Changes of Conformation in Albumin with Temperature by Molecular Dynamics Simulations

This work presents the analysis of the conformation of albumin in the temperature range of 300K–312K, i.e., in the physiological range. Using molecular dynamics simulations, we calculate values of the backbone and dihedral angles for this molecule. We analyze the global dynamic properties of albumin treated as a chain. In this range of temperature, we study parameters of the molecule and the conformational entropy derived from two angles that reflect global dynamics in the conformational space. A thorough rationalization, based on the scaling theory, for the subdiffusion Flory–De Gennes type exponent of 0.4 unfolds in conjunction with picking up the most appreciable fluctuations of the corresponding statistical-test parameter. These fluctuations coincide adequately with entropy fluctuations, namely the oscillations out of thermodynamic equilibrium. Using Fisher’s test, we investigate the conformational entropy over time and suggest its oscillatory properties in the corresponding time domain. Using the Kruscal–Wallis test, we also analyze differences between the root mean square displacement of a molecule at various temperatures. Here we show that its values in the range of 306K–309K are different than in another temperature. Using the Kullback–Leibler theory, we investigate differences between the distribution of the root mean square displacement for each temperature and time window.

[1]  B. Ranjbar,et al.  Conformational study of human serum albumin in pre-denaturation temperatures by differential scanning calorimetry, circular dichroism and UV spectroscopy. , 2006, Journal of biochemistry and molecular biology.

[2]  S. Curry,et al.  Binding of the General Anesthetics Propofol and Halothane to Human Serum Albumin , 2000, The Journal of Biological Chemistry.

[3]  L. Van Melderen,et al.  Thermodynamic Stability of the Transcription Regulator PaaR2 from Escherichia coli O157:H7. , 2019, Biophysical journal.

[4]  J. Tanner.  Empirical power laws for the radii of gyration of protein oligomers. , 2016, Acta crystallographica. Section D, Structural biology.

[5]  Dominique P Pioletti,et al.  Impact of synovial fluid flow on temperature regulation in knee cartilage. , 2015, Journal of biomechanics.

[6]  Amos Maritan,et al.  Flory theory for polymers , 2013, Journal of physics. Condensed matter : an Institute of Physics journal.

[7]  Wei Zhang,et al.  A point‐charge force field for molecular mechanics simulations of proteins based on condensed‐phase quantum mechanical calculations , 2003, J. Comput. Chem..

[8]  Adam Gadomski.  On (sub)mesoscopic scale peculiarities of diffusion driven growth in an active matter confined space, and related (bio)material realizations , 2019, Biosyst..

[9]  T. Obremski.  Practical Nonparametric Statistics (2nd ed.) , 1981 .

[10]  P. Biswas,et al.  Conformational Entropy of Intrinsically Disordered Proteins from Amino Acid Triads , 2015, Scientific Reports.

[11]  W. Krause,et al.  Rheopexy of synovial fluid and protein aggregation , 2006, Journal of The Royal Society Interface.

[12]  Gert Vriend,et al.  New ways to boost molecular dynamics simulations , 2015, J. Comput. Chem..

[13]  David J. Groggel,et al.  Practical Nonparametric Statistics , 2000, Technometrics.

[14]  Jacob Klein,et al.  Supramolecular synergy in the boundary lubrication of synovial joints , 2015, Nature Communications.

[15]  Zhongmin Jin,et al.  Biotribology of articular cartilage--a review of the recent advances. , 2008, Medical engineering & physics.

[16]  J. Jumper,et al.  Loss of conformational entropy in protein folding calculated using realistic ensembles and its implications for NMR-based calculations , 2014, Proceedings of the National Academy of Sciences.

[17]  A. Ravve,et al.  Principles of Polymer Chemistry , 1995 .

[18]  Andrey G. Cherstvy,et al.  Anomalous diffusion models and their properties: non-stationarity, non-ergodicity, and ageing at the centenary of single particle tracking. , 2014, Physical chemistry chemical physics : PCCP.

[19]  J. Tanner,et al.  Determination of protein oligomeric structure from small‐angle X‐ray scattering , 2018, Protein science : a publication of the Protein Society.

[20]  L. Nilsson,et al.  Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K , 2001 .

[21]  P. Gennes.  Scaling Concepts in Polymer Physics , 1979 .

[22]  A. Liwo,et al.  Design of a knowledge-based force field for off-lattice simulations of protein structure. , 1997, Acta biochimica Polonica.

[23]  T. C. B. McLeish,et al.  Polymer Physics , 2009, Encyclopedia of Complexity and Systems Science.

[24]  O. Yli-Harja,et al.  Robust Fisher's Test for Periodicity Detection in Noisy Biological Time Series , 2007, 2007 IEEE International Workshop on Genomic Signal Processing and Statistics.

[25]  Adam Gadomski,et al.  Entropy Production Associated with Aggregation into Granules in a Subdiffusive Environment , 2018, Entropy.

[26]  G. Fredrickson.  The theory of polymer dynamics , 1996 .

[27]  J. Quastel.  Diffusion in Disordered Media , 1996 .

[28]  Tom C. Lubensky,et al.  Flory exponents for generalized polymer problems , 1980 .

[29]  Krzysztof Domino,et al.  The use of copula functions for predictive analysis of correlations between extreme storm tides , 2014 .

[30]  Korbinian Strimmer,et al.  Identifying periodically expressed transcripts in microarray time series data , 2008, Bioinform..

[31]  Marco Grimaldo,et al.  Dynamics of proteins in solution , 2019, Quarterly Reviews of Biophysics.