Novel dynamic residue network analysis approaches to study allosteric modulation: SARS-CoV-2 Mpro and its evolutionary mutations as a case study

Amamuddy, Olivier Sheik; Boateng, Rita Afriyie; Barozi, Victor; Nyamai, Dorothy Wavinya; Bishop, Özlem Taştan

doi:10.1016/j.csbj.2021.11.016

Cited by 16 publications

(34 citation statements)

References 91 publications

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“…Asymmetric protomer behavior of the dimeric proteins was discussed previously. 37 Here, we observed only minor differences. Yet, due to these differences, we have not identified any persistent hubs .…”

Section: Resultsmentioning

confidence: 44%

“…We recently reported a similar switch behavior between the protomers in the presence of mutations in the homodimeric SARS-CoV-2 M pro protein, too. 37 Like in most mutant systems, protomer A of S315N explored more conformational space than the WT along PC2, whereas in protomer B it was along PC1. A distinctively spread-out conformational space in contrast to the WT was noted in S315R, S457I, and G593D in both protomers and along both PCs.…”

Section: Resultsmentioning

confidence: 99%

“…At the protomer level, comparative analysis was done between WT and each mutant protein separately, and within each paired system, each protomer of a mutant was compared to the protomers of WT (or vice versa); this was done since protomer dynamics can switch between identical copies of a protomer in a homodimer 37 ( Figure 4 ). We further supported this analysis by the root-mean-square fluctuation (RMSF) calculations ( Figure S3 ).…”

Section: Resultsmentioning

confidence: 99%

“…Visual inspection of the residue mapping to the 3D KatG WT structure, unexpectedly, showed that the identified BC hubs form large hub ensembles as communication regions rather than clearly defined single communication paths, as identified in our previous studies. 37 , 38 These hubs are mostly located at or around the interface region ( Figure S5 ). In addition to conferring structural stability, 61 protein interface residues are also known to be involved in inter- and intraprotein communication.…”

Section: Resultsmentioning

confidence: 99%

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Deciphering Isoniazid Drug Resistance Mechanisms on Dimeric Mycobacterium tuberculosis KatG via Post-molecular Dynamics Analyses Including Combined Dynamic Residue Network Metrics

et al. 2022

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Resistance mutations in Mycobacterium tuberculosis ( Mtb ) catalase peroxidase protein (KatG), an essential enzyme in isoniazid (INH) activation, reduce the sensitivity of Mtb to first-line drugs, hence presenting challenges in tuberculosis (TB) management. Thus, understanding the mutational imposed resistance mechanisms remains of utmost importance in the quest to reduce the TB burden. Herein, effects of 11 high confidence mutations in the KatG structure and residue network communication patterns were determined using extensive computational approaches. Combined traditional post-molecular dynamics analysis and comparative essential dynamics revealed that the mutant proteins have significant loop flexibility around the heme binding pocket and enhanced asymmetric protomer behavior with respect to wild-type (WT) protein. Heme contact analysis between WT and mutant proteins identified a reduction to no contact between heme and residue His270, a covalent bond vital for the heme-enabled KatG catalytic activity. Betweenness centrality calculations showed large hub ensembles with new hubs especially around the binding cavity and expanded to the dimerization domain via interface in the mutant systems, providing possible compensatory allosteric communication paths for the active site as a result of the mutations which may destabilize the heme binding pocket and the loops in its vicinity. Additionally, an interesting observation came from Eigencentrality hubs, most of which are located in the C-terminal domain, indicating relevance of the domain in the protease functionality. Overall, our results provide insight toward the mechanisms involved in KatG-INH resistance in addition to identifying key regions in the enzyme functionality, which can be used for future drug design.

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Section: Resultsmentioning

confidence: 44%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Deciphering Isoniazid Drug Resistance Mechanisms on Dimeric Mycobacterium tuberculosis KatG via Post-molecular Dynamics Analyses Including Combined Dynamic Residue Network Metrics

et al. 2022

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“…1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 17 , 18 , 19 , 20 , 21 , 22 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 63 , 64 .…”

Section: Uncited Referencesunclassified

Identification of Aloe-derived natural products as prospective lead scaffolds for SARS-CoV-2 main protease (Mpro) inhibitors

Hicks

Kandel

Lampe

2022

Bioorganic & Medicinal Chemistry Letters

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Green and efficient one-pot three-component synthesis of novel drug-like furo[2,3-d]pyrimidines as potential active site inhibitors and putative allosteric hotspots modulators of both SARS-CoV-2 MPro and PLPro

Mousavi

Zeynizadeh

Rimaz

2023

Bioorganic Chemistry

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Novel dynamic residue network analysis approaches to study allosteric modulation: SARS-CoV-2 Mpro and its evolutionary mutations as a case study

Cited by 16 publications

References 91 publications

Deciphering Isoniazid Drug Resistance Mechanisms on Dimeric Mycobacterium tuberculosis KatG via Post-molecular Dynamics Analyses Including Combined Dynamic Residue Network Metrics

Deciphering Isoniazid Drug Resistance Mechanisms on Dimeric Mycobacterium tuberculosis KatG via Post-molecular Dynamics Analyses Including Combined Dynamic Residue Network Metrics

Identification of Aloe-derived natural products as prospective lead scaffolds for SARS-CoV-2 main protease (Mpro) inhibitors

Green and efficient one-pot three-component synthesis of novel drug-like furo[2,3-d]pyrimidines as potential active site inhibitors and putative allosteric hotspots modulators of both SARS-CoV-2 MPro and PLPro

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