INFORMATION COMPLEXITY OF PROTEIN FOLDING

描述：蛋白质折叠问题的一个基本原理是 氨基酸序列中包含的信息足以说明 蛋白质的三维折叠结构。的目标是 目前的研究是利用以下技术来理解和量化这一概念 信息与复杂性理论。这些技术的潜在应用 蛋白质结构测定和预测的方法也将是 探索过了。从信息论的观点来看，蛋白质折叠 可以被设想为一种通信过程，通过该过程，序列 信息被传输到三维结构。有一种 关于这样的信息传递，人们可以提出的问题数量。多么 许多信息从序列转移到结构？多冗余啊 是信息吗？是不是通过蛋白质折叠进行的信息传递是“嘈杂的” 还是无声的“沟通渠道？”这些问题是由 利用我们最近在理解 热力学熵、信息熵和信息熵之间的关系 算法复杂性。该序列的信息内容为 由信息熵确定，并且 三维结构与算法的复杂性有关。这个 算法复杂性是对最短计算时间的度量 结构的表示形式。有了这些数量，信息 序列和结构数据的内容(或数据压缩)将是 下定决心。使用最大熵技术，共享或相互 序列和结构之间的信息也将被确定。 这些共享信息的知识将被用来开发 蛋白质折叠的“沟通渠道”。这种方法也可以使用 对结构预测算法进行定量比较。长期目标 是将这些共享信息合并到最大熵算法中 用于X-射线和核磁共振结构测定。这种方法还将提供 一种联合优化X射线和核磁共振确定结构的算法 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.