Side-chain torsional potentials: effect of dipeptide, protein, and solvent environment

Gelin, Bruce R.; Karplus, Martin

doi:10.1021/bi00574a022

Cited by 295 publications

(153 citation statements)

References 13 publications

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“…However, the latter was carried out using the cruder force field representation within the program CHARMM. 38,39 Using the DFT data the MM2 force field was augmented to reproduce the dihedral angle energy for the O-C-C-Cl dihedral. To do this we followed the dihedral fitting procedure of Cheung et al, 40 to fit to the torsional terms in the force field.…”

Section: Temperature Dependent Twist Inversion Moleculementioning

confidence: 99%

Predictions of molecular chirality and helical twisting powers: A theoretical study

Earl

Wilson

2003

The Journal of Chemical Physics

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A theoretical study of a number of chiral molecules has been undertaken using a molecular Monte Carlo simulation approach coupled with calculations of molecular chirality based on a chirality order parameter. Results for a variety of TADDOL (α,α,α′,α′-tetraaryl-1,3-dioxolan-4,5dimethanol) derivatives show good agreement with experimental findings for the sign, magnitude, and the temperature dependence of the helical twisting power (HTP). For a photochromic chiral dopant with variable HTP we are able to model the reduction in the HTP when photoisomerization occurs. Our studies on a liquid crystalline material with a single chiral center have reproduced a temperature dependent twist inversion in the material. We discuss the temperature and solvent dependence of the helical twisting power and argue that in all the systems studied here, preferential selection of certain molecular conformations at different temperatures and in different solvents are able to explain the observed experimental behavior of the HTP.

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Section: Temperature Dependent Twist Inversion Moleculementioning

confidence: 99%

Predictions of molecular chirality and helical twisting powers: A theoretical study

Earl

Wilson

2003

The Journal of Chemical Physics

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“…In continuous space methods (7,8), any side chain torsion angle may be sampled. Discrete space methods are based on the assumption that side chains exist in energetically preferred conformations called rotamers, which are local minima conformers that have been sampled by statistical analysis of known structures (9)(10)(11)(12)(13)(14). Discrete space methods cannot predict conformations that are not present in the rotamer database.…”

mentioning

confidence: 99%

A stochastic algorithm for global optimization and for best populations: A test case of side chains in proteins

Glick

Rayan

Goldblum

2002

Proc. Natl. Acad. Sci. U.S.A.

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The problem of global optimization is pivotal in a variety of scientific fields. Here, we present a robust stochastic search method that is able to find the global minimum for a given cost function, as well as, in most cases, any number of best solutions for very large combinatorial ''explosive'' systems. The algorithm iteratively eliminates variable values that contribute consistently to the highest end of a cost function's spectrum of values for the full system. Values that have not been eliminated are retained for a full, exhaustive search, allowing the creation of an ordered population of best solutions, which includes the global minimum. We demonstrate the ability of the algorithm to explore the conformational space of side chains in eight proteins, with 54 to 263 residues, to reproduce a population of their low energy conformations. The 1,000 lowest energy solutions are identical in the stochastic (with two different seed numbers) and full, exhaustive searches for six of eight proteins. The others retain the lowest 141 and 213 (of 1,000) conformations, depending on the seed number, and the maximal difference between stochastic and exhaustive is only about 0.15 Kcal͞mol. The energy gap between the lowest and highest of the 1,000 low-energy conformers in eight proteins is between 0.55 and 3.64 Kcal͞mol. This algorithm offers real opportunities for solving problems of high complexity in structural biology and in other fields of science and technology.M any problems in life sciences and in other fields of science and technology are of high complexity, thus requiring sophisticated methods of searching and scoring to achieve the ability to study and to simulate them by means of a computer simulation. An excellent search method coupled with a highly reliable scoring method should allow comparisons to some natural phenomena. In this article, we have taken the approach of comparing best populations found by a stochastic search method to a full, exhaustive search, as the crucial test of this method. However, comparisons to experimental results also are included. The problem chosen to exemplify this method is the positions of side chains in proteins, which is essential for both theoretical and experimental purposes. On the theoretical side, it is a subproblem in de novo protein structure prediction. It is essential for structure-based drug design (1), for inverse folding and threading algorithms (2), for predicting the effect of mutations on structure (3), for ab initio predictions of tertiary structure (4), for homology-based modeling (5), and others. From the x-ray crystallographer's point of view, it could speed the placement of side chains using the electron density maps of the main chain before refinement calculations. The main limitation is the large amount of possible conformations that each side chain may adopt (6). An exhaustive search of all possible conformations is beyond the scope of state of the art computers.Current strategies for side chain addition to a given backbone differ in three categories. The ...

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“…Besides the Poisson-Boltzmann and generalized Born-type approaches, another category of implicit models describes the solvent effect in terms of the dielectric screening of electrostatic interaction within the protein molecule. For example, this can be done by defining the dielectric coefficient as a simple function of distance (71,72) and as a more detailed function involving solvent-excluded volume (73), the distance of a charge from the protein surface, and the degree of exposure of a charge point to the solvent (74).…”

Section: Protein Structures In Aqueous Environmentmentioning

confidence: 99%

Scoring Functions for De Novo Protein Structure Prediction Revisited

Ngan

Hung²,

Liu³

et al. 2008

Protein Structure Prediction

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SummaryDe novo protein structure prediction methods attempt to predict tertiary structures from sequences based on general principles that govern protein folding energetics and/or statistical tendencies of conformational features that native structures acquire, without the use of explicit templates. A general paradigm for de novo prediction involves sampling the conformational space, guided by scoring functions and other sequence-dependent biases, such that a large set of candidate ("decoy") structures are generated, and then selecting native-like conformations from those decoys using scoring functions as well as conformer clustering. High-resolution refinement is sometimes used as a final step to fine-tune native-like structures. There are two major classes of scoring functions. Physics-based functions are based on mathematical models describing aspects of the known physics of molecular interaction. Knowledge-based functions are formed with statistical models capturing aspects of the properties of native protein conformations. We discuss the implementation and use of some of the scoring functions from these two classes for de novo structure prediction in this chapter.

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Side-chain torsional potentials: effect of dipeptide, protein, and solvent environment

Cited by 295 publications

References 13 publications

Predictions of molecular chirality and helical twisting powers: A theoretical study

Predictions of molecular chirality and helical twisting powers: A theoretical study

A stochastic algorithm for global optimization and for best populations: A test case of side chains in proteins

Scoring Functions for De Novo Protein Structure Prediction Revisited

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