B Harihar, S Selvaraj
Department of Bioinformatics, Bharathidasan University, Tiruchirappalli, Tamilnadu, India.
In the past decade many approaches have been proposed for the prediction of folding rates. The long-range order proposed by Gromiha and Selvaraj, 2001 1 is one of the successful topological descriptor for predicting the folding rates of proteins that fold through a two-state mechanism. Predicting the unfolding rates of proteins is also an equally challenging problem. However, only a limited number of models have been proposed for predicting the unfolding rates of two-state proteins. In the present work, 30 two-state proteins characterized using a consensus set of experimental conditions were taken and the parameter long-range order 1 (LRO) derived from their 3D structures were related with their experimental unfolding rates ln(k u ). From the total data set of 30 proteins used, five slow unfolding proteins with very low unfolding rates were considered to be outliers and were not included in our data set. Except all-beta structural class, LRO of both the all-alpha and mixed-class proteins showed a strong inverse correlation of -0.99 and -0.88 with experimental ln(k u ). LRO showed a correlation of -0.62 with experimental ln(k u ) for all-beta proteins. For predicting the unfolding rates, a simple statistical method has been used and linear regression equations were developed for individual structural classes of proteins using LRO and the results obtained showed a better agreement with experimental results. In the present work, LRO predicts the experimental ln(k u ) for all the two-state proteins with better accuracy using back-check and jack-knife methods and showed a minimal average deviation. Results observed from our present work strongly evidence that long-range contacts observed in the final native state of proteins plays a crucial role in deciding the unfolding rates of proteins and shows that the topological quantities derived from the 3D structures of proteins is an important descriptor in determining its unfolding rate. The present method can be used to predict the unfolding rates of proteins with known 3D structures that fold through a two-state mechanism by computing long-range interactions observed in their 3D structures 2 . It is expected that further experimental studies will shed light on these predictions. Read more…