Project description:A number of state-of-the-art protein structure prediction servers have been developed by researchers working in the Bioinformatics Unit at University College London. The popular PSIPRED server allows users to perform secondary structure prediction, transmembrane topology prediction and protein fold recognition. More recent servers include DISOPRED for the prediction of protein dynamic disorder and DomPred for domain boundary prediction. These servers are available from our software home page at http://bioinf.cs.ucl.ac.uk/software.html.

| S-EPMC1160171 | biostudies-literature

Protein annotation and modelling servers at University College London.

Project description:The UCL Bioinformatics Group web portal offers several high quality protein structure prediction and function annotation algorithms including PSIPRED, pGenTHREADER, pDomTHREADER, MEMSAT, MetSite, DISOPRED2, DomPred and FFPred for the prediction of secondary structure, protein fold, protein structural domain, transmembrane helix topology, metal binding sites, regions of protein disorder, protein domain boundaries and protein function, respectively. We also now offer a fully automated 3D modelling pipeline: BioSerf, which performed well in CASP8 and uses a fragment-assembly approach which placed it in the top five servers in the de novo modelling category. The servers are available via the group web site at http://bioinf.cs.ucl.ac.uk/.

| S-EPMC2896093 | biostudies-literature

UCL Cancer Institute

Project description:Organisation EGAO00000001178

| EGAO00000001178 | EGA

The Francis Crick Institute, London, UK

Project description:Organisation EGAO00000000857

| EGAO00000000857 | EGA

Division of Genetics and Molecular Medicine, King's College London, London, UK

Project description:Organisation EGAO00000000053

| EGAO00000000053 | EGA

Systemic Anti-Cancer Therapy and Metastatic Cancer Are Independent Mortality Risk Factors during Two UK Waves of the COVID-19 Pandemic at University College London Hospital.

Project description:An increased mortality risk was observed in patients with cancer during the first wave of COVID-19. Here, we describe determinants of mortality in patients with solid cancer comparing the first and second waves of COVID-19. A retrospective analysis encompassing two waves of COVID-19 (March-May 2020; December 2020-February 2021) was performed. 207 patients with cancer were matched to 452 patients without cancer. Patient demographics and oncological variables such as cancer subtype, staging and anti-cancer treatment were evaluated for association with COVID-19 mortality. Overall mortality was lower in wave two compared to wave one, HR 0.41 (95% CI: 0.30-0.56). In patients with cancer, mortality was 43.6% in wave one and 15.9% in wave two. In hospitalized patients, after adjusting for age, ethnicity and co-morbidities, a history of cancer was associated with increased mortality in wave one but not wave two. In summary, the second UK wave of COVID-19 is associated with lower mortality in hospitalized patients. A history of solid cancer was not associated with increased mortality despite the dominance of the more transmissible B.1.1.7 SARS-CoV-2 variant. In both waves, metastatic disease and systemic anti-cancer treatment appeared to be independent risk factors for death within the combined cancer cohort.

| S-EPMC8657102 | biostudies-literature

King's College London, St Thomasâ€™ Campus, Lambeth Palace Road,London, UK

Project description:Organisation EGAO00000000373

| EGAO00000000373 | EGA