Dataset Information

DNB-based on-chip motif finding: A high-throughput method to profile different types of protein-DNA interactions.

ABSTRACT: Here, we report a sensitive DocMF system that uses next-generation sequencing chips to profile protein-DNA interactions. Using DocMF, we successfully identified a variety of endonuclease recognition sites and the protospacer adjacent motif (PAM) sequences of different CRISPR systems. DocMF can simultaneously screen both 5' and 3' PAMs with high coverage. For SpCas9, we found noncanonical 5'-NAG-3' (~5%) and 5'-NGA-3' (~1.6%), in addition to its common PAMs, 5'-NGG-3' (~89.9%). More relaxed PAM sequences of two uncharacterized Cas endonucleases, VeCas9 and BvCas12a, were extensively characterized using DocMF. Moreover, we observed that dCas9, a DNA binding protein lacking endonuclease activity, preferably bound to the previously reported 5'-NGG-3' sequence. In summary, our studies demonstrate that DocMF is the first tool with the capacity to exhaustively assay both the binding and the cutting properties of different DNA binding proteins.

SUBMITTER: Li Z

PROVIDER: S-EPMC7399529 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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DNB-based on-chip motif finding: A high-throughput method to profile different types of protein-DNA interactions.

Li Zhuokun Z Wang Xiaojue X Xu Dongyang D Zhang Dengwei D Wang Dan D Dai Xuechen X Wang Qi Q Li Zhou Z Gu Ying Y Ouyang Wenjie W Zhao Shuchang S Huang Baoqian B Gong Jian J Zhao Jing J Chen Ao A Shen Yue Y Dong Yuliang Y Zhang Wenwei W Xu Xun X Xu Chongjun C Jiang Yuan Y

Science advances 20200731 31

Here, we report a sensitive DocMF system that uses next-generation sequencing chips to profile protein-DNA interactions. Using DocMF, we successfully identified a variety of endonuclease recognition sites and the protospacer adjacent motif (PAM) sequences of different CRISPR systems. DocMF can simultaneously screen both 5' and 3' PAMs with high coverage. For SpCas9, we found noncanonical 5'-NAG-3' (~5%) and 5'-NGA-3' (~1.6%), in addition to its common PAMs, 5'-NGG-3' (~89.9%). More relaxed PAM s ...[more]

PMID: 32789179

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Project description:BackgroundSupervised learning and many stochastic methods for predicting protein-protein interactions require both negative and positive interactions in the training data set. Unlike positive interactions, negative interactions cannot be readily obtained from interaction data, so these must be generated. In protein-protein interactions and other molecular interactions as well, taking all non-positive interactions as negative interactions produces too many negative interactions for the positive interactions. Random selection from non-positive interactions is unsuitable, since the selected data may not reflect the original distribution of data.ResultsWe developed a bootstrapping algorithm for generating a negative data set of arbitrary size from protein-protein interaction data. We also developed an efficient boosting algorithm for finding interacting motif pairs in human and virus proteins. The boosting algorithm showed the best performance (84.4% sensitivity and 75.9% specificity) with balanced positive and negative data sets. The boosting algorithm was also used to find potential motif pairs in complexes of human and virus proteins, for which structural data was not used to train the algorithm. Interacting motif pairs common to multiple folds of structural data for the complexes were proven to be statistically significant. The data set for interactions between human and virus proteins was extracted from BOND and is available at http://virus.hpid.org/interactions.aspx. The complexes of human and virus proteins were extracted from PDB and their identifiers are available at http://virus.hpid.org/PDB_IDs.html.ConclusionWhen the positive and negative training data sets are unbalanced, the result via the prediction model tends to be biased. Bootstrapping is effective for generating a negative data set, for which the size and distribution are easily controlled. Our boosting algorithm could efficiently predict interacting motif pairs from protein interaction and sequence data, which was trained with the balanced data sets generated via the bootstrapping method.

Dataset Information

DNB-based on-chip motif finding: A high-throughput method to profile different types of protein-DNA interactions.

Publications

DNB-based on-chip motif finding: A high-throughput method to profile different types of protein-DNA interactions.

Similar Datasets

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