The structural determination of peptide:HLA (human leucocyte antigen) class I complexes by X-ray crystallography has provided valuable information for understanding how peptides bind to individual HLA class I molecules and how this may influence the immune response. We compared 101 crystal structures of 9-mer peptide:HLA class I complexes available in the protein data bank (PDB) by performing a contact analysis using the Contact Map Analysis webserver http://ligin.weizmann.ac.il/cma. An InterSystems Caché 'post-relational' database containing residue position, amino acid (AA) and buried surface that contact a particular peptide position was then created allowing data comparison for all the structures (Pocketcheck). The analysis illustrates that the HLA class I residues 24, 45, 63 and 67 show high contact frequencies to both the p1 and/or p2 position of bound peptides, indicating that they might influence the nature of a peptide anchor. To determine the influence of these residues we utilized soluble HLA technology and mass spectrometry to analyze peptides derived from HLA-B*44:06 since it differs from the previously described allele B*44:02 by seven AA exchanges located in the alpha 1 domain (residues 24, 32, 41, 45, 63, 67 and 80). HLA-B*44:06 features an anchor motif of P or A at p2 and Y or W at the C-terminal. Additionally B*44:06-derived peptides feature an auxiliary anchor motif at p1, comprising D or E. Our results illustrate that structural analysis can provide valuable information to understand allogenicity and provides a further step towards intelligent HLA mismatching.

http://www.peptidecheck.org.

SummaryCurrently, the amount of sequenced and classified MHC class I genes of the common marmoset is limited, in spite of the wide use of this species as an animal model for biomedical research. In this study, 480 clones of MHC class I G locus (Caja‐G) cDNA sequences were obtained from 21 common marmosets. Up to 10 different alleles were detected in each common marmoset, leading to the assumption that the Caja‐G loci duplicated in the marmoset genome. In the investigated population, four alleles occurred more often, giving evidence for higher immunological advantage of these alleles. In contrast to the human non‐classical MHC class I genes, Caja‐G shows high rates of polymorphism at the relevant peptide‐binding sites, despite its phylogenetic relationship to the non‐classical HLA‐G. Our results provide information for better understanding of the immunological properties of the common marmoset and confirm the theory of a gene conversion of the Caja‐G due to its detected plasticity and the absence of any known HLA‐A equivalent.

The study objective was to test the hypothesis that having histocompatible children increases the risk of rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), possibly by contributing to the persistence of fetal cells acquired during pregnancy. We conducted a case control study using data from the UC San Francisco Mother Child Immunogenetic Study and studies at the Inova Translational Medicine Institute. We imputed human leukocyte antigen (HLA) alleles and minor histocompatibility antigens (mHags). We created a variable of exposure to histocompatible children. We estimated an average sequence similarity matching (SSM) score for each mother based on discordant mother−child alleles as a measure of histocompatibility. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals. A total of 138 RA, 117 SLE, and 913 control mothers were analyzed. Increased risk of RA was associated with having any child compatible at HLA-B (OR 1.9; 1.2-3.1), DPB1 (OR 1.8; 1.2-2.6) or DQB1 (OR 1.8; 1.2-2.7). Compatibility at mHag ZAPHIR was associated with reduced risk of SLE among mothers carrying the HLA-restriction allele B*07:02 (n = 262; OR 0.4; 0.2-0.8). Our findings support the hypothesis that mother−child histocompatibility is associated with risk of RA and SLE.

The German Corona Consensus (GECCO) established a uniform dataset in FHIR format for exchanging and sharing interoperable COVID-19 patient specific data between health information systems (HIS) for universities. For sharing the COVID-19 information with other locations that use openEHR, the data are to be converted in FHIR format. In this paper, we introduce our solution through a web-tool named “openEHR-to-FHIR” that converts compositions from an openEHR repository and stores in their respective GECCO FHIR profiles. The tool provides a REST web service for ad hoc conversion of openEHR compositions to FHIR profiles.

Background Extraction of medical terms and their corresponding values from semi-structured and unstructured texts of medical reports can be a time-consuming and error-prone process. Methods of natural language processing (NLP) can help define an extraction pipeline for accomplishing a structured format transformation strategy. Objectives In this paper, we build an NLP pipeline to extract values of the classification of malignant tumors (TNM) from unstructured and semi-structured pathology reports and import them further to a structured data source for a clinical study. Our research interest is not focused on standard performance metrics like precision, recall, and F-measure on the test and validation data. We discuss how with the help of software programming techniques the readability of rule-based (RB) information extraction (IE) pipelines can be improved, and therefore minimize the time to correct or update the rules, and efficiently import them to another programming language. Methods The extract rules were manually programmed with training data of TNM classification and tested in two separate pipelines based on design specifications from domain experts and data curators. Firstly we implemented each rule directly in one line for each extraction item. Secondly, we reprogrammed them in a readable fashion through decomposition and intention-revealing names for the variable declaration. To measure the impact of both methods we measure the time for the fine-tuning and programming of the extractions through test data of semi-structured and unstructured texts. Results We analyze the benefits of improving through readability of the writing of rules, through parallel programming with regular expressions (REGEX), and the Apache Uima Ruta language (AURL). The time for correcting the readable rules in AURL and REGEX was significantly reduced. Complicated rules in REGEX are decomposed and intention-revealing declarations were reprogrammed in AURL in 5 min. Conclusion We discuss the importance of factor readability and how can it be improved when programming RB text IE pipelines. Independent of the features of the programming language and the tools applied, a readable coding strategy can be proven beneficial for future maintenance and offer an interpretable solution for understanding the extraction and for transferring the rules to other domains and NLP pipelines.

Data quality of diffraction experiments depends on several factors: a) The diffractometer and the area detector hardware, b) the sample, c) the experimental procedure and d) the data reduction approach and software.The talk will highlight key aspects of each of these factors. The hardware revolves around the notions of absolute detectivity, overhead, minimizing systematic errors and diffractometer access.The sample choice, mounting, protection environment is controlled within reason by the user.The experimental procedure comprises the choice of wavelength, the geometric strategy, the mode of scan and detector operation and the decision on absolute detectivity vs. redundancy.The data reduction software has to be optimized at extracting consistently area detector data not only under good conditions, but also under real life flaws of the practical experimental procedure. There have been several attempts over the years to define which positions in the HLA binding groove (pockets) influence the specificity of bound amino acids at each position in the peptide. The structural determination of the HLA molecule by X-ray crystallography has provided valuable information for understanding how peptides bind to HLA. Originally, six pockets (A-F) were defined by calculating the surface of the binding groove based upon the crystal structure of HLA-A2 [1]. Since then, x-ray crystallography has been performed for a variety of HLA alleles bound to a range of peptides, which has lead to broader pocket definitions [2]. KeywordsSeveral studies performed peptide sequencing for allelic variants to understand the magnitude of certain mismatches on peptide specificity. We have previously described the ability of distinct HLA variants to present peptides of >10 amino acids in length [3]. In these cases it is especially important to be able to define which positions within the HLA binding cleft are in contact with a given peptide and thus influence the sequence of the peptides selected. The knowledge about individual peptide features allowed for the crystallographic analysis of selected pMHC complexes.The Protein data bank was searched for all deposited structures of peptide:HLA-complexes http://rcsb.org/pdb and those were submitted to the contact map analysis webserver http://ligin.weizmann.ac.il/cma. The output html was piped into an InterSystems Caché "post-relational" database allowing objectoriented data storage. Tabular data with residue position, amino acid and buried surface that contact a particular peptide position were compared for >100 HLA class I structures.These new dynamic definitions increase the precision of peptide prediction and support the characterization of individual weights for individual amino acids. This knowledge facilitates a rating of the allogenicity of mismatches and will be a further step towards intelligent HLA mismatching. Despite the success of macromolecular crystallography (MX) and the enormous advances in the field, one of the major hurdles still to be surmounted is the availability of well-diffractin...

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