Potently neutralizing and protective human antibodies against SARS-CoV-2.

The ongoing pandemic of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a major threat to global health1 and the medical countermeasures available so far are limited2,3. Moreover, we currently lack a thorough understanding of the mechanisms of humoral immunity to SARS-CoV-24. Here we analyse a large panel of human monoclonal antibodies that target the spike (S) glycoprotein5, and identify several that exhibit potent neutralizing activity and fully block the receptor-binding domain of the S protein (SRBD) from interacting with human angiotensin-converting enzyme 2 (ACE2). Using competition-binding, structural and functional studies, we show that the monoclonal antibodies can be clustered into classes that recognize distinct epitopes on the SRBD, as well as distinct conformational states of the S trimer. Two potently neutralizing monoclonal antibodies, COV2-2196 and COV2-2130, which recognize non-overlapping sites, bound simultaneously to the S protein and neutralized wild-type SARS-CoV-2 virus in a synergistic manner. In two mouse models of SARS-CoV-2 infection, passive transfer of COV2-2196, COV2-2130 or a combination of both of these antibodies protected mice from weight loss and reduced the viral burden and levels of inflammation in the lungs. In addition, passive transfer of either of two of the most potent ACE2-blocking monoclonal antibodies (COV2-2196 or COV2-2381) as monotherapy protected rhesus macaques from SARS-CoV-2 infection. These results identify protective epitopes on the SRBD and provide a structure-based framework for rational vaccine design and the selection of robust immunotherapeutic agents.

Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design.

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.

Comparative modeling and docking of chemokine-receptor interactions with Rosetta.

Chemokine receptors are a subset of G protein-coupled receptors defined by the distinct property of binding small protein ligands in the chemokine family. Chemokine receptors recognize their ligands by a mechanism that is distinct from other class A GPCRs that bind peptides or small molecules. For this reason, structural information on other ligand-GPCR interactions are only indirectly relevant to understanding the chemokine receptor interface. Additionally, the experimentally determined structures of chemokine-GPCR complexes represent less than 3% of the known interactions of this complex, multi-ligand/multi-receptor network. To enable predictive modeling of the remaining 97% of interactions, a general in silico protocol was designed to utilize existing chemokine receptor crystal structures, co-crystal structures, and NMR ensembles of chemokines bound to receptor fragments. This protocol was benchmarked on the ability to predict each of the three published co-crystal structures, while being blinded to the target structure. Averaging ensembles selected from the top-ranking models reproduced up to 84% of the intermolecular contacts found in the crystal structure, with the lowest Cα-RMSD of the complex at 3.3 Å. The chemokine receptor N-terminus, unresolved in crystal structures, was included in the modeling and recapitulates contacts with known sulfotyrosine binding pockets seen in structures derived from experimental NMR data. This benchmarking experiment suggests that realistic homology models of chemokine-GPCR complexes can be generated by leveraging current structural data.

Expanding the toolkit for membrane protein modeling in Rosetta.

Motivation: A range of membrane protein modeling tools has been developed in the past 5-10 years, yet few of these tools are integrated and make use of existing functionality for soluble proteins. To extend existing methods in the Rosetta biomolecular modeling suite for membrane proteins, we recently implemented RosettaMP, a general framework for membrane protein modeling. While RosettaMP facilitates implementation of new methods, addressing real-world biological problems also requires a set of accessory tools that are used to carry out standard modeling tasks. Results: Here, we present six modeling tools, including de novo prediction of single trans-membrane helices, making mutations and refining the structure with different amounts of flexibility, transforming a protein into membrane coordinates and optimizing its embedding, computing a Rosetta energy score, and visualizing the protein in the membrane bilayer. We present these methods with complete protocol captures that allow non-expert modelers to carry out the computations. Availability and Implementation: The presented tools are part of the Rosetta software suite, available at www.rosettacommons.org . Contact: julia.koehler.leman@gmail.com. Supplementary information: Supplementary data are available at Bioinformatics online.

Holistic Approach to Partial Covalent Interactions in Protein Structure Prediction and Design with Rosetta.

Partial covalent interactions (PCIs) in proteins, which include hydrogen bonds, salt bridges, cation-π, and π-π interactions, contribute to thermodynamic stability and facilitate interactions with other biomolecules. Several score functions have been developed within the Rosetta protein modeling framework that identify and evaluate these PCIs through analyzing the geometry between participating atoms. However, we hypothesize that PCIs can be unified through a simplified electron orbital representation. To test this hypothesis, we have introduced orbital based chemical descriptors for PCIs into Rosetta, called the PCI score function. Optimal geometries for the PCIs are derived from a statistical analysis of high-quality protein structures obtained from the Protein Data Bank (PDB), and the relative orientation of electron deficient hydrogen atoms and electron-rich lone pair or π orbitals are evaluated. We demonstrate that nativelike geometries of hydrogen bonds, salt bridges, cation-π, and π-π interactions are recapitulated during minimization of protein conformation. The packing density of tested protein structures increased from the standard score function from 0.62 to 0.64, closer to the native value of 0.70. Overall, rotamer recovery improved when using the PCI score function (75%) as compared to the standard Rosetta score function (74%). The PCI score function represents an improvement over the standard Rosetta score function for protein model scoring; in addition, it provides a platform for future directions in the analysis of small molecule to protein interactions, which depend on partial covalent interactions.

Combination of Cα-H Hydrogen Bonds and van der Waals Packing Modulates the Stability of GxxxG-Mediated Dimers in Membranes.

The GxxxG motif is frequently found at the dimerization interface of a transmembrane structural motif called GASright, which is characterized by a short interhelical distance and a right-handed crossing angle between the helices. In GASright dimers, such as glycophorin A (GpA), BNIP3, and members of the ErbB family, the backbones of the helices are in contact, and they invariably display networks of 4 to 8 weak hydrogen bonds between Cα-H carbon donors and carbonyl acceptors on opposing helices (Cα-H···O═C hydrogen bonds). These networks of weak hydrogen bonds at the helix-helix interface are presumably stabilizing, but their energetic contribution to dimerization has yet to be determined experimentally. Here, we present a computational and experimental structure-based analysis of GASright dimers of different predicted stabilities, which show that a combination of van der Waals packing and Cα-H hydrogen bonding predicts the experimental trend of dimerization propensities. This finding provides experimental support for the hypothesis that the networks of Cα-H hydrogen bonds are major contributors to the free energy of association of GxxxG-mediated dimers. The structural comparison between groups of GASright dimers of different stabilities reveals distinct sequence as well as conformational preferences. Stability correlates with shorter interhelical distances, narrower crossing angles, better packing, and the formation of larger networks of Cα-H hydrogen bonds. The identification of these structural rules provides insight on how nature could modulate stability in GASright and finely tune dimerization to support biological function.

A frequent, GxxxG-mediated, transmembrane association motif is optimized for the formation of interhelical Cα-H hydrogen bonds.

Carbon hydrogen bonds between Cα-H donors and carbonyl acceptors are frequently observed between transmembrane helices (Cα-H···O=C). Networks of these interactions occur often at helix-helix interfaces mediated by GxxxG and similar patterns. Cα-H hydrogen bonds have been hypothesized to be important in membrane protein folding and association, but evidence that they are major determinants of helix association is still lacking. Here we present a comprehensive geometric analysis of homodimeric helices that demonstrates the existence of a single region in conformational space with high propensity for Cα-H···O=C hydrogen bond formation. This region corresponds to the most frequent motif for parallel dimers, GASright, whose best-known example is glycophorin A. The finding suggests a causal link between the high frequency of occurrence of GASright and its propensity for carbon hydrogen bond formation. Investigation of the sequence dependency of the motif determined that Gly residues are required at specific positions where only Gly can act as a donor with its "side chain" Hα. Gly also reduces the steric barrier for non-Gly amino acids at other positions to act as Cα donors, promoting the formation of cooperative hydrogen bonding networks. These findings offer a structural rationale for the occurrence of GxxxG patterns at the GASright interface. The analysis identified the conformational space and the sequence requirement of Cα-H···O=C mediated motifs; we took advantage of these results to develop a structural prediction method. The resulting program, CATM, predicts ab initio the known high-resolution structures of homodimeric GASright motifs at near-atomic level.

Protocols for Molecular Modeling with Rosetta3 and RosettaScripts.

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987-2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed "RosettaScripts" for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta's ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

A Gly-zipper motif mediates homodimerization of the transmembrane domain of the mitochondrial kinase ADCK3.

Interactions between α-helices within the hydrophobic environment of lipid bilayers are integral to the folding and function of transmembrane proteins; however, the major forces that mediate these interactions remain debated, and our ability to predict these interactions is still largely untested. We recently demonstrated that the frequent transmembrane association motif GASright, the GxxxG-containing fold of the glycophorin A dimer, is optimal for the formation of extended networks of Cα-H hydrogen bonds, supporting the hypothesis that these bonds are major contributors to association. We also found that optimization of Cα-H hydrogen bonding and interhelical packing is sufficient to computationally predict the structure of known GASright dimers at near atomic level. Here, we demonstrate that this computational method can be used to characterize the structure of a protein not previously known to dimerize, by predicting and validating the transmembrane dimer of ADCK3, a mitochondrial kinase. ADCK3 is involved in the biosynthesis of the redox active lipid, ubiquinone, and human ADCK3 mutations cause a cerebellar ataxia associated with ubiquinone deficiency, but the biochemical functions of ADCK3 remain largely undefined. Our experimental analyses show that the transmembrane helix of ADCK3 oligomerizes, with an interface based on an extended Gly-zipper motif, as predicted by our models. The data provide strong evidence for the hypothesis that optimization of Cα-H hydrogen bonding is an important factor in the association of transmembrane helices. This work also provides a structural foundation for investigating the role of transmembrane association in regulating the biological activity of ADCK3.

Structural informatics, modeling, and design with an open-source Molecular Software Library (MSL).

We present the Molecular Software Library (MSL), a C++ library for molecular modeling. MSL is a set of tools that supports a large variety of algorithms for the design, modeling, and analysis of macromolecules. Among the main features supported by the library are methods for applying geometric transformations and alignments, the implementation of a rich set of energy functions, side chain optimization, backbone manipulation, calculation of solvent accessible surface area, and other tools. MSL has a number of unique features, such as the ability of storing alternative atomic coordinates (for modeling) and multiple amino acid identities at the same backbone position (for design). It has a straightforward mechanism for extending its energy functions and can work with any type of molecules. Although the code base is large, MSL was created with ease of developing in mind. It allows the rapid implementation of simple tasks while fully supporting the creation of complex applications. Some of the potentialities of the software are demonstrated here with examples that show how to program complex and essential modeling tasks with few lines of code. MSL is an ongoing and evolving project, with new features and improvements being introduced regularly, but it is mature and suitable for production and has been used in numerous protein modeling and design projects. MSL is open-source software, freely downloadable at http://msl-libraries.org. We propose it as a common platform for the development of new molecular algorithms and to promote the distribution, sharing, and reutilization of computational methods.

Modeling the complete chemokine-receptor interaction.

Chemokines are soluble, secreted proteins that induce chemotaxis of leukocytes and other cells. Migratory cells can sense the chemokine concentration gradient following chemokine binding and activation of chemokine receptors, a subset of the G protein-coupled receptor (GPCR) superfamily. Chemokine receptor signaling plays a central role in cell migration during inflammatory responses as well as in cancer and other diseases. Given their important role in mediating essential pathologic and physiologic processes, chemokines and their receptors are attractive targets for therapeutic development. A better understanding of the molecular basis of chemokine-GPCR interactions will aid in the understanding of the mechanistic basis for chemokine function in disease-related processes, as well as aid in the design of new therapeutics. High resolution protein structures are critical for determining these mechanisms and investigating the interactions between approximately 50 chemokines and 20 chemokine receptors. Currently, three unique structures of chemokine-GPCR complexes have been determined and have greatly broadened our knowledge of this large protein-protein interaction. While these structures represent only a small fraction of clinically relevant chemokines and receptors, they can be exploited as scaffolds for homology modeling to understand the chemokine-GPCR interactions. This chapter presents a specialized methodology to construct and validate models of chemokine-GPCR complexes using the Rosetta software suite.