Molecular Modeling Programs
Visual
Molecular Dynamics
by
NIH Biomedical Research Center for
Macromolecular Modeling and BioinformaticsVMD is designed for modeling, visualization, and analysis of biological systems such as proteins, nucleic acids, lipid bilayer assemblies, etc. It may be used to view more general molecules, as VMD can read standard Protein Data Bank (PDB) files and display the contained structure. VMD provides a wide variety of methods for rendering and coloring a molecule: simple points and lines, CPK spheres and cylinders, licorice bonds, backbone tubes and ribbons, cartoon drawings, and others. VMD can be used to animate and analyze the trajectory of a molecular dynamics (MD) simulation. In particular, VMD can act as a graphical front end for an external MD program by displaying and animating a molecule undergoing simulation on a remote computer.
Molecular Dynamics
by
NIH Biomedical Research Center for
Macromolecular Modeling and BioinformaticsNAMD, recipient of a 2002 Gordon Bell Award and a 2012 Sidney Fernbach Award, is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. Based on Charm++ parallel objects, NAMD scales to hundreds of cores for typical simulations and beyond 500,000 cores for the largest simulations. NAMD uses the popular molecular graphics program VMD for simulation setup and trajectory analysis, but is also file-compatible with AMBER, CHARMM, and X-PLOR. NAMD is distributed free of charge with source code. You can build NAMD yourself or download binaries for a wide variety of platforms. Our tutorials show you how to use NAMD and VMD for biomolecular modeling
Molecular Dynamics
by
Erik Lindahl, David van der Spoel, Berk Hess
GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles
It is primarily designed for biochemical molecules like proteins, lipids and nucleic acids that have a lot of complicated bonded interactions, but since GROMACS is extremely fast at calculating the nonbonded interactions (that usually dominate simulations) many groups are also using it for research on non-biological systems, e.g. polymers.
GROMACS supports all the usual algorithms you expect from a modern molecular dynamics implementation, (check the online reference or manual for details), but there are also quite a few features that make it stand out from the competition:
Molecular Dynamics
by Jay Ponder's LabThe Tinker molecular modeling software is a complete and general package for molecular mechanics and dynamics, with some special features for biopolymers. Tinker has the ability to use any of several common parameter sets, such as Amber (ff94, ff96, ff98, ff99, ff99SB), CHARMM (19, 22, 22/CMAP), Allinger MM (MM2-1991 and MM3-2000), OPLS (OPLS-UA, OPLS-AA), Merck Molecular Force Field (MMFF), Liam Dang's polarizable model, AMOEBA (2004, 2009, 2013, 2017, 2018) polarizable atomic multipole force fields, AMOEBA+ that adds charge penetration effects, and our new HIPPO (Hydrogen-like Interatomic Polarizable POtential) force field. Parameter sets for other force field models are under consideration for future releases.
In October 2000, Folding@home was officially released. The main software core engine was the Tinker molecular dynamics (MD) code. Tinker was chosen as the first scientific core due to its versatility and well laid out software design. In particular, Tinker was the only code to support a wide variety of MD force fields and solvent models. With the Tinker core, we were able to make several advances, including the first folding of a small protein starting purely from sequence (subsequently published in Nature).
Tinker official site
Tinker Users Guide
Build molecular structures with Tinker and files format
Start Using Tinker
Tinker file examples for NVT, NPT (*.xyz, *.key, *.dyn, *.arc)
Tinker tutorial
Tinker tutorial for box, protein, free energy
Molecular Modeling
Books
Molecular Modeling and Simulation: An Interdisciplinary Guide
by Review of previous edition: “I am often asked by physicists, mathematicians and engineers to recommend a book that would be useful to get them started in computational molecular biology. I am also often approached by my colleagues in computational biology to recommend a solid textbook for a graduate course in the area. Tamar Schlick has written the book that I will be recommending to both groups. Tamar has done an amazing job in writing a book that is both suitably accessible for beginners, and suitably rigorous for experts.” J. J. Collins, Boston University, USA. “Molecular modeling … is now an important branch of modern biochemistry. … Schlick has brought her unique interdisciplinary expertise to the subject. … One of the most distinguished characteristics of the book is that it makes the reading really fun … and the material accessible. … a crystal clear logical presentation … . Schlick has added a unique title to the collection of mathematical biology textbooks … . a valuable introduction to the field of computational molecular modeling. It is a unique textbook … .” Hong Qian, SIAM Review, 2005.
Molecular Modeling and Prediction of Bioactivity
byMuch of chemistry, molecular biology, and drug design, are centered around the relationships between chemical structure and measured properties of compounds and polymers, such as viscosity, acidity, solubility, toxicity, enzyme binding, and membrane penetration. For any set of compounds, these relationships are by necessity complicated, particularly when the properties are of biological nature. To investigate and utilize such complicated relationships, henceforth abbreviated SAR for structure-activity relationships, and QSAR for quantitative SAR, we need a description of the variation in chemical structure of relevant compounds and biological targets, good measures of the biological properties, and, of course, an ability to synthesize compounds of interest. In addition, we need reasonable ways to construct and express the relationships, i. e. , mathematical or other models, as well as ways to select the compounds to be investigated so that the resulting QSAR indeed is informative and useful for the stated purposes. In the present context, these purposes typically are the conceptual understanding of the SAR, and the ability to propose new compounds with improved property profiles. Here we discuss the two latter parts of the SARlQSAR problem, i. e. , reasonable ways to model the relationships, and how to select compounds to make the models as "good" as possible. The second is often called the problem of statistical experimental design, which in the present context wecall statistical molecular design, SMD. 1.
Computational Chemistry: A Practical Guide for Applying Techniques to Real World Problems
byA practical, easily accessible guide for bench-top chemists, this book focuses on accurately applying computational chemistry techniques to everyday chemistry problems.
Provides nonmathematical explanations of advanced topics in computational chemistry.
Focuses on when and how to apply different computational techniques.
Addresses computational chemistry connections to biochemical systems and polymers.
Provides a prioritized list of methods for attacking difficult computational chemistry problems, and compares advantages and disadvantages of various approximation techniques.
Describes how the choice of methods of software affects requirements for computer memory and processing time.