Better Kernels and Coding Schemes Lead to Improvements in SVM-based Secondary Structure Prediction
The accurate prediction of a protein's secondary structure plays an increasingly critical role in predicting its function and tertiary structure, as it is utilized by many of the current state-of-the-art methods for remote homology, fold recognition, and ab initio structure prediction.
We developed a new secondary structure prediction algorithm called YASSPP that uses a pair of cascaded models constructed from two sets of binary SVM-based models. YASSPP uses an input coding scheme that combines both position-specific and non-position specific information, utilizes a kernel function designed to capture the sequence conservation signals around the local window of each residue, and constructs a second-level model by incorporating both the three-state predictions produced by the first-level model and information about the original sequence.
Experiments on three standard datasets (RS126, PB513, and EVA common subset 4) show that YASSPP is capable of producing the highest Q3 and SOV scores than that achieved by existing widely used schemes such as PSIPRED, SSPro~4.0, SAM-T99sec, as well as previously developed SVM-based schemes. On the EVA dataset it achieves a Q3 and SOV score of 79.34% and 78.65%, which are considerably higher than the best reported scores of 77.64% and 76.05%, respectively.
The YASSPP prediction server is available at http://bioinfo.cs.umn.edu/yasspp.