Thomas G Flower

Bio: Thomas G Flower is an academic researcher from California Institute for Quantitative Biosciences. The author has contributed to research in topics: Autophagosome & ESCRT. The author has an hindex of 7, co-authored 13 publications receiving 237 citations. Previous affiliations of Thomas G Flower include National Institute for Medical Research & Francis Crick Institute.

Structure of SARS-CoV-2 ORF8, a rapidly evolving immune evasion protein.

Abstract: The molecular basis for the severity and rapid spread of the COVID-19 disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is largely unknown. ORF8 is a rapidly evolving accessory protein that has been proposed to interfere with immune responses. The crystal structure of SARS-CoV-2 ORF8 was determined at 2.04-A resolution by X-ray crystallography. The structure reveals a ∼60-residue core similar to SARS-CoV-2 ORF7a, with the addition of two dimerization interfaces unique to SARS-CoV-2 ORF8. A covalent disulfide-linked dimer is formed through an N-terminal sequence specific to SARS-CoV-2, while a separate noncovalent interface is formed by another SARS-CoV-2-specific sequence, 73YIDI76 Together, the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities.

Structure of SARS-CoV-2 ORF8, a rapidly evolving coronavirus protein implicated in immune evasion.

Abstract: Author(s): Flower, Thomas G; Buffalo, Cosmo Z; Hooy, Richard M; Allaire, Marc; Ren, Xuefeng; Hurley, James H | Abstract: The molecular basis for the severity and rapid spread of the COVID-19 disease caused by SARS-CoV-2 is largely unknown. ORF8 is a rapidly evolving accessory protein that has been proposed to interfere with immune responses. The crystal structure of SARS-CoV-2 ORF8 was determined at 2.04 A resolution by x-ray crystallography. The structure reveals a ~60 residue core similar to SARS-CoV ORF7a with the addition of two dimerization interfaces unique to SARS-CoV-2 ORF8. A covalent disulfide-linked dimer is formed through an N-terminal sequence specific to SARS-CoV-2, while a separate non-covalent interface is formed by another SARS-CoV-2-specific sequence, 73 YIDI 76 . Together the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities.

MRE11 stability is regulated by CK2-dependent interaction with R2TP complex

Abstract: The MRN (MRE11–RAD50–NBS1) complex is essential for repair of DNA double-strand breaks and stalled replication forks. Mutations of the MRN complex subunit MRE11 cause the hereditary cancer-susceptibility disease ataxia-telangiectasia-like disorder (ATLD). Here we show that MRE11 directly interacts with PIH1D1, a subunit of heat-shock protein 90 cochaperone R2TP complex, which is required for the assembly of large protein complexes, such as RNA polymerase II, small nucleolar ribonucleoproteins and mammalian target of rapamycin complex 1. The MRE11-PIH1D1 interaction is dependent on casein kinase 2 (CK2) phosphorylation of two acidic sequences within the MRE11 C terminus containing serines 558/561 and 688/689. Conversely, the PIH1D1 phospho-binding domain PIH-N is required for association with MRE11 phosphorylated by CK2. Consistent with these findings, depletion of PIH1D1 resulted in MRE11 destabilization and affected DNA-damage repair processes dependent on MRE11. Additionally, mutations of serines 688/689, which abolish PIH1D1 binding, also resulted in decreased MRE11 stability. As depletion of R2TP frequently leads to instability of its substrates and as truncation mutation of MRE11 lacking serines 688/689 leads to decreased levels of the MRN complex both in ATLD patients and an ATLD mouse model, our results suggest that the MRN complex is a novel R2TP complex substrate and that their interaction is regulated by CK2 phosphorylation.

Crystallographic molecular replacement using an in silico-generated search model of SARS-CoV-2 ORF8.

Abstract: The majority of crystal structures are determined by the method of molecular replacement (MR). The range of application of MR is limited mainly by the need for an accurate search model. In most cases, pre-existing experimentally determined structures are used as search models. In favorable cases, ab initio predicted structures have yielded search models adequate for MR. The ORF8 protein of SARS-CoV-2 represents a challenging case for MR using an ab initio prediction because ORF8 has an all β-sheet fold and few orthologs. We previously determined experimentally the structure of ORF8 using the single anomalous dispersion (SAD) phasing method, having been unable to find an MR solution to the crystallographic phase problem. Following a report of an accurate prediction of the ORF8 structure, we assessed whether the predicted model would have succeeded as an MR search model. A phase problem solution was found, and the resulting structure was refined, yielding structural parameters equivalent to the original experimental solution.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Highly accurate protein structure prediction with AlphaFold

Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

Highly accurate protein structure prediction for the human proteome

Abstract: Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally-determined structure1. Here we dramatically expand structural coverage by applying the state-of-the-art machine learning method, AlphaFold2, at scale to almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model, and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions likely to be disordered. Finally, we provide some case studies illustrating how high-quality predictions may be used to generate biological hypotheses. Importantly, we are making our predictions freely available to the community via a public database (hosted by the European Bioinformatics Institute at https://alphafold.ebi.ac.uk/ ). We anticipate that routine large-scale and high-accuracy structure prediction will become an important tool, allowing new questions to be addressed from a structural perspective.

The MRE11–RAD50–NBS1 Complex Conducts the Orchestration of Damage Signaling and Outcomes to Stress in DNA Replication and Repair

Abstract: Genomic instability in disease and its fidelity in health depend on the DNA damage response (DDR), regulated in part from the complex of meiotic recombination 11 homolog 1 (MRE11), ATP-binding cassette-ATPase (RAD50), and phosphopeptide-binding Nijmegen breakage syndrome protein 1 (NBS1). The MRE11-RAD50-NBS1 (MRN) complex forms a multifunctional DDR machine. Within its network assemblies, MRN is the core conductor for the initial and sustained responses to DNA double-strand breaks, stalled replication forks, dysfunctional telomeres, and viral DNA infection. MRN can interfere with cancer therapy and is an attractive target for precision medicine. Its conformations change the paradigm whereby kinases initiate damage sensing. Delineated results reveal kinase activation, posttranslational targeting, functional scaffolding, conformations storing binding energy and enabling access, interactions with hub proteins such as replication protein A (RPA), and distinct networks at DNA breaks and forks. MRN biochemistry provides prototypic insights into how it initiates, implements, and regulates multifunctional responses to genomic stress.