Complex Systems In Presence Research Articles

Computational investigation on the effect of the peptidomimetic inhibitors (NPT100-18A and NPT200-11) on the α-synuclein and lipid membrane interactions

Parkinson’s disease (PD) is associated with α-synuclein (α-Syn), a presynaptic protein that binds to cell membranes. The molecular pathophysiology of PD most likely begins with the binding of α-Syn to membranes. Recently, two peptidomimetic inhibitors (NPT100-18A and NPT200-11) were identified to potentially interact with α-Syn and affect the interaction of α-Syn with the membrane. In this study, the effect of the two peptidomimetic inhibitors on the α-Syn-membrane interaction was demonstrated. DFT calculations were performed for optimization of the two inhibitors, and the nucleophilicity (N) and electrophilicity (ω) of NPT100-18A and NPT200-11 were calculated to be 3.90 and 3.86 (N); 1.06 and 1.04 (ω), respectively. Using the docking tool (CB-dock2), the two α-Syn-peptidomimetic inhibitor complexes (α-Syn-NPT100-18A and α-Syn-NPT200-11) have been prepared. Then all-atom molecular dynamics (MD) simulation was carried out on the α-Syn (control), α-Syn-NPT100-18A and α-Syn-NPT200-11 complex systems in presence of DOPE: DOPS: DOPC (5:3:2) lipid bilayer. From the conformational dynamics analysis, the 3-D structure of α-Syn was found to be stable, and the helices present in the regions (1–37) and (45–95) of α-Syn were found to be retained in the presence of the two peptidomimetic inhibitors. The electron density profile analysis revealed the binding modes of NAC and C-terminal region of α-Syn (in the presence of NPT200-11 inhibitor) with lipid membrane are in the close vicinity from the lipid bilayer centre. Our findings in this study on α-Syn-membrane interactions may be useful for developing a new therapeutic approach for treating PD and other neurodegenerative disorders. Communicated by Ramaswamy H. Sarma

An Information Geometric Perspective on the Complexity of Macroscopic Predictions Arising from Incomplete Information

Motivated by the presence of deep connections among dynamical equations, experimental data, physical systems, and statistical modeling, we report on a series of findings uncovered by the authors and collaborators during the last decade within the framework of the so-called Information Geometric Approach to Chaos (IGAC). The IGAC is a theoretical modeling scheme that combines methods of information geometry with inductive inference techniques to furnish probabilistic descriptions of complex systems in presence of limited information. In addition to relying on curvature and Jacobi field computations, a suitable indicator of complexity within the IGAC framework is given by the so-called information geometric entropy (IGE). The IGE is an information geometric measure of complexity of geodesic paths on curved statistical manifolds underlying the entropic dynamics of systems specified in terms of probability distributions. In this manuscript, we discuss several illustrative examples wherein our modeling scheme is employed to infer macroscopic predictions when only partial knowledge of the microscopic nature of a given system is available. Finally, we include comments on the strengths and weaknesses of the current version of our proposed theoretical scheme in our concluding remarks.