We report a new approach of using statistical context-based scores as encoded features to train neural networks to achieve secondary structure prediction accuracy improvement. The context-based scores are pseudo-potentials derived by evaluating statistical, high-order inter-residue interactions, which estimate the favorability of a residue adopting certain secondary structure conformation within its amino acid environment. Encoding these context-based scores as important training and prediction features provides a way to address a long-standing difficulty in neural network-based secondary structure predictions of taking interdependency among secondary structures of neighboring residues into account. Our computational results have shown that the context-based scores are effective features to enhance the prediction accuracy of secondary structure predictions. An overall 7-fold cross-validated Q3 accuracy of 82.74% and Segment Overlap Accuracy (SOV) accuracy of 86.25% are achieved on a set of more than 7987 protein chains with, at most, 25% sequence identity. The Q3 prediction accuracy on benchmarks of CB513, Manesh215, Carugo338, as well as CASP9 protein chains is higher than popularly used secondary structure prediction servers, including Psipred, Profphd, Jpred, Porter (ab initio), and Netsurf. More significant improvement is observed in the SOV accuracy, where more than 4% enhancement is observed, compared to the server with the best SOV accuracy. A Q8 accuracy of >70% (71.5%) is also found in eight-state secondary structure prediction. The majority of the Q3 accuracy improvement is contributed from correctly identifying β-sheets and α-helices. When the context-based scores are incorporated, there are 15.5% more residues predicted with >90% confidence. These high-confidence predictions usually have a rather high accuracy (averagely ~95%). The three- and eight-state prediction servers (SCORPION) implementing our methods are available online.
Understanding the duration of antibodies to the SARS-CoV-2 virus that causes COVID-19 is important to controlling the current pandemic. Participants from the Texas Coronavirus Antibody REsponse Survey (Texas CARES) with at least one nucleocapsid protein antibody test were selected for a longitudinal analysis of antibody duration. A linear mixed model was fit to data from participants (n= 4,553) with one to three antibody tests over 11 months (10/1/2020-9/16/2021), and models fit showed that expected antibody response after COVID-19 infection robustly increases for 100 days post-infection, and predicts individuals may remain antibody positive from natural infection beyond 500 days, depending on age, body mass index, smoking or vaping use, and disease severity (hospitalized or not; symptomatic or not).
Background: The prevalence of long-term symptoms of coronavirus disease 2019 (COVID-19) in nonhospitalized pediatric populations in the United States is not well described. The objective of this analysis was to examine the presence of persistent COVID symptoms in children by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody status. Methods: Data were collected between October 2020 and May 2022 from the Texas Coronavirus Antibody REsponse Survey, a statewide prospective population-based survey among 5-90 years old. Serostatus was assessed by the Roche Elecsys Anti-SARS-CoV-2 Immunoassay for detection of antibodies to the SARS-CoV-2 nucleocapsid protein. Self-reported antigen/polymerase chain reaction COVID-19 test results and persistent COVID symptom status/type/duration were collected simultaneously. Risk ratios for persistent COVID symptoms were calculated versus adults and by age group, antibody status, symptom presence/severity, variant, body mass index and vaccine status. Results: A total of 82 (4.5% of the total sample [n = 1813], 8.0% pre-Delta, 3.4% Delta and beyond) participants reported persistent COVID symptoms (n = 27 [1.5%] 4–12 weeks, n = 58 [3.3%] >12 weeks). Compared with adults, all pediatric age groups had a lower risk for persistent COVID symptoms regardless of length of symptoms reported. Additional increased risk for persistent COVID symptoms >12 weeks included severe symptoms with initial infection, not being vaccinated and having unhealthy weight (body mass index ≥85th percentile for age and sex). Conclusions: These findings highlight the existence of nonhospitalized youth who may also experience persistent COVID symptoms. Children and adolescents are less likely to experience persistent COVID symptoms than adults and more likely to be symptomatic, experience severe symptoms and have unhealthy weight compared with children/adolescents without persistent COVID symptoms.
BackgroundDisulfide bonds play an important role in protein folding and structure stability. Accurately predicting disulfide bonds from protein sequences is important for modeling the structural and functional characteristics of many proteins.MethodsIn this work, we introduce an approach of enhancing disulfide bonding prediction accuracy by taking advantage of context-based features. We firstly derive the first-order and second-order mean-force potentials according to the amino acid environment around the cysteine residues from large number of cysteine samples. The mean-force potentials are integrated as context-based scores to estimate the favorability of a cysteine residue in disulfide bonding state as well as a cysteine pair in disulfide bond connectivity. These context-based scores are then incorporated as features together with other sequence and evolutionary information to train neural networks for disulfide bonding state prediction and connectivity prediction.ResultsThe 10-fold cross validated accuracy is 90.8% at residue-level and 85.6% at protein-level in classifying an individual cysteine residue as bonded or free, which is around 2% accuracy improvement. The average accuracy for disulfide bonding connectivity prediction is also improved, which yields overall sensitivity of 73.42% and specificity of 91.61%.ConclusionsOur computational results have shown that the context-based scores are effective features to enhance the prediction accuracies of both disulfide bonding state prediction and connectivity prediction. Our disulfide prediction algorithm is implemented on a web server named "Dinosolve" available at: http://hpcr.cs.odu.edu/dinosolve.
Currently, methods for conducting multiple treatment propensity scoring in the presence of high‐dimensional covariate spaces that result from “big data” are lacking—the most prominent method relies on inverse probability treatment weighting (IPTW). However, IPTW only utilizes one element of the generalized propensity score (GPS) vector, which can lead to a loss of information and inadequate covariate balance in the presence of multiple treatments. This limitation motivates the development of a novel propensity score method that uses the entire GPS vector to establish a scalar balancing score that, when adjusted for, achieves covariate balance in the presence of potentially high‐dimensional covariates. Specifically, the generalized propensity score cumulative distribution function (GPS‐CDF) method is introduced. A one‐parameter power function fits the CDF of the GPS vector and a resulting scalar balancing score is used for matching and/or stratification. Simulation results show superior performance of the new method compared to IPTW both in achieving covariate balance and estimating average treatment effects in the presence of multiple treatments. The proposed approach is applied to a study derived from electronic medical records to determine the causal relationship between three different vasopressors and mortality in patients with non‐traumatic aneurysmal subarachnoid hemorrhage. Results suggest that the GPS‐CDF method performs well when applied to large observational studies with multiple treatments that have large covariate spaces.
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