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。这些当前基于同源的方法显式或隐式地假设范例序列产生结构屈服函数。由于蛋白质的功能依赖于柔韧性、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会改善功能预测。已经开发了从序列和局部无序预测灵活性的算法，以及预测在两个结构化状态之间窥探的序列的算法。序列复杂性也可能是移动性的间接衡量标准，通过对已知例子的研究，还可以发现其他衡量标准。我们建议确定基于同源性的预测的相互作用，这些预测仅使用由氨基酸序列确定的结构信息和运动信息的显式表示的序列信息。结构信息将通过螺旋、薄片等、预测和疏水矩计算来表示。运动信息将通过灵活性预测、切换序列预测、有序/无序预测、序列复杂性以及在工作过程中发现的新的度量来表示。由于不同的功能会涉及不同程度的运动参数，因此计划在顺序族的基础上应用这种组合方法。对于结构和运动信息的表示，提出了一种新的比较，称为属性轮廓。Gritskov/Eisenberg 1D简档和相关属性简档的集合将使用神经网络数据模型组合成单个预测。预测结果将与实验观测结果进行比较，并使用折叠式方法进行评估。如果成功，这项工作将改进从氨基酸序列预测蛋白质功能，这一点很重要，因为从各种基因组计划中产生了大量功能未知的蛋白质序列。运动和无序是描述蛋白质结构/功能的重要因素。例如，紫杉醇最近被证明与Bcl-2中的一个无序环结合。阿尔茨海默病、传染性海绵状脑病、帕金森病以及金黄色葡萄球菌、口蹄疫病毒和(可能)艾滋病毒等感染性病原体严重依赖于蛋白质的无序区域。因此，理解序列、灵活性、秩序/无序、复杂性、结构和功能之间的相互作用显然关系到人类的健康。

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.