Proteins backbone

Tjandra N, Szabo A and Bax A 1996 Protein backbone dynamics and N-15 chemical shift anisotropy from quantitative measurement of relaxation interference effected. Am. Chem. Soc. 118 6986-91... 

Figure C3.2.18.(a) Model a-helix, (b) hydrogen bonding contacts in tire helix, and (c) schematic representation of tire effective Hamiltonian interactions between atoms in tire protein backbone. From [23].

In light of tire tlieory presented above one can understand tliat tire rate of energy delivery to an acceptor site will be modified tlirough tire influence of nuclear motions on tire mutual orientations and distances between donors and acceptors. One aspect is tire fact tliat ultrafast excitation of tire donor pool can lead to collective motion in tire excited donor wavepacket on tire potential surface of tire excited electronic state. Anotlier type of collective nuclear motion, which can also contribute to such observations, relates to tire low-frequency vibrations of tire matrix stmcture in which tire chromophores are embedded, as for example a protein backbone. In tire latter case tire matrix vibration effectively causes a collective motion of tire chromophores togetlier, witliout direct involvement on tire wavepacket motions of individual cliromophores. For all such reasons, nuclear motions cannot in general be neglected. In tliis connection it is notable tliat observations in protein complexes of low-frequency modes in tlie... 

Phosphate release from actin. (a) Monomeric actin with ADP and Pi bound. The protein backbone (tube), ADP (grey spheres), and Ca -Pi (black spheres) are shown. The orientation of the spring indicates the pulling direction during P, unbinding. (b) Force exerted on the deprotonated (solid line) and protonated (dashed line) phosphate during the SMD simulations. 

Fig. 10. Conformational flooding accelerates conformational transitions and makes them accessible for MD simulations. Top left snapshots of the protein backbone of BPTI during a 500 ps-MD simulation. Bottom left a projection of the conformational coordinates contributing most to the atomic motions shows that, on that MD time scale, the system remains in its initial configuration (CS 1). Top right Conformational flooding forces the system into new conformations after crossing high energy barriers (CS 2, CS 3,. . . ). Bottom right The projection visualizes the new conformations they remain stable, even when the applied flooding potentials (dashed contour lines) is switched off.

Fig. 1. Exit route of xenon in simulations of the extraction process. The xenon atom is solid black. The atoms of the residues surrounding the exit path are shown a.s spheres, and the protein backbone is shown as a thin curve. On the left, the xenon is viewed through the exit between residues on the right, the view is from (ho side and the direction of the tug is marked with a line.

L. Holm and C. Sander, Database algorithm for generating protein backbone and side-chain co-ordinates from a trace, J. Mol. Biol. 218 (1991), 183-194. 

M.J. Sippl, M. Hendlich and P. Lackner, Assembly of polypeptide and protein backbone conformations from low energy ensembles of short fragments. Protein Sci. 1 (1992), 625-640. 

Figure 7-16. Superimpasition of the X-ray structure of the tetracycline repressor class D dimer (dark, protein database entry 2TRT) with the calculated geometrical average of a 3 ns MD simulation (light trace). Only the protein backbone C trace Is shown, The secondary structure elements and the tertiary structure are almost perfectly reproduced and maintained throughout the whole production phase of the calculation,...

The comparison of both data sources qualitatively shows a similar picture. Regions of high mobflity are located especially between the secondary structure elements, which are marked on the abscissa of the plot in Figure 7-17. Please remember that the fluctuations plotted in this example also include the amino acid side chains, not only the protein backbone. This is the reason why the side chains of large and flexible amino acids like lysine or arginine can increase the fluctuations dramatically, although the corresponding backbone remains almost immobile. In these cases, it is useful to analyze the fluctuations of the protein backbone and side chains individually. 

Perform a conformation search of the protein backbone using the meso-scale side-chain representation. 

Amino acid-derived hormones include the catecholamines, epinephrine and norepinephrine (qv), and the thyroid hormones, thyroxine and triiodothyronine (see Thyroid AND ANTITHYROID PREPARATIONS). Catecholamines are synthesized from the amino acid tyrosine by a series of enzymatic reactions that include hydroxylations, decarboxylations, and methylations. Thyroid hormones also are derived from tyrosine iodination of the tyrosine residues on a large protein backbone results in the production of active hormone. 

A recent survey analyzed the accuracy of tliree different side chain prediction methods [134]. These methods were tested by predicting side chain conformations on nearnative protein backbones with <4 A RMSD to the native structures. The tliree methods included the packing of backbone-dependent rotamers [129], the self-consistent mean-field approach to positioning rotamers based on their van der Waals interactions [145],... 

ES Huang, P Koehl, M Levitt, RV Pappu, JW Ponder. Accuracy of side-chain prediction upon near-native protein backbones generated by ab initio folding methods. Proteins 33 204-217, 1998. 

Fig. 2. Protein backbone representations of (a) the 2[4Fe-4S] ferredoxin from Peptococcus aerogenes, (b) the proposed structure of the FA/FB-binding protein of PSl based on the 4 A crystsd structure (25), and (c) the [3Fe-4S][4Fe-4S] ferredoxin from Sulfolo-bus acidocaldarius. Ligands to clusters Fa and Fb, important residues as well as the loop extension (see text) EU e highlighted in darker gray.

Draw a line structure that shows the various ways in which water molecules form hydrogen bonds with a protein backbone. 

Begin by drawing a section of protein backbone. Because the problem asks only about hydrogen bonds to the backbone, the side chains are not involved in this problem and may be designated simply as R. 

Conventional MS in the energy domain has contributed a lot to the understanding of the electronic ground state of iron centers in proteins and biomimetic models ([55], and references therein). However, the vibrational properties of these centers, which are thought to be related to their biological function, are much less studied. This is partly due to the fact that the vibrational states of the iron centers are masked by the vibrational states of the protein backbone and thus techniques such as Resonance Raman- or IR-spectroscopy do not provide a clear picture of the vibrational properties of these centers. A special feature of NIS is that it directly reveals the fraction of kinetic energy due to the Fe motion in a particular vibrational mode. 

In summary, NIS provides an excellent tool for the study of the vibrational properties of iron centers in proteins. In spectroscopies like Resonance Raman and IR, the vibrational states of the iron centers are masked by those of the protein backbone. A specific feature of NIS is that it is an isotope-selective technique (e.g., for Fe). Its focus is on the metal-ligand bond stretching and bending vibrations which exhibit the most prominent contributions to the mean square displacement of the metal atom. 

This is particularly important for the carbohydrates near the point of glycosylation to the protein backbone. 

Glycoproteins1 are macromolecules containing one carbohydrate chain (or several) covalently linked to a protein backbone. They include... 

Figure 1. A two-dimensional representation that illustrates the tracing of the interaction lines to give the peak-pass-peak-pass chain representative of the protein backbone, side chains and disulphide bridge. Circles represent passes and squares peaks. 

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