ABSTRACT
Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep graph convolution (DGC) network-based framework, DGCddG, to predict the changes of protein-protein binding affinity after mutation. DGCddG incorporates multi-layer graph convolution to extract a deep, contextualized representation for each residue of the protein complex structure. The mined channels of the mutation sites by DGC is then fitted to the binding affinity with a multi-layer perceptron. Experiments with results on multiple datasets show that our model can achieve relatively good performance for both single and multi-point mutations. For blind tests on datasets related to angiotensin-converting enzyme 2 binding with the SARS-CoV-2 virus, our method shows better results in predicting ACE2 changes, may help in finding favorable antibodies. Code and data availability: https://github.com/lennylv/DGCddG.
Subject(s)
COVID-19 , Humans , Protein Binding/genetics , COVID-19/genetics , SARS-CoV-2/genetics , Mutation/genetics , Point MutationSubject(s)
COVID-19/genetics , Chromosomes, Human, Pair 3/genetics , Epigenome , Gene Editing , Genetic Loci , Membrane Transport Proteins/genetics , Receptors, CCR/genetics , SARS-CoV-2/genetics , COVID-19/metabolism , Cell Line , Chromosomes, Human, Pair 3/metabolism , Humans , Membrane Transport Proteins/metabolism , Receptors, CCR/metabolism , SARS-CoV-2/metabolism , Severity of Illness IndexABSTRACT
OBJECTIVE: To analyse the incidence, risk factors and impact of acute kidney injury (AKI) on the prognosis of patients with COVID-19. DESIGN: Meta-analysis. DATA SOURCES: PubMed, Embase, CNKI and MedRxiv of Systematic Reviews from 1 January 2020 to 15 May 2020. STUDY SELECTION: Studies examining the following demographics and outcomes were included: patients' age; sex; incidence of and risk factors for AKI and their impact on prognosis; COVID-19 disease type and incidence of continuous renal replacement therapy (CRRT) administration during COVID-19 infection. RESULTS: A total of 79 research articles, including 49 692 patients with COVID-19, met the systemic evaluation criteria. The mortality rate and incidence of AKI in patients with COVID-19 in China were significantly lower than those in patients with COVID-19 outside China. A significantly higher proportion of patients with COVID-19 from North America were aged ≥65 years and also developed AKI. European patients with COVID-19 had significantly higher mortality and a higher CRRT rate than patients from other regions. Further analysis of the risk factors for COVID-19 combined with AKI showed that age ≥60 years and severe COVID-19 were independent risk factors for AKI, with an OR of 3.53, 95% CI (2.92-4.25) and an OR of 6.07, 95% CI (2.53-14.58), respectively. The CRRT rate in patients with severe COVID-19 was significantly higher than in patients with non-severe COVID-19, with an OR of 6.60, 95% CI (2.83-15.39). The risk of death in patients with COVID-19 and AKI was significantly increased, with an OR of 11.05, 95% CI (9.13-13.36). CONCLUSION: AKI was a common and serious complication of COVID-19. Older age and having severe COVID-19 were independent risk factors for AKI. The risk of in-hospital death was significantly increased in patients with COVID-19 complicated by AKI.