HAPTIC: Heuristic Affinity Prediction Tool for Immune Complexes
Use HAPTIC to estimate affinity of paratopes for cognate flexibly disordered peptidic antigens, allowing for variable-length B-cell epitopes.
Sample HAPTIC Input and Output:
Notes:
- HAPTIC estimates epitope-paratope binding affinity as dissocation-constant
values for variable-length candidate epitopes of conformationally
disordered peptidic antigen sequences.
- Paratope-footprint radius (in Å) determines maximum length of rigid
poly-L-proline II (PPII) helical segment (assuming 3.1 Å rise per
residue) in a candidate epitope.
- Temperature of immunization is normal body temperature for immunized animal
species (e.g., 310.15 K = 37°C for human/mouse [default] or
312.35 K = 39.2°C for rabbit).
- Temperature of immunoassay is for experiment to measure epitope-paratope
binding affinity, with default value assumed for ambient/room temperature of
298.15K = 25°C.
- Input must be provided as peptidic sequence data in FASTA format using only
the 20 standard amino-acid residue codes (ACDEFGHIKLMNPQRSTVWY).
- Posttranslational/other covalent modifications such as oxidation,
phosphorylation and glycosylation are neglected.
- Each input sequence is partitioned into candidate epitopes (i.e.,
tripeptide and longer segments).
- Excessively long candidate epitopes (i.e., comprising excessively long
run/s of consecutive proline residues and/or deemed too bulky to fit paratope)
are excluded from analysis.
- Candidate epitopes are ranked by estimated affinity at temperature of
immunization, such that top-ranking candidate epitope is identified as
predicted most immunodominant epitope.
References:
- Caoili SE (2006) A structural-energetic basis for B-cell epitope prediction. Protein & Peptide Letters 13(7):743-751 (PMID: 17018020)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- Caoili SE (2022) Prediction of variable-length B-cell epitopes for antipeptide paratopes using the program HAPTIC. Protein & Peptide Letters (online ahead of print), doi: 10.2174/0929866529666220203101808 (PMID: 35125075)
Author Information:
Salvador Eugenio C. Caoili (email:
badong@post.upm.edu.ph)
Department of Biochemistry and Molecular Biology, College of Medicine, University of the Philippines Manila
Room 101, Medical Annex Building (Salcedo Hall), 547 Pedro Gil Street, Ermita, Manila 1000, Philippines
Telephone/Fax: +63 526 0377 / +63 526 4197