Prediction of Protein Secondary Structures From Conformational Biases

We use LINUS, a procedure developed by Srinivasan and Rose, to provide a physical interpretation of and to predict the secondary structures of proteins. The secondary structure type at a given site is identified by the largest conformational bias during short time simulations. We examine the rate of successful prediction as a function of temperature and the interaction window. At high temperatures, there is a large propensity for the establishment of $\beta$-strands whereas $\alpha$-helices appear only when the temperature is lower than a certain threshold value. It is found that there exists an optimal temperature at which the correct secondary structures are predicted most accurately. We find that this temperature is close to the peak temperature of the specific heat. Changing the interaction window or carrying out longer simulations approaching equilibrium lead to little change in the optimal success rate. Our findings are in accord with the observation by Srinivasan and Rose that the secondary structures are mainly determined by local interactions and they appear in the early stage of folding.