Prediction of protein structures from protein sequences using computers is an important step to discover proteins' 3D conformation structures and their functions and hence has profound theoretical and practical significance in areas such as protein engineering and drug design. In this talk, we will discuss our new results in protein secondary structure and Transmembrane protein prediction using Support Vector Machines. We will also discuss how to use a combination of Support Vector Machine and Decision Tree to understand how a prediction is reached through rule extraction. Clearly, a good interpretation is useful for guiding biological experiments and may lead further prediction improvement. A novel approach of rule clustering for super-rule generation will also be briefly discussed.