{"categories":[{"label":"Bio-Informatics","url":"https://skillfed.io/packages/category/scientific-engineering-bio-informatics"}],"enrichment":{"capability":"Unified Python interface to multiple MHC binding, presentation, immunogenicity, and antigen processing prediction tools, returning structured peptide results with affinity, percentile rank, and other prediction kinds.","skillfed_tags":["immunoinformatics","epitope-prediction","vaccine-design"],"use_cases":["Scan a protein sequence for immunogenic peptides that bind strongly to a patient's HLA alleles.","Rank candidate neoantigen peptides by predicted MHC-I affinity and presentation likelihood for personalized cancer vaccine design.","Predict TCR recognition of a peptide-MHC complex using paired CDR3 sequences and allele information.","Generate flat DataFrames of peptide predictions across multiple samples with different HLA genotypes for cohort analysis.","Combine multiple prediction kinds (affinity, stability, immunogenicity) to filter and prioritize epitope candidates.","Integrate MHC predictions into a larger immunoinformatics pipeline alongside variant calling and protein annotation."],"what_it_does":"mhctools wraps multiple published MHC prediction algorithms into a single Python interface, handling peptide-MHC binding affinity, surface presentation likelihood, stability, immunogenicity, antigen processing, and TCR recognition. It abstracts away the differences between underlying predictors (NetMHCpan, MHCflurry, NetCleave, Pepsickle, and others) and returns results as structured PeptideResult objects with consistent accessors for each prediction kind, or as pandas DataFrames for bulk analysis.\n\nThe package is designed for immunology and vaccine research workflows: scanning proteins for candidate epitopes, ranking peptides by MHC binding strength and percentile rank, predicting presentation likelihood across multiple HLA alleles, and handling multi-sample cohort predictions where each patient has a different HLA genotype. It supports both single-allele and haplotype-level predictions, with metadata describing whether each prediction kind is MHC-dependent and which MHC class (I or II) it applies to.","worth_installing":"Yes. mhctools is actively maintained, has low install friction, carries no known vulnerabilities, and uses a permissive license. It is the standard unified interface for MHC prediction in Python research workflows. Install it if you need to predict peptide-MHC binding, presentation, or immunogenicity; the main gotcha is that some wrapped predictors may require additional model downloads or environment setup beyond the base package."},"id":"mhctools","links":{"html":"https://skillfed.io/packages/mhctools","md":"https://skillfed.io/packages/mhctools.md","pypi":"https://pypi.org/project/mhctools/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-11","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"mhctools","python_support":"supports_current","summary":"Python interface to MHC binding, presentation, immunogenicity, and antigen processing predictors"},"popularity":{"monthly_downloads":105773,"position":12680,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.31.5"}