T Cell Epitope Predictions.

Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.

Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

Challenges in distinguishing functional proteins from polyproteins in databases: implications for drug discovery.

This opinion article addresses a major issue in molecular biology and drug discovery by highlighting the complications that arise from combining polyproteins and their functional products within the same database entry. This problem, exemplified by the discovery of novel inhibitors for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease, has an influence on our ability to retrieve precise data and hinders the development of targeted therapies. It also emphasizes the need for improved database practices and underscores their significance in advancing scientific research. Furthermore, it emphasizes the need of learning from the SARS-CoV-2 pandemic in order to improve global preparedness for future health crises.

ChIP-GPT: a managed large language model for robust data extraction from biomedical database records.

Increasing volumes of biomedical data are amassing in databases. Large-scale analyses of these data have wide-ranging applications in biology and medicine. Such analyses require tools to characterize and process entries at scale. However, existing tools, mainly centered on extracting predefined fields, often fail to comprehensively process database entries or correct evident errors-a task humans can easily perform. These tools also lack the ability to reason like domain experts, hindering their robustness and analytical depth. Recent advances with large language models (LLMs) provide a fundamentally new way to query databases. But while a tool such as ChatGPT is adept at answering questions about manually input records, challenges arise when scaling up this process. First, interactions with the LLM need to be automated. Second, limitations on input length may require a record pruning or summarization pre-processing step. Third, to behave reliably as desired, the LLM needs either well-designed, short, 'few-shot' examples, or fine-tuning based on a larger set of well-curated examples. Here, we report ChIP-GPT, based on fine-tuning of the generative pre-trained transformer (GPT) model Llama and on a program prompting the model iteratively and handling its generation of answer text. This model is designed to extract metadata from the Sequence Read Archive, emphasizing the identification of chromatin immunoprecipitation (ChIP) targets and cell lines. When trained with 100 examples, ChIP-GPT demonstrates 90-94% accuracy. Notably, it can seamlessly extract data from records with typos or absent field labels. Our proposed method is easily adaptable to customized questions and different databases.

Translational informatics for human microbiota: data resources, models and applications.

With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models and a summary of the translational informatics applied to microbiota data. We first review the existing databases, knowledge bases, knowledge graphs and standardizations of microbiome data. Then, the high-throughput sequencing techniques for the microbiome and the informatics tools for their analyses are compared. Finally, translational informatics for the microbiome, including biomarker discovery, personalized treatment and smart healthcare for complex diseases, are discussed.

Structural coverage of the human interactome.

Complex biological processes in cells are embedded in the interactome, representing the complete set of protein-protein interactions. Mapping and analyzing the protein structures are essential to fully comprehending these processes' molecular details. Therefore, knowing the structural coverage of the interactome is important to show the current limitations. Structural modeling of protein-protein interactions requires accurate protein structures. In this study, we mapped all experimental structures to the reference human proteome. Later, we found the enrichment in structural coverage when complementary methods such as homology modeling and deep learning (AlphaFold) were included. We then collected the interactions from the literature and databases to form the reference human interactome, resulting in 117 897 non-redundant interactions. When we analyzed the structural coverage of the interactome, we found that the number of experimentally determined protein complex structures is scarce, corresponding to 3.95% of all binary interactions. We also analyzed known and modeled structures to potentially construct the structural interactome with a docking method. Our analysis showed that 12.97% of the interactions from HuRI and 73.62% and 32.94% from the filtered versions of STRING and HIPPIE could potentially be modeled with high structural coverage or accuracy, respectively. Overall, this paper provides an overview of the current state of structural coverage of the human proteome and interactome.

Update of the UMD-VHL database: classification of 164 challenging variants based on genotype-phenotype correlation among 605 entries.

BACKGROUND: The von Hippel-Lindau (VHL) disease is a hereditary tumour syndrome caused by germline mutations in VHL tumour suppressor gene. The identification of VHL variants requires accurate classification which has an impact on patient management and genetic counselling. METHODS: The TENGEN (French oncogenetics network of neuroendocrine tumors) and PREDIR (French National Cancer Institute network for Inherited predispositions to kidney cancer) networks have collected VHL genetic variants and clinical characteristics of all VHL-suspected patients analysed from 2003 to 2021 by one of the nine laboratories performing VHL genetic testing in France. Identified variants were registered in a locus-specific database, the Universal Mutation Database-VHL database (http://www.umd.be/VHL/). RESULTS: Here we report the expert classification of 164 variants, including all missense variants (n=124), all difficult interpretation variants (n=40) and their associated phenotypes. After initial American College of Medical Genetics classification, first-round classification was performed by the VHL expert group followed by a second round for discordant and ambiguous cases. Overall, the VHL experts modified the classification of 87 variants including 30 variants of uncertain significance that were as (likely)pathogenic variants for 19, and as likely benign for 11. CONCLUSION: Consequently, this work has allowed the diagnosis and influenced the genetic counselling of 45 VHL-suspected families and can benefit to the worldwide VHL community, through this review.

A comparative medical genomics approach may facilitate the interpretation of rare missense variation.

PURPOSE: To determine the degree to which likely causal missense variants of single-locus traits in domesticated species have features suggestive of pathogenicity in a human genomic context. METHODS: We extracted missense variants from the Online Mendelian Inheritance in Animals database for nine animals (cat, cattle, chicken, dog, goat, horse, pig, rabbit and sheep), mapped coordinates to the human reference genome and annotated variants using genome analysis tools. We also searched a private commercial laboratory database of genetic testing results from >400 000 individuals with suspected rare disorders. RESULTS: Of 339 variants that were mappable to the same residue and gene in the human genome, 56 had been previously classified with respect to pathogenicity: 31 (55.4%) pathogenic/likely pathogenic, 1 (1.8%) benign/likely benign and 24 (42.9%) uncertain/other. The odds ratio for a pathogenic/likely pathogenic classification in ClinVar was 7.0 (95% CI 4.1 to 12.0, p<0.0001), compared with all other germline missense variants in these same 220 genes. The remaining 283 variants disproportionately had allele frequencies and REVEL scores that supported pathogenicity. CONCLUSION: Cross-species comparisons could facilitate the interpretation of rare missense variation. These results provide further support for comparative medical genomics approaches that connect big data initiatives in human and veterinary genetics.

Role of autophagy-related genes in liver cancer prognosis.

Autophagy, a highly conserved process of protein and organelle degradation, has emerged as a critical regulator in various diseases, including cancer progression. In the context of liver cancer, the predictive value of autophagy-related genes remains ambiguous. Leveraging chip datasets from the TCGA and GTEx databases, we identified 23 differentially expressed autophagy-related genes in liver cancer. Notably, five key autophagy genes, PRKAA2, BIRC5, MAPT, IGF1, and SPNS1, were highlighted as potential prognostic markers, with MAPT showing significant overexpression in clinical samples. In vitro cellular assays further demonstrated that MAPT promotes liver cancer cell proliferation, migration, and invasion by inhibiting autophagy and suppressing apoptosis. Subsequent in vivo studies further corroborated the pro-tumorigenic role of MAPT by suppressing autophagy. Collectively, our model based on the five key genes provides a promising tool for predicting liver cancer prognosis, with MAPT emerging as a pivotal factor in tumor progression through autophagy modulation.

Review of predicting protein stability changes upon variations.

Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high-throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state-of-the-art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.