ArticleNucleic acids research2017
NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Automating neoantigen selection for personalized cancer vaccine design.medRxiv : the preprint server for health sciences · 2026Article
- Review: application and opportunities for machine learning and artificial intelligence in preclinical immunogenicity risk assessment.Frontiers in immunology · 2026Review
- Computational Methods in Immunoinformatics: Epitope Discovery and Diagnostic Applications.ACS omega · 2025Review
- On the impact of local protein structure features on prediction of major histocompatibility complex class I and II antigen presentation.Briefings in bioinformatics · 2025Article
- Machine learning approaches enable the discovery of therapeutics across domains.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- NetMHCpan-4.2: improved prediction of CD8+ epitopes by use of transfer learning and structural features.Frontiers in immunology · 2025Article
- Developing Vaccines in Pancreatic Adenocarcinoma: Trials and Tribulations.Current oncology (Toronto, Ont.) · 2024Review
- In Silico Identification of Peanut Peptides Suitable for Allergy Immunotherapy in HLA-DRB1*03:01-Restricted Patients.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification.Molecular & cellular proteomics : MCP · 2024Review
- A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes.NAR cancer · 2024Article
- Transfer learning improves pMHC kinetic stability and immunogenicity predictions.Immunoinformatics (Amsterdam, Netherlands) · 2024Article
- MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes.Briefings in bioinformatics · 2024Article
- MONET: a database for prediction of neoantigens derived from microsatellite loci.Frontiers in immunology · 2024Article
- IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition.Frontiers in immunology · 2024Article
- Integrating machine learning to advance epitope mapping.Frontiers in immunology · 2024Review
- Neoantigen-targeted TCR-engineered T cell immunotherapy: current advances and challenges.Biomarker research · 2023Review
- The OSUMMER lines: A series of ultraviolet-accelerated NRAS-mutant mouse melanoma cell lines syngeneic to C57BL/6.Pigment cell & melanoma research · 2023Article
- High-throughput characterization of HLA-E-presented CD94/NKG2x ligands reveals peptides which modulate NK cell activation.Nature communications · 2023Article
- Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design.Pathogens and global health · 2023Review
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2 authors.
Funding
Abstract
Peptides are extensively used to characterize functional or (linear) structural aspects of receptor-ligand interactions in biological systems, e.g. SH2, SH3, PDZ peptide-recognition domains, the MHC membrane receptors and enzymes such as kinases and phosphatases. NNAlign is a method for the identification of such linear motifs in biological sequences. The algorithm aligns the amino acid or nucleotide sequences provided as training set, and generates a model of the sequence motif detected in the data. The webserver allows setting up cross-validation experiments to estimate the performance of the model, as well as evaluations on independent data. Many features of the training sequences can be encoded as input, and the network architecture is highly customizable. The results returned by the server include a graphical representation of the motif identified by the method, performance values and a downloadable model that can be applied to scan protein sequences for occurrence of the motif. While its performance for the characterization of peptide-MHC interactions is widely documented, we extended NNAlign to be applicable to other receptor-ligand systems as well. Version 2.0 supports alignments with insertions and deletions, encoding of receptor pseudo-sequences, and custom alphabets for the training sequences. The server is available at http://www.cbs.dtu.dk/services/NNAlign-2.0.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.