Jarnot Patryk
Department of Computer Networks and Systems, Silesian University of Technology, 44-100, Gliwice, Poland.
Sci Rep. 2024 Dec 30;14(1):31777. doi: 10.1038/s41598-024-82548-8.
Methods for scoring matrix adjustment decrease the significance of biased residues to better detect homology between protein sequences. This is because non-homologous proteins often contain fragments with non-standard compositions that are strikingly similar to each other. However, these fragments are also functionally important in proteins and are receiving an increasing attention from the scientific community. In this study, we described why the gold standard method for scoring matrix adjustment is unable to emphasise frequent amino acids. Further, we used BLAST to align collagen-like domains with and without the scoring matrix adjustment and compared the results. We found that the scoring matrices were adjusted in the opposite direction to the optimal state. Therefore, turning off the adjustment improved alignment quality of collagen-like domains, but scoring matrices still need refinement. This study provides a detailed analysis of why the gold standard method fails, and opens doors for new methods to adjust scoring matrices for functional motifs with non-standard compositions.
评分矩阵调整方法降低了有偏差残基的显著性，以便更好地检测蛋白质序列之间的同源性。这是因为非同源蛋白质通常包含具有非标准组成的片段，这些片段彼此惊人地相似。然而，这些片段在蛋白质中也具有重要的功能，并且正受到科学界越来越多的关注。在本研究中，我们描述了为什么评分矩阵调整的金标准方法无法强调频繁出现的氨基酸。此外，我们使用BLAST对有无评分矩阵调整的类胶原结构域进行比对，并比较结果。我们发现评分矩阵朝着与最优状态相反的方向进行了调整。因此，关闭调整可提高类胶原结构域的比对质量，但评分矩阵仍需改进。本研究详细分析了金标准方法失败的原因，并为调整具有非标准组成的功能基序的评分矩阵的新方法打开了大门。
