Early computational detection of potential high-risk SARS-CoV-2 variants.

The ongoing COVID-19 pandemic is leading to the discovery of hundreds of novel SARS-CoV-2 variants daily. While most variants do not impact the course of the pandemic, some variants pose an increased risk when the acquired mutations allow better evasion of antibody neutralisation or increased transmissibility. Early detection of such high-risk variants (HRVs) is paramount for the proper management of the pandemic. However, experimental assays to determine immune evasion and transmissibility characteristics of new variants are resource-intensive and time-consuming, potentially leading to delays in appropriate responses by decision makers. Presented herein is a novel in silico approach combining spike (S) protein structure modelling and large protein transformer language models on S protein sequences to accurately rank SARS-CoV-2 variants for immune escape and fitness potential. Both metrics were experimentally validated using in vitro pseudovirus-based neutralisation test and binding assays and were subsequently combined to explore the changing landscape of the pandemic and to create an automated Early Warning System (EWS) capable of evaluating new variants in minutes and risk-monitoring variant lineages in near real-time. The system accurately pinpoints the putatively dangerous variants by selecting on average less than 0.3% of the novel variants each week. The EWS flagged all 16 variants designated by the World Health Organization (WHO) as variants of interest (VOIs) if applicable or variants of concern (VOCs) otherwise with an average lead time of more than one and a half months ahead of their designation as such.

Early computational detection of potential high-risk SARS-CoV-2 variants

The ongoing COVID-19 pandemic is leading to the discovery of hundreds of novel SARS-CoV-2 variants daily. While most variants do not impact the course of the pandemic, some variants pose an increased risk when the acquired mutations allow better evasion of antibody neutralisation or increased transmissibility. Early detection of such high-risk variants (HRVs) is paramount for the proper management of the pandemic. However, experimental assays to determine immune evasion and transmissibility characteristics of new variants are resource-intensive and time-consuming, potentially leading to delays in appropriate responses by decision makers. Presented herein is a novel in silico approach combining spike (S) protein structure modelling and large protein transformer language models on S protein sequences to accurately rank SARS-CoV-2 variants for immune escape and fitness potential. Both metrics were experimentally validated using in vitro pseudovirus-based neutralisation test and binding assays and were subsequently combined to explore the changing landscape of the pandemic and to create an automated Early Warning System (EWS) capable of evaluating new variants in minutes and risk-monitoring variant lineages in near real-time. The system accurately pinpoints the putatively dangerous variants by selecting on average less than 0.3% of the novel variants each week. The EWS flagged all 16 variants designated by the World Health Organisation (WHO) as variants of interest (VOIs) if applicable or variants of concern (VOCs) otherwise with an average lead time of more than one and a half months ahead of their designation as such.

DELTEX E3 ligases ubiquitylate ADP-ribosyl modification on protein substrates.

Ubiquitylation had been considered limited to protein lysine residues, but other substrates have recently emerged. Here, we show that DELTEX E3 ligases specifically target the 3' hydroxyl of the adenosine diphosphate (ADP)-ribosyl moiety that can be linked to a protein, thus generating a hybrid ADP-ribosyl-ubiquitin modification. Unlike other known hydroxyl-specific E3s, which proceed via a covalent E3~ubiqutin intermediate, DELTEX enzymes are RING E3s that stimulate a direct ubiquitin transfer from E2~ubiquitin onto a substrate. However, DELTEXes follow a previously unidentified paradigm for RING E3s, whereby the ligase not only forms a scaffold but also provides catalytic residues to activate the acceptor. Comparative analysis of known hydroxyl-ubiquitylating active sites points to the recurring use of a catalytic histidine residue, which, in DELTEX E3s, is potentiated by a glutamate in a catalytic triad-like manner. In addition, we determined the hydrolase specificity profile of this modification, identifying human and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enzymes that could reverse it in cells.

Early Computational Detection of Potential High Risk SARS-CoV-2 Variants (preprint)

The ongoing COVID-19 pandemic is leading to the discovery of hundreds of novel SARS-CoV-2 variants on a daily basis. While most variants do not impact the course of the pandemic, some variants pose significantly increased risk when the acquired mutations allow better evasion of antibody neutralisation in previously infected or vaccinated subjects, or increased transmissibility. Early detection of such high risk variants (HRVs) is paramount for the proper management of the pandemic. However, experimental assays to determine immune evasion and transmissibility characteristics of new variants are resource-intensive and time-consuming, potentially leading to delayed appropriate responses by decision makers. Here we present a novel in silico approach combining Spike protein structure modelling and large protein transformer language models on Spike protein sequences, to accurately rank SARS-CoV-2 variants for immune escape and fitness potential. We validate our immune escape and fitness metrics with in vitro pVNT and binding assays. These metrics can be combined into an automated Early Warning System (EWS) capable of evaluating new variants in minutes and risk monitoring variant lineages in near real-time. The EWS flagged 12 out of 13 variants, designated by the World Health Organisation (WHO, Alpha-Omicron) as potentially dangerous, on average two months ahead of them being designated as such, demonstrating its ability to help increase preparedness against future variants. Omicron was flagged by the EWS on the day its sequence was made available, with immune evasion and binding metrics subsequently confirmed through our in vitro experiments.

Barriers and facilitators for implementing paediatric telehealth: Rapid review of user perspectives

Introduction: COVID-19 has brought to the fore an urgent need for secure information and communication technology (ICT) supported healthcare delivery, as the pertinence of infection control and social distancing continues. Telehealth for pediatric care incorporates additional layers of complexity compared with adult services for a variety of reasons including logistical, privacy, parental consent, child assent, child welfare, and quality concerns. There is no systematic evidence synthesis available that outlines the implementation issues for incorporating telehealth to pediatric services generally, or how users perceive these issues. Methods: We conducted a rapid mixed-methods evidence synthesis to identify barriers, facilitators, and documented stakeholder experiences of implementing pediatric telehealth, to inform the pandemic response. A systematic search was undertaken by a research librarian in MEDLINE for relevant studies. All identified records were blind double-screened by two reviewers. Implementation-related data were extracted, and studies quality appraised using the Mixed-Methods Appraisal Tool. Qualitative findings were analyzed thematically and then mapped to the Consolidated Framework for Implementation Research. Quantitative findings about barriers and facilitators for implementation were narratively synthesized. Results: We identified 27 eligible studies (19 quantitative;5 mixed-methods, 3 qualitative). Important challenges highlighted from the perspective of the healthcare providers included issues with ICT proficiency, lack of confidence in the quality/reliability of the technology, connectivity issues, concerns around legal issues, increased administrative burden and/ or fear of inability to conduct thorough examinations with reliance on subjective descriptions. Facilitators included clear dissemination of the aims of ICT services, involvement of staff throughout planning and implementation, sufficient training, and cultivation of telehealth champions. Families often expressed preference for in-person visits but those who had tried tele-consultations, lived far from clinics, or perceived increased convenience with technology considered telemedicine more favorably. Concerns from parents included the responsibility of describing their child's condition in the absence of an examination. Conclusion: Healthcare providers and families who have experienced tele-consultations generally report high satisfaction and usability for such services. The use of ICT to facilitate pediatric healthcare consultations is feasible for certain clinical encounters and can work well with appropriate planning and quality facilities in place.

The association between PICU to ed telemedicine consultations on changes in severity of illness

INTRODUCTION: Children and adolescents transferring from emergency departments (EDs) with video telemedicine programs have been associated with lower severity of illness (SOI) on admission to Pediatric Intensive Care Units (PICUs) compared to patients transferred from EDs without telemedicine programs. Very little is known about the relationship between telemedicine use and SOI in the ED. We hypothesize telemedicine will be used on patients with higher SOI in comparison to telephone consultations. METHODS: A prospective crossover-cluster randomized controlled trial was conducted to compare telemedicine and telephone consultations conducted on critically ill children. Patients age 0-14 from 15 participating EDs with established telemedicine programs were included if they were transferred to the regional PICU. Patients were randomized to either telemedicine or telephone consultation based on four 6-month blocks. Data was analyzed using 'intention to treat' analysis, as well as 'as treated' and 'per protocol'. RePEAT (Revised Pediatric Emergency Assessment Tool) and PRISA (Pediatric Risk of Admission) scores were used to estimate SOI in the ED and Pediatric Index of Mortality (PIM) was used to assess SOI at the time of PICU admission. RESULTS: This study enrolled 696 patients, 373 were admitted to the PICU. Telemedicine and telephone cohorts were comparable by sex, age and race/ethnicity. PIM-2/3 scores (median -4.97 telemed, -5.1 telephone, p = 0.95) and mean RePEAT scores (1.67 telemed, 1.71 telephone, p=0.61) were not statistically significant between the groups whether using the intention to treat or as treated analysis. PRISA scores were similar in the intention to treat analysis, (19.5 telemed, 21.7 telephone, p=0.15) but were significantly higher in the telemedicine cohort (mean=22, SD=13.09) than in the telephone cohort (mean=19, SD=12.82), p= 0.039) in the as treated analysis. CONCLUSIONS: Despite randomization of telemedicine and telephone consultations among patients presenting to 15 EDs as part of a multi-institutional trial, physicians did not adhere to telephone consultations when patients had higher SOI and instead preferentially used telemedicine. More data is needed to understand the effect of telemedicine consultation on transfer rates, SOI, and optimal application in the post-COVID era.

Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning (preprint)

The SARS-CoV-2 pandemic has created a global race for a cure. One approach focuses on designing a novel variant of the human angiotensin-converting enzyme 2 (ACE2) that binds more tightly to the SARS-CoV-2 spike protein and diverts it from human cells. Here we formulate a novel protein design framework as a reinforcement learning problem. We generate new designs efficiently through the combination of a fast, biologically-grounded reward function and sequential action-space formulation. The use of Policy Gradients reduces the compute budget needed to reach consistent, high-quality designs by at least an order of magnitude compared to standard methods. Complexes designed by this method have been validated by molecular dynamics simulations, confirming their increased stability. This suggests that combining leading protein design methods with modern deep reinforcement learning is a viable path for discovering a Covid-19 cure and may accelerate design of peptide-based therapeutics for other diseases.