Mumey Brendan M, Bailey Brian W, Kirkpatrick Bonnie, Jesaitis Algirdas J, Angel Thomas, Dratz Edward A
Department of Computer Science, Montana State University, Bozeman, MT 59717-3880, USA.
J Comput Biol. 2003;10(3-4):555-67. doi: 10.1089/10665270360688183.
Antibodies that bind to protein surfaces of interest can be used to report the three-dimensional structure of the protein as follows: Proteins are composed of linear polypeptide chains that fold together in complex spatial patterns to create the native protein structure. These folded structures form binding sites for antibodies. Antibody binding sites are typically "assembled" on the protein surface from segments that are far apart in the primary amino acid sequence of the target proteins. Short amino acid probe sequences that bind to the active region of each antibody can be used as witnesses to the antibody epitope surface and these probes can be efficiently selected from random sequence peptide libraries. This paper presents a new method to align these antibody epitopes to discontinuous regions of the one-dimensional amino acid sequence of a target protein. Such alignments of the epitopes indicate how segments of the protein sequence must be folded together in space and thus provide long-range constraints for solving the 3-D protein structure. This new antibody-based approach is applicable to the large fraction of proteins that are refractory to current approaches for structure determination and has the additional advantage of requiring very small amounts of the target protein. The binding site of an antibody is a surface, not just a continuous linear sequence, so the epitope mapping alignment problem is outside the scope of classical string alignment algorithms, such as Smith-Waterman. We formalize the alignment problem that is at the heart of this new approach, prove that the epitope mapping alignment problem is NP-complete, and give some initial results using a branch-and-bound algorithm to map two real-life cases. Initial results for two validation cases are presented for a graph-based protein surface neighbor mapping procedure that promises to provide additional spatial proximity information for the amino acid residues on the protein surface.
与感兴趣的蛋白质表面结合的抗体可用于报告蛋白质的三维结构，具体如下：蛋白质由线性多肽链组成，这些多肽链以复杂的空间模式折叠在一起，形成天然蛋白质结构。这些折叠结构形成了抗体的结合位点。抗体结合位点通常是由目标蛋白质一级氨基酸序列中相距甚远的片段在蛋白质表面“组装”而成。与每种抗体的活性区域结合的短氨基酸探针序列可作为抗体表位表面的见证，并且这些探针可从随机序列肽库中高效筛选。本文提出了一种新方法，将这些抗体表位与目标蛋白质一维氨基酸序列的不连续区域进行比对。这种表位比对表明蛋白质序列的片段必须如何在空间中折叠在一起，从而为解析三维蛋白质结构提供远程限制。这种基于抗体的新方法适用于目前难以用结构测定方法解析的大部分蛋白质，并且具有所需目标蛋白质量非常少的额外优势。抗体的结合位点是一个表面，而不仅仅是一个连续的线性序列，因此表位图谱比对问题超出了经典字符串比对算法（如史密斯-沃特曼算法）的范围。我们将这种新方法核心的比对问题形式化，证明表位图谱比对问题是NP完全问题，并给出了使用分支定界算法对两个实际案例进行图谱分析的一些初步结果。针对基于图形的蛋白质表面邻域图谱程序给出了两个验证案例的初步结果，该程序有望为蛋白质表面的氨基酸残基提供额外的空间邻近信息。
