The MM3 force field for amides, polypeptides and proteins

Lii, Jenn‐Huei; Allinger, Norman L.

doi:10.1002/jcc.540120208

Cited by 210 publications

(107 citation statements)

References 30 publications

(1 reference statement)

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“…12; the rotational barrier used for the amide bond was that for formamide (12). These data lead to S,, = 38.78, S,,, = 25.55, SVi, = 4.04, S , = 6.28, and = 74.65 cal deg-I mol-I.…”

Section: = X[hcon(me)ph (1) + 2meoh (L)]mentioning

confidence: 94%

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The tetrahedral intermediate from the hydration of N-methylformanilide

Guthrie¹,

Barker²,

Cullimore³

et al. 1993

Can. J. Chem.

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. Can. J. Chem. 71, 2109Chem. 71, (1993.Heats of hydrolysis of N-methylformanilide dimethyl acetal have been measured in basic solution. The heat of formation of N-methylformanilide was obtained by determining the equilibrium constant in aqueous solution for its formation from formic acid and N-methylaniline as a function of temperature: m : (~) = -33.78 * 1.85 kcal/mol. These data permit the calculation of the heat of formation of N-methylfonnanilide dimethyl acetal, AH;(l) = -72.95 * 1.85 kcal/mol. The free energy of formation of the tetrahedral intermediate in the hydrolysis of N-methylformanilide was calculated by methods we have previously reported. Consideration of the energetics of the intermediates and the known rates of reaction leads to the conclusion that the rate-determining step for alkaline hydrolysis is cleavage of the C-N bond. Operant en solution basique, on a mesurC les chaleurs d'hydrolyse l'acktal dimkthylique de la N-mCthylformanilide. On a obtenu la chaleur de formation de la N-mCthylformanilide en determinant la constante d'kquilibre en fonction de la temperature (en solution aqueuse) pour sa formation 2 partir de l'acide formique et la N-methylaniline: m;(1) = -33,78 * 1,85 kcal/mol. Ces donnCes permettent de calculer la chaleur de formation de I'acCtal dimkthylique de la N-mCthylformanilide, mY(1) = -72,95 * 1,85 kcal/mol. Utilisant des methodes dkcrites anterieurement, on a calculC 1'Cnergie libre de formation de I'intermCdiaire tCtraCdrique implique dans l'hydrolyse de la N-mCthylformanilide. Une considCration des energies des intermediaires et des constantes de vitesse connues mkne i la conclusion que 1'Ctape qui dktermine la vitesse de la reaction de l'hydrolyse alcaline est le clivage de la liaison C-N.[Traduit par la redaction]

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“…12; the rotational barrier used for the amide bond was that for formamide (12). These data lead to S,, = 38.78, S,,, = 25.55, SVi, = 4.04, S , = 6.28, and = 74.65 cal deg-I mol-I.…”

Section: = X[hcon(me)ph (1) + 2meoh (L)]mentioning

confidence: 94%

“…The standard entropy of N,N-dimethylformamide was calculated from the vibrational frequencies (12), moments of inertia and reduced moments of inertia calculated from the MM2 geometry, and values for the rotational barriers from ref. 12; the rotational barrier used for the amide bond was that for formamide (12).…”

Section: = X[hcon(me)ph (1) + 2meoh (L)]mentioning

confidence: 99%

The tetrahedral intermediate from the hydration of N-methylformanilide

Guthrie¹,

Barker²,

Cullimore³

et al. 1993

Can. J. Chem.

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“…34 Accordingly, the majority of force fields no longer include explicit terms for hydrogen bonding as was done in earlier force fields, 14,35 although exceptions exist. 20 Notable is that the current electrostatic models do not explicitly treat electronic polarizability. Instead, polarizability is included implicitly by choosing partial atomic charges that overestimate molecular dipoles.…”

Section: Potential Energy Functionsmentioning

confidence: 99%

Empirical force fields for biological macromolecules: Overview and issues

2004

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Empirical force field-based studies of biological macromolecules are becoming a common tool for investigating their structure-activity relationships at an atomic level of detail. Such studies facilitate interpretation of experimental data and allow for information not readily accessible to experimental methods to be obtained. A large part of the success of empirical force field-based methods is the quality of the force fields combined with the algorithmic advances that allow for more accurate reproduction of experimental observables. Presented is an overview of the issues associated with the development and application of empirical force fields to biomolecular systems. This is followed by a summary of the force fields commonly applied to the different classes of biomolecules; proteins, nucleic acids, lipids, and carbohydrates. In addition, issues associated with computational studies on "heterogeneous" biomolecular systems and the transferability of force fields to a wide range of organic molecules of pharmacological interest are discussed.

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“…(5) Minimization in Cartesian coordinate space (e.g., steepest descents, conjugate-gradient, Fletcher-Powell) may be performed following the conformational selection but prior to Metropolis sampling. We used the DREIDING force field (Mayo et al, 1990), which yields excellent results for structural minimization; however, other force fields, i.e., AMBER (Weiner et al, 1986), CHARMM (Brooks et al, 1983), MM3 (Lii & Allinger, 1991), and UFF (Rappe et al, 1992), can be utilized as well.…”

Section: The Monte Cad0 Methodsmentioning

confidence: 99%

De novo prediction of polypeptide conformations using dihedral probability grid Monte Carlo methodology

et al. 1995

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We tested the dihedral probability grid Monte Carlo (DPG-MC) methodology to determine optimal conformations of polypeptides by applying it to predict the low energy ensemble for two peptides whose solution NMR structures are known: integrin receptor peptide (YGRGDSP, Type I1 P-turn) and S3 a-helical peptide (YMSEDEL KAAEAAFKRHGPT).DPG-MC involves importance sampling, local random stepping in the vicinity of a current local minima, and Metropolis sampling criteria for acceptance or rejection of new structures. Internal coordinate values are based on side-chain-specific dihedral angle probability distributions (from analysis of high-resolution protein crystal structures). Important features of DPG-MC are: (1) Each DPG-MC step selects the torsion angles (6, $, x) from a discrete grid that are then applied directly to the structure. The torsion angle increments can be taken as S = 60, 30, 15, 10, or 5", depending on the application. (2) DPG-MC utilizes a temperature-dependent probability function ( P ) in conjunction with Metropolis sampling to accept or reject new structures.For each peptide, we found close agreement with the known structure for the low energy conformational ensemble located with DPG-MC. This suggests that DPG-MC will be useful for predicting conformations of other polypeptides.Keywords: computational chemistry; importance sampling; Monte Carlo; peptide conformation; protein conformation; protein folding A full understanding of protein function requires knowledge of the three-dimensional structure. Unfortunately, experimentally determined structures for most proteins are unavailable. Consequently, it is essential to develop approaches for predicting secondary and tertiary structures of proteins.In the past decade, several approaches to protein structure prediction have evolved, including: (1) lattice search methods

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The MM3 force field for amides, polypeptides and proteins

Cited by 210 publications

References 30 publications

The tetrahedral intermediate from the hydration of N-methylformanilide

The tetrahedral intermediate from the hydration of N-methylformanilide

Empirical force fields for biological macromolecules: Overview and issues

De novo prediction of polypeptide conformations using dihedral probability grid Monte Carlo methodology

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