9 May

Secondary structural ensembles of the SARS-CoV-2 RNA genome in infected cells (Nature Communications)

SARS-CoV-2 is a betacoronavirus with a single-stranded, positive-sense, 30-kilobase RNA genome responsible for the ongoing COVID-19 pandemic. Although population average structure models of the genome were recently reported, there is little experimental data on native structural ensembles, and most structures lack functional characterization. Here we report secondary structure heterogeneity of the entire SARS-CoV-2 genome in two lines of infected cells at single nucleotide resolution. Our results reveal alternative RNA conformations across the genome and at the critical frameshifting stimulation element (FSE) that are drastically different from prevailing population average models. Importantly, we find that this structural ensemble promotes frameshifting rates much higher than the canonical minimal FSE and similar to ribosome profiling studies. Our results highlight the value of studying RNA in its full length and cellular context. The genomic structures detailed here lay groundwork for coronavirus RNA biology and will guide the design of SARS-CoV-2 RNA-based therapeutics.

9 May

Pandemic strategies with computational and structural biology against COVID-19: A retrospective (Computational and Structural Biotechnology Journal)

The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic, has dominated all aspects of life since 2020. Research studies on the virus and exploration of therapeutic and preventive strategies have been moving at rapid rates to control the pandemic. In the field of bioinformatics or computational and structural biology, recent research strategies have used multiple disciplines to compile large datasets to uncover statistical correlations and significance, visualize and model proteins, perform molecular dynamics simulations, and employ the help of artificial intelligence and machine learning to harness computational processing power to further the research on COVID-19, including drug screening, drug design, vaccine development, prognosis prediction, and outbreak prediction. These recent developments should help us better understand the viral disease and develop the much-needed therapies and strategies for the management of COVID-19.

9 May

Fighting SARS-CoV-2 with structural biology methods (Nature Methods)

Comment in Nature Methods highlights the importance of structural biology in fight against SAR-Cov-2. High-resolution structural information is critical for rapid development of vaccines and therapeutics against emerging human pathogens. Structural biology methods have been at the forefront of research on SARS-CoV-2 since the beginning of the COVID-19 pandemic. These technologies will continue to be powerful tools to fend off future public health threats.

2 Nov 2021

MicroED structure of the human adenosine receptor determined from a single nanocrystal in LCP (PNAS)

Microcrystal electron diffraction (MicroED) is a cryogenic electron microscopy (cryo-EM) method that determines protein structures from submicron crystals. G protein–coupled receptors (GPCRs) are membrane proteins that are critically important drug targets. These proteins require crystallization in lipidic cubic phase (LCP), making standard MicroED approaches intractable for investigating these samples. Here, T. Gonen et. al. show that GPCR microcrystals grown in an LCP can be made amenable for MicroED by converting the LCP to the sponge phase and then ion-beam milling the crystals into thin lamellae. Theirfindings provide the basis for solving GPCR structures using MicroED, with future applications in structure-based drug discovery.

2 Nov 2021

AI revolutions in biology -The joys and perils of AlphaFold (EMBO reports)

Last December, the artificial intelligence (AI) AlphaFold achieved what had been considered impossible: near-perfect protein fold predictions. The shockwaves reverberated well beyond the scientific community: one of the grand challenges in biology, how to fold a protein, appeared to have been solved finally—something that most of us did not expect to happen in our lifetimes. Notwithstanding all the justified excitement about AlphaFold, this achievement does not mean though that AI will make experimental structural biology or its practitioners and tools redundant. Structural biology will remain essential for understanding how proteins work and how they dynamically interact with each other.

7 Sep 2021

Maturation of the matrix and viral membrane of HIV-1 (Science)

Gag, the primary structural protein of HIV-1, is recruited to the plasma membrane for virus assembly by its matrix (MA) domain. Gag is subsequently cleaved into its component domains, causing structural maturation to repurpose the virion for cell entry. J. A. G. Briggs et. al. determined the structure and arrangement of MA within immature and mature HIV-1 through cryo–electron tomography. They found that MA rearranges between two different hexameric lattices upon maturation. In mature HIV-1, a lipid extends out of the membrane to bind with a pocket in MA. Their data suggest that proteolytic maturation of HIV-1 not only assembles the viral capsid surrounding the genome but also repurposes the membrane-bound MA lattice for an entry or postentry function and results in the partial removal of up to 2500 lipids from the viral membrane.

7 Sep 2021

Polyclonal antibody responses to HIV Env immunogens resolved using cryoEM (Nature Communications)

Engineered ectodomain trimer immunogens based on BG505 envelope glycoprotein are widely utilized as components of HIV vaccine development platforms. In this study, Ward, Andrew B. et. al. used rhesus macaques to evaluate the immunogenicity of several stabilized BG505 SOSIP constructs both as free trimers and presented on a nanoparticle. They applied a cryoEM-based method for high-resolution mapping of polyclonal antibody responses elicited in immunized animals (cryoEMPEM). Mutational analysis coupled with neutralization assays were used to probe the neutralization potential at each epitope. They demonstrate that cryoEMPEM data can be used for rapid, high-resolution analysis of polyclonal antibody responses without the need for monoclonal antibody isolation. This approach allowed to resolve structurally distinct classes of antibodies that bind overlapping sites. In addition to comprehensive mapping of commonly targeted neutralizing and non-neutralizing epitopes in BG505 SOSIP immunogens, their analysis revealed that epitopes comprising engineered stabilizing mutations and of partially occupied glycosylation sites can be immunogenic.

7 Sep 2021

Structural basis for target site selection in RNA-guided DNA transposition systems (Science)

CRISPR-associated transposition systems allow guide RNA-directed integration of a single DNA cargo in one orientation at a fixed distance from a programmable target sequence. J. E. Peters, E. Kellog et. al. used cryo-electron microscopy (cryo-EM) to define the mechanism that underlies this process by characterizing the transposition regulator, TnsC, from a type V-K CRISPR-transposase system. In this scenario, polymerization of adenosine triphosphate-bound TnsC helical filaments could explain how polarity information is passed to the transposase. TniQ caps the TnsC filament, representing a universal mechanism for target information transfer in Tn7/Tn7-like elements. Transposase-driven disassembly establishes delivery of the element only to unused protospacers. Finally, TnsC transitions to define the fixed point of insertion, as revealed by structures with the transition state mimic ADP_AlF3. These mechanistic findings provide the underpinnings for engineering CRISPR-associated transposition systems for research and therapeutic applications.

20 Aug 2021

Accurate prediction of protein structures and interactions using a three-track neural network (Science)

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. D. Baker et. al. explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network called RosseTTAFold produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo–electron microscopy structure modelling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. They make the method RoseTTAFold available to the scientific community to speed biological research.

20 Aug 2021

Highly accurate protein structure prediction for the human proteome (Nature)

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, Kathryn Tunyasuvunakool, Jonas Adler, Demis Hassabis et. al. markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers 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) has very high confidence. They 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 that are likely to be disordered. Finally, they provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. They are making their predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.

20 Aug 2021

Highly accurate protein structure prediction with AlphaFold (Nature)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort, the structures of around 100,000 unique proteins have been determined, but this represents a small fraction of the billions of known protein sequences. 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’—has been an important open research problem for more than 50 years. Despite recent progress, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here, John Jumper, Richard Evans, Demis Hassabis et. al.provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. They validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14), 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.