Discovery of a single-subunit oligosaccharyltransferase that enables glycosylation of full-length IgG antibodies in .

Human immunoglobulin G (IgG) antibodies are one of the most important classes of biotherapeutic agents and undergo glycosylation at the conserved N297 site in the C2 domain, which is critical for IgG Fc effector functions and anti-inflammatory activity. Hence, technologies for producing authentically glycosylated IgGs are in high demand. While attempts to engineer for this purpose have been described, they have met limited success due in part to the lack of available oligosaccharyltransferase (OST) enzymes that can install linked glycans within the QYNST sequon of the IgG C2 domain.

Contextual protein and antibody encodings from equivariant graph transformers.

The optimal residue identity at each position in a protein is determined by its structural, evolutionary, and functional context. We seek to learn the representation space of the optimal amino-acid residue in different structural contexts in proteins. Inspired by masked language modeling (MLM), our training aims to transduce learning of amino-acid labels from non-masked residues to masked residues in their structural environments and from general (e.

Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies.

Antibodies have the capacity to bind a diverse set of antigens, and they have become critical therapeutics and diagnostic molecules. The binding of antibodies is facilitated by a set of six hypervariable loops that are diversified through genetic recombination and mutation. Even with recent advances, accurate structural prediction of these loops remains a challenge.

Hallucinating structure-conditioned antibody libraries for target-specific binders.

Antibodies are widely developed and used as therapeutics to treat cancer, infectious disease, and inflammation. During development, initial leads routinely undergo additional engineering to increase their target affinity. Experimental methods for affinity maturation are expensive, laborious, and time-consuming and rarely allow the efficient exploration of the relevant design space.

Simultaneous prediction of antibody backbone and side-chain conformations with deep learning.

Antibody engineering is becoming increasingly popular in medicine for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution modeling of these loops is essential for effective antibody engineering and design.

Induced fit with replica exchange improves protein complex structure prediction.

Despite the progress in prediction of protein complexes over the last decade, recent blind protein complex structure prediction challenges revealed limited success rates (less than 20% models with DockQ score > 0.4) on targets that exhibit significant conformational change upon binding. To overcome limitations in capturing backbone motions, we developed a new, aggressive sampling method that incorporates temperature replica exchange Monte Carlo (T-REMC) and conformational sampling techniques within docking protocols in Rosetta.

Structural basis for peptide substrate specificities of glycosyltransferase GalNAc-T2.

The polypeptide -acetylgalactosaminyl transferase (GalNAc-T) enzyme family initiates -linked mucin-type glycosylation. The family constitutes 20 isoenzymes in humans. GalNAc-Ts exhibit both redundancy and finely tuned specificity for a wide range of peptide substrates.

Deep Learning in Protein Structural Modeling and Design.

Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields, including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design.

Geometric potentials from deep learning improve prediction of CDR H3 loop structures.

Motivation: Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure.

The structure of the colorectal cancer-associated enzyme GalNAc-T12 reveals how nonconserved residues dictate its function.

Polypeptide acetylgalactosaminyl transferases (GalNAc-Ts) initiate mucin type -glycosylation by catalyzing the transfer of -acetylgalactosamine (GalNAc) to Ser or Thr on a protein substrate. Inactive and partially active variants of the isoenzyme GalNAc-T12 are present in subsets of patients with colorectal cancer, and several of these variants alter nonconserved residues with unknown functions. While previous biochemical studies have demonstrated that GalNAc-T12 selects for peptide and glycopeptide substrates through unique interactions with its catalytic and lectin domains, the molecular basis for this distinct substrate selectivity remains elusive.

Computational affinity maturation of camelid single-domain intrabodies against the nonamyloid component of alpha-synuclein.

Improving the affinity of protein-protein interactions is a challenging problem that is particularly important in the development of antibodies for diagnostic and clinical use. Here, we used structure-based computational methods to optimize the binding affinity of VNAC1, a single-domain intracellular antibody (intrabody) from the camelid family that was selected for its specific binding to the nonamyloid component (NAC) of human α-synuclein (α-syn), a natively disordered protein, implicated in the pathogenesis of Parkinson's disease (PD) and related neurological disorders. Specifically, we performed ab initio modeling that revealed several possible modes of VNAC1 binding to the NAC region of α-syn as well as mutations that potentially enhance the affinity between these interacting proteins.

Tilting the balance between canonical and noncanonical conformations for the H1 hypervariable loop of a llama VHH through point mutations.

Nanobodies are single-domain antibodies found in camelids. These are the smallest naturally occurring binding domains and derive functionality via three hypervariable loops (H1-H3) that form the binding surface. They are excellent candidates for antibody engineering because of their favorable characteristics like small size, high solubility, and stability.