Ng Julio, Pevzner Pavel A
Bioinformatics Program, University of California, San Diego, La Jolla, California 92093-0419, USA.
J Proteome Res. 2008 Jan;7(1):89-95. doi: 10.1021/pr070214g.
Identification of fusion proteins has contributed significantly to our understanding of cancer progression, yielding important predictive markers and therapeutic targets. While fusion proteins can be potentially identified by mass spectrometry, all previously found fusion proteins were identified using genomic (rather than mass spectrometry) technologies. This lack of MS/MS applications in studies of fusion proteins is caused by the lack of computational tools that are able to interpret mass spectra from peptides covering unknown fusion breakpoints (fusion peptides). Indeed, the number of potential fusion peptides is so large that the existing MS/MS database search tools become impractical even in the case of small genomes. We explore computational approaches to identifying fusion peptides, propose an algorithm for solving the fusion peptide identification problem, and analyze the performance of this algorithm on simulated data. We further illustrate how this approach can be modified for human exons prediction.
融合蛋白的鉴定对我们理解癌症进展做出了重大贡献，产生了重要的预测标志物和治疗靶点。虽然融合蛋白有可能通过质谱法鉴定，但所有先前发现的融合蛋白都是使用基因组（而非质谱）技术鉴定的。在融合蛋白研究中缺乏MS/MS应用，是由于缺乏能够解释覆盖未知融合断点的肽段（融合肽）的质谱图的计算工具。实际上，潜在融合肽的数量如此之多，以至于即使在小基因组的情况下，现有的MS/MS数据库搜索工具也变得不切实际。我们探索了鉴定融合肽的计算方法，提出了一种解决融合肽鉴定问题的算法，并分析了该算法在模拟数据上的性能。我们进一步说明了如何修改此方法用于人类外显子预测。
