Hallucinating symmetric protein assemblies.

Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here, we use deep network hallucination to generate a wide range of symmetric protein homo-oligomers given only a specification of the number of protomers and the protomer length. Crystal structures of seven designs are very similar to the computational models (median root mean square deviation: 0.6 angstroms), as are three cryo-electron microscopy structures of giant 10-nanometer rings with up to 1550 residues and C33 symmetry; all differ considerably from previously solved structures. Our results highlight the rich diversity of new protein structures that can be generated using deep learning and pave the way for the design of increasingly complex components for nanomachines and biomaterials.

Robust deep learning-based protein sequence design using ProteinMPNN.

Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning-based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using x-ray crystallography, cryo-electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target-binding proteins.

A systematic review and meta-analysis of patient data from the West Africa (2013-16) Ebola virus disease epidemic.

BACKGROUND: Over 28 000 individuals were infected with Ebola virus during the West Africa (2013-2016) epidemic, yet there has been criticism of the lack of robust clinical descriptions of Ebola virus disease (EVD) illness from that outbreak. OBJECTIVES: To perform a meta-analysis of published data from the epidemic to describe the clinical presentation, evolution of disease, and predictors of mortality in individuals with EVD. To assess the quality and utility of published data for clinical and public health decision-making. DATA SOURCES: Primary articles available in PubMed and published between January 2014 and May 2017. ELIGIBILITY: Studies that sequentially enrolled individuals hospitalized for EVD and that reported acute clinical outcomes. METHODS: We performed meta-analyses using random-effect models and assessed heterogeneity using the I2 method. We assessed data representativeness by comparing meta-analysis estimates with WHO aggregate data. We examined data utility by examining the availability and compatibility of data sets. RESULTS: In all, 3653 articles were screened and 34 articles were included, representing 16 independent cohorts of patients (18 overlapping cohorts) and at least 6168 individuals. The pooled estimate for case fatality rate was 51% (95% CI 46%-56%). However, pooling of estimates for clinical presentation, progression, and predictors of mortality in individuals with EVD were hampered by significant heterogeneity, and inadequate data on clinical progression. Our assessment of data quality found that heterogeneity was largely unexplained, and data availability and compatibility were poor. CONCLUSIONS: We have quantified a missed opportunity to generate reliable estimates of the clinical manifestations of EVD during the West Africa epidemic. Clinical data standards and data capture platforms are urgently needed.