Protein Dynamics and Function

Author(s):  
Hans Frauenfelder
2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Rasim B. Aslanov ◽  
Leman M. Dashdemirova ◽  
Oktay Z. Alekperov ◽  
Azad R. Abdurahimov ◽  
Oktay K. Gasymov

The relationship between structure, dynamics, and function of biomolecules is a fundamental interest of biophysics. Protein dynamics drastically vary in temporal and spatial scales. The function of a particular protein determines the significance of a distinct type of dynamics. Here, we investigate the influence of hydration water on the dynamics of a protein called silk fibroin. Particular interest is to investigate the protein dynamics using thermal decay of the free radicals induced by ultraviolet irradiation. The full decay of the free radicals occurs at very wide temperature region (120 K–340 K). Three distinct regions with transition points of ∼135 K, 205 K, and 279 K are apparent in the thermal decay curves of hydrated fibroin samples. The first transition (∼135 K) that leads 2–6% increase of total spins was observed only in the decay curves of fibroin submerged in 40% and 50% glycerol. The second transition (∼205 K) was invariant for all samples, hydrated and dry fibroins. The third transition of 279 K common for all hydrated fibroin samples was shifted about 84 K to a higher temperature of 363 K in dry fibroin. The thermal transitions at 205 K and 279 K are weakly and strongly, respectively, coupled to water molecules. Nature of the free radicals participated in these transitions was identified. The significance of the findings for protein dynamics is discussed.


2014 ◽  
Vol 13 (06) ◽  
pp. 1450053 ◽  
Author(s):  
Meng Zhan ◽  
Suhong Li ◽  
Fan Li

Accurate prediction of the Debye–Waller temperature factor of proteins is of significant importance in the study of protein dynamics and function. This work explores the utility of wavelets for improving the performance of Gaussian network model (GNM). We propose two wavelet transformed Gaussian network models (wtGNM), namely a scale-one wtGNM and a scale-two wtGNM. Based on a set of 113 protein structures, it shows that the mean correlation with experimental results for the scale-one wtGNM is 0.714 and that for the scale-two wtGNM is 0.738. In contrast, the mean correlation for the original GNM is 0.594. Therefore, the wtGNM is a potential algorithm for improving the GNM prediction of protein B-factors.


1988 ◽  
Vol 47 (2-3) ◽  
pp. 155-163 ◽  
Author(s):  
Sándor Damjanovich ◽  
Margit Balázs ◽  
János Szöllösi ◽  
Lajos Trón ◽  
Béla Somogyi

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