Machine learning methods for deciphering the human immune repertoire language. Deciphering the immune information (immune status, antigen binding) encoded into antibody and T-cell repertoires is of paramount importance for the development of vaccines, diagnostics and therapeutics and requires machine learning approaches (artificial intelligence). We focus on developing deep learning approaches to learn how to read and write the immune repertoire language.
Single-cell immune repertoire and immuno-mass spectrometry methods. Our research focuses on developing experimental platforms that link droplet-based high-throughput single-cell sequencing with the large-scale mass-spectrometry analysis of serum antibody repertoires in order to resolve the diversity and specificity of the effector serum antibody repertoire at single antibody resolution.
Scheffer L, Reber EE, Mehta BB, Pavlović M, Chernigovskaya M, Richardson E, Akbar R, Lund-Johansen F, Greiff V, Haff IH, Sandve GK(2024) Predictability of antigen binding based on short motifs in the antibody CDRH3 Brief Bioinform, 25(6) DOI 10.1093/bib/bbae537, PubMed 39438077
Riccabona JR, Spoendlin FC, Fischer AM, Loeffler JR, Quoika PK, Jenkins TP, Ferguson JA, Smorodina E, Laustsen AH, Greiff V, Forli S, Ward AB, Deane CM, Fernández-Quintero ML(2024) Assessing AF2's ability to predict structural ensembles of proteins Structure(in press) DOI 10.1016/j.str.2024.09.001, PubMed 39332396
Bashour H, Smorodina E, Pariset M, Zhong J, Akbar R, Chernigovskaya M, Lê Quý K, Snapkow I, Rawat P, Krawczyk K, Sandve GK, Gutierrez-Marcos J, Gutierrez DN, Andersen JT, Greiff V(2024) Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability Commun Biol, 7(1), 922 DOI 10.1038/s42003-024-06561-3, PubMed 39085379