SGER: Hidden Markov models for three-dimensional packing arrangements in proteins

EIA-0229454 Bystroff，ChristopherRensselaer Polytechnic InstituteSGER：蛋白质三维包装排列的隐马尔可夫模型我们的目标是设计一个隐马尔可夫模型（HMM），它可以捕获蛋白质中非局部相互作用的序列依赖性。该模型将专注于已知存在于蛋白质中但其序列相似性无法通过当前方法检测到的经常性子结构。这些经常性的包装安排，或核心，被编码在HMM状态之间的空间关系，代表个别残基。新的算法将允许预测蛋白质链通过3D状态的自我避免路径。该方法测试了蛋白质折叠的拓扑驱动假设，如果成功的话，它将代表我们从序列预测蛋白质结构的能力的进步。该方法广泛适用于可以在两个空间中描述的系统，顺序/时间和空间，并且（1）存在已知示例的大型数据库，（2）导致所有示例的共同基本原理。 应用包括天气预测、大脑功能、生态系统和自然语言。

EIA-0229454Bystroff, ChristopherRensselaer Polytechnic InstituteSGER: Hidden Markov Models for Three-Dimensional Packing Arrangements in ProteinsOur goal is to design a hidden Markov model (HMM) that captures the sequence dependence of non-local interactions in proteins. The model will focus on recurrent substructures that are known to exist in proteins but whose sequence similarity is undetectable by current methods. These recurrent packing arrangements, or cores, are encoded in the spatial relationships between the HMM states, which represent individual residues. New algorithms will allow the prediction of the protein chain's self-avoiding path through the 3D states. The approach tests the topology-driven hypothesis for protein folding, and if successful it will represent an advance in our ability to predict protein structure from sequence.The approach is broadly applicable to systems that can be described in two spaces, sequential/temporal and spatial, and for which (1) a large database of known examples exists, and (2) a common underlying principle led to all of the examples. Applications include weather prediction, brain function, ecosystems and natural languages.