Biology subjects

Lund-Johansen, F.

Publications and source records attributed to Lund-Johansen, F..

6 recordsLinked to original sources

bioRxiv2024

Baselining the Buzz. Trastuzumab-HER2 Affinity, and Beyond!

Strong antibody-antigen binding is the primary consideration when developing an efficacious therapeutic antibody. In recent years, much work has been devoted to applying complex machine learning models to this cause, yet simple baselines are often lacking. Here, we show that the widely used sequence alignment method, BLOSUM, can yield diverse, binder-enriched libraries from a single starting antibody. Using Trastuzumab-HER2 as a model system, we experimentally validated 720 novel designs generated with five different computational methods using surface plasmon resonance. The BLOSUM substitution matrix outperformed all four deep learning design approaches tested, achieving an estimated minimum binder enrichment of 12.5% and producing nine sub-nanomolar binders. These results underscore the importance of comparing against simple baselines and set a benchmark to guide future computational antibody library design. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/586756v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1597ee9org.highwire.dtl.DTLVardef@9af4b6org.highwire.dtl.DTLVardef@1380e61org.highwire.dtl.DTLVardef@1380d29_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Multiplexed measurement of binding- and neutralizing antibodies to SARS-CoV-2 variants in 12.000 post-vaccine sera

Diagnostic assays currently used to monitor the efficacy of COVID-19 vaccines measure levels of antibodies to the receptor-binding domain of ancestral SARS-CoV-2 (RBDwt). However, the predictive value for protection against new variants of concern (VOCs) has not been firmly established. Here, we used bead-based arrays and flow cytometry to measure binding of antibodies to spike proteins and receptor-binding domains (RBDs) from VOCs in 12,000 sera. Effects of sera on RBD-ACE2 interactions were measured as a proxy for neutralizing antibodies. The samples were obtained from healthy individuals or patients on immunosuppressive therapy who had received two to four doses of COVID-19 vaccines and from COVID-19 convalescents. The results show that anti-RBDwt titers correlate with the levels of binding- and neutralizing antibodies against the Alpha, Beta, Gamma, Delta, Epsilon and Omicron variants. The benefit of multiplexed analysis lies in the ability to measure a wide range of anti-RBD titers using a single dilution of serum for each assay. The reactivity patterns also yield an internal reference for neutralizing activity and binding antibody units per milliliter (BAU/ml). Results obtained with sera from vaccinated healthy individuals and patients confirmed and extended results from previous studies on time-dependent waning of antibody levels and effects of immunosuppressive agents. We conclude that anti-RBDwt titers correlate with levels of neutralizing antibodies against VOCs and propose that our method may be implemented to enhance the precision and throughput of immunomonitoring.

immunology↗

bioRxiv2021

Public T-cell epitopes shared among SARS-CoV-2 variants are presented on prevalent HLA class I alleles

T-cell epitopes with broad population coverage may form the basis for a new generation of SARS-CoV-2 vaccines. However, published studies on immunoprevalence are limited by small test cohorts, low frequencies of antigen-specific cells and lack of data correlating eluted HLA ligands with T-cell responsiveness. As the protective role of pre-existing cross-reactivity to homologous peptides is unclear, we aimed to identify SARS-CoV-2-specific minimal epitopes recognized by CD8 T-cells among 48 peptides eluted from prevalent HLA alleles, and an additional 84 predicted binders, in a large cohort of convalescents (n=83) and pre-pandemic control samples (n=19). We identified nine conserved SARS-CoV-2-specific epitopes restricted by four of the most prevalent HLA class I alleles in the Norwegian study cohort, to which responding CD8 T cells were detected in 70-100% of convalescents expressing the relevant HLA allele. Only two of these were derived from the Spike protein, included in current vaccines. We found a strong correlation between immunoprevalence and immunodominance. Thus, the CD8 T-cell response to SARS-CoV-2 is more focused than previously believed. Using a new algorithm, we predict that a vaccine including these epitopes could induce a T-cell response in 83% of Caucasians.

immunology↗

bioRxiv2021

In silico proof of principle of machine learning-based antibody design at unconstrained scale

Generative machine learning (ML) has been postulated to be a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody binding parameters. The simulation framework enables both the computation of antibody-antigen 3D-structures as well as functions as an oracle for unrestricted prospective evaluation of the antigen specificity of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (1D) data can be used to design native-like conformational (3D) epitope-specific antibodies, matching or exceeding the training dataset in affinity and developability variety. Furthermore, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Finally, we validated that the antibody design insight gained from simulated antibody-antigen binding data is applicable to experimental real-world data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design. HighlightsO_LIA large-scale dataset of 70M [3 orders of magnitude larger than the current state of the art] synthetic antibody-antigen complexes, that reflect biological complexity, allows the prospective evaluation of antibody generative deep learning C_LIO_LICombination of generative learning, synthetic antibody-antigen binding data, and prospective evaluation shows that deep learning driven antibody design and discovery at an unconstrained level is feasible C_LIO_LITransfer learning (low-N learning) coupled to generative learning shows that antibody-binding rules may be transferred across unrelated antibody-antigen complexes C_LIO_LIExperimental validation of antibody-design conclusions drawn from deep learning on synthetic antibody-antigen binding data C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/451480v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@1d947bcorg.highwire.dtl.DTLVardef@1f4c51corg.highwire.dtl.DTLVardef@b8df67org.highwire.dtl.DTLVardef@1530ea5_HPS_FORMAT_FIGEXP M_FIG We leverage large synthetic ground-truth data to demonstrate the (A,B) unconstrained deep generative learning-based generation of native-like antibody sequences, (C) the prospective evaluation of conformational (3D) affinity, paratope-epitope pairs, and developability. (D) Finally, we show increased generation quality of low-N-based machine learning models via transfer learning. C_FIG

immunology↗

bioRxiv2021

A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction

Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: The lack of a unified ML formalization of immunological antibody specificity prediction problems and the unavailability of large-scale synthetic benchmarking datasets of real-world relevance. Here, we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based 3D-antibody-antigen binding structures with ground-truth access to conformational paratope, epitope, and affinity. We formalized common immunological antibody specificity prediction problems as ML tasks and confirmed that for both sequence and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework thus enables real-world relevant development and benchmarking of ML strategies for biotherapeutics design. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/451258v3_ufig1.gif" ALT="Figure 1"> View larger version (86K): org.highwire.dtl.DTLVardef@4c3a6aorg.highwire.dtl.DTLVardef@8f1deborg.highwire.dtl.DTLVardef@1f55ec6org.highwire.dtl.DTLVardef@2135dd_HPS_FORMAT_FIGEXP M_FIG The software framework Absolut! enables (A,B) the generation of virtually arbitrarily large numbers of synthetic 3D-antibody-antigen structures, (C,D) the formalization of antibody specificity as machine learning (ML) tasks as well as the exploration of ML strategies for real-world antibody-antigen binding or paratope-epitope prediction. C_FIG HighlightsO_LISoftware framework Absolut! to generate an arbitrarily large number of synthetic 3D-antibody-antigen structures that contain biological layers of antibody-antigen binding complexity that render ML predictions challenging C_LIO_LIImmunological antibody specificity prediction problems formalized as machine learning tasks for which the in silico complexes are immediately usable as benchmark datasets C_LIO_LIExploration of machine learning prediction accuracy as a function of architecture, dataset size, choice of negatives, and sequence-structure encoding C_LIO_LIRelative ML performance learnt on Absolut! datasets transfers to experimental datasets C_LI

immunology↗

bioRxiv2021

Spatial-proteomics reveal in-vivo phospho-signaling dynamics at subcellular resolution

Dynamic change in subcellular localization of signaling proteins is a general concept that eukaryotic cells evolved for eliciting a coordinated response to stimuli. Mass spectrometry (MS)-based proteomics in combination with subcellular fractionation can provide comprehensive maps of spatio-temporal regulation of cells, but involves laborious workflows that does not cover the phospho-proteome level. Here we present a high-throughput workflow based on sequential cell fractionation to profile the global proteome and phospho-proteome dynamics across six distinct subcellular fractions. We benchmarked the workflow by studying spatio-temporal EGFR phospho-signaling dynamics in-vitro in HeLa cells and in-vivo in mouse tissues. Finally, we investigated the spatio-temporal stress signaling, revealing cellular relocation of ribosomal proteins in response to hypertonicity and muscle contraction. Proteomics data generated in this study can be explored through https://SpatialProteoDynamics.github.io.

systems biology↗