Development of Molecular Mechanical Force Fields for Studying

Biological Systems

Junmei Wang

Green Center for Systems Biology

University of Texas Southwestern Medical Center

2/12/2015

Career Goal

Develop a set of high quality molecular mechanical force fields in order to

1. to accurately model protein-ligand interactions2. to successfully study dynamics of biomolecules

Quantify “high quality”1. Structures: rmsd <= 2.0Å2. Energies: rmse <= 1.0 kcal/mol 3. Protein folding: successful rate >= 60%

Functional Forms

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Direction 1: Molecular Mechanical Force Field Development

GAFF2

Biomolecular MMFF• Using GAFF2 vdW• From “general” to “special”• Targeted for drug design

PTM MMFF• 500 plus PTM residues• More than 70% proteins are

modified• Submitted a R01 grant with Piotr

Cieplak at Sanford Burnham Medical Research Institute

Polarizable MMFF• AMBER force field consortium (R01)• Fast charge methods• Exploration on functional form: lone

pairs and charge transfer

MMFF Toolkits• Antechamber2 package• Online MM toolkit• Online MM database• Submitted a NSF SSE

(Scientific Software Elements) Grant

Philosophy

1. Robustness vs Accuracy2. Physical charges3. A new atom type is introduced only when it

is necessary and well-justified4. Critical assessment (blind test, challenges

(CASP, SAMPL)

Force Field Parameterization Strategies

Experimental data:: vibrational frequencies, pure liquid/solid properties, solvation free energies

crystal/NMR structures, Ramachandran, B-factors,J-J couplings, order parameters

Binding data: ki, IC50

QM data: optimized geometries, conformational energies, interaction energies, electric moments, electrostatic potentials, electron densities, etc.

Model compound selection

Equilibrium bond length/angles

Partial Charges

van der Waals

Bond stretching and angle bending force constants

Torsional angle

Satisfactory?

Exit

Evaluation

Yes

No

Direction 2: Application of MD in Bio-Systems

MD Simulations to study biological processes1. Using external forces2. Through collaborations 3. Collaborators: Dr. Paul Blount and all Green Center faculty

Direction 2: Application of MD in Bio-Systems

Crystal simulations1. Evaluate force fields2. Structure refinement3. Collaboration with Rama, Doeke and Ian4. Performed crystal simulations for 12 protein crystals5. Performed crystal simulations with external electric field 6. Two manuscripts in preparation

Direction 2: Application of MD in Bio-Systems

Protein folding by MD simulations1. Evaluate force fields2. Understand the mechanisms of protein folding3. GB-MD can speed up protein folding about 20 fold because GB

has no viscosity Trp-cage folded within 200 ns (3.1 s)Tryptophan Zipper 2 folded within 50 ns (1.2 s)

4. Plan to run protein folding for the following systems1. Trpcage: 2. Tryptophan Zipper3. WW domain4. Villin headpiece: 36 aa5. Protein G

Benchmark: GBMD for PDZ3 (115 aa)BIOHPC: 225.6 ns per day

Direction 3: Rational Protein Design

Identify site-site correlation with physics-based approaches

1. Normal model analysis

2. Quasi-harmonic analysis

3. Non-equilibrium MD simulations by applying an external

electric field (constant or pulsed) to whole systems or selected

residues

4. MD simulations followed by correlation analysis of the residue-

residue interaction energies (Kong and Karplus)

5. Anisotropic thermal diffusion (ATD, Ota and Agard)

6. MD simulations followed by time-correlation analysis

7. Rigid residue scanning (Kalescky, Liu and Tao)

8. Conditional activity (Milo Lin) ?

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Direction 3: Rational Protein Design

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Wang et al. J. Chem. Info. Model., 52, 1199-1212, 2012.

Protein Design Scoring Functions

1. naa of Eref are parameterized through decoy discrimination

2. Rosetta decoy set: 114 targets and each has 24,000 decoys

3. TS is obtained by SAS calculation using our model

4. Ecorr can be estimated by Eqs. 2 and 3. Cij is the correlation calculated with the designed protein sequence as a perturbation.

5. wij are determined by Cij

Direction 3: Rational Protein Design

Integrate site-site correlation into a physical scoring function and conduct rational protein design

1. Four levels of integration2. Monte Carlo Metropolis algorithm is used to conduct protein design. 3. Use Dunbrack lab’s side chain rotamer libraries 4. Test the computational protocols with PSD-95 PDZ domain

Why is this a promising project?1. One R21 grant was awarded2. In collaboration with the Ranganathan Lab3. Implement into AMBER package

4. Plan to submit a R01 grant this year

Direction 4: Computer-Aided Drug Design (CADD) Core Facility

Mission: To provide virtual screening service to those who want to develop small molecules to inhibit their protein or nucleic acid targets

Services1. Binding free energy calculation with MM-PB/GBSA 2. Homology modeling with Modeller 3. Pharmacophore modeling using Phase and Galahad, Tuplets4. 2D/3D-QSAR, HQSAR modeling with Sybyl 5. Targeted library construction6. Docking protocol construction with Glide

Direction 4: Computer-Aided Drug Design (CADD) Core Facility

Databases: UTSW Screening Library ZINC (UCSF), 35 million purchasable compounds

GoalScreen at least one million compounds per day with Glide docking

Hardware1. Linux cluster (320 CPU cores, $60K)2. Sybyl, Schrodinger ($5K)3. Plan to apply for a Welch grant and NIH equipment grant (PA-

15-089) to support the core facilityHire one research scientist operate the core facility

Computer-Aided Drug Design

Homology Modeling

Structures Inhibitors

Primary HTS Patents literature

Pharmacophore

X-ray or NMR

ADMETDrug Likeness Analysis

DockingProtocol

QSAR

Similarity and Substructure Search queries

Structure-based drug design Ligand-based drug design

hits

Compound libraries

Compound Acquisition Test

inhibitors

Efficiency

Reliability

2D-Similarity Search

Pharmacophore

Molecular Docking

MM-GB/PBSA

Experiment

database

Lead Identification Through Virtual Screening Using A Set of Hierarchical Filters

Wang, J.*; Kang, X.; Kollman, P. A.; Kuntz, I. D. J. Med. Chem. . 48, 2432-2444