TAILIEUCHUNG - Báo cáo sinh học: "Scoring function to predict solubility mutagenesis"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Scoring function to predict solubility mutagenesis. | AMR algorithms for D MOLECULAR BIOLOGY Scoring function to predict solubility mutagenesis Tian et al. Tian et al. Algorithms for Molecular Biology 2010 5 33 http content 5Z1 33 7 October 2010 BioMed Central Tian et al. Algorithms for Molecular Biology 2010 5 33 http content 5 1 33 AMR ALGORITHMS FOR MOLECULAR BIOLOGY RESEARCH Open Access Scoring function to predict solubility mutagenesis Ye Tian1 Christopher Deutsch2 Bala Krishnamoorthy1 Abstract Background Mutagenesis is commonly used to engineer proteins with desirable properties not present in the wild type WT protein such as increased or decreased stability reactivity or solubility. Experimentalists often have to choose a small subset of mutations from a large number of candidates to obtain the desired change and computational techniques are invaluable to make the choices. While several such methods have been proposed to predict stability and reactivity mutagenesis solubility has not received much attention. Results We use concepts from computational geometry to define a three body scoring function that predicts the change in protein solubility due to mutations. The scoring function captures both sequence and structure information. By exploring the literature we have assembled a substantial database of 137 single- and multiplepoint solubility mutations. Our database is the largest such collection with structural information known so far. We optimize the scoring function using linear programming LP methods to derive its weights based on training. Starting with default values of 1 we find weights in the range 0 2 so that predictions of increase or decrease in solubility are optimized. We compare the LP method to the standard machine learning techniques of support vector machines SVM and the Lasso. Using statistics for leave-one-out LOO 10-fold and 3-fold cross validations CV for training and prediction we demonstrate that the LP method performs the best overall. For the LOOCV the LP .

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