Decrypting the Information Exchange Pathways across the Spliceosome Machinery

Saltalamacchia, Andrea; Casalino, Lorenzo; Borišek, Jure; Batista, Víctor S.; Rivalta, Ivan; Magistrato, Alessandra

doi:10.1021/jacs.0c02036

Cited by 43 publications

(67 citation statements)

References 48 publications

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“…A recent study calculated network betweenness of spliceosome C complex factors and supported a well-established function of Prp8 as key communication conduit within spliceosome (Brow, 2002;Saltalamacchia et al, 2020). Our analysis provides further insights.…”

Section: Discussionsupporting

confidence: 72%

“…However, these studies are difficult to interpret with respect to molecular mechanisms since snRNAs are not part of the STRING database and without structural data the intermolecular contacts that cause changes in biological function are unclear. Recently, network theory analysis of a spliceosome structure combined with molecular dynamic simulations were used to identify putative information exchange pathways among splicing machinery components (Clf1, Cwc2, and Prp8) in the yeast C complex spliceosome (Saltalamacchia et al, 2020; Shao et al, 2020b). It is not clear, though, how the C complex structural network is assembled or changes during splicing.…”

Section: Introductionmentioning

confidence: 99%

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Network Theory Reveals Principles of Spliceosome Structure and Dynamics

Kaur

Feltz

Sun

et al. 2021

Preprint

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Cryo-EM has revolutionized structural biology of the spliceosome and dozens of distinct spliceosome structures representing much of the splicing cycle have now been determined. However, comparison of these structures is challenging due to extreme compositional and conformational dynamics of the splicing machinery and the thousands of intermolecular interactions created or dismantled as splicing progresses. We have used network theory to quantitatively analyze the dynamic interactions of splicing factors throughout the splicing cycle by constructing structure-based networks from every protein-protein, protein-RNA, and RNA-RNA interaction found in eight different spliceosome structures. Our networks reveal that structural modules comprising the spliceosome are highly dynamic with factors oscillating between modules during each stage along with large changes in the algebraic connectivities of the networks. Overall, the spliceosome's connectivity is focused on the active site in part due to contributions from non-globular proteins and components of the NTC. Many key components of the spliceosome including Prp8 and the U2 snRNA exhibit large shifts in both eigenvector and betweenness centralities during splicing. Other factors show transiently high betweenness centralities only at certain stages thereby suggesting mechanisms for regulating splicing by briefly bridging otherwise poorly connected network nodes. These observations provide insights into the organizing principles of spliceosome architecture and provide a framework for comparative network analysis of similarly complex and dynamic macromolecular machines.

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Section: Discussionsupporting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Network Theory Reveals Principles of Spliceosome Structure and Dynamics

Kaur

Feltz

Sun

et al. 2021

Preprint

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“…In it, the C α atoms of each amino acid are the nodes and the correlation between them are the edges connecting the nodes. The number of nodes in our system is N=3438, which makes it one of the largest systems studied previously with this method (60–65)(Comparable to the work by Saltalamacchia et al on a splicosome complex involving 4804 C α atomd and 270 phosphorus atoms in the network (69)). We then calculated the betweenness centrality (BC), a graph theoretical measure that provides a way to quantify the amount of information that flows via the nodes and edges of a network.…”

Section: Resultsmentioning

confidence: 99%

Distant Residues Modulate Conformational Opening in SARS-CoV-2 Spike Protein

Ray

Andricioaei

2020

Preprint

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Infection by SARS-CoV-2 involves the attachment of the receptor binding domain (RBD) of its spike proteins to the ACE2 receptors on the peripheral membrane of host cells. Binding is initiated by a down to up conformational change in the spike protein, an opening which presents the RBD to the receptor. To date, computational and experimental studies for therapeutics have concentrated, for good reason, on the RBD. However, the RBD region is highly prone to mutations, and therefore will possibly arise drug resistance. In contrast, we here focus on the correlations between the RBD and residues distant to it in the spike protein. We thereby provide a deeper understanding of the role of distant residues in the molecular mechanism of infection. Predictions of key mutations in distant allosteric binding sites are provided, with implications for therapeutics. Identifying these emerging mutants can also go a long way towards pre-designing vaccines for future outbreaks. The model we use, based on time-independent component analysis (tICA) and protein graph connectivity network, is able to identify multiple residues that exhibit long-distance coupling with the RBD opening. Mutation on these residues can lead to new strains of coronavirus with different degrees of transmissibility and virulence. The most ubiquitous D614G mutation and the A570D mutation of the highly contagious UK SARS-CoV-2 variant are predicted ab-initio from our model. Conversely, broad spectrum therapeutics like drugs and monoclonal antibodies can be generated targeting these key distant but conserved regions of the spike protein.Significance statementThe novel coronavirus (SARS-CoV-2) pandemic resulted in the largest economic and public health crises in recent times. Significant drug design effort, against SARS-CoV-2, is focused on the receptor binding domain (RBD) of the spike protein, although this region is prone to mutations causing therapeutic resistance. We applied deep data analysis methods on all-atom molecular dynamics simulations to identify key non-RBD residues that play a crucial role in spike-receptor binding and infection. Because the non-RBD residues are typically conserved across multiple coronaviruses, they can not only be targeted by broad spectrum antibodies and drugs against existing strains, but can also offer predictive insights into how to design adaptable antiviral therapeutic framework against spike of strains that might appear during future epidemics.

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“…Due to the strategic location of K417, halfway of L1/4 and L3 in the RBM, tweezing the α1helix@ACE2, we computed the cross-correlation matrices based on the Pearson's correlation coefficient and the per-residue sum of the Cross-Correlation coefficient (CCc) for the residues at the RBM/ACE2 interface (Figure S3). [26][27] Stunningly, SA RBD/ACE2 and K417 RBD/ACE2 exhibit the largest CCcs all over the RBM, with the difference of SA RBD/ACE2 being more marked at the L1/4 and L3 regions.…”

Section: Figurementioning

confidence: 97%

“…Since an allosteric communication among SARS-CoV-2 mutations has been recently speculated, [28][29][30] we then applied dynamical NetWork theory Analysis (NWA) to decrypt the information-exchange pathways underlying the observed RBD functional dynamics and to decode whether RBD mutants can enhance the allosteric cross-talk between critical RDB's structural elements. 26,31 In NWA, the protein is represented as a correlation-based weighted network. The nodes (the residues' center of mass) are connected by edges whose numerical value (weight) indicates the correlation-strength between residues pairs (i.e.…”

Section: Figurementioning

confidence: 99%

Allosteric Cross-Talk Among SARS-CoV-2 Spike’s Receptor-Binding Domain Mutations Triggers an Effective Hijacking of Human Cell Receptor

Spinello

Saltalamacchia

Borišek

et al. 2021

Preprint

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The rapid and relentless emergence of novel highly transmissible SARS-CoV-2 variants, possibly decreasing vaccine efficacy, currently represents a formidable medical and societal challenge. These variants frequently hold mutations on the Spike protein’s Receptor-Binding Domain (RBD), which, binding to the Angiotensin-Converting Enzyme 2 (ACE2) receptor, mediates viral entry into the host cells.Here, all-atom Molecular Dynamics simulations and Dynamical Network Theory of the wild-type and mutant RBD/ACE2 adducts disclose that while the N501Y mutation (UK variant) enhances the Spike’s binding affinity towards ACE2, the N501Y, E484K and K417N mutations (South African variant) aptly adapt to increase SARS-CoV-2 propagation via a two-pronged strategy: (i) effectively grasping ACE2 through an allosteric signaling between pivotal RBD structural elements; and (ii) impairing the binding of antibodies elicited by infected/vaccinated patients. This information, unlocking the molecular terms and evolutionary strategies underlying the increased virulence of emerging SARS-CoV-2 variants, set the basis for developing the next-generation anti-COVID-19 therapeutics.TOC GRAPHICS

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Decrypting the Information Exchange Pathways across the Spliceosome Machinery

Cited by 43 publications

References 48 publications

Network Theory Reveals Principles of Spliceosome Structure and Dynamics

Network Theory Reveals Principles of Spliceosome Structure and Dynamics

Distant Residues Modulate Conformational Opening in SARS-CoV-2 Spike Protein

Allosteric Cross-Talk Among SARS-CoV-2 Spike’s Receptor-Binding Domain Mutations Triggers an Effective Hijacking of Human Cell Receptor

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