Utilizing Protein Structure to Identify Non-Random Somatic Mutations

Motivation: Human cancer is caused by the accumulation of somatic mutations in tumor suppressors and oncogenes within the genome. In the case of oncogenes, recent theory suggests that there are only a few key "driver" mutations responsible for tumorigenesis. As there have been significant pharmacological successes in developing drugs that treat cancers that carry these driver mutations, several methods that rely on mutational clustering have been developed to identify them. However, these methods consider proteins as a single strand without taking their spatial structures into account. We propose a new methodology that incorporates protein tertiary structure in order to increase our power when identifying mutation clustering. Results: We have developed a novel algorithm, iPAC: identification of Protein Amino acid Clustering, for the identification of non-random somatic mutations in proteins that takes into account the three dimensional protein structure. By using the tertiary information, we are able to detect both novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of clustering based on existing methods. For example, by combining the data in the Protein Data Bank (PDB) and the Catalogue of Somatic Mutations in Cancer, our algorithm identifies new mutational clusters in well known cancer proteins such as KRAS and PI3KCa. Further, by utilizing the tertiary structure, our algorithm also identifies clusters in EGFR, EIF2AK2, and other proteins that are not identified by current methodology

Equilibrium Binding Model for CpG DNA-Dependent Dimerization of Toll-like Receptor 9 Ectodomain.

Microbial nucleic acids in the extracellular milieu are recognized in vertebrates by Toll-like receptors (TLRs), one of the most important families of innate immune receptors. TLR9 recognizes single-stranded unmethylated CpG DNA in endosomes. DNA binding induces TLR9 dimerization and activation of a potent inflammatory response. To provide insights on how DNA ligands induce TLR9 dimerization, we developed a detailed theoretical framework for equilibrium ligand binding, modeling the binding of the ssDNA at the two main sites on the TLR9 ectodomain. Light scattering and fluorescence anisotropy assays performed with recombinant TLR9 ectodomain and a panel of agonistic and antagonistic DNA ligands provide data that restrain the binding parameters, identify the likely ligand binding intermediates, and suggest cooperative modes of binding. This work brings us one step closer to establishing a rigorous biochemical understanding of how TLRs are activated by their ligands.This work was supported by: -US NIH grant R01-GM102869 -Wellcome Trust Senior Research Fellowships 101908/Z/13/Z and 217191/Z/19/Z to Y.M

An Endogenous Retrovirus from Human Hookworm Encodes an Ancient Phlebovirus-Like Class II Envelope Fusion Protein

Within the parasitic nematode Ancylostoma ceylanicum, a ~20 million-year-old Bel/Pao LTR retrotransposon encodes an ancient viral class II envelope fusion protein termed Atlas Gc. Typically, retroviruses and related degenerate retrotransposons encode a hemagglutinin-like class I envelope fusion protein. A subset of Bel/Pao LTR retrotransposons within the phylum Nematoda have acquired a phlebovirus-like envelope gene and utilized the encoded fusion machinery to escape the genome as intact exogenous retroviruses. This includes C. elegans retroelement 7 virus which was recently reclassified as a member of the genus Semotivirus. A 3.76 Å cryoEM reconstruction confirms Atlas Gc as a closely related phleboviral homologue and class II fusion protein in a novel case of gene exaptation. Preliminary biophysical and biochemical characterization indicate Atlas Gc functions under specific physiological conditions targeting late-endosomal membranes, much like modern viral class II envelope fusion proteins. Phylogenetic analyses support the reclassification of the Atlas endogenous retrovirus and five other A. ceylanicum ERVs as novel semotiviruses of Belpaoviridae of the new viral order of reverse-transcribing viruses Ortervirales

Imaging and visualizing SARS-CoV-2 in a new era for structural biology.

The SARS-CoV-2 pandemic has had a global impact and has put scientific endeavour in the spotlight, perhaps more than any previous viral outbreak. Fortuitously, the pandemic came at a time when decades of research in multiple scientific fields could be rapidly brought to bear, and a new generation of vaccine platforms was on the cusp of clinical maturity. SARS-CoV-2 also emerged at the inflection point of a technological revolution in macromolecular imaging by cryo-electron microscopy, fuelled by a confluence of major technological advances in sample preparation, optics, detectors and image processing software, that complemented pre-existing techniques. Together, these advances enabled us to visualize SARS-CoV-2 and its components more rapidly, in greater detail, and in a wider variety of biologically relevant contexts than would have been possible even a few years earlier. The resulting ultrastructural information on SARS-CoV-2 and how it interacts with the host cell has played a critical role in the much-needed accelerated development of COVID-19 vaccines and therapeutics. Here, we review key imaging modalities used to visualize SARS-CoV-2 and present select example data, which have provided us with an exceptionally detailed picture of this virus

Leveraging protein quaternary structure to identify oncogenic driver mutations.

BACKGROUND: Identifying key "driver" mutations which are responsible for tumorigenesis is critical in the development of new oncology drugs. Due to multiple pharmacological successes in treating cancers that are caused by such driver mutations, a large body of methods have been developed to differentiate these mutations from the benign "passenger" mutations which occur in the tumor but do not further progress the disease. Under the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of algorithms that identify these clusters has become a critical area of research. RESULTS: We have developed a novel methodology, QuartPAC (Quaternary Protein Amino acid Clustering), that identifies non-random mutational clustering while utilizing the protein quaternary structure in 3D space. By integrating the spatial information in the Protein Data Bank (PDB) and the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), QuartPAC is able to identify clusters which are otherwise missed in a variety of proteins. The R package is available on Bioconductor at: http://bioconductor.jp/packages/3.1/bioc/html/QuartPAC.html . CONCLUSION: QuartPAC provides a unique tool to identify mutational clustering while accounting for the complete folded protein quaternary structure.This work was supported in part by NSF Grant DMS 1106738 (GR, HZ); NIH Grants GM59507 and CA154295 (HZ), and GM102869 (YM); and Wellcome Trust Grant 101908/Z/13/Z (YM)

A Spatial Simulation Approach to Account for Protein Structure When Identifying Non-Random Somatic Mutations

Background: Current research suggests that a small set of "driver" mutations are responsible for tumorigenesis while a larger body of "passenger" mutations occurs in the tumor but does not progress the disease. Due to recent pharmacological successes in treating cancers caused by driver mutations, a variety of of methodologies that attempt to identify such mutations have been developed. Based on the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of cluster identification algorithms has become critical. Results: We have developed a novel methodology, SpacePAC (Spatial Protein Amino acid Clustering), that identifies mutational clustering by considering the protein tertiary structure directly in 3D space. By combining the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC) and the spatial information in the Protein Data Bank (PDB), SpacePAC is able to identify novel mutation clusters in many proteins such as FGFR3 and CHRM2. In addition, SpacePAC is better able to localize the most significant mutational hotspots as demonstrated in the cases of BRAF and ALK. The R package is available on Bioconductor at: http://www.bioconductor.org/packages/release/bioc/html/SpacePAC.html Conclusion: SpacePAC adds a valuable tool to the identification of mutational clusters while considering protein tertiary structureComment: 16 pages, 8 Figures, 4 Table

MDA5 disease variant M854K prevents ATP-dependent structural discrimination of viral and cellular RNA.

Funder: National Institute for Health Research (NIHR)Funder: Medical Research CouncilFunder: Biotechnology and Biological Sciences Research CouncilOur innate immune responses to viral RNA are vital defenses. Long cytosolic double-stranded RNA (dsRNA) is recognized by MDA5. The ATPase activity of MDA5 contributes to its dsRNA binding selectivity. Mutations that reduce RNA selectivity can cause autoinflammatory disease. Here, we show how the disease-associated MDA5 variant M854K perturbs MDA5-dsRNA recognition. M854K MDA5 constitutively activates interferon signaling in the absence of exogenous RNA. M854K MDA5 lacks ATPase activity and binds more stably to synthetic Alu:Alu dsRNA. CryoEM structures of MDA5-dsRNA filaments at different stages of ATP hydrolysis show that the K854 sidechain forms polar bonds that constrain the conformation of MDA5 subdomains, disrupting key steps in the ATPase cycle- RNA footprint expansion and helical twist modulation. The M854K mutation inhibits ATP-dependent RNA proofreading via an allosteric mechanism, allowing MDA5 to form signaling complexes on endogenous RNAs. This work provides insights on how MDA5 recognizes dsRNA in health and disease.Human Frontier Science Progra

CryoEM structures of MDA5-dsRNA filaments at different stages of ATP hydrolysis

Summary Double-stranded RNA (dsRNA) is a potent proinflammatory signature of viral infection. Long cytosolic dsRNA is recognized by MDA5. The cooperative assembly of MDA5 into helical filaments on dsRNA nucleates the assembly of a multiprotein type-I-interferon signaling platform. Here, we determined cryoEM structures of MDA5-dsRNA filaments with different helical twists and bound nucleotide analogs, at resolutions sufficient to build and refine atomic models. The structures identify the filament forming interfaces, which encode the dsRNA binding cooperativity and length specificity of MDA5. The predominantly hydrophobic interface contacts confer flexibility, reflected in the variable helical twist within filaments. Mutation of filament-forming residues can result in loss or gain of signaling activity. Each MDA5 molecule spans 14 or 15 RNA base pairs, depending on the twist. Variations in twist also correlate with variations in the occupancy and type of nucleotide in the active site, providing insights on how ATP hydrolysis contributes to MDA5-dsRNA recognition. eTOC Structures of MDA5 bound to double-stranded RNA reveal a flexible, predominantly hydrophobic filament forming interface. The filaments have variable helical twist. Structures determined with ATP and transition state analogs show how the ATPase cycle is coupled to changes in helical twist, the mode of RNA binding and the length of the RNA footprint of MDA5. Highlights CryoEM structures of MDA5-dsRNA filaments determined for three catalytic states Filament forming interfaces are flexible and predominantly hydrophobic Mutation of filament-forming residues can cause loss or gain of IFN-β signaling ATPase cycle is coupled to changes in filament twist and size of the RNA footprintWellcome Trust Senior Research Fellowship 101908/Z/13/Z European Research Council Horizon 2020 Research and Innovation Programme, award ERC-CoG-648432 MEMBRANEFUSION EM17434 from the Wellcome Trust, MRC and BBSR

A Graph Theoretic Approach to Utilizing Protein Structure to Identify Non-Random Somatic Mutations

Background: It is well known that the development of cancer is caused by the accumulation of somatic mutations within the genome. For oncogenes specifically, current research suggests that there is a small set of "driver" mutations that are primarily responsible for tumorigenesis. Further, due to some recent pharmacological successes in treating these driver mutations and their resulting tumors, a variety of methods have been developed to identify potential driver mutations using methods such as machine learning and mutational clustering. We propose a novel methodology that increases our power to identify mutational clusters by taking into account protein tertiary structure via a graph theoretical approach. Results: We have designed and implemented GraphPAC (Graph Protein Amino Acid Clustering) to identify mutational clustering while considering protein spatial structure. Using GraphPAC, we are able to detect novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of prior clustering based on current methods. Specifically, by utilizing the spatial information available in the Protein Data Bank (PDB) along with the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), GraphPAC identifies new mutational clusters in well known oncogenes such as EGFR and KRAS. Further, by utilizing graph theory to account for the tertiary structure, GraphPAC identifies clusters in DPP4, NRP1 and other proteins not identified by existing methods. The R package is available at: http://bioconductor.org/packages/release/bioc/html/GraphPAC.html Conclusion: GraphPAC provides an alternative to iPAC and an extension to current methodology when identifying potential activating driver mutations by utilizing a graph theoretic approach when considering protein tertiary structure.Comment: 25 pages, 8 figures, 3 Table

Pattern Recognition and Signaling Mechanisms of RIG-I and MDA5.

Most organisms rely on innate immune receptors to recognize conserved molecular structures from invading microbes. Two essential innate immune receptors, RIG-I and MDA5, detect viral double-stranded RNA in the cytoplasm. The inflammatory response triggered by these RIG-I-like receptors (RLRs) is one of the first and most important lines of defense against infection. RIG-I recognizes short RNA ligands with 5'-triphosphate caps. MDA5 recognizes long kilobase-scale genomic RNA and replication intermediates. Ligand binding induces conformational changes and oligomerization of RLRs that activate the signaling partner MAVS on the mitochondrial and peroxisomal membranes. This signaling process is under tight regulation, dependent on post-translational modifications of RIG-I and MDA5, and on regulatory proteins including unanchored ubiquitin chains and a third RLR, LGP2. Here, we review recent advances that have shifted the paradigm of RLR signaling away from the conventional linear signaling cascade. In the emerging RLR signaling model, large multimeric signaling platforms generate a highly cooperative, self-propagating, and context-dependent signal, which varies with the subcellular localization of the signaling platform