公开(公告)号：US20060009913A1

公开(公告)日：2006-01-12

申请号：US10901576

申请日：2004-07-29

申请人： Rene Trabanino ,  Nagarajan Vaidehi ,  Spencer Hall ,  William Goddard ,  Wely Floriano

发明人： Rene Trabanino ,  Nagarajan Vaidehi ,  Spencer Hall ,  William Goddard ,  Wely Floriano

IPC分类号： G06F19/00 ,  G01N33/48

CPC分类号： G16B15/00 ,  G16B30/00

摘要： The invention provides computer-implemented methods and apparatus implementing a hierarchical protocol using multiscale molecular dynamics and molecular modeling methods to predict the presence of transmembrane regions in proteins, such as G-Protein Coupled Receptors (GPCR), and protein structural models generated according to the protocol. The protocol features a coarse grain sampling method, such as hydrophobicity analysis, to provide a fast and accurate procedure for predicting transmembrane regions. Methods and apparatus of the invention are useful to screen protein or polynucleotide databases for encoded proteins with transmembrane regions, such as GPCRs.

公开(公告)号：US20050136481A1

公开(公告)日：2005-06-23

申请号：US10918531

申请日：2004-08-13

申请人： Rene Trabanino ,  Nagarajan Vaidehi ,  Spencer Hall ,  William Goddard ,  Wely Floriano

发明人： Rene Trabanino ,  Nagarajan Vaidehi ,  Spencer Hall ,  William Goddard ,  Wely Floriano

IPC分类号： G01N33/48 ,  G01N33/50 ,  G01N33/53 ,  G06F19/16 ,  G06F19/00

CPC分类号： G16B15/00 ,  G16B30/00

摘要： The invention provides computer-implemented methods and apparatus implementing a hierarchical protocol using multiscale molecular dynamics and molecular modeling methods to predict the structure of transmembrane proteins such as G-Protein Coupled Receptors (GPCR), and protein structural models generated according to the protocol. The protocol features a combination of coarse grain sampling methods, such as hydrophobicity analysis, followed by coarse grain molecular dynamics and atomic level molecular dynamics, including accurate continuum solvation. Also included are energy optimization to determine the rotation of helices in the (seven-helical) TM bundle, and optimization of the helix translations along their axes and rotational optimization using hydrophobic moment of the helices, to provide a fast and accurate procedure for predicting GPCR tertiary structure.

摘要翻译： 本发明提供了计算机实现的方法和装置，其使用多尺度分子动力学和分子建模方法来实现分层协议，以预测跨膜蛋白如G-蛋白偶联受体（GPCR）的结构和根据该方案产生的蛋白质结构模型。 该协议具有粗粒度采样方法的组合，如疏水性分析，其次是粗粒分子动力学和原子级分子动力学，包括精确的连续溶剂化。 还包括能量优化以确定（七螺旋）TM束中的螺旋的旋转，以及使用其螺旋的疏水力矩优化沿其轴的螺旋平移和旋转优化，以提供用于预测GPCR的快速和准确的程序 三级结构。