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Functionalization Workbench

By AbderRahman N Sobh1, Jasper Taylor Maniates-Selvin1, Kaustubh Bhalerao1, Adri van Duin2, Chen-Yu Li1

1. University of Illinois at Urbana-Champaign 2. Penn State University

Using molecular dynamic simulation - explore the interactions between a molecular structure and substrate when they are linked together.

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Version 1.0c - published on 14 Apr 2014

doi:10.4231/D38C9R435 cite this

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    Main Menu Representations 1 Representations 2 Licorice Representation Change Simulation



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This tool is designed to serve as an interactive method of exploring graphical simulation of environments characterized by a molecular structure attached to a substrate through an anchoring development. The code for Visual Molecular Dynamics (VMD) and Molecular Dynamics (NAMD) was developed by the Theoretical and Computational Biophysics Group at the University of Illinois.

Until recently, much of our understanding of multi-body interactions came from the analysis of an imaginary system involving two objects, often referred to as the two-body problem. This is a simple approach because two-body problems can typically be reduced to two one-body problems and those can be solved analytically and exactly. For hundreds of years, this has also been an effective approach resulting in insight into principles of gravitational orbit, electricity and magnetism, and momentum. As physical problems increase in complexity, however, involving three or more bodies, they become much more difficult to solve. Eventually, in more complex systems, it becomes simply infeasible to approach the problems analytically. One approach to this problem is to run an experiment and record observations in order to discover new insight. In a sense this is a very intuitive way to understand the universe, and something that has been done since the dawn of science. Often, however, a deeper understanding required to explain why we observe the behavior that we do. To answer the question we develop “models”, or frameworks to explain the underlying behavior. As discussed earlier, we were limited for quite some time to models involving two bodies. Since the 1950s, however, with the advent of both computational methods and machinery, we have been able to tackle more complex problems using numerical methods.

Numerical methods provide the approximate solution as opposed to the exact solution achieved by analytical methods. While many numerical algorithms exist, they share in common the ability to provide, in a finite number of calculations, insight into a system consisting of infinite potential interactions. Some commonly known, elementary, numerical methods involve integration of a function. Most students of introductory calculus courses will be familiar with the concept of Riemann sums – specifically the rectangular rule or trapezoidal rule. These methods allow for the approximate calculation of the area under nearly any Cartesian curve, by subdividing the curve into a set of individual rectangles or trapezoids. While it is beyond the scope of this discussion to provide detail on these methods, the key is that the methods allow for approximation of a function using a finite number of calculations.

Within molecular dynamics, similar algorithms exist. A popular molecular dynamics package, developed at the University of Illinois at Urbana Champaign, known as NAMD (Not just Another Molecular Dynamics program) employs several algorithms for reducing problems into simpler, smaller, tasks. These reduced problems are then solved using a mixture of different calculation methods including numerical approximation methods. Scientists often need to further add some sort of noise simulation into their models to get even greater insight into the problem. Simulations run by NAMD and other molecular dynamics programs, then, output data that can be analyzed.

A common way to analyze such data is by visualization. VMD (visual molecular dynamics), also developed at Illinois, provides a means for analyzing and visualizing the data, which may be in the form of individual molecules or molecular interaction time-series data. VMD is valuable for understanding both form and function – it can reveal the structure molecules take up as well as how that structure is changed upon interacting with another species so that the function of individual domains may be determined. Molecular dynamics and visualization is increasingly being used in the life sciences and bioengineering disciplines to both understand and develop better diagnostic devices and pharmaceuticals.

This tool will help you get familiar with the VMD software and provide a sense of how powerful the software can be. In the tool, a model of DNA attached to a gold substrate is provided. When the model is displayed, you can explore different visualizations, pan, zoom, rotate, and even play a video of the simulation.

To learn more about the topics discussed here visit the following links: Course on Atomic Scale Simulation by David Ceperley at the University of Illinois at Urbana-Champaign Molecular Dynamics Lecture Course on Biomolecular Physics by Klaus Schulten and Taekjip Ha at the University of Illinois at Urbana-Champaign VMD walkthrough Overview of some NAMD algorithms Klaus Schulten’s Laboratory (Active Developers of the VMD and NAMD software packages) VMD tutorial Molecular Dynamics Simulation, Elementary Methods by J. M. Haile Discussion of VMD from developer John Stone

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Visual Molecular Dynamics (VMD) and Molecular Dynamics (NAMD) developed by the Theoretical and Computational Biophysics Group at the University of Illinois.

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NanoBio Node, University of Illinois Champaign-Urbana


Theoretical Computational Biophysics Group and VMD development Website:

Cite this work

Researchers should cite this work as follows:

  • Humphrey, W., Dalke, A. and Schulten, K., "VMD - Visual Molecular Dynamics", J. Molec. Graphics, 1996, vol. 14, pp. 33-38.
  • AbderRahman N Sobh; Jasper Taylor Maniates-Selvin; Kaustubh Bhalerao; Adri van Duin; Chen-Yu Li (2014), "Functionalization Workbench," (DOI: 10.4231/D38C9R435).

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Tags, a resource for nanoscience and nanotechnology, is supported by the National Science Foundation and other funding agencies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.