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題 名 | Artificial Intelligence Techniques for Analyzing the 3-D Structure of Proteins: Designing New Proteins |
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作 者 | Mehta,B. V.; Mehta,S. B.; Rabelo,L. C.; Kopchick,J. J.; | 書刊名 | 醫學工程 |
卷 期 | 8:6 1996.12[民85.12] |
頁 次 | 頁86-95 |
分類號 | 410.1644 |
關鍵詞 | Protein; 3-D structure; Artificial intelligence; |
語 文 | 英文(English) |
英文摘要 | Proteins are an essential part of all living organisms and are associated with growth, repair and reproductivity of living cells. All proteins are polymers of a finite set of twenty naturally occurring amino acids. A protein is a polymeric substance made up of one or more polypeptide chains. Each polypeptide chain comprises of many amino acids arranged in a particular sequence. However, these amino acids can combine in virtually infinite number of ways to create a myriad of protein species, each with a unique three dimensional structure. The many specific tasks served by proteins are largely dependent on their structural form. The structure of proteins can be classified into three main levels, (a) Primary, (b) Secondary and (c) Tertiary. The fourth structural level (Quaternary) is considered if the protein consists of more than a single chain. The three dimensional structure of a protein is its secondary and tertiary structure. The sequence of amino acids in a protein determines how a particular portion of the polypeptide chain folds into one of the secondary structures. The uncertainty in the way a protein molecular sequence folds has undergone significant research. The Several techniques have been developed for protein secondary structure prediction using different type of statistical and neural network methodologies. In this paper, to predict the three dimensional structure of the newly designed protein( or mutated protein) a energy minimization technique along with the Fuzzy ARTmap paradigm, Probabilistic Neural Network(PNN) technique, and Generalized Regression Neural Network (GRNN) methods are used for the artificial neural network scheme and a modified Chou-Fassman algorithm is used for the statistical technique. The method was utilized on the bovine (bGH) and human (hGH) growth hormones and its agonist and antagonists. The in-house molecular modeling package was used to perform mutations on the first and third alpha helices of the bGH and hGH molecules to simulate breaking the helical structure and strengthening the helical structure, and predicting the new structure using the coupled minimization-NN and minimization-CF techniques. The results of this new technique are compared with the existing techniques. The growth patterns observed in the genetically altered mice from experiments conducted in the Edison biotechnology laboratory were compared with the same mutations performed on the CAD system using the modeling and prediction software. |
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