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Using a coarse-grained model, self-organized assembly of proteins (e.g. CorA and its inner segment iCorA) is studied by examining quantities such as contact profile, radius of gyration, and structure factor as a function of protein concentration at a range of low (native phase) to high (denature phase) temperatures. Visual inspections show distinct structures, i.e. isolated globular bundles to entangled network on multiple length scales in dilute to crowded protein concentrations. In native phase, the radius of gyration of the protein does not vary much with the protein concentration while that of its inner segment increases systematically. In contrast, the radius of gyration of the protein shows enormous growth with the concentration due to entanglement while that of the inner segment remains almost constant in denatured phase. The multi-scale morphology of the collective assembly is quantified by estimating the effective dimension D of protein from scaling of the structure factor: collective assembly from inner segments remains globular (D aroud 3) at almost all length scales in its native phase while that from protein chains shows sparsely distributed morphology with D around 2 in entire temperature range due to entanglement except in crowded environment at low temperature where D around 2.6. Higher morphological response of chains with only the inner-segments due to selective interactions in its native phase may be more conducive to self-organizing mechanism than that of the remaining segments of the protein chains.
It is shown that the density of modes of the vibrational spectrum of globular proteins is universal, i.e., regardless of the protein in question it closely follows one universal curve. The present study, including 135 proteins analyzed with a full at
A coarse-grained simulation model eliminates microscopic degrees of freedom and represents a polymer by a simplified structure. A priori, two classes of coarse-grained models may be distinguished: those which are designed for a specific polymer and r
We introduce a formulation for normal mode analyses of globular proteins that significantly improves on an earlier, 1-parameter formulation (M. Tirion, PRL 77, 1905 (1996)) that characterized the slow modes associated with protein data bank structure
The algorithm for Monte Carlo simulation of parton-level events based on an Artificial Neural Network (ANN) proposed in arXiv:1810.11509 is used to perform a simulation of $Hto 4ell$ decay. Improvements in the training algorithm have been implemented
Machine learning encompasses a set of tools and algorithms which are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable in