The structural knowledge of transmembrane proteins is difficult to attain experimentally, as the wet-lab experimental methods are time consuming, expensive, require good infrastructure and contain high false positive results. Hence, the need of in-silico methods for protein secondary structure prediction is being driven by above listed limitations. Over a number of years, various transmembrane region predictors has been developed using computational approach. In this book, a connectionist (ANN-Artificial Neural Network) model has been developed for prediction of alpha helical transmembrane...
The structural knowledge of transmembrane proteins is difficult to attain experimentally, as the wet-lab experimental methods are time consuming, expe...
