Deep Learning-Based Comparative Prediction and Functional Analysis of Intrinsically Disordered Regions in SARS-CoV-2
Sidra Ilyas, Abdul Manan, Donghun Lee
International Journal of Molecular Sciences, doi:10.3390/ijms26073411
This study explores the role of intrinsically disordered regions (IDRs) in the SARS-CoV-2 proteome and their potential as targets for small-molecule drug discovery. Experimentally validated intrinsic disordered regions from the literature were utilized to assess the prediction of intrinsic disorder across a selection of SARS-CoV-2 proteins. The disorder propensities of proteins using four deep learning-based disorder prediction models: ADOPT, PONDR ® VLXT, PONDR ® VSL2, and flDPnn, were analyzed. ADOPT, VSL2, and VLXT identified a flexible linker (129-147), while VSL2 and VLXT predicted disorder in the Cu(II) binding region (163-167) of NSP1. ADOPT did not predict disordered regions in NSP11; however, VSL2 and VLXT identified disorder in the experimentally validated regions. The IDR in ORF3a is crucial for protein localization and immune modulation, affecting inflammatory pathways. VSL2 predicted significant disorder in the N-terminal domain (18-23), which aligns with experimental data (1-41), overlapping with the TRAF-binding motif, while ADOPT indicated high disorder in the C-terminal domain (255-275), consistent with VSL2 and flDPnn. All tools identified disorder in the N-terminal (1-68), central linker (181-248), and C-terminal (370-419) regions of the nucleocapsid (N) protein, suggesting flexibility and accuracy. The S2 subunit of the spike protein displayed more predicted disorder than the S1 subunit across ADOPT, VSL2, and flDPnn. These IDRs are essential for viral functions, like protein localization, immune modulation, receptor binding, and membrane fusion. This study highlights the importance of IDR in modulating key inflammatory pathways, suggesting that they could serve as promising targets for small-molecule drug development to combat COVID-19.
Statistical Analysis The statistical analysis was conducted using Python 3.6, employing the SciPy statistics library. A Chi-squared test was conducted to evaluate the relationship between experimentally validated and predicted disorder data from the ADOPT, VLXT, VSL2, and flDPnn models. This statistical test was employed to determine whether there was a significant association between the observed frequencies of disorder classifications and the expected frequencies under the assumption of independence. By comparing the distribution of predicted and experimentally validated data, the Chi-squared test allowed us to evaluate the degree of agreement between computational predictions and experimental data, thereby providing a quantitative measure of the models' predictive reliability.
Conclusions The analysis of intrinsically disordered regions (IDRs) in various SARS-CoV-2 proteins highlights their essential roles in viral replication, host interactions, and immune modulation. Due to their structural plasticity, IDRs enable dynamic protein conformations that facilitate crucial interactions with viral RNA, host cell membranes, and immune pathways. In the SARS-CoV-2 replicase polyprotein 1ab, multiple IDRs, including the flexible linker/spacer in NSP1, the Cu(II)-binding domain in NSP1, the RNA-binding domain in the polymerase, and the lipid-binding region in NSP6, are integral to viral replication and protein synthesis. Their structural flexibility and dynamic interactions with..
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