Journal Articles

Experiences Using MOE in Academia

by Jeffry D. Madura
Department of Chemistry & Biochemistry, Duquesne University
320 Mellon Hall, 600 Forbes Ave., Pittsburgh, PA 15282
e-mail:
phone: (412) 396-4129

Jeffry D. Madura

MOE (Molecular Operating Environment) by Chemical Computing Group is the next generation in chemical computing software. The program, MOE, is flexible and robust. The flexibility comes from its open architecture, which means that source code is provided, as well as the ability to modify the source code and incorporate modifications into the executable. MOE is also robust in that a number of different application modules included with the program cover a wide range of computational chemistry fields. Additionally, the capability to look at an algorithm, change it and test the changes makes MOE an excellent platform from which to teach and a great tool with which to do research. It is these features, as well as others, that make MOE easily fit into a research program in which highly specialized and compute intensive codes are written.

This article briefly touches upon five different areas in which we are currently using MOE in our research and in the classroom:

  1. Docking takes a look at the docking of substrates to a metalloenzyme.

  2. Poisson-Boltzmann Electrostatics explains the application of the Poisson-Boltzmann solver to compute small molecule solvation, visualize protein electrostatics and ultimately calculate free energies of binding between a substrate and receptor.

  3. Gaussian Molecular Orbitals examines the use of the programmability feature of MOE to parse and visualize a Gaussian98 cube file containing molecular orbital data.

  4. Dynamics Animation explains how MOE is used as an analysis tool by reading the output from a molecular dynamics trajectory file into a MOE database. This database can be used to animate the trajectory or analyze the results from a simulation.

  5. In the Classroom describes the use of MOE in the classroom.

Docking

Recent advances in protein structure determination, either via X-ray crystallography, NMR or computer models, are providing the necessary data for chemists, biochemists and pharmacologists to design and study substrates for these proteins. Identifying the binding sites of these newly determined protein structures has lead to the development of a variety of docking strategies. The main goal of automated docking procedures is to predict the "best" substrate–enzyme complex. MOE-Dock, which is based on the algorithms in AutoDock, can perform such automated docking. We are currently using MOE-Dock to study substrate-enzyme interactions in metalloenzyme systems. More specifically, our efforts have been focused on the docking of sulfonamides to carbonic anhydrase II and IV that are of interest due to their application in glaucoma therapy. Using a standard protocol it is possible to correctly predict the correct orientation of substrates to a specific isozyme.

We started with the solved X-ray structure for 4-Aminobenzenesulfonamide (AMS) bound to CA II. We used this system to establish the appropriate parameters for docking substrates to metalloenzymes. Using the newly developed docking parameters, we then ran MOE-Dock to dock other sulfonamides to CA II and CA IV. The following figure illustrates the five "best" MOE-Dock predicted sulfonamide-CA II structures. The purple ball and stick structure is the AMS X-ray structure. The green sphere represents the zinc atom while the blue sticks are the three CA histidines complexed to the zinc.

MOE-Dock Predicted
Sulfonamide-CA II Structures

This example illustrates that MOE-Dock provides reasonable enzyme-substrate complexes. While doing our docking studies, x-ray structures of sulfonamide-CA IV complexes were not known. However, after completing our docking calculations, we became aware of x-ray coordinates for (R)-4-ethylamino-3,4-dihydro-2-(3-methoxy)propyl-2H-thieno[3,2-e]-1,2-thiazine-6-sulfonamide-1,1-dioxide (AZP) bound to CA II and CA IV by Stams. In comparing our results with their experimental results, we found that MOE-Dock typically predicted enzyme-substrate complexes to within 2.5 – 3 Ä.

Poisson-Boltzmann Electrostatics

Electrostatic forces play a dominant role in many chemical and biochemical phenomena. In a few cases, electrostatic interactions can be studied analytically. Still, for all other systems, numerical methods must be used to solve the Poisson-Boltzmann equation. Over the past 10 years several methods to solve the Poisson-Boltzmann equation have been proposed. The MOE-Electrostatics tool uses the multi-grid approach. Since its introduction we have been evaluating MOE-Electrostatics in computing the free energy of solvation for small molecules, protein electrostatics and binding free energy between enzymes and substrates. For the free energy of solvation tests we took 15 molecules, representative of the functional groups of amino acids, from the PARSE paper of Sitkoff et al. Using various combinations of radii and charges we computed the free energy of solvation. The results of those calculations are presented in the following figure. This is a summary of solvation free energy for 15 molecules representing the amino acid functional groups using MOE-Electrostatics, Delphi and UHBD. The best fit, based on these calculations, is to use MMFF94 radii with PARSE charges.

Comparison of PB Solver Methods and Forcefields

Using MOE-Electrostatic one is able to compute the electrostatic potential around biomolecules. We used this capability to calculate the electrostatic potential around various proteins and used MOE's visualization feature to construct GRASP-like images as shown below:

Electrostatic Potential

The above figure is the result of a MOE-Electrostatic calculation on hen egg-white lysozyme. Mapped onto the molecular surface is the electrostatic potential. Red indicates positive potential; blue, negative potential; and white, zero potential. The substrate NAG3 is shown to highlight the location of the active site.

We are currently modifying MOE-Electrostatic to accurately calculate the binding free energy between an enzyme and a substrate.

Gaussian Molecular Orbitals

Carbanions that are stabilized by carbonyl, cyano and nitro groups are widely used in synthesis while some of them are important biochemical intermediates. In order to understand the nature of the cyano substituent on cyclic systems, we have been performing electronic structure calculations using Gaussian98. We used the graphics capabilities of MOE to visualize some of the data generated from Gaussian98. Specifically, we have written SVL code to parse the Gaussian98 cube file so that we can display molecular orbitals. The following picture is a screen snapshot of the highest occupied molecular orbital (HOMO) for cyclopentanecarbonitrile, 2-methyl anion.

Molecular Orbital (HOMO) for Cyclopentanecarbonitrile, 2-Methyl Anion

The SVL code was easily written in an afternoon, and can be modified to change the mode in which the orbital is displayed.

Dynamics Animation

Antifreeze proteins (commonly known as Thermal hysterisis proteins) kinetically inhibit ice crystal growth. Development of any hypothesis based on macroscopic observations such as freezing point depression, altered ice crystal morphology and reduced cellular damage requires an understanding of the problem on the molecular level. The focus of our ice/water and ice/antifreeze protein/water simulations is to develop a molecular level understanding of the binding mechanism, thermodynamics, kinetics, and molecular recognition at the interfacial region. In this project we are studying an antifreeze protein at the ice/water interface using the molecular dynamics program DLPOLY 2.11 while visualizing and analyzing the results using MOE.

We initially performed a 1.3 ns simulation of a large ice/water system using DLPOLY 2.11 so that we could observe the various properties of the interface. Such properties include the z-density profile, diffusional and orientational profiles. We used MOE to read the trajectory files generated by DLPOLY 2.11 and put the data into MOE's database. A small SVL program was written to read the trajectory file and write the data into MOE's database format. Using this database we then animated the trajectory and computed the desired properties. Below is a snapshot from the simulation of the ice/water interface after 1.2 ns of simulation time. The system contains 906 ice/water TIP3 molecules at a temperature of 225K.

Ice/Water Interface in MOE

We used MOE to construct the ice/antifreeze protein/water interface shown in the next figure:

Protein/Water Interface in MOE

This system contains 13810 ice/water molecules and two antifreeze peptides for a total of 42 334 atoms. As the simulation progressed, we read the trajectory files into MOE and analyzed the results.

In the Classroom

MOE is an extremely versatile software with which to teach. The program can transverse the different levels in which one wishes to instruct their students. For instance MOE can be used as a black-box in Freshman and Organic classes. Here students use the program to draw, manipulate and visualize structures as well as perform basic calculations. In upper-level classes, students use MOE to explore the algorithms behind the methods taught in earlier courses. We are presently using both of these aspects of MOE in our Computational Chemistry skills course offered to incoming graduate students and senior undergraduates. The students are initially introduced to MOE through several visualization and computational exercises which include drawing numerous organic structures, assigning a forcefield and performing a molecular mechanics energy minimization. The various minimization methods are discussed during lecture and SVL code is presented as part of the discussion. Later on in the course, students are taught molecular dynamics methods. In this case the students are encouraged to look at the dynamics code in MOE, make modifications and test the results of their changes.

Summary

In the above discussion I have provided a sample of the uses and experiences we have explored with MOE. The program fits very nicely into our research and education program for several reasons. These reasons include the ease, flexibility, and robustness of MOE. The program is very straightforward to learn and use. One of MOE's greatest strengths is that not only does MOE run on many different platforms, its use and operation is consistent across all systems. For example, MOE is small enough to run on a laptop with the same capabilities as it does on a large UNIX workstation. MOE's second greatest strength is that the user can make changes or improvements to existing algorithms in MOE and not have to wait until the next release of the program. Moreover, the user can add new functionality to MOE. MOE is robust in the range of application modules supplied with the program. The number of new applications increases with each new release, which is currently twice a year.

Find out about CCG's new agreement for academic uses of MOE.

References

DelPhi Nicholls, A., and B. Honig (1991). "A rapid finite difference algorithm, utilizing successive over­relaxation to solve the Poisson­Boltzmann equation." J. Comp. Chem. 12, 435­445.

DL_POLY is a package of molecular simulation routines written by W. Smith and T. R. Forester copyright The Council for the Central Laboratory of the Research Councils, Daresbury Laboratory at Daresbury, Nr. Warrington (1996).  Smith, W., and Forester, T., 1996, J. Molec. Graphics, 14, 136.

Gaussian 98, Revision A.3, M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, V. G. Zakrzewski, J. A. Montgomery, Jr., R. E. Stratmann, J. C. Burant, S. Dapprich, J. M. Millam, A. D. Daniels, K. N. Kudin, M. C. Strain, O. Farkas, J. Tomasi, V. Barone, M. Cossi, R. Cammi, B. Mennucci, C. Pomelli, C. Adamo, S. Clifford, J. Ochterski, G. A. Petersson, P. Y. Ayala, Q. Cui, K. Morokuma, D. K. Malick, A. D. Rabuck, K. Raghavachari, J. B. Foresman, J. Cioslowski, J. V. Ortiz, B. B. Stefanov, G. Liu, A. Liashenko, P. Piskorz, I. Komaromi, R. Gomperts, R. L. Martin, D. J. Fox, T. Keith, M. A. Al-Laham, C. Y. Peng, A. Nanayakkara, C. Gonzalez, M. Challacombe, P. M. W. Gill, B. Johnson, W. Chen, M. W. Wong, J. L. Andres, C. Gonzalez, M. Head-Gordon, E. S. Replogle, and J. A. Pople, Gaussian, Inc., Pittsburgh PA, 1998.

GRASP Nicholls, A.; K. Sharp; B. Honig. (1991) "Protein Folding and Association: Insights from the Interfacial and Thermodynamic Properties of Hydrocarbons." Proteins. 11, 281-296.

MMFF94 T. A. Halgren and R. B. Nachbar, (1996) " Merck Molecular Force Field. IV. Conformational Energies and Geometries for MMFF94." J. Comput. Chem., 17, 587-615.

Sitkoff, D.; K. A. Sharp; B. Honig. (1994) "Accurate Calculation of Hydration Free Energies Using Macroscopic Solvent Models." J. Phys. Chem. 98, 1978-1988.

Stams, T; Y. Chen; P. A. Boriack-Sjodin; J. D. Hurt; J. Liao; J. A. May; T. Dean; P. Laipis; D. N. Silverman and D. W. Christianson (1998). "Structure of Murin Carbonic Anhydrase IV and Human Carbonic Anhydrase II Complexed with Brinzolamide: Molecular Basis of Isozyme- Drug Discrimination." Protein Science. 7, 556-563.

UHBD Madura, J. D.; J. M. Briggs; R. C. Wade; M. E. Davis; B. A. Luty; A. Ilin; J. Antosiewicz; M. K. Gilson; B. Bagheri; L. R. Scott and J. A. McCammon (1995). "Electrostatics and Diffusion of Molecules in Solution: Simulations with the University of Houston Brownian Dynamics Program." Computer Physics Communications. 91, 57-95.