Genome-wide Structure Prediction of Globular and Transmembrane Helical Proteins

基于序列信息的蛋白质结构预测对基因组序列数据的解释具有重要价值。这种方法将减少必须通过实验确定的蛋白质结构的数量，以获得一个有机体的结构补体。该方法将二级结构预测和基于距离的扭角动力学等工具与基于补片的三级接触预测和自适应思想相结合。第一类蛋白质将是具有α -螺旋结构的蛋白质，这类蛋白质的预测是最成功的。该方法将扩展到预测跨膜螺旋的排列。β -薄片蛋白结构的进一步扩展将涉及基序检测和基序积累到更大的结构中。这些方法将被优化和评分，以帮助选择最佳的模型结构。结果将是自动化的高精度软件，可用于全基因组序列。

Prediction of protein structure based on sequence information is of great value for the interpretation of genomic sequence data. Such method would reduce the number of protein structure that must be determined experimentally to obtain a structural complement for an organism. The new method integrates tools such as secondary structure prediction and distance-based torsion angle dynamics with the idea of patch-based tertiary contact prediction and bootstrapping. The first class of proteins will be those that take on an alpha-helical structure, the class of proteins for which prediction has been most successful. The method will be extended to predict arrangements of transmembrane helices. A further extension to the structure of beta-sheet proteins will involve motif detection and accumulation of motifs into larger structures. The methods will be optimized and scored to aid in selecting the best model structure. The result will be automated high accuracy software that can be used on whole genome sequences.