Extended antibody-framework-to-antigen distance observed exclusively with broad HIV-1-neutralizing antibodies recognizing glycan-dense surfaces.
Myungjin LeeAnita ChangelaJason GormanReda RawiTatsiana BylundCara W ChaoBob C LinMark K LouderAdam S OliaBaoshan ZhangNicole A Doria-RoseSusan Zolla-PaznerLawrence ShapiroGwo-Yu ChuangPeter D KwongPublished in: Nature communications (2021)
Antibody-Framework-to-Antigen Distance (AFAD) - the distance between the body of an antibody and a protein antigen - is an important parameter governing antibody recognition. Here, we quantify AFAD for ~2,000 non-redundant antibody-protein-antigen complexes in the Protein Data Bank. AFADs showed a gaussian distribution with mean of 16.3 Å and standard deviation (σ) of 2.4 Å. Notably, antibody-antigen complexes with extended AFADs (>3σ) were exclusively human immunodeficiency virus-type 1 (HIV-1)-neutralizing antibodies. High correlation (R2 = 0.8110) was observed between AFADs and glycan coverage, as assessed by molecular dynamics simulations of the HIV-1-envelope trimer. Especially long AFADs were observed for antibodies targeting the glycosylated trimer apex, and we tested the impact of introducing an apex-glycan hole (N160K); the cryo-EM structure of the glycan hole-targeting HIV-1-neutralizing antibody 2909 in complex with an N160K-envelope trimer revealed a substantially shorter AFAD. Overall, extended AFADs exclusively recognized densely glycosylated surfaces, with the introduction of a glycan hole enabling closer recognition.
Keyphrases
- human immunodeficiency virus
- antiretroviral therapy
- hiv infected
- hiv positive
- hepatitis c virus
- hiv testing
- hiv aids
- molecular dynamics simulations
- dengue virus
- protein protein
- men who have sex with men
- small molecule
- healthcare
- deep learning
- amino acid
- zika virus
- big data
- cell surface
- escherichia coli
- artificial intelligence
- biofilm formation