Quality Assessment of Protein Models

Abstract

Quality assessment (QA) is to judge the quality of a protein model without knowing its native structure. It plays an important role in the process of protein structure prediction by guiding us to select an appropriate combination of templates out of many possibilities [1]. We formulate the QA problem as mapping and regression. Protein models are mapped into R n space by extracting n features from each model, and the space is divided into subspaces according to the feature values in order to represent models’ closeness (between 0 and 1) to the native structure. For the feature values, we measure the degree of agreement between the predicted property from the sequence analysis and the calculated one from the 3D model. Properties that we use include the secondary structure, solvent accessibility, hydrophobicity, and energy components from MODELLER [2], DFIRE [3], and TASSER [4]. One advantage of using the consensustype feature is that models with similar quality are prone to be clustered together in R n space, and thus they tend to be well classified. Assigning closeness values to regions of R n space is carried out using decoy structures generated during the CASP7 with known native structures [1] by applying Support Vector Machine regression techniques (SVM). SVM that we used has the character of non-linearity which can be applied to the linearly non-separable cases. In other words, for given features, SVM is more efficient than the linear method such as linear programming. The proposed method was used to select final models during the CASP8 prediction. We will discuss both the usefulness and the limitation of the method for better protein modeling.