a probabilistic fragment-based protein structure prediction algorithm概率fragment-based蛋白质结构预测算法.pdfVIP

A Probabilistic Fragment-Based Protein Structure Prediction Algorithm 1 1 1,2 1,2 David Simoncini , Francois Berenger , Rojan Shrestha , Kam Y. J. Zhang * 1 Zhang Initiative Research Unit, Advanced Science Institute, RIKEN, Wako, Saitama, Japan, 2 Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan Abstract Conformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representation of proteins. It is during this coarse-grained phase that fragment-based methods sample intensely the conformational space. If the native-like region is sampled more, the accuracy of the final all-atom predictions may be improved accordingly. In this work we present EdaFold, a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm. Fragment-based approaches build protein models by assembling short fragments from known protein structures. Whereas the probability mass functions over the fragment libraries are uniform in the usual case, we propose an algorithm that learns from previously generated decoys and steers the search toward native-like regions. A comparison with Rosetta AbInitio protocol shows that EdaFold is able to generate models with lower energies and to enhance the percentage of near-native coarse-grained