Mechanism of unfolding of human prion protein - Singh, R. K.; Chamachi, N. G.; Chakrabarty, S.; Mukherjee, A. (Journal of Physical Chemistry B, 2017)
Misfolding and aggregation of prion proteins are associated with several neurodegenerative diseases. Therefore, understanding the mechanism of the misfolding process is of enormous interest in the scientific community. It has been speculated and widely discussed that the native cellular prion protein (PrPC) form needs to undergo substantial unfolding to a more stable PrPC* state, which may further oligomerize into the toxic scrapie (PrPSc) form. Here, we have studied the mechanism of the unfolding of the human prion protein (huPrP) using a set of extensive well-tempered metadynamics simulations. Through multiple microsecond-long metadynamics simulations, we find several possible unfolding pathways. We show that each pathway leads to an unfolded state of lower free energy than the native state. Thus, our study may point to the signature of a PrPC*. form that corresponds to a global minimum on the conformational free-energy landscape. Moreover, we find that these global minima states do not involve an increased beta-sheet content, as was assumed to be a signature of PrPSc formation in previous simulation studies. We have further analyzed the origin of metastability of the PrPC form through free energy surfaces of the chopped helical segments to show that the helices, particularly H2 and H3 of the prion protein, have the tendency to form either a random coil or a beta-structure. Therefore, the secondary structural elements of the prion protein are only weakly stabilized by tertiary contacts and solvation forces so that relatively weak perturbations induced by temperature, pressure, pH, and so forth can lead to substantial unfolding with characteristics of intrinsically disordered proteins.

Prediction of coal ash fusion temperatures using computational intelligence based models - Tambe, S.S.; Naniwadekar, M.; Tiwary, S.; Mukherjee, A.; Das, T. B. (International Journal of Coal Science and Technology, 2018)
In the coal-based combustion and gasification processes, the mineral matter contained in the coal (predominantly oxides), is left as an incombustible residue, termed ash. Commonly, ash deposits are formed on the heat absorbing surfaces of the exposed equipment of the combustion/gasification processes. These deposits lead to the occurrence of slagging or fouling and, consequently, reduced process efficiency. The ash fusion temperatures (AFTs) signify the temperature range over which the ash deposits are formed on the heat absorbing surfaces of the process equipment. Thus, for designing and operating the coal-based processes, it is important to have mathematical models predicting accurately the four types of AFTs namely initial deformation temperature, softening temperature, hemispherical temperature, and flow temperature. Several linear/nonlinear models with varying prediction accuracies and complexities are available for the AFT prediction. Their principal drawback is their applicability to the coals originating from a limited number of geographical regions. Accordingly, this study presents computational intelligence (CI) based nonlinear models to predict the four AFTs using the oxide composition of the coal ash as the model input. The CI methods used in the modeling are genetic programming (GP), artificial neural networks, and support vector regression. The notable features of this study are that the models with a better AFT prediction and generalization performance, a wider application potential, and reduced complexity, have been developed. Among the CI-based models, GP and MLP based models have yielded overall improved performance in predicting all four AFTs.