We describe here a general Amber force field (GAFF) for organic molecules. GAFF is designed to be compatible with existing Amber force fields for proteins and nucleic acids, and has parameters for most organic and pharmaceutical molecules that are composed of H, C, N, O, S, P, and halogens. It uses a simple functional form and a limited number of atom types, but incorporates both empirical and heuristic models to estimate force constants and partial atomic charges. The performance of GAFF in test cases is encouraging. In test I, 74 crystallographic structures were compared to GAFF minimized structures, with a root-mean-square displacement of 0.26 A, which is comparable to that of the Tripos 5.2 force field (0.25 A) and better than those of MMFF 94 and CHARMm (0.47 and 0.44 A, respectively). In test II, gas phase minimizations were performed on 22 nucleic acid base pairs, and the minimized structures and intermolecular energies were compared to MP2/6-31G* results. The RMS of displacements and relative energies were 0.25 A and 1.2 kcal/mol, respectively. These data are comparable to results from Parm99/RESP (0.16 A and 1.18 kcal/mol, respectively), which were parameterized to these base pairs. Test III looked at the relative energies of 71 conformational pairs that were used in development of the Parm99 force field. The RMS error in relative energies (compared to experiment) is about 0.5 kcal/mol. GAFF can be applied to wide range of molecules in an automatic fashion, making it suitable for rational drug design and database searching.

/pdf/development-and-testing-of-a-general-amber-force-field-28v6k2tn5g.pdf

Development and testing of a general amber force field.

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.

/pdf/scalable-molecular-dynamics-with-namd-51o1z7rwp8.pdf

Scalable molecular dynamics with NAMD

CHARMM (Chemistry at HARvard Macromolecular Mechanics) is a highly flexible computer program which uses empirical energy functions to model macromolecular systems. The program can read or model build structures, energy minimize them by first- or second-derivative techniques, perform a normal mode or molecular dynamics simulation, and analyze the structural, equilibrium, and dynamic properties determined in these calculations. The operations that CHARMM can perform are described, and some implementation details are given. A set of parameters for the empirical energy function and a sample run are included.

CHARMM: A program for macromolecular energy, minimization, and dynamics calculations

New protein parameters are reported for the all-atom empirical energy function in the CHARMM program. The parameter evaluation was based on a self-consistent approach designed to achieve a balance between the internal (bonding) and interaction (nonbonding) terms of the force field and among the solvent−solvent, solvent−solute, and solute−solute interactions. Optimization of the internal parameters used experimental gas-phase geometries, vibrational spectra, and torsional energy surfaces supplemented with ab initio results. The peptide backbone bonding parameters were optimized with respect to data for N-methylacetamide and the alanine dipeptide. The interaction parameters, particularly the atomic charges, were determined by fitting ab initio interaction energies and geometries of complexes between water and model compounds that represented the backbone and the various side chains. In addition, dipole moments, experimental heats and free energies of vaporization, solvation and sublimation, molecular volume...

/pdf/all-atom-empirical-potential-for-molecular-modeling-and-2nds0dxw12.pdf

All-atom empirical potential for molecular modeling and dynamics studies of proteins.

NAMD is a parallel molecular dynamics code designed for high‐performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high‐end parallel platforms, as well as tens of processors on low‐cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 1781–1802, 2005

/pdf/scalable-molecular-dynamics-with-namd-3j7xyc70g4.pdf

Scalable Molecular Dynamics with NAMD

We present the development of a force field for simulation of nucleic acids and proteins. Our approach began by obtaining equilibrium bond lengths and angles from microwave, neutron diffraction, and prior molecular mechanical calculations, torsional constants from microwave, NMR, and molecular mechanical studies, nonbonded parameters from crystal packing calculations, and atomic charges from the fit of a partial charge model to electrostatic potentials calculated by ab initio quantum mechanical theory. The parameters were then refined with molecular mechanical studies on the structures and energies of model compounds. For nucleic acids, we focused on methyl ethyl ether, tetrahydrofuran, deoxyadenosine, dimethyl phosphate, 9-methylguanine-l-methylcytosine hydrogen-bonded complex, 9-methyladenine-l-methylthymine hydrogen-bonded complex, and 1,3-dimethyluracil base-stacked dimer. Bond, angle, torsional, nonbonded, and hydrogen-bond parameters were varied to optimize the agreement between calculated and experimental values for sugar pucker energies and structures, vibrational frequencies of dimethyl phosphate and tetrahydrofuran, and energies for base pairing and base stacking. For proteins, we focused on 4>,'lt maps of glycyl and alanyl dipeptides, hydrogen-bonding interactions involving the various protein polar groups, and energy refinement calculations on insulin. Unlike the models for hydrogen bonding involving nitrogen and oxygen electron donors, an adequate description of sulfur hydrogen bonding required explicit inclusion of lone pairs. There are two fundamental problems in simulating the struc­ tural and energetic properties of molecules: the first is how to choose an analytical been placed E(R) which correctly describes the energy of the system in terms of its 3N degrees of freedom. The second is how the simulation can search or span conforma­ tional space (R) in order to answer questions posed by the scientist interested in the properties of the system. For complex systems, solution to the first problem are an es­ sential first step in attacking the second problem, and thus, considerable effort has been placed in developing analytical functions that are simple enough to allow one to simulate the properties of complex molecules yet accurate enough to obtain meaningful estimates for structures and energies. In the case of the structures and thermodynamic stabilities of saturated hydrocarbons in inert solvents or the gas phase, the first problem has been essentially solved by molecular mechanics ap­ proaches of Allinger, I Ermer and Lifson,2 and their co-workers. However, for polar and ionic molecules in condensed phases, unsolved questions remain as to the best form of the analytical function E(R). In the area of proteins and peptides, seminal work has come from the Scheraga 3 and Lifson 4 schools. The Scheraga group has used both crystal packing (intermolecular) and con­ formational properties of peptides to arrive at force fields ECEPP, UNECEPP, and EPEN for modeling structural and thermodynamic properties of peptides and proteins. Levitt, using the energy refinement software developed in the Lifson group, has proposed a force field for proteins based on calculations on lysozyme,S and Gelin and Karplus have adapted this software along with many parameters from the Scheraga studies to do molecular dynamics

A new force field for molecular mechanical simulation of nucleic acids and proteins

We describe a computer program we have been developing to build models of molecules and calculate their interactions using empirical energy approaches. The program is sufficiently flexible and general to allow modeling of small molecules, as well as polymers. As an illustration, we present applications of the program to study the conformation of actinomycin D. In particular, we study the rotational isomerism about the D-Val-, L-Pro, and L-Pro-Sar amide bonds as well as comparing the energy and structure of the Sobell model and the x-ray structure of actinomycin D.

AMBER: Assisted model building with energy refinement. A general program for modeling molecules and their interactions

Color-coded computer graphics representations of the electrostatic potentials of trypsin, trypsin-inhibitor, prealbumin and its thyroxine complex, fragments of double-helical DNA, and a netropsin--DNA complex illustrate the electrostatic and topographic complementarity in macromolecule-ligand interactions. This approach is powerful in revealing intermolecular specificity and shows promise of having predictive value in drug design.

https://www.pnas.org/content/pnas/79/12/3754.full.pdf

Electrostatic potential molecular surfaces

Molecular mechanics simulation of protein-ligand interactions: binding of thyroid hormone analogs to prealbumin

We present the results of molecular-mechanics studies on base-paired dinucleoside phosphates and hexanucleoside pentaphosphates. Starting from B-DNA-like conformations, we have refined the nucleic acid conformations, allowing all degrees of freedom to relax. The calculated energies of different base sequences are used to investigate the basis for the different stabilities of DNA polymers of different sequences, as found by Wells et al. [J. Mol. Biol.54, 465–497, 1970]. Our calculations appear to reproduce the relative melting temperatures of sequence isomers as well or better than any of the previous calculations which addresssed the question of nucleotide stability. We offer a detailed physical explanation of three observations, that poly(dA)·poly(dT) melts higher than poly[d(A-T)]·poly[d(A-T)] by 6°C, that poly(dG)·poly(dC) melts lower than poly[d(G-C)]·poly[d(G-C)] by 12°C, and that poly(dA-dG)·poly(dT-dC) melts lower than poly(dA-dC)·poly(dT-dG) by 6°C. The dihedral angles found after refinement are similar to those of Levitt [Proc. Natl. Acad. Sci. USA75, 640–644, 1978] and differ by ∼20° from the B-DNA values. We also see evidence of base tilting and twisting similar to that found by Levitt. The two main differences in the results of our calculations and Levitt's lie in the sugar puckers [we find mainly C(2′)endo] and in the tendency to stay in local “torsional” minima (we find a number of examples of C(3′)endo sugar puckering and ω′ = trans rather than gauche). Both of these results are dependent on the nature of the potential function used in our study. However, our finding of movement from local torsional minima is suggestive of the significant flexibility of double-stranded deoxynucleotides.

Studies of nucleotide conformations and interactions. The relative stabilities of double-helical B-DNA sequence isomers.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Paul K. Weiner

Author Tools

Chat about Author