INFORMATION COMPLEXITY OF PROTEIN FOLDING

描述：蛋白质折叠问题的一个基本原则是， 氨基酸序列中包含的信息足以决定 蛋白质的三维折叠结构。 的目标 目前的研究是理解和量化这一想法使用的技术， 信息与复杂性理论 这些潜在应用 蛋白质结构测定和预测的方法也将是 探讨了 从信息论的角度来看，蛋白质折叠 可以被设想为一种通信过程，通过该通信过程， 信息被传送到三维结构。 有 关于这种信息传递，人们可以问很多问题。 如何 大量的信息从序列转移到结构？ 多多余 是信息？ 是通过蛋白质折叠的信息传递， 或无噪音”的沟通渠道 这些问题是由 利用我们在理解上的最新进展 热力学熵、信息熵和 算法复杂度 序列的信息内容是 从信息熵中确定，以及 三维结构与算法复杂度有关。 的 算法复杂度是最短计算时间的度量。 一种结构的表现。 有了这些量， 序列和结构数据的内容（或数据压缩）将被 测定 使用最大熵技术， 还将确定序列和结构之间信息。 对这一共享信息的了解将用于开发 蛋白质折叠的“沟通通道”。 这种方法也可以用于 定量比较结构预测算法。 一个长期目标 就是将这些共享信息整合到最大熵算法中 用于X射线和NMR结构测定。 这种方法还将提供 X射线和核磁共振联合优化的结构确定算法 数据

DESCRIPTION: A basic tenet of the protein folding problem is that the information contained in the amino acid sequence is sufficient to dictate the three-dimensional, folded structure of a protein. The goal of the present study is to understand and quantify this idea using techniques of information and complexity theory. Potential applications of these approaches to protein structure determination and prediction will also be explored. From an information theoretical point of view, protein folding can be envisioned as a communication process by which the sequence information is transmitted to the three-dimensional structure. There are a number of questions one can ask regarding such information transfer. How much information is transferred from sequence to structure? How redundant is the information? Is information transfer via protein folding, a "noisy or noiseless" communication channel? These questions are approached by taking advantages of recent advances in our understanding of the relationship between thermodynamic entropy, information entropy and algorithmic complexity. The information content of the sequence is determined from the information entropy, and the content of the three-dimensional structure is related to the algorithmic complexity. The algorithmic complexity is a measure of the shortest computational representation of a structure. With these quantities, the information content (or data compression) of sequence and structural data will be determined. Using maximum entropy techniques, the shared or mutual information between sequence and structure will also be determined. Knowledge of this shared information will be used to develop models of the "communication channel" of protein folding. This approach can also be used to quantitatively compare structure prediction algorithms. A long term goal is to incorporate this shared information into a maximum entropy algorithm for X-ray and NMR structure determination. This approach will also provide an algorithm for determining structures by jointly optimizing X-ray and NMR data.