PT  - JOURNAL ARTICLE
AU  - Sean O’Donoghue
AU  - Andrea Schafferhans
AU  - Neblina Sikta
AU  - Sandeep Kaur
AU  - Christian Stolte
AU  - James Procter
AU  - Bosco Ho
AU  - Stuart Anderson
AU  - Matt Adcock
AU  - Christian Dallago
AU  - Nicola Bordin
AU  - Burkhard Rost
TI  - Systematic modeling of SARS-CoV-2 protein structures
AID  - 10.1101/2020.07.16.207308
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.07.16.207308
4099  - http://biorxiv.org/content/early/2020/07/17/2020.07.16.207308.short
4100  - http://biorxiv.org/content/early/2020/07/17/2020.07.16.207308.full
AB  - The COVID-19 pandemic spawned by SARS-CoV-2 requires quick characterisation of the protein structures comprising the viral proteome. As experimentally determined 3D structures become available, these data can be augmented by high-throughput generation of homology models, thereby helping researchers leverage structural data to gain detailed insights into the molecular mechanisms underlying COVID-19. These insights, in turn, help in generating hypotheses aimed at identifying druggable targets for the development of therapeutic interventions, including vaccines.We present an online resource that provides 872 structural models, derived from all current entries in the PDB that have detectable sequence similarity to any of the SARS-CoV-2 proteins. The matching of sequence-to-structure alignments were generated by aligning pairs of Hidden Markov Models (HMMs) via HHblits. The structures are presented in the Aquaria molecular graphics systems, which was designed to facilitate overlay of sequence features, e.g., single nucleotide polymorphisms and posttranslational modifications from UniProt. Aquaria has recently been enhanced to include a much richer set of sequence features from UniProt, and predictions from PredictProtein and CATH.Our resource provides researchers with a wealth of information on the molecular mechanisms of COVID-19; the information can easily be accessed, and, to the best of our knowledge, is currently not available at other resources. The resource provides an immediate visual overview of what is known - and not known - about the 3D structure of the viral proteome, thereby helping direct future research. An accompanying video (https://youtu.be/J2nWQTlJNaY) explains how to used the resource and some the novel insights gained into COVID infection. The COVID-19 models - together with 32,717 sequence features - are available at https://aquaria.ws/covid19.Competing Interest StatementThe authors have declared no competing interest.