Dataset Information

Computational Prediction of Hot Spots and Binding Site of Inhibitor NSC23766 on Rac1 Binding With Tiam1.

ABSTRACT: Rac1 is a small signaling protein, which belongs to the Rho subfamily of Ras superfamily. It is activated by binding GTP and inactivated by exchanging GDP for GTP. The ability of nucleotide exchange depends on guanine nucleotide exchange factors (GEFs) family proteins. T-lymphoma invasion and metastasis factor 1 (Tiam1) is a member of GEFs. Rac1 participates in multiple signaling pathways and regulates various cellular events by interacting with GEFs. Particularly, it is involved in the development and progression of various kinds of tumors. In this paper, we have studied the detailed interaction between Rac1 and Tiam1. Seven residues on Rac1 are predicted to be important for the interaction with Tiam1, i.e. E31, Y32, D38, N39, Y64, D65 and W56. All these residues are located on the switch 1 and 2 domains which are the interface between Rac1 and Tiam1, except W56. In addition, we analyzed how inhibitor NSC23766 interacts with Rac1. Our docking results show that NSC23766 binds to the same region as Tiam1. Several residues, i.e. F37, D38, N39, W56, Y64, L67, L70 and S71, contribute much to binding free energy. These findings are very useful for the structure-based design of inhibitors toward Rac1.

SUBMITTER: Zheng C

PROVIDER: S-EPMC7884829 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Computational Prediction of Hot Spots and Binding Site of Inhibitor NSC23766 on Rac1 Binding With Tiam1.

Zheng Chunwen C Wu Xiaodong X Zeng Ruijie R Lin Lirui L Xu Liyan L Li Enmin E Dong Geng G

Frontiers in chemistry 20210202

Rac1 is a small signaling protein, which belongs to the Rho subfamily of Ras superfamily. It is activated by binding GTP and inactivated by exchanging GDP for GTP. The ability of nucleotide exchange depends on guanine nucleotide exchange factors (GEFs) family proteins. T-lymphoma invasion and metastasis factor 1 (Tiam1) is a member of GEFs. Rac1 participates in multiple signaling pathways and regulates various cellular events by interacting with GEFs. Particularly, it is involved in the developm ...[more]

PMID: 33604328

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Project description:Modern gene therapy methods have limited control over where a therapeutic viral vector inserts into the host genome. Vector integration can activate local gene expression, which can cause cancer if the vector inserts near an oncogene. Viral integration hot-spots or 'common insertion sites' (CIS) are scrutinized to evaluate and predict patient safety. CIS are typically defined by a minimum density of insertions (such as 2-4 within a 30-100 kb region), which unfortunately depends on the total number of observed VIS. This is problematic for comparing hot-spot distributions across data sets and patients, where the VIS numbers may vary.We develop two new methods for defining hot-spots that are relatively independent of data set size. Both methods operate on distributions of VIS across consecutive 1 Mb 'bins' of the genome. The first method 'z-threshold' tallies the number of VIS per bin, converts these counts to z-scores, and applies a threshold to define high density bins. The second method 'BCP' applies a Bayesian change-point model to the z-scores to define hot-spots. The novel hot-spot methods are compared with a conventional CIS method using simulated data sets and data sets from five published human studies, including the X-linked ALD (adrenoleukodystrophy), CGD (chronic granulomatous disease) and SCID-X1 (X-linked severe combined immunodeficiency) trials. The BCP analysis of the human X-linked ALD data for two patients separately (774 and 1627 VIS) and combined (2401 VIS) resulted in 5-6 hot-spots covering 0.17-0.251% of the genome and containing 5.56-7.74% of the total VIS. In comparison, the CIS analysis resulted in 12-110 hot-spots covering 0.018-0.246% of the genome and containing 5.81-22.7% of the VIS, corresponding to a greater number of hot-spots as the data set size increased. Our hot-spot methods enable one to evaluate the extent of VIS clustering, and formally compare data sets in terms of hot-spot overlap. Finally, we show that the BCP hot-spots from the repopulating samples coincide with greater gene and CpG island density than the median genome density.The z-threshold and BCP methods are useful for comparing hot-spot patterns across data sets of disparate sizes. The methodology and software provided here should enable one to study hot-spot conservation across a variety of VIS data sets and evaluate vector safety for gene therapy trials.

Dataset Information

Computational Prediction of Hot Spots and Binding Site of Inhibitor NSC23766 on Rac1 Binding With Tiam1.

Publications

Computational Prediction of Hot Spots and Binding Site of Inhibitor NSC23766 on Rac1 Binding With Tiam1.

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