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