Allostery in a protein involves effector binding at an allosteric site that changes the structure and/or dynamics at a distant, functional site. In addition to the chemical equilibrium of ligand binding, allostery involves a conformational equilibrium between one protein substate that binds the effector and a second substate that less strongly binds the effector. We run molecular dynamics simulations using simple, smooth energy landscapes to sample specific ligand-induced conformational transitions, as defined by the effector-bound and effector-unbound protein structures. These simulations can be performed using our web server (http://salilab.org/allosmod/). We then develop a set of features to analyze the simulations and capture the relevant thermodynamic properties of the allosteric conformational equilibrium. These features are based on molecular mechanics energy functions, stereochemical effects, and structural/dynamic coupling between sites. Using a machine-learning algorithm on a data set of 10 proteins and 179 mutations, we predict both the magnitude and the sign of the allosteric conformational equilibrium shift by the mutation; the impact of a large identifiable fraction of the mutations can be predicted with an average unsigned error of 1k(B)T. With similar accuracy, we predict the mutation effects for an 11th protein that was omitted from the initial training and testing of the machine-learning algorithm. We also assess which calculated thermodynamic properties contribute most to the accuracy of the prediction.

Motivation

Structural characterization of protein interactions is necessary for understanding and modulating biological processes. On one hand, X-ray crystallography or NMR spectroscopy provide atomic resolution structures but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling assembly structures from individual components frequently suffer from high false-positive rate, rarely resulting in a unique solution.

Results

Here, we present a combined approach that computationally integrates data from a variety of fast and accessible experimental techniques for rapid and accurate structure determination of protein-protein complexes. The integrative method uses atomistic models of two interacting proteins and one or more datasets from five accessible experimental techniques: a small-angle X-ray scattering (SAXS) profile, 2D class average images from negative-stain electron microscopy micrographs (EM), a 3D density map from single-particle negative-stain EM, residue type content of the protein-protein interface from NMR spectroscopy and chemical cross-linking detected by mass spectrometry. The method is tested on a docking benchmark consisting of 176 known complex structures and simulated experimental data. The near-native model is the top scoring one for up to 61% of benchmark cases depending on the included experimental datasets; in comparison to 10% for standard computational docking. We also collected SAXS, 2D class average images and 3D density map from negative-stain EM to model the PCSK9 antigen-J16 Fab antibody complex, followed by validation of the model by a subsequently available X-ray crystallographic structure.

Staphylococcus aureus is both an important pathogen and a human commensal. To explore this ambivalent relationship between host and microbe, we analysed the memory humoral response against IsdB, a protein involved in iron acquisition, in four healthy donors. Here we show that in all donors a heavily biased use of two immunoglobulin heavy chain germlines generated high affinity (pM) antibodies that neutralize the two IsdB NEAT domains, IGHV4-39 for NEAT1 and IGHV1-69 for NEAT2. In contrast to the typical antibody/antigen interactions, the binding is primarily driven by the germline-encoded hydrophobic CDRH-2 motifs of IGHV1-69 and IGHV4-39, with a binding mechanism nearly identical for each antibody derived from different donors. Our results suggest that IGHV1-69 and IGHV4-39, while part of the adaptive immune system, may have evolved under selection pressure to encode a binding motif innately capable of recognizing and neutralizing a structurally conserved protein domain involved in pathogen iron acquisition.

The commercial success of bispecific antibodies generally has been hindered by the complexities associated with generating appropriate molecules for both research scale and large scale manufacturing purposes. Bispecific IgG (BsIgG) based on two antibodies that use an identical common light chain can be combined with a minimal set of Fc mutations to drive heavy chain heterodimerization in order to address these challenges. However, the facile generation of common light chain antibodies with properties similar to traditional monoclonal antibodies has not been demonstrated and they have only been used sparingly. Here, we describe the design of a synthetic human antibody library based on common light chains to generate antibodies with biochemical and biophysical properties that are indistinguishable to traditional therapeutic monoclonal antibodies. We used this library to generate diverse panels of well-behaved, high affinity antibodies toward a variety of epitopes across multiple antigens, including mouse 4-1BB, a therapeutically important T cell costimulatory receptor. Over 200 BsIgG toward 4-1BB were generated using an automated purification method we developed that enables milligram-scale production of BsIgG. This approach allowed us to identify antibodies with a wide range of agonistic activity that are being used to further investigate the therapeutic potential of antibodies targeting one or more epitopes of 4-1BB.

The disialoganglioside GD2 is overexpressed on several solid tumors, and monoclonal antibodies targeting GD2 have substantially improved outcomes for children with high-risk neuroblastoma. However, approximately 40% of patients with neuroblastoma still relapse, and anti-GD2 has not mediated significant clinical activity in any other GD2⁺ malignancy. Macrophages are important mediators of anti-tumor immunity, but tumors resist macrophage phagocytosis through expression of the checkpoint molecule CD47, a so-called 'Don't eat me' signal. In this study, we establish potent synergy for the combination of anti-GD2 and anti-CD47 in syngeneic and xenograft mouse models of neuroblastoma, where the combination eradicates tumors, as well as osteosarcoma and small-cell lung cancer, where the combination significantly reduces tumor burden and extends survival. This synergy is driven by two GD2-specific factors that reorient the balance of macrophage activity. Ligation of GD2 on tumor cells (a) causes upregulation of surface calreticulin, a pro-phagocytic 'Eat me' signal that primes cells for removal and (b) interrupts the interaction of GD2 with its newly identified ligand, the inhibitory immunoreceptor Siglec-7. This work credentials the combination of anti-GD2 and anti-CD47 for clinical translation and suggests that CD47 blockade will be most efficacious in combination with monoclonal antibodies that alter additional pro- and anti-phagocytic signals within the tumor microenvironment.