European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK.
Department of Chemistry - BMC, Uppsala University, Uppsala, Sweden.
Cell Rep. 2022 May 3;39(5):110764. doi: 10.1016/j.celrep.2022.110764.
Linear motifs have an integral role in dynamic cell functions, including cell signaling. However, due to their small size, low complexity, and frequent mutations, identifying novel functional motifs poses a challenge. Viruses rely extensively on the molecular mimicry of cellular linear motifs. In this study, we apply systematic motif prediction combined with functional filters to identify human linear motifs convergently evolved also in viral proteins. We observe an increase in the sensitivity of motif prediction and improved enrichment in known instances. We identify >7,300 non-redundant motif instances at various confidence levels, 99 of which are supported by all functional and structural filters. Overall, we provide a pipeline to improve the identification of functional linear motifs from interactomics datasets and a comprehensive catalog of putative human motifs that can contribute to our understanding of the human domain-linear motif code and the associated mechanisms of viral interference.
线性基序在包括细胞信号转导在内的动态细胞功能中起着重要作用。然而，由于它们体积小、复杂性低且经常发生突变，因此识别新的功能基序具有挑战性。病毒广泛依赖于细胞线性基序的分子模拟。在这项研究中，我们应用系统的基序预测结合功能筛选，来识别在病毒蛋白中也发生趋同进化的人类线性基序。我们观察到基序预测的灵敏度提高，并且在已知实例中得到了更好的富集。我们在不同置信水平下识别出超过 7300 个非冗余的基序实例，其中 99 个实例得到了所有功能和结构筛选的支持。总的来说，我们提供了一种从相互作用组数据集中识别功能线性基序的方法，以及一个全面的人类假定基序目录，这有助于我们理解人类域-线性基序密码和相关的病毒干扰机制。
