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Proteins achieve their functional, active, and operative three dimensional native structures by overcoming the possibility of being trapped in non-native energy minima present in the energy landscape. The enormous and intricate interactions that play an important role in protein folding also determine the stability of the proteins. The large number of stabilizing/destabilizing interactions makes proteins to be only marginally stable as compared to the other competing structures. Therefore, there are some possibilities that they become trapped in the non-native conformations and thus get misfolded. These misfolded proteins lead to several debilitating diseases. This work performs a comparative study of some existing foldability criteria in the computational design of misfold resistant protein sequences based on self-consistent mean field theory. The foldability criteria selected for this study are Ef, Δ, and Φ that are commonly used in protein design procedures to determine the most efficient foldability criterion for the design of misfolding resistant proteins. The results suggest that the foldability criterion Δ is significantly better in designing a funnel energy landscape stabilizing the target state. The results also suggest that inclusion of negative design features is important for designing misfolding resistant proteins, but more information about the non-native conformations in terms of Φ leads to worse results compared to even simple positive design. The sequences designed using Δ show better resistance to misfolding in the Monte Carlo simulations performed in the study.

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Bimetallic Ag-Pd nanoparticles supported on TiO2 have been prepared using Ocimum tenuiflorum leaf extract and characterized by UVvisible, SEM, SEM-EDS, TEM, SAED pattern and XPS analysis. This heterogeneous catalyst is efficient for oxidation reactions of alcohol and can be reused upto 5th cycles. Use of low cost and non-toxic TiO2 as support is an additional advantage to this catalyst.

The three dimensional native structure plays an important role in determining the function of a protein. However, structure determination is tedious and costly, so prediction of protein three dimensional structures is a very important as well as a challenging task in computational biophysics. Prediction of dihedral angle is particularly helpful for predicting tertiary structure of proteins as knowledge of backbone torsion angles significantly narrow down the conformational search space for tertiary structure prediction. Dihedral angles provide a detailed description of local conformation of a protein. With the advancement of machine learning and other relevant techniques, dihedral angle prediction may establish itself as a fascinating supplement to secondary structure prediction. Over the last two decades, research in this direction has led to development of several dihedral angle prediction methods. In this article we critically review available methods for protein dihedral angle prediction with an emphasis on deep learning based real value angle prediction methods. We believe this review will provide important insights into the state of the art of protein dihedral angle prediction.