HUT2: HAPTIC3/HEPTAD2 User Toolkit

Links to web interfaces for components of HUT2 (cf. HUT):

1. HIP2: represents peptidic antigens using FASTA-style format (e.g., for use with HAPTIC3 and HEPTAD2; cf. HIP)
2. HAPTIC3: estimates affinity of paratopes for cognate disordered peptidic antigens in a solvent system characterized by temperature, pH, dielectric constant, ionic strength and donor/acceptor parameters for hydrogen-bonding/similar interactions, allowing for variable-length B-cell epitopes with conformational flexibility (cf. HAPTIC2)
3. HEPTAD2: HAPTIC3-like epitope prediction tool for analyzing peptidic antigens each comprising exactly one cysteine residue pair with an intramolecular disulfide bond (cf. HEPTAD)
4. SETH: estimates ionic strength, dielectric constant (i.e., relative permittivity), dipole-ion interaction cutoff distance and donor/acceptor parameters for hydrogen-bonding/similar interactions from solvent-system composition and temperature, for HAPTIC3/HEPTAD2.

Notes:

1. HUT2 is a more comprehensive and generally applicable replacement for HUT.
2. HIP2 facilitates tagging of antigen residues for exclusion from downstream analyses by HAPTIC3 and HEPTAD2.
3. HAPTIC3 (unlike HAPTIC2) captures composition-dependent solvent-system effects (e.g., [de]protonation, charge screening and competition for hydrogen-bonding partners).
4. HEPTAD2 (unlike HAPTIC3) expects each input peptidic antigen sequence to contain exactly two cysteine residues, which are assumed to form a disulfide-bonded pair.
5. SETH facilitates estimation of solvent-system input parameters for HAPTIC3 and HEPTAD2.

References:

1. Caoili SE (2006) A structural-energetic basis for B-cell epitope prediction. Protein & Peptide Letters 13(7):743-751 (PMID: 17018020)
2. Caoili SE (2010) Immunization with peptide-protein conjugates: impact on benchmarking B-cell epitope prediction for vaccine design. Protein & Peptide Letters 17(3):386-398 (PMID: 19594433)
3. Caoili SE (2012) On the meaning of affinity limits in B-cell epitope prediction for antipeptide antibody-mediated immunity. Advances in Bioinformatics 2012:346765, doi: 10.1155/2012/346765 (PMID: 23209458)
4. Caoili SE (2014) Benchmarking B-cell epitope prediction with quantitative dose-response data on antipeptide antibodies: towards novel pharmaceutical product development. BioMed Research International 2014:867905, doi: 10.1155/2014/867905 (PMID: 24949474)
5. Caoili SE (2014) Hybrid methods for B-cell epitope prediction. Methods in Molecular Biology 1184:245-283, doi: 10.1007/978-1-4939-1115-8_14 (PMID: 25048129)
6. Caoili SE (2015) An integrative structure-based framework for predicting biological effects mediated by antipeptide antibodies. Journal of Immunological Methods 427:19-29, doi: 10.1016/j.jim.2015.09.002 (PMID: 26410103)
7. Caoili SE (2021) Beyond B-cell epitopes: curating positive data on antipeptide paratope binding to support peptide-based vaccine design. Protein & Peptide Letters 28(8):953-962, doi: 10.2174/0929866528666210218215624 (PMID: 33602065)
8. Caoili SE (2022) Prediction of variable-length B-cell epitopes for antipeptide paratopes using the program HAPTIC. Protein & Peptide Letters 29(4):328-339, doi: 10.2174/0929866529666220203101808 (PMID: 35125075)
9. Caoili SE (2022) Comprehending B-cell epitope prediction to develop vaccines and immunodiagnostics. Frontiers in Immunology 13:908459, doi: 10.3389/fimmu.2022.908459 (PMID: 35874755)
10. Caoili SE (2024) B-cell epitope prediction for antipeptide paratopes with the HAPTIC2/HEPTAD User Toolkit (HUT). Methods in Molecular Biology 2821:9-32, doi: 10.1007/978-1-0716-3914-6_2 (PMID: 38997477)

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