Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Department of Computer Science, Kasetsart University, Bangkok, Thailand.
Bioinformatics. 2019 Dec 15;35(24):5313-5314. doi: 10.1093/bioinformatics/btz568.
Identification of the amino-acid motifs in proteins that are targeted for post-translational modifications (PTMs) is of great importance in understanding regulatory networks. Information about targeted motifs can be derived from mass spectrometry data that identify peptides containing specific PTMs such as phosphorylation, ubiquitylation and acetylation. Comparison of input data against a standardized 'background' set allows identification of over- and under-represented amino acids surrounding the modified site. Conventionally, calculation of targeted motifs assumes a random background distribution of amino acids surrounding the modified position. However, we show that probabilities of amino acids depend on (i) the type of the modification and (ii) their positions relative to the modified site. Thus, software that identifies such over- and under-represented amino acids should make appropriate adjustments for these effects. Here we present a new program, PTM-Logo, that generates representations of these amino acid preferences ('logos') based on position-specific amino-acid probability backgrounds calculated either from user-input data or curated databases.
PTM-Logo is freely available online at http://sysbio.chula.ac.th/PTMLogo/ or https://hpcwebapps.cit.nih.gov/PTMLogo/.
Supplementary data are available at Bioinformatics online.
鉴定蛋白质中受翻译后修饰（PTM）靶向的氨基酸基序对于理解调控网络非常重要。关于靶向基序的信息可以从鉴定含有特定 PTM（如磷酸化、泛素化和乙酰化）的肽的质谱数据中获得。将输入数据与标准化的“背景”集进行比较，可以鉴定修饰位点周围过度和不足的氨基酸。传统上，靶向基序的计算假设修饰位置周围的氨基酸具有随机背景分布。然而，我们表明，氨基酸的概率取决于（i）修饰的类型和（ii）它们相对于修饰位点的位置。因此，识别这些过度和不足的氨基酸的软件应该对这些影响进行适当的调整。在这里，我们提出了一个新的程序，PTM-Logo，它根据从用户输入数据或经过整理的数据库计算的位置特异性氨基酸概率背景，生成这些氨基酸偏好的表示（“标志”）。
PTM-Logo 可在 http://sysbio.chula.ac.th/PTMLogo/ 或 https://hpcwebapps.cit.nih.gov/PTMLogo/ 在线免费获得。
补充数据可在 Bioinformatics 在线获得。
