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Predict Protein 3 D Fold Using Knowledge Base Energy Function

The proteins three-dimensional structures can provide the essential information for protein function, and also provide a key to design drugs targeted at proteins. Hence, it is important to develop an efficient method for protein 3D structure prediction. In this paper, a fold recognition method of threading approach is developed to predict protein 3D structure from 2D structure. The 2D structure for the query sequence can be predicted first. Then an algorithm is developed to find the possible 3D folds as candidate folds that have similar 2D structure to the query protein. This approach can filter out 90.9% to 99.22% impossible folds. Next, query protein is substituted into all possible candidate folds according to the alignment with template protein, and their statistical knowledge base energy functions are calculated. A fold with minimum energy function will be selected as 3D structure of query protein. The weights of three scores of energy function can be optimized by using genetic algorithm. The accuracy of predicting a correct fold among 637 folds can research to 56.7%.