title: A sequence-based two-level method for the prediction of type I secreted RTX proteins.
abstract: Many Gram-negative bacteria use the type I secretion system (T1SS) to translocate a wide range of substrates (type I secreted RTX proteins, T1SRPs) from the cytoplasm across the inner and outer membrane in one step to the extracellular space. Since T1SRPs play an important role in pathogen-host interactions, identifying them is crucial for a full understanding of the pathogenic mechanism of T1SS. However, experimental identification is often time-consuming and expensive. In the post-genomic era, it becomes imperative to predict new T1SRPs using information from the amino acid sequence alone when new proteins are being identified in a high-throughput mode. In this study, we report a two-level method for the first attempt to identify T1SRPs using sequence-derived features and the random forest (RF) algorithm. At the full-length sequence level, the results show that the unique feature of T1SRPs is the presence of variable numbers of the calcium-binding RTX repeats. These RTX repeats have a strong predictive power and so T1SRPs can be well distinguished from non-T1SRPs. At another level, different from that of the secretion signal, we find that a sequence segment located at the last 20-30 C-terminal amino acids may contain important signal information for T1SRP secretion because obvious differences were shown between the corresponding positions of T1SRPs and non-T1SRPs in terms of amino acid and secondary structure compositions. Using five-fold cross-validation, overall accuracies of 97% at the full-length sequence level and 89% at the secretion signal level were achieved through feature evaluation and optimization. Benchmarking on an independent dataset, our method could correctly predict 63 and 66 of 74 T1SRPs at the full-length sequence and secretion signal levels, respectively. We believe that this study will be useful in elucidating the secretion mechanism of T1SS and facilitating hypothesis-driven experimental design and validation.
authors: College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China. yzguo@scu.edu.cn liml@scu.edu.cn.
authors: Jiesi
authors: J
authors: Luo
authors: Wenling
authors: W
authors: Li
authors: Zhongyu
authors: Z
authors: Liu
authors: Yanzhi
authors: Y
authors: Guo
authors: Xuemei
authors: X
authors: Pu
authors: Menglong
authors: M
authors: Li
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000737:chemistry* / Q000378:metabolism; D006090:Gram-Negative Bacteria / Q000737:chemistry* / Q000502:physiology; D016905:Gram-Negative Bacterial Infections / Q000378:metabolism / Q000382:microbiology; D054884:Host-Pathogen Interactions; D006801:Humans; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011487:Protein Conformation; D000069177:Type I Secretion Systems / Q000737:chemistry* / Q000378:metabolism
chemical_list: D001426:Bacterial Proteins; D000069177:Type I Secretion Systems