systems only with 200-300 atoms are okay to deal with
• One way out is a hybrid QM/MM scheme - where part of the system is described by QM, and the rest by MM
• Challenge is to describe the QM/MM interphase
Inadequacy of Classical MD -Role of Multiscale QM/MM
Inadequacy of Classical MD -Role of Multiscale QM/MM
qmmm
+
+
Inadequacy of Classical MD -Role of Multiscale QM/MM
qmmm
+
+
1. nonphysical polarization of QM electrons by point-like MM atoms.
2. Lack of polarizable description of the MM atoms.
3. Lack of Pauli exclusion repulsion of QM electrons by MM atoms.
Problems in modeling HQM/MM:
Gromacs CPMD
pdb2gmx
grompp
reads .pdf and FFcreates .gro and .top
mdrun
reads .mdp .gro .topcreates .tpr
reads index.ndx, raises QM flags & deletes QM atoms from force calc.
reads .tpr
enters md_loop
ends md_loop
calls force()
calls interface
creates CPMDinput file;
calls cpmd & sleeps
Interface activates;Calculates Ext_fld;performs scf_loop;
calculates f[][];activates gmx & sleeps
cpmd.f
append force
control.f
scf_loop
receives f[][]
http://www.tougaloo.edu/research/qmmm/
V (r) = |φ(r ') |2
| r − r ' |d 3r '∫ = qj
1r−e−2αr
r−αe−2αr
0 1 2 3 4Distance (a.u.)
-3
-2
-1
0
QM
-MM
Ele
ctro
static
Pot
entia
l (a.
u.)
Eichinger et al [xx]Allessandro et al [xx]Present (l=1.0)Present (l=1.3)Pure Coulomb
Non-physical polarization of QM charges:
Oxydase part of iNOS Protein
Heme Reactive States: Oxidation of ArgLudwig and Marletta Structure 7, R73 (1999)
FeII
FeIII
e-
Heme Reactive States: Oxidation of ArgLudwig and Marletta Structure 7, R73 (1999)
FeIIO2FeIIIO2.-[ ]
O2
FeII
FeIII
e-
Heme Reactive States: Oxidation of ArgLudwig and Marletta Structure 7, R73 (1999)
FeIIIO2H
H4B
H3B.
FeIIO2FeIIIO2.-[ ]
O2
FeII
FeIII
e-
Heme Reactive States: Oxidation of ArgLudwig and Marletta Structure 7, R73 (1999)
FeIIIO2H
H4B
H3B.
FeIIO2FeIIIO2.-[ ]
O2
FeII
FeIII
e-(FeO)3+H2O
H+
Heme Reactive States: Oxidation of ArgLudwig and Marletta Structure 7, R73 (1999)
FeIIIO2H
H4B
H3B.
FeIIO2FeIIIO2.-[ ]
O2
FeII
FeIII
e-
Arg NHA
(FeO)3+H2O
H+
Heme Reactive States: Oxidation of ArgLudwig and Marletta Structure 7, R73 (1999)
Stability of Heme Monomer Spin states in presence of BH4 and ARG
H4B
Trp463 Arg
199Cys
200
Heme
O2
Arg-substrate
Oxydation state
Spin state
RelativeEnergy
(CPMD)
RelativeEnergy
(Gaussian)
InteractionwithBH4
InteractionwithARG
+2 3 -5.59-14.98
xxxxxxxx
-25.160.000
-125.440.0000
+2 5
-6.01-1.851.580.19
xxxxxxxx-7.98xxxx
-24.55-24.560.0000.000
-122.360.000
-121.830.000
+3 4 -0.38 xxxx -22.34 -100.18
+3 6
-7.5942.09-13.028.55
25.01-10.8418.4320.04
-22.24-17.140.000.00
-106.050.00
-105.400.00
Effect of BH4 and ARG on Heme Monomer Spin states
H-abstraction from a DNA Base-Pair subunit by OH radical
Acknowledgement
• Dr. Bernard Brooks, NHLBI/NIH
• Dr. Valentin Gogonea, CSU, Ohio
• Dr. Robert Latour, Clemson University
• Dr. Mohammad Abolfath, U. of Texas
• Mr. Shawn Cole, Tougaloo College
• Mr. Marcus Johnson, Tougaloo College
Thanks to all of you.
• NCMHD-RIMI (NIH)
• MS-INBRE (NIH)
• Sub-project from NIH R1-grant, Clemson
• Special Volunteer, NHLBI/NIH
Collaborators
Funding