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    • 3. 发明授权
    • Protein fold recognition using sequence-derived predictions
    • 蛋白质折叠识别使用序列导出的预测
    • US06512981B1
    • 2003-01-28
    • US09071279
    • 1998-05-01
    • David EisenbergDaniel Fischer
    • David EisenbergDaniel Fischer
    • G06F1900
    • G06F19/16G01N33/6803
    • A computer-assisted method for assigning an amino acid probe sequence to a known three-dimensional protein structure. In particular, the invention includes a method for using the amino acid sequence of a probe plus sequence-derived properties of the probe in making fold assignments. The method includes inputting into a computer system a string p1, p2 . . . pn describing the amino acid sequence of the probe sequence and at least one sequence-derived property for the probe sequence; inputting into a computer system a string t1, t2 . . . tm of structural properties for each member of a library of known 3D structures; executing an alignment algorithm in the computer to compute an alignment score indicating the optimal alignment of the string p1, p2 . . . pn to each string t1, t2 . . . tm by applying a combined compatibility function g(pi, tj); determining the statistical significance of each alignment score to determine a best-fit alignment score; and applying the best-fit alignment score to indicate or select the corresponding 3D protein structure from the library for output to a user. The invention includes a computer program implementation of such a method.
    • 用于将氨基酸探针序列分配给已知的三维蛋白质结构的计算机辅助方法。 特别地,本发明包括使用探针的氨基酸序列加上探针的序列衍生性质进行折叠分配的方法。 该方法包括向计算机系统输入字符串p1,p2。 。 。 pn描述探针序列的氨基酸序列和探针序列的至少一个序列衍生性质; 向计算机系统输入字符串t1,t2。 。 。 已知3D结构库的每个成员的结构特性的tm; 在计算机中执行对准算法来计算指示字符串p1,p2的最佳对齐的对齐分数。 。 。 pn到每个字符串t1,t2。 。 。 通过应用组合的兼容性函数g(pi,tj); 确定每个比对得分的统计学显着性以确定最佳拟合比对得分; 并应用最佳拟合对准分数来指示或选择相应的3D蛋白质结构从库输出给用户。 本发明包括这种方法的计算机程序实现。
    • 5. 发明授权
    • Determining the functions and interactions of proteins by comparative analysis
    • 通过比较分析确定蛋白质的功能和相互作用
    • US06892139B2
    • 2005-05-10
    • US09712363
    • 2000-11-13
    • David EisenbergSergio H. RotsteinEdward M. Marcotte
    • David EisenbergSergio H. RotsteinEdward M. Marcotte
    • G06F19/00G01N33/48C12Q1/68G01N33/50
    • G06F19/18G06F19/22
    • The invention provides novel methods for characterizing the function of nucleic acids and polypeptides. The invention provides a novel method for identifying a nucleic acid or a polypeptide sequence that may be a target for a drug. The invention provides a novel method for identifying a nucleic acid or a polypeptide sequence that may be essential for the growth or viability of an organism. The characterization is based on use of methods of the invention comprising algorithms that can identify functional relationships between diverse sets of non-homologous nucleic acid and polypeptide sequences. The invention provides a computer program product, stored on a computer-readable medium, for identifying a nucleic acid or a polypeptide sequence that may be essential for the growth or viability of an organism. The invention provides a computer program product, stored on a computer-readable medium, for identifying a nucleic acid or a polypeptide sequence that may be a target for a drug. The invention provides a computer system, comprising a processor and a computer program product of the invention.
    • 本发明提供用于表征核酸和多肽的功能的新方法。 本发明提供了用于鉴定可能是药物靶标的核酸或多肽序列的新方法。 本发明提供了一种用于鉴定可能对生物体的生长或活力至关重要的核酸或多肽序列的新方法。 该表征基于本发明的方法的使用,其包括可以鉴定不同组非同源核酸和多肽序列之间的功能关系的算法。 本发明提供了一种计算机程序产品,其存储在计算机可读介质上,用于鉴定可能对生物体的生长或活力至关重要的核酸或多肽序列。 本发明提供了一种计算机程序产品,其存储在计算机可读介质上,用于鉴定可能是药物靶标的核酸或多肽序列。 本发明提供一种包括本发明的处理器和计算机程序产品的计算机系统。
    • 10. 发明授权
    • Method to identify protein sequences that fold into a known
three-dimensional structure
    • 鉴定折叠成已知三维结构的蛋白质序列的方法
    • US5436850A
    • 1995-07-25
    • US218685
    • 1994-03-28
    • David EisenbergJames U. BowieRoland Luthy
    • David EisenbergJames U. BowieRoland Luthy
    • C07K1/00G01N33/68G06F17/50G06F19/00C12Q1/68
    • G06F19/16C07K1/00G01N33/68
    • A computer-assisted method for identifying protein sequences that fold into a known three-dimensional structure. The inventive method attacks the inverse protein folding problem by finding target sequences that are most compatible with profiles representing the structural environments of the residues in known three-dimensional protein structures. The method starts with a known three-dimensional protein structure and determines three key features of each residue's environment within the structure: (1) the total area of the residue's side-chain that is buried by other protein atoms, inaccessible to solvent; (2) the fraction of the side-chain area that is covered by polar atoms (O, N) or water, and (3) the local secondary structure. Based on these parameters, each residue position is categorized into an environment class. In this manner, a three-dimensional protein structure is converted into a one-dimensional environment string, which represents the environment class of each residue in the folded protein structure. A 3D structure profile table is then created containing score values that represent the frequency of finding any of the 20 common amino acids structures at each position of the environment string. These frequencies are determined from a database of known protein structures and aligned sequences. The method determines the most favorable alignment of a target protein sequence to the residue positions defined by the environment string, and determines a "best fit" alignment score, S.sub.ij, for the target sequence. Each target sequence may then be further characterized by a ZScore, which is the number of standard deviations that S.sub.ij for the target sequence is above the mean alignment score for other target sequences of similar length.
    • 用于鉴定折叠成已知三维结构的蛋白质序列的计算机辅助方法。 本发明的方法通过找到与表示已知三维蛋白质结构中的残基的结构环境的图谱最相容的靶序列来攻击逆蛋白质折叠问题。 该方法从已知的三维蛋白质结构开始,确定结构内每个残基环境的三个关键特征:(1)被其他蛋白质原子掩埋的残留物侧链的总面积,溶剂不可及; (2)由极性原子(O,N)或水覆盖的侧链区域的分数,以及(3)局部二级结构。 基于这些参数,每个残差位置被分类为一个环境类。 以这种方式,将三维蛋白质结构转化为一维环境串,其代表折叠的蛋白质结构中每个残基的环境类别。 然后创建3D结构简档表,其中包含表示在环境字符串的每个位置找到20个常见氨基酸结构中的任何一个的频率的评分值。 这些频率由已知蛋白质结构和比对序列的数据库确定。 该方法确定目标蛋白质序列与由环境序列定义的残基位置的最佳比对,并确定靶序列的“最佳拟合”比对分数Sij。 然后可以通过ZScore进一步表征每个靶序列,ZScore是靶序列的Sij高于具有相似长度的其他靶序列的平均比对分数的标准偏差数。