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  • 1
    Electronic Resource
    Electronic Resource
    Insight into protein topology from Monte Carlo simulations (2002)
    College Park, Md. : American Institute of Physics (AIP)
    The Journal of Chemical Physics 117 (2002), S. 3499-3503 
    Details
    ISSN: 1089-7690
    Source: AIP Digital Archive
    Topics: Physics , Chemistry and Pharmacology
    Notes: Understanding the sequence elements coding for protein topologies is an important step toward determining structures from sequences. Here we use a Monte Carlo approach to generate the equilibrium conformations of the 56 amino acid B1 domain of protein G and several fragments, corresponding to progressive elongations from its N-terminus. This method, which is not biased towards any particular topology and starts from random conformations, yields α/β topologies deviating by 3 Å rms from the experimental structure. It is found that this simple protein model helps clarify the diversity of rate limiting steps that is observed in proteins sharing 15%–80% sequence identities with protein G. © 2002 American Institute of Physics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1063/1.1494427
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  • 2
    Electronic Resource
    Electronic Resource
    A new spectroscopic molecular mechanics force field. Parameters for proteins (1995)
    College Park, Md. : American Institute of Physics (AIP)
    The Journal of Chemical Physics 102 (1995), S. 8586-8605 
    Details
    ISSN: 1089-7690
    Source: AIP Digital Archive
    Topics: Physics , Chemistry and Pharmacology
    Notes: We present the development of a new spectroscopic molecular mechanics potential for proteins. SPASIBA merges the torsional, van der Waals, electrostatic, and hydrogen bond potentials of AMBER with the Urey–Bradley–Shimanouchi terms for bond lengths and bond angles. To begin, SPASIBA was consistently parametrized to structural, energies, and vibrational frequency data of model compounds representative of the 16 nonaromatic acids: n-alkanes, alcohols, acids, ethyl methyl sulfide, methyl sulfide in water, ethanethiol, dimethyl disulfide, guanidium ion, propionamide, N-methylacetamide, N-methylisobutyramide, and N-isopropylacetamide. The parameters were then transferred to N-acetyl-X methylamides (where X=Gly, L-Ala, L-Pro), the L-Leu, L-Cys, and L-Thr amino acids blocked by the carboxylate and ammonium ions, and the right-handed deca-alanine and Gly-L-Pro-Gly-Gly peptides. Results show that SPASIBA reproduces vibrational frequencies (with much higher accuracy than present molecular mechanics potentials), as well as potential energy distributions of normal modes. For the 532 fundamental frequencies considered for refining the force field, the mean frequency error is 13 cm−1. This force field, which can be incorporated in molecular dynamics simulations, is also found to provide useful insights into the conformation/vibrational spectrum relationships. This ability is illustrated on the Gly and L-Ala dipeptides, as well as the polypeptides. © 1995 American Institute of Physics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1063/1.468848
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  • 3
    Electronic Resource
    Electronic Resource
    From polypeptide sequences to structures using Monte Carlo simulations and an optimized potential (1999)
    College Park, Md. : American Institute of Physics (AIP)
    The Journal of Chemical Physics 111 (1999), S. 2301-2310 
    Details
    ISSN: 1089-7690
    Source: AIP Digital Archive
    Topics: Physics , Chemistry and Pharmacology
    Notes: The ab initio prediction of the structure of a polypeptide from its sequence necessarily requires the detection of the lowest energy forms which correspond to the native state of the polypeptide. A potential for modeling the energy hypersurface of polypeptides using a hybrid level of description is optimized for the structures of four training peptides which have been shown experimentally to adopt α, ββ, αβ, and ββα conformations in aqueous solution. This potential is then used in diffusion process-controlled Monte Carlo simulations to predict the native structures of this training set of peptides and a test set of 20 peptides which were not themselves used during the optimization of the potential. Starting from various fully extended conformations, all simulations lead to an ensemble of conformations compatible with experimental results. These conformations include simple motifs such as coil, α helix, β-turn, β-hairpin, βα, and coil-α conformations, but also more complex motifs such as turnlike, ββα, βββ, and α-helical hairpin conformations. © 1999 American Institute of Physics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1063/1.479501
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  • 4
    Electronic Resource
    Electronic Resource
    Finding the low-energy forms of avian pancreatic polypeptide with the diffusion-process-controlled Monte Carlo method (1998)
    College Park, Md. : American Institute of Physics (AIP)
    The Journal of Chemical Physics 109 (1998), S. 1567-1574 
    Details
    ISSN: 1089-7690
    Source: AIP Digital Archive
    Topics: Physics , Chemistry and Pharmacology
    Notes: Ab initio folding of the avian pancreatic polypeptide using a diffusion-process-controlled Monte Carlo method is presented. This method differs from other Monte Carlo methods in that two successive conformations must be kinetically connected in a small period of time. The 36-residue polypeptide is represented using a hybrid level of structure description: the backbone is treated at an all-atom level, while the side chains are modeled as spheres. The conformations are evaluated on the basis of pairwise contact energies between the side chains, a main chain hydrogen bonding potential, and local bonded potentials. Starting from various extended conformations, the chain reaches the basin of lowest energy in ∼1000–3500 Monte Carlo steps and the predicted conformations deviate by ∼3.0 Å rms from the x-ray structure. The eight trajectories suggest a three-step mechanism: (1) early formation of the α helix in the region 14–33, (2) cooperative formation of long-range interactions, and (3) stabilization of the polyprolinelike conformation in the region 1–8 in the final steps of folding. © 1998 American Institute of Physics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1063/1.476708
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  • 5
    Electronic Resource
    Electronic Resource
    A diffusion process-controlled Monte Carlo method for finding the global energy minimum of a polypeptide chain. I. Formulation and test on a hexadecapeptide (1997)
    College Park, Md. : American Institute of Physics (AIP)
    The Journal of Chemical Physics 106 (1997), S. 5260-5270 
    Details
    ISSN: 1089-7690
    Source: AIP Digital Archive
    Topics: Physics , Chemistry and Pharmacology
    Notes: Finding the global energy minimum region of a polypeptide chain, independently of the starting conformation and in a reasonable computational time, is of fundamental interest. To approach this problem, a new Monte Carlo method is proposed and applied to the hexadecapeptide model Ac-(AAQAA)3Y(NH2), in which the global energy minimum conformation, an α helix, is known. In order to reduce the available conformational space, the backbone dihedral angles φ and ψ are restricted to a discrete set of ten regions and the side chains are modeled by a two-point representation. The energy used in these off-lattice simulations is of Amber type with a simplified hydrophobic potential. The novelty of the method is that, prior to the minimization of the energy, the move from the current conformation to the next must satisfy a kinetic requirement. The kinetic requirement is that there exists an upper bound on the escape time from the current conformation. From diffusion consideration it is shown that the escape time correlates with the angular deviations of the residues. The effectiveness of the approach is illustrated by a total of 25 biased simulations (i.e., using specific probabilities for the ten φ–ψ regions) and five unbiased simulations (i.e., the 10 regions are equiprobable before application of the kinetic requirement), starting from various conformations. It is found that all biased and unbiased simulations find the global minimum energy structure in ∼102–103 Monte Carlo steps, although the estimated probability of getting the full α helix is ∼10−11–10−16. © 1997 American Institute of Physics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1063/1.473525
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  • 6
    Electronic Resource
    Electronic Resource
    Folding a 20 amino acid αβ peptide with the diffusion process-controlled Monte Carlo method (1997)
    College Park, Md. : American Institute of Physics (AIP)
    The Journal of Chemical Physics 107 (1997), S. 1941-1947 
    Details
    ISSN: 1089-7690
    Source: AIP Digital Archive
    Topics: Physics , Chemistry and Pharmacology
    Notes: In this study we report on the application of the diffusion process-controlled Monte Carlo method to a 20 amino acid αβ peptide (Ac-E-T-Q-A-A-L-L-A-A-Q-K-A-Y-H-P-M-T-M-T-G-Am). The polypeptide chain is represented by a set of 126 particles, the side chains are modeled by spheres, and the backbone dihedral angles φ and ψ of each of the amino acid residue are essentially restricted to a set of ten high probability regions, although the whole φ-ψ space may be visited in the course of the simulation. The method differs from other off-lattice Monte Carlo methods, in that the escape time from one accepted conformation to the next is estimated and limited at each iteration. The conformations are evaluated on the basis of pairwise nonbonded side chain energies derived from statistical distributions of contacts in real proteins and a simple main chain hydrogen bonding potential. As a result of four simulations starting from random extended conformations and one starting from a structure consistent with NMR data, the lowest-energy conformation (i.e., the αβ fold) is detected in ∼103 Monte Carlo steps, although the estimated probability of getting the αβ motif is ∼10−12. The predicted conformations deviate by 3.0 Å rms from a model structure compatible with the experimental results. In this work further evidence is provided that this method is useful in determining the lowest-energy region of medium-size polypeptide chains. © 1997 American Institute of Physics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1063/1.474546
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  • 7
    Electronic Resource
    Electronic Resource
    Conformational studies of neuroactive ligands. 1. Force field and vibrational spectra of crystalline acetylcholine (1989)
    s.l. : American Chemical Society
    The @journal of physical chemistry 〈Washington, DC〉 93 (1989), S. 1338-1350 
    Details
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology , Physics
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1021/j100341a033
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  • 8
    Electronic Resource
    Electronic Resource
    Conformational studies of neuroactive ligands. 2. Solution-state conformations of acetylcholine (1989)
    s.l. : American Chemical Society
    The @journal of physical chemistry 〈Washington, DC〉 93 (1989), S. 1351-1357 
    Details
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology , Physics
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1021/j100341a034
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  • 9
    Electronic Resource
    Electronic Resource
    Ab initio polypeptide structure prediction (2000)
    Springer
    Theoretical chemistry accounts 104 (2000), S. 1-6 
    Details
    ISSN: 1432-2234
    Keywords: Key words: Peptide structure prediction – Chain representation – Effective potential – Global optimization approaches
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract. The biological activity of a polypeptide strongly depends on its 3D structure. Ab initio prediction of the native structure from the sequence of amino acids has long motivated the development of an optimum energy model such that interactions present in the native conformation are stronger than those present in nonnative conformations and of algorithms capable of finding the basin of lowest free energy among an astronomically large number of possible conformations. Despite recent progress in our understanding of the factors responsible for both polypeptide stability and formation, computer simulations of polypeptide models are still far from being practical software tools for biologists. In this work, state-of-the-art computer simulations aimed at ab initio structure prediction in aqueous solution are reviewed and their strengths and weaknesses are highlighted.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s002149900095
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  • 10
    Electronic Resource
    Electronic Resource
    A truncated Newton minimizer adapted for CHARMM and biomolecular applications (1994)
    New York, NY [u.a.] : Wiley-Blackwell
    Journal of Computational Chemistry 15 (1994), S. 532-552 
    Details
    ISSN: 0192-8651
    Keywords: Computational Chemistry and Molecular Modeling ; Biochemistry
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Computer Science
    Notes: We report the adaptation of the truncated Newton minimization package TNPACK for CHARMM and biomolecular energy minimization. TNPACK is based on the preconditioned linear conjugate-gradient technique for solving the Newton equations. The structure of the problem - sparsity of the Hessian - is exploited for preconditioning. Experience with the new version of TNPACK is presented on a series of molecular systems of biological and numerical interest: alanine dipeptide (N-methyl-alanyl-acetamide), a dimer of N-methyl-acetamide, deca-alanine, mellitin (26 residues), avian pancreatic polypeptide (36 residues), rubredoxin (52 residues), bovine pancreatic trypsin inhibitor (58 residues), a dimer of insulin (99 residues), and lysozyme (130 residues). Detailed comparisons among the minimization algorithms available in CHARMM, particularly those used for large-scale problems, are presented along with new mathematical developments in TNPACK. The new TNPACK version performs significantly better than ABNR, the most competitive minimizer in CHARMM, for all systems tested in terms of CPU time when curvature information (Hessian/vector product) is calculated by a finite-difference of gradients (the numeric option of TNPACK). The remaining derivative quantities are, however, evaluated analytically in TNPACK. The CPU gain is 50% or more (speedup factors of 1.5 to 2.5) for the largest molecular systems tested and even greater for smaller systems (CPU factors of 1 to 4 for small systems and 1 to 5 for medium systems). TNPACK uses curvature information to escape from undesired configurational regions and to ensure the identification of true local minima. It converges rapidly once a convex region is reached and achieves very low final gradient norms, such as of order 10-8, with little additional work. Even greater overall CPU gains are expected for large-scale minimization problems by making the architectures of CHARMM and TNPACK more compatible with respect to the second-derivative calculations. © 1994 by John Wiley & Sons, Inc.
    Additional Material: 4 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/jcc.540150506
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