Reduced structural flexibility of eplet amino acids in HLA proteins

International audienceThe proteins encoded in the HLA (Human Leukocyte Antigen) system are largely responsible for the compatibility in organ transplants. To date, the molecular determinants involved in recognizing HLA antigens by recipient antibodies are unknown. Here we explore flexibility as a potential determinant. For this purpose, we compare in terms of N-RMSF (Normalized Root Mean Square Fluctuation) amino acids labeled as confirmed eplets (regions defined around polymorphic amino acids) against amino acids that have not been reported as eplets. We found that eplet amino acids tend to be less flexible than non-eplet amino acids, which would indicate that the antibodies would have a preference for binding with less mobile regions

Consistency and Reproducibility of Grades in Higher Education: A Case Study in Deep Learning

Evaluating the performance of students in higher education is essential for gauging the effectiveness of teaching methods and achieving greater equality of opportunities for all. In this study, we investigate the correlation between two teachers' grading practices in a deep learning course at the master's level, offered at CentraleSup\'elec. The two teachers, who have distinct teaching styles, were responsible for marking the final project oral presentation. Our results indicate a significant positive correlation (0.76) between the two teachers' grading practices, suggesting that their assessments of students' performance are consistent. Although consistent with each other, grades do not seem to be fully reproducible from one examiner to the other suggesting serious drawbacks of only using one examiner for oral projects. Furthermore, we observed that the maximum difference between the grades assigned by the two examiners was 12.5%, highlighting the potential impact of inter-examiner variability on students' final grades.Comment: 5 pages, 1 figure (2 images), 1 tabl

Python Eplet Load Calculator (PELC) package

PELC is a Python package designed to calculate efficiently the HLA Eplet Load (based on the EpRegistry database) between donors and recipients. v0.5.4 includes the latest version of EpRegistry (V2023-08-25).If you use this software, please cite it as below. Lhotte, R., Clichet, V., Usureau, C. & Taupin, J. (2022). Python Eplet Load Calculator (PELC) package (Version 0.5.4.1) [Computer software]. https://doi.org/10.5281/zenodo.725480

HLA-EpiCheck : A B-cell epitope prediction tool on HLA antigens using molecular dynamics simulation data

International audienceIn the context of organ transplantation, recipient’s antibodies against donor-specific HLA antigens are the main reason for transplant loss.The prediction of B-cell (antibody) epitopes on HLA antigens is therefore a key challenge on the way to improving the matching stepbetween donor and recipient from a structural point of view. Here, we present HLA-EpiCheck, a B-cell epitope prediction tool that relies onan unprecedented dataset of short Molecular Dynamics (MD) simulations of 207 HLA antigens. We use hydrophobic properties, electrostaticcharges, flexibility and solvent accessibility as descriptors calculated on patches sampled from MD trajectories. Then, we train an ExtremelyRandomized Trees machine learning model. This model outperforms the state-of-the-art DiscoTope 3.0 tool for B-cell epitope predictionon HLA antigens

Improving HLA typing imputation accuracy and eplet identification with local next‐generation sequencing training data

International audienceAssessing donor/recipient HLA compatibility at the eplet level requires second field DNA typings but these are not always available. These can be estimated from lower‐resolution data either manually or with computational tools currently relying, at best, on data containing typing ambiguities. We gathered NGS typing data from 61,393 individuals in 17 French laboratories, for loci A, B, and C (100% of typings), DRB1 and DQB1 (95.5%), DQA1 (39.6%), DRB3/4/5, DPB1, and DPA1 (10.5%). We developed HaploSFHI, a modified iterative maximum likelihood algorithm, to impute second field HLA typings from low‐ or intermediate‐resolution ones. Compared with the reference tools HaploStats, HLA‐EMMA, and HLA‐Upgrade, HaploSFHI provided more accurate predictions across all loci on two French test sets and four European‐independent test sets. Only HaploSFHI could impute DQA1, and solely HaploSFHI and HaploStats provided DRB3/4/5 imputations. The improved performance of HaploSFHI was due to our local and nonambiguous data. We provided explanations for the most common imputation errors and pinpointed the variability of a low number of low‐resolution haplotypes. We thus provided guidance to select individuals for whom sequencing would optimize incompatibility assessment and cost‐effectiveness of HLA typing, considering not only well‐imputed second field typing(s) but also well‐imputed eplets