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

EIA-0229454 Bystroff，ChristopherRensselaer理工学院SGER：蛋白质中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)，它捕获蛋白质中非局部相互作用的序列相关性。该模型将重点放在已知存在于蛋白质中但其序列相似性无法用现有方法检测到的重复亚结构上。这些递归的堆积排列或核心被编码在HMM状态之间的空间关系中，HMM状态代表单个残基。新的算法将允许通过3D状态预测蛋白质链的自我回避路径。该方法测试了蛋白质折叠的拓扑驱动假说，如果成功，它将代表着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序列/时间和空间两个空间中描述的系统，并且对于这些系统，(1)存在一个已知示例的大型数据库，(2)一个共同的潜在原理导致了所有示例。应用包括天气预报、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.