The disclosure provides compositions comprising at least one assembly comprising a peptide and a major histocompatibility complex (MHC), wherein the peptide is an integral component of the MHC, wherein the peptide is attached to a surface at its C-terminus through a linker and wherein the peptide is synthesized on the surface. In certain embodiments, the compositions comprise a plurality of assemblies in a spatially-ordered array. The disclosure provides methods for making and using these compositions.

The present invention relates to compositions and methods employing conjugates that include a self-assembly and disassembly (SADA) polypeptide and a binding domain. The present invention encompasses the recognition that conjugates with a SADA polypeptide have certain improved biological properties. SADA-conjugates are described, along with uses thereof (e.g., as therapeutic or diagnostic agents) and methods of manufacture.

A method for quantifying the activity of the proteins/enzymes involved in the conjugation of the SUMO/Ubiquitin/Nedd8 proteins in a cell of a biological sample the method including: a) a step of contacting a cellular extract of cell with each protein of a subgroup of at least 3 proteins wherein the at least 3 proteins corresponds to the proteins essentially including or only including the sequences SEQ ID NO: 1 to 3, b) a step of simultaneously measuring ubiquitination, sumoylation and neddylation level of each of the at least 3 proteins to obtain a first value for ubiquitin, SUMO and Nedd8.

The present disclosure relates to compositions and methodology for revealing binding sites between proteins, proteins and nucleic acids, or proteins and small molecules. The disclosure provides rapid and direct positive identification and sequencing of the contact region between such molecules, and can be applied to individual interacting pairs, as well as large-scale or global interactions.

The present invention provides assays and assay systems for use in spatially encoded biological assays. The invention provides an assay system comprising an assay capable of high levels of multiplexing where reagents are provided to a biological sample in defined spatial patterns; instrumentation capable of controlled delivery of reagents according to the spatial patterns; and a decoding scheme providing a readout that is digital in nature.

Provided herein are methods and compositions relating to libraries of optimized antibodies having nucleic acids encoding for an antibody comprising modified sequences. Libraries described herein include variegated libraries comprising nucleic acids each encoding for a predetermined variant of at least one predetermined reference nucleic acid sequence. Further described herein are protein libraries generated when the nucleic acid libraries are translated. Further described herein are cell libraries expressing variegated nucleic acid libraries described herein.

Provided herein are methods and compositions relating to libraries of optimized antibodies having nucleic acids encoding for an antibody comprising modified sequences. Libraries described herein include variegated libraries comprising nucleic acids each encoding for a predetermined variant of at least one predetermined reference nucleic acid sequence. Further described herein are protein libraries generated when the nucleic acid libraries are translated. Further described herein are cell libraries expressing variegated nucleic acid libraries described herein.

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.

Provided herein are in vitro methods of assaying an in vivo level of high molecular weight (HMW) species of a therapeutic protein. In exemplary embodiments, the method comprises (a) incubating a mixture comprising (i) a sample comprising the therapeutic protein and (ii) serum, or a depleted fraction thereof; and (b) assaying the level of HMW species of the therapeutic protein present in the mixture at one or more time points after step (a). Also methods of determining the in vivo reversibility of HMW species of a therapeutic protein are provided herein. In exemplary instances, the method comprises (A) assaying the in vivo level of high molecular weight (HMW) species of a therapeutic protein according to a presently disclosed in vitro method, and (B) comparing the level(s) of HMW species present in the mixture to the level of HMW species present in the sample prior to the incubating step.

Methods and devices for identifying agents that block binding or activation of peripheral membrane proteins are disclosed.