Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?

T cells are essential immune cells responsible for identifying and eliminating pathogens. Through interactions between their T-cell antigen receptors (TCRs) and antigens presented by major histocompatibility complex molecules (MHCs) or MHC-like molecules, T cells discriminate foreign and self peptides. Determining the fundamental principles that govern these interactions has important implications in numerous medical contexts. However, reconstructing a map between T cells and their antagonist antigens remains an open challenge for the field of immunology, and success of in silico reconstructions of this relationship has remained incremental. In this Perspective, we discuss the role that new state-of-the-art deep-learning models for predicting protein structure may play in resolving some of the unanswered questions the field faces linking TCR and peptide-MHC properties to T-cell specificity. We provide a comprehensive overview of structural databases and the evolution of predictive models, and highlight the breakthrough AlphaFold provided the field.

Meta-analysis prediction intervals are under reported in sport and exercise medicine.

AIM: Prediction intervals are a useful measure of uncertainty for meta-analyses that capture the likely effect size of a new (similar) study based on the included studies. In comparison, confidence intervals reflect the uncertainty around the point estimate but provide an incomplete summary of the underlying heterogeneity in the meta-analysis. This study aimed to estimate (i) the proportion of meta-analysis studies that report a prediction interval in sports medicine; and (ii) the proportion of studies with a discrepancy between the reported confidence interval and a calculated prediction interval. METHODS: We screened, at random, 1500 meta-analysis studies published between 2012 and 2022 in highly ranked sports medicine and medical journals. Articles that used a random effect meta-analysis model were included in the study. We randomly selected one meta-analysis from each article to extract data from, which included the number of estimates, the pooled effect, and the confidence and prediction interval. RESULTS: Of the 1500 articles screened, 866 (514 from sports medicine) used a random effect model. The probability of a prediction interval being reported in sports medicine was 1.7% (95% CI = 0.9%, 3.3%). In medicine the probability was 3.9% (95% CI = 2.4%, 6.6%). A prediction interval was able to be calculated for 220 sports medicine studies. For 60% of these studies, there was a discrepancy in study findings between the reported confidence interval and the calculated prediction interval. Prediction intervals were 3.4 times wider than confidence intervals. CONCLUSION: Very few meta-analyses report prediction intervals and hence are prone to missing the impact of between-study heterogeneity on the overall conclusions. The widespread misinterpretation of random effect meta-analyses could mean that potentially harmful treatments, or those lacking a sufficient evidence base, are being used in practice. Authors, reviewers, and editors should be aware of the importance of prediction intervals.

The availability of task-specific feedback does not affect 20 km time trial cycling performance or test-retest reliability in trained cyclists.

OBJECTIVES: This study examined the influence of the availability of task-specific feedback on 20 km time trial (20TT) cycling performance and test-retest reliability. DESIGN: Thirty trained, club-level cyclists completed two 20TT's on different days, with (feedback, FB) or without (no-feedback, NFB) task-specific feedback (i.e., power output, cadence, gear and heart rate [HR]). Elapsed distance was provided in both conditions. METHODS: During trials, ergometer variables and HR were continuously recorded, and a rating of perceived exertion (RPE) was collected every 2 km. Data were analysed using linear mixed-effects models in a Bayesian framework, and Cohen's d was calculated for standardised differences. The reliability of finish time and mean power output (PO) was determined via multiple indices, including intraclass correlations (ICC). RESULTS: Performance, pacing behaviour, and RPE were not statistically different between conditions. The posterior mean difference [95% credible interval] between TT1 and TT2 for FB and NFB was 10s [-5, 25] and -2s [-17, 14], respectively. In TT2, HR was statistically higher (∼8bmin-1) in FB compared to NFB after 13 km (d = 2.08-2.25). However, this result was explained by differences in maximal HR. Finish time (FB: ICC= 0.99; NFB: ICC=0.99) and mean power output (FB: ICC=0.99; NFB: ICC=0.99) in each condition were substantially reliable. CONCLUSIONS: The availability of task-specific information did not affect 20TT performance or reliability. Except for elapsed distance, task-specific feedback should be withheld from trained cyclists when evaluating interventions that may affect performance, to prevent participants from recalling previous performance settings.