BIOINFORMATICS, DISORDERED PROTEINS, AND FUNCTION

生物信息学的一个主要目标是从序列中预测蛋白质的结构/功能;目前最成功的方法是使用序列同源性。 目前这些基于同源性的方法明确或隐含地假设范式序列产生结构产生功能。 由于蛋白质功能取决于灵活性，运动，甚至在某些情况下缺乏结构（无序），将各种运动预测与结构预测相结合应该可以改善功能预测。 从序列和局部无序预测灵活性的算法已经被开发出来，预测在两个结构化状态之间告密的序列的算法也已经被开发出来。 序列复杂性也可能是流动性的间接测量，并且通过研究已知的例子可以发现其他测量。 我们建议确定的相互作用的同源性为基础的预测，使用序列信息，只有明确表示的结构信息和运动信息确定从氨基酸序列。 结构信息将由螺旋、片层等表示，预测，并通过疏水矩计算。 运动信息将被表示为灵活性预测，开关序列预测，顺序/无序预测，序列复杂性，以及新的措施，如果有的话，在这项工作的过程中被发现。 由于不同的功能会在不同程度上涉及运动参数，因此计划在层序族的基础上应用这种组合方法。 新的比较，称为属性配置文件，提出了结构和运动信息的表示。 Gribskov/Eisenberg 1D配置文件集和相关属性配置文件将使用神经网络数据模型合并为单个预测。 预测结果将与实验观察结果进行比较，并使用折刀法进行评估。 如果成功，这项工作将改善从氨基酸序列预测蛋白质功能的能力，这一点很重要，因为各种基因组计划中有大量功能不明的蛋白质序列。 运动和无序是蛋白质结构/功能表征中需要添加的重要部分。 例如，最近已经显示紫杉醇与Bcl-2中的无序环结合。 阿尔茨海默病、传染性海绵状脑病、帕金森病和感染性病原体如金黄色葡萄球菌、口蹄疫病毒和（可能）HIV严重依赖于蛋白质的无序区域。 因此，理解顺序、灵活性、有序/无序、复杂性、结构和功能的相互作用显然与人类健康有关。

A major objective of bioinformatics is to predict protein structure/function from sequence; the most successful current methods use sequence homology. These current homology-based methods explicitly or implicitly assume the paradigm sequence yields structure yields function. Since protein function depends on flexibility, movement, or even lack of structure (disorder) in some cases, combining various motion predictions with structure predictions should improve prediction of function. Algorithms for predicting flexibility from sequence and local disorder have been developed, as have algorithms to predict sequences that snitch between two structured states. Sequence complexity might also be an indirect measure of mobility and still other measures could be discovered by the study of known examples. We propose to determine the interplay of homology-based predictions that use sequence information only with explicit representations of both structure information and motion information as determined from amino acid sequence. Structural information will be represented by helix, sheet, etc., predictions, and by hydrophobic moment calculations. Motion information will be represented by flexibility prediction, switch sequence prediction, order / disorder prediction, sequence complexity, and new measures if any are discovered in the course of this work. Since different functions would involve motional parameters to different extents, the plan is to apply this combined approach on a sequence family basis. Novel comparisons, called Attribute Profiles, are proposed for the representation of structure and motion information. Sets of Gribskov/Eisenberg 1D Profiles and associated Attribute Profiles will be combined into single predictions using neural network data models. Prediction outcomes will be compared with experimental observations and evaluated using the jackknife method. If successful, this work will improve prediction of protein function from amino acid sequence, which is important given the significant numbers of protein sequences with undetermined functions coming out of the various genome projects. Motion and disorder are important pieces that need to be added to the characterization of protein structure/function. For example, taxol has been shown recently to bind to a disordered loop in Bcl-2. Alzheimer disease, transmissible spongiform encephalopathies, Parkinson disease and infectious agents such as Staphylococcal aureus, foot-and-mouth disease virus, and (perhaps) HIV depend critically on disordered regions of protein. Thus, understanding the interplay of sequence, flexibility, order / disorder, complexity, structure and function clearly relates to human health.