It has been demonstrated that certain compounds bind to TNF and stabilise a conformation of trimeric TNF that binds to the TNF receptor. Antibodies which selectively bind to complexes of such compounds with TNF superfamily members are disclosed. These antibodies may be used to detect further compounds with the same activity, and as target engagement biomarker.

The invention is in the field of TNF signalling. Compounds have been identified which are capable of modulating signalling of TNF trimers through receptors. Methods of identifying such compounds are therefore provided. The compounds themselves have utility in therapy.

It has been demonstrated that certain compounds bind to TNF and stabilise a conformation of trimeric TNF that binds to the TNF receptor. Antibodies which selectively bind to complexes of such compounds with TNF superfamily members are disclosed. These antibodies may be used to detect further compounds with the same activity, and as target engagement biomarker.

The present invention provides compositions and methods that can be used to determine a peptide signature for an antibody repertoire in a sample comprising multiple antibodies. The method can be used to characterize a phenotype in a sample, such as providing a diagnosis, prognosis or theranosis of a medical condition.

A method for identifying a protein associated with a receptor-ligand interaction is described. The method comprises providing a population of engineered cells comprising a targeting library targeting specific gene expression, contacting the population of cells with a recombinant toxin fusion for sufficient time, and identifying proteins in the selection pool of cells by sequencing one or more of the nucleic acid molecule comprised in the selection pool of cells, thereby identifying the target gene. Toxin-resistant cell lines, toxin-producing cell lines, recombinant toxin fusions, probes and methods producing same, and kits thereof, are also provided.

A composition for charactering an internalizing antibody is provided. The composition includes an antibody-binding moiety and a detectable agent.

Provided herein are methods for design of engineered polypeptides that recapitulate molecular structure features of a predetermined portion of a reference protein structure, e.g., an antibody epitope or a protein binding site. A Machine Learning (ML) model is trained by labeling blueprint records generated from a reference target structure with scores calculated based on computational protein modeling of polypeptide structures generated by the blueprint records. The method may include training an ML model based on a first set of blueprint records, or representations thereof, and a first set of scores, each blueprint record from the first set of blueprint records associated with each score from the first set of scores. After the training, the machine learning model may be executed to generate a second set of blueprint records. A set of engineered polypeptides are then generated based on the second set of blueprint records.

The invention comprises systems, methods and arrays for identification and optimization of novel peptide binders to protein targets. Embodiments include steps of peptide binder discovery, core peptide maturation, N-terminal and C-terminal extension and kinetics analysis of the final peptide binder.

It has been demonstrated that certain compounds bind to TNF and stabilise a conformation of trimeric TNF that binds to the TNF receptor. Antibodies which selectively bind to complexes of such compounds with TNF superfamily members are disclosed. These antibodies may be used to detect further compounds with the same activity, and as target engagement biomarker.

Compositions and methods are provided for the identification of peptide sequences that are ligands for a T cell receptor (TCR) of interest, in a given MHC context.