Efficiency of simulated annealing for peptides with increasing geometrical restrictions

Baysal, Canan; Meirovitch, Hagai

doi:10.1002/(sici)1096-987x(19991130)20:15<1659::aid-jcc6>3.0.co;2-f

Cited by 21 publications

(20 citation statements)

References 50 publications

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“…However, in many cases this process is not efficient. For linear peptides [39], the MCM method [37] is significantly more efficient than simulated annealing as generator of low-energy minimized structures. Another approach to enhance sampling in protein folding and peptides simulations is to perform simulations in the so-called generalized ensembles.…”

Section: Conformations In the Solid Statementioning

confidence: 99%

Natural peptide analgesics: the role of solution conformation

Spadaccini¹,

Temussi²

2001

CMLS, Cell. Mol. Life Sci.

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Endogenous opioids have been studied extensively since their discovery, in the hope of finding a perfect analgesic, devoid of the secondary effects of alkaloid opioids. However, the design of selective opioid agonists has proved very difficult. First, structural studies of peptides in general are hampered by their intrinsic flexibility. Second, the relationship between constitution and the so-called 'bioactive conformation' is far from obvious. Ideally, a direct structural study of the complex between a peptide and its receptor should answer both questions, but such a study is not possible, because opioid receptors are large membrane proteins, difficult to study by standard structural techniques. Thus, conformational studies of opioid peptides are still important for drug design and also for indirect receptor mapping. This review deals with conformational studies of natural opioid peptides in several solvents that mimic in part the different environments in which the peptides exert their action. None of the structural investigations yields a convincing bioactive conformation, but the global conformation of longer peptides in biomimetic environments can shed light on the interaction with receptors.

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Section: Conformations In the Solid Statementioning

confidence: 99%

Natural peptide analgesics: the role of solution conformation

Spadaccini¹,

Temussi²

2001

CMLS, Cell. Mol. Life Sci.

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“…Therefore, MCMAB is also much more efficient than simulated annealing that was found to be inferior to MCM. [19][20][21][22][23] Because MCMAB does not rely on structural organization, one would expect that tailoring its main features to available clustering techniques would enhance its efficiency even further. The present results suggest that MCMAB can also be improved by considering additional angular-energy correlations, such as those involving 1 -2 , 460 352 36740 1667 697 697 882 882 6589 1811 2492 1514 2572 1407 9437 5598 334 463 3294 2301 2128 2292 18136 4319 315 315 4938 7126 3760 2082 9100 3674 17698 3762 585 585 295 295 9740 3565 5962 1917 1335 1335 2523 1792 1850 3955 8773 4730 3036 1855 8964 2853 16801 8070 15594 5548 29908 …”

Section: Resultsmentioning

confidence: 99%

Efficient Conformational Search Method for Peptides and Proteins: Monte Carlo Minimization with an Adaptive Bias

Ozkan

Meirovitch

2003

J. Phys. Chem. B

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The energy function of a protein consists of a tremendous number of minima. Locating the global energy minimum (GEM), which corresponds to the native structure, is a severe problem in global optimization. The commonly used Monte Carlo minimization (MCM) method is based on a random selection of torsional angle values. We suggest selecting these values with biased probabilities depending on the increased structureenergy correlations as the GEM is approached during the search. Our method applied to models of the 5-residue peptide Leu-enkephalin finds the GEM ∼2.7 faster than MCM.

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“…This angular criterion, which is based on energetic considerations, has been found to be suitable for a short peptide, whereas for a long peptide or loop, an additional criterion, such as the RMSD between structures should be employed (see discussion in Ref. 53). Each selected structure then becomes a "seed" for an MC or MD simulation that spans the related wide microstate.…”

Section: Methodology For Treating Flexibilitymentioning

confidence: 99%

“…This optimization requires extensive conformational search for low-energy minimized structures, which is carried out with our highly efficient local torsional deformations (LTD) method. 46,52,53 From a large sample of structures generated by LTD (using E tot with the optimized ASPs) one identifies a relatively small group of low-energy structures that are significantly different. Each of the latter becomes a "seed" for Monte Carlo simulation which spans its vicinity, the free energies (hence the relative populations) are calculated from the MC samples with the local states (LS) method, 54,55 and averages of various properties over the samples' contributions weighted by the populations can be calculated and compared with the experiment.…”

Section: Introductionmentioning

confidence: 99%

Solvation parameters for predicting the structure of surface loops in proteins: Transferability and entropic effects

Das

Meirovitch

2003

Proteins

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A new procedure for optimizing parameters of implicit solvation models introduced by us has been applied successfully first to cyclic peptides and more recently to three surface loops of ribonuclease A (Das and Meirovitch, Proteins 2001; 43:303-314) using the simplified model E tot ‫؍‬ E FF ( ‫؍‬ nr) ؉ ⌺ i i A i , where i are atomic solvation parameters (ASPs) to be optimized, A i is the solvent accessible surface area of atom i, E FF ( ‫؍‬ nr) is the AMBER force-field energy of the loop-loop and looptemplate interactions with a distance-dependent dielectric constant, ‫؍‬ nr, where n is a parameter. The loop is free to move while the protein template is held fixed in its X-ray structure; an extensive conformational search for energy minimized loop structures is carried out with our local torsional deformation method. The optimal ASPs and n are those for which the structure with the lowest minimized energy [E tot (n, i )] becomes the experimental X-ray structure, or less strictly, the energy gap between these structures is within 2-3 kcal/mol. To check if a set of ASPs can be defined, which is transferable to a large number of loops, we optimize individual sets of ASPs (based on n ‫؍‬ 2) for 12 surface loops from which an "averaged" best-fit set is defined. This set is then applied to the 12 loops and an independent "test" group of 8 loops leading in most cases to very small RMSD values; thus, this set can be useful for structure prediction of loops in homology modeling. For three loops we also calculate the free energy gaps to find that they are only slightly smaller than their energy counterparts, indicating that only larger n will enable reducing too large gaps. Because of its simplicity, this model allowed carrying out an extensive application of our methodology, providing thereby a large number of benchmark results for comparison with future calculations based on n > 2 as well as on more sophisticated solvation models with as yet unknown performance for loops. Proteins 2003;51:470 -483.

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Efficiency of simulated annealing for peptides with increasing geometrical restrictions

Cited by 21 publications

References 50 publications

Natural peptide analgesics: the role of solution conformation

Natural peptide analgesics: the role of solution conformation

Efficient Conformational Search Method for Peptides and Proteins: Monte Carlo Minimization with an Adaptive Bias

Solvation parameters for predicting the structure of surface loops in proteins: Transferability and entropic effects

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