Improving the affinity of a high-affinity protein-protein interaction is a challenging problem that has practical applications in the development of therapeutic biomolecules. We used a combination of structure-based computational methods to optimize the binding affinity of an antibody fragment to the I-domain of the integrin VLA1. Despite the already high affinity of the antibody (Kd ;7 nM) and the moderate resolution (2.8 Å ) of the starting crystal structure, the affinity was increased by an order of magnitude primarily through a decrease in the dissociation rate. We determined the crystal structure of a high-affinity quadruple mutant complex at 2.2 Å . The structure shows that the design makes the predicted contacts. Structural evidence and mutagenesis experiments that probe a hydrogen bond network illustrate the importance of satisfying hydrogen bonding requirements while seeking higheraffinity mutations. The large and diverse set of interface mutations allowed refinement of the mutant binding affinity prediction protocol and improvement of the single-mutant success rate. Our results indicate that structure-based computational design can be successfully applied to further improve the binding of high-affinity antibodies.Keywords: antibody; affinity maturation; computational protein design; protein-protein interactions; binding energy prediction Supplemental material: see www.proteinscience.orgComputational techniques for small molecule design have recently become an established part of the drug discovery process, and many studies have been published in which structure-based design has led to high-affinity molecules (Jorgensen 2004). In contrast, there has been considerably less usage of computational design techniques in the field of protein engineering. This is due in part to the effectiveness of directed evolution experimental techniques (Crameri et al. 1996;Hanes et al. 1998), the computational complexity of treating full proteins, and the relative scarcity of structural information on engineered proteins. Very recently there have been a number of successes in computational protein design, such as the redesign of an internal domain-domain interface of an endonuclease (Chevalier et al. 2002), the design of a novel protein fold , the design of specific enzymatic activity into a periplasmic binding protein (Dwyer et al. 2004), and alteration of DNase-inhibitor pair binding specificity (Kortemme et al. 2004). It is now foreseeable that biomolecule therapeutic design could be addressed using computational techniques.Antibodies are the most widely used format for protein therapeutic applications for a variety of reasons, including high affinity and the ability to trigger immune responses

Bispecific immunoglobulin-like antibodies capable of engaging multiple antigens represent a promising new class of therapeutic agents. Engineering of these molecules requires optimization of the molecular properties of one of the domain components. Here, we present a detailed crystallographic and computational characterization of the stabilization patterns in the lymphotoxin-beta receptor (LTbetaR) binding Fv domain of an anti-LTbetaR/anti-TNF-related apoptosis inducing ligand receptor-2 (TRAIL-R2) bispecific immunoglobulin-like antibody. We further describe a new hierarchical structure-guided approach toward engineering of antibody-like molecules to enhance their thermal and chemical stability.

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2020) Functional activity of anti-LINGO-1 antibody opicinumab requires target engagement at a secondary binding site, mAbs, 12:1, 1713648, ABSTRACT LINGO-1 is a membrane protein of the central nervous system (CNS) that suppresses myelination of axons. Preclinical studies have revealed that blockade of LINGO-1 function leads to CNS repair in demyelinating animal models. The anti-LINGO-1 antibody Li81 (opicinumab), which blocks LINGO-1 function and shows robust remyelinating activity in animal models, is currently being investigated in a Phase 2 clinical trial as a potential treatment for individuals with relapsing forms of multiple sclerosis (AFFINITY: clinical trial.gov number NCT03222973). Li81 has the unusual feature that it contains two LINGO-1 binding sites: a classical site utilizing its complementarity-determining regions and a cryptic secondary site involving Li81 light chain framework residues that recruits a second LINGO-1 molecule only after engagement of the primary binding site. Concurrent binding at both sites leads to formation of a 2:2 complex of LINGO-1 with the Li81 antigen-binding fragment, and higher order complexes with intact Li81 antibody. To elucidate the role of the secondary binding site, we designed a series of Li81 variant constructs that eliminate it while retaining the classic site contacts. These Li81 mutants retained the high affinity binding to LINGO-1, but lost the antibody-induced oligodendrocyte progenitor cell (OPC) differentiation activity and myelination activity in OPC-dorsal root ganglion neuron cocultures seen with Li81. The mutations also attenuate antibody-induced internalization of LINGO-1 on cultured cortical neurons, OPCs, and cells over-expressing LINGO-1. Together these studies reveal that engagement at both LINGO-1 binding sites of Li81 is critical for robust functional activity of the antibody. ARTICLE HISTORY

Proteins, especially antibodies, are widely used as therapeutic and diagnostic agents. Computational protein design is a powerful tool for improving the affinity and stability of these molecules. We describe a protein design method which employs the dead-end elimination (DEE) and A* discrete search algorithms with a few improvements aimed at making the procedure more useful for actual projects to design proteins for better affinity or stability. DEE/A* and related algorithms allow vast search spaces of protein sequences and their alternative side chain conformations ("rotamers") to be systematically explored, to find those with the best free energy of folding or binding. To maximize a protein design project's chance of success, it needs to find a diverse set of sequences to experimentally synthesize. It should also find structures that score well, not only on the pairwise-additive energy function which DEE/A* and related search algorithms must use, but also on a post-search energy function with accurate treatment of solvation effects. Straight DEE/A*, however, typically finds vast numbers of very similar low-energy conformations, making it infeasible to find a diverse set of sequences or conformations. Herein, we describe a three-level DEE/A* procedure that uses DEE/A* at the level of sequences, at the level of rotamers, and at an intermediate "fleximer" level, to ensure a wide variety of sequences as well as a diverse set of conformations for each sequence.A physics-based method is also described herein for calculating the free energy of folding based on a thermodynamic cycle with a model of the unfolded state. The free energies of both folding and binding may be used for the final evaluation of the designed structures. For example, when designing for improved affinity (binding), we can also ensure that stability is not degraded by screening on the free energy of folding.