Linear conformational correlate

Concept of cyclic conformations with linear conformational correlates. More information on the details of the PPP yS-spiral can be obtained by considering a series of cyclic analogs. The concept being utilized here is that of cyclic conformations with linear conformational correlates. The idea is that a helix with a large number of residues per turn can have a low pitch and only small changes in torsion angles are sufficient to interconvert between cyclic and linear helical conformations. With this... 

Because of a smaller rotation of 16° in the propeller conformation (E-conformation), the conformation dependence of the substituent effect correlations is not very serious in this system. Nevertheless, the non-linear behaviour should be similar to that in the trityl carbocation system. From comparison of the plots in Figs 8 and 15, the plot for Y = p-MeO for the benzhydryl cation should be related to the correlation for the T-conformation and that for Y = P-NO2 should be related to the P-conformer correlation. The difference in the slopes gives no clue as to the intrinsic selectivity (p) of this system. 

D.W. Urry, T.L. TVapane, H. Sugano, and K.U. Prasad, Sequential polypeptides of elastin cyclic conformational correlates of the linear polypentapep-tide. J Am Chem Soc 103,2080-2089,1981. 

In the rest of simulations, the peptide is more or less tilled. In more than half of simulations, the peptide doesn t stay linear, but change its main chain conformation (more to the tilting). The peptide bends like a bow (Barlow et al. called it curved peptide [86]), or even breaks the helical stmcture (kinked as mentioned by the same authors)—see Fig. 8.4. Amount of bending is different for each simulation, but we were not able to find correlation between composition of system and amount of bending or place of break. But peptide keeps best the linear conformation of ide-... 

The second application of the CFTI approach described here involves calculations of the free energy differences between conformers of the linear form of the opioid pentapeptide DPDPE in aqueous solution [9, 10]. DPDPE (Tyr-D-Pen-Gly-Phe-D-Pen, where D-Pen is the D isomer of /3,/3-dimethylcysteine) and other opioids are an interesting class of biologically active peptides which exhibit a strong correlation between conformation and affinity and selectivity for different receptors. The cyclic form of DPDPE contains a disulfide bond constraint, and is a highly specific S opioid [llj. Our simulations provide information on the cost of pre-organizing the linear peptide from its stable solution structure to a cyclic-like precursor for disulfide bond formation. Such... 

The second problem involves the measurement of pKa values for carbonyl and thiocarbonyl derivatives. Grieg and Johnson (157) have pointed out that the measurement of pKa values for very weak bases (11) is an inaccurate and arbitrary process. Of particular difficulty for our purposes is the fact that different carbonyl derivatives may require different acidity functions. As a result of this situation, no attempt was made to make correlations of pKa data for carbonyl and thiocarbonyl derivatives with eq. (2). Because accurate pKa values can be measured for imines, these values were correlated with eq. (2), although the conformational problem remains. The imine sets were first studied by Charton and Charton (73), who correlated them with eq. (2). No correlations of data for carbonyl or thiocarbonyl derivatives with eq. (2) are extant in the literature. Bhaskar, Gosavi, and Rao (158) have reported that AG values for complex formation of substituted thioureas with iodine are a linear function of the Taft a values. Drago, Wenz, and Carlson (159) have reported similar results for complex formation between iodine and substituted amides. Oloffson (160) has reported a linear relationship between -AH for the complex of substituted N,N-dimethylamides with SbCls and the ffj constants. 

Secondly, as a measure of nonlinearity, the calculation conforms more closely to that concept than the correlation coefficient does. As a contrast, we can consider terms such as precision and accuracy, where high precision and high accuracy mean data with small values of standard deviation> while low precision and low accuracy mean large values of the measure. Thus, for those two characteristics, the measured value changes in opposition to the concept. If we were to use the correlation coefficient calculation as the measure of nonlinearity, we would have the same situation. However, by defining the linearity calculation the way did, the calculation now runs parallel to the concept a calculated value of zero means no nonlinearity while increasing values of the calculation corresponds to increasing nonlinearity. 

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