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Characterization of subcellular localization of eukaryotic clamp loader/unloader and its regulatory mechanism

ABSTRACT: Proliferating cell nuclear antigen (PCNA) plays a critical role as a processivity clamp for eukaryotic DNA polymerases and a binding platform for many DNA replication and repair proteins. The enzymatic activities of PCNA loading and unloading have been studied extensively in vitro. However, the subcellular locations of PCNA loaders, replication complex C (RFC) and CTF18-RFC-like-complex (RLC), and PCNA unloader ATAD5-RLC remain elusive, and the role of their subunits RFC2-5 is unknown. Here we used protein fractionation to determine the subcellular localization of RFC and RLCs and affinity purification to find molecular requirements for the newly defined location. All RFC/RLC proteins were detected in the nuclease-resistant pellet fraction. RFC1 and ATAD5 were not detected in the non-ionic detergent-soluble and nuclease-susceptible chromatin fractions, independent of cell cycle or exogenous DNA damage. We found that small RFC proteins contribute to maintaining protein levels of the RFC/RLCs. RFC1, ATAD5, and RFC4 co-immunoprecipitated with lamina-associated polypeptide 2 (LAP2) α which regulates intranuclear lamin A/C. LAP2α knockout consistently reduced detection of RFC/RLCs in the pellet fraction, while marginally affecting total protein levels. Our findings strongly suggest that PCNA-mediated DNA transaction occurs through regulatory machinery associated with nuclear structures, such as the nuclear matrix.

SUBMITTER: Park S

PROVIDER: S-EPMC8575788 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features.ResultsHere, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM). In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets; 1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after five-fold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively; best accuracies reported till date on the same datasets.ConclusionThese results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search based results. The presently developed modules are implemented as web server "ESLpred2" available at http://www.imtech.res.in/raghava/eslpred2/.

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Characterization of subcellular localization of eukaryotic clamp loader/unloader and its regulatory mechanism

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