Redesign of a cross-reactive antibody to dengue virus with broad-spectrum activity and increased in vivo potency.

Affinity improvement of proteins, including antibodies, by computational chemistry broadly relies on physics-based energy functions coupled with refinement. However, achieving significant enhancement of binding affinity (>10-fold) remains a challenging exercise, particularly for cross-reactive antibodies. We describe here an empirical approach that captures key physicochemical features common to antigen-antibody interfaces to predict protein-protein interaction and mutations that confer increased affinity. We apply this approach to the design of affinity-enhancing mutations in 4E11, a potent cross-reactive neutralizing antibody to dengue virus (DV), without a crystal structure. Combination of predicted mutations led to a 450-fold improvement in affinity to serotype 4 of DV while preserving, or modestly increasing, affinity to serotypes 1-3 of DV. We show that increased affinity resulted in strong in vitro neutralizing activity to all four serotypes, and that the redesigned antibody has potent antiviral activity in a mouse model of DV challenge. Our findings demonstrate an empirical computational chemistry approach for improving protein-protein docking and engineering antibody affinity, which will help accelerate the development of clinically relevant antibodies.

Quantitative biochemical rationale for differences in transmissibility of 1918 pandemic influenza A viruses.

The human adaptation of influenza A viruses is critically governed by the binding specificity of the viral surface hemagglutinin (HA) to long (chain length) alpha2-6 sialylated glycan (alpha2-6) receptors on the human upper respiratory tissues. A recent study demonstrated that whereas the 1918 H1N1 pandemic virus, A/South Carolina/1/1918 (SC18), with alpha2-6 binding preference transmitted efficiently, a single amino acid mutation on HA resulted in a mixed alpha2-3 sialylated glycan (alpha2-3)/alpha2-6 binding virus (NY18) that transmitted inefficiently. To define the biochemical basis for the observed differences in virus transmission, in this study, we have developed an approach to quantify the multivalent HA-glycan interactions. Analysis of the molecular HA-glycan contacts showed subtle changes resulting from the single amino acid variations between SC18 and NY18. The effect of these changes on glycan binding is amplified by multivalency, resulting in quantitative differences in their long alpha2-6 glycan binding affinities. Furthermore, these differences are also reflected in the markedly distinct binding pattern of SC18 and NY18 HA to the physiological glycans present in human upper respiratory tissues. Thus, the dramatic lower binding affinity of NY18 to long alpha2-6 glycans, as against a mixed alpha2-3/6 binding, correlates with its inefficient transmission. In summary, this study establishes a quantitative biochemical correlate for influenza A virus transmission.

Glycomics: a pathway to a class of new and improved therapeutics.

Complex glycans that are located at the surface of cells, deposited in the extracellular matrix and attached to soluble signalling molecules have a crucial role in the phenotypic expression of cellular genotypes. However, owing to their structural complexity and some redundancy in terms of structures that elicit a function, the therapeutic potential of complex glycans has not been well exploited, with a few notable exceptions. This review outlines recent advances that promise to increase our ability to use complex glycans as therapeutics. Opportunities for the development of further structure-function relationships for these complex molecules are also discussed.

Networks link antigenic and receptor-binding sites of influenza hemagglutinin: mechanistic insight into fitter strain propagation.

Influenza viral passaging through pre-vaccinated mice shows that emergent antigenic site mutations on the viral hemagglutinin (HA) impact host receptor-binding affinity and, therefore, the evolution of fitter influenza strains. To understand this phenomenon, we computed the Significant Interactions Network (SIN) for each residue and mapped the networks of antigenic site residues on a representative H1N1 HA. Specific antigenic site residues are 'linked' to receptor-binding site (RBS) residues via their SIN and mutations within "RBS-linked" antigenic residues can significantly influence receptor-binding affinity by impacting the SIN of key RBS residues. In contrast, other antigenic site residues do not have such "RBS-links" and do not impact receptor-binding affinity upon mutation. Thus, a potential mechanism emerges for how immunologic pressure on RBS-linked antigenic residues can contribute to evolution of fitter influenza strains by modulating the host receptor-binding affinity.

Atomic interaction networks in the core of protein domains and their native folds.

Vastly divergent sequences populate a majority of protein folds. In the quest to identify features that are conserved within protein domains belonging to the same fold, we set out to examine the entire protein universe on a fold-by-fold basis. We report that the atomic interaction network in the solvent-unexposed core of protein domains are fold-conserved, extraordinary sequence divergence notwithstanding. Further, we find that this feature, termed protein core atomic interaction network (or PCAIN) is significantly distinguishable across different folds, thus appearing to be "signature" of a domain's native fold. As part of this study, we computed the PCAINs for 8698 representative protein domains from families across the 1018 known protein folds to construct our seed database and an automated framework was developed for PCAIN-based characterization of the protein fold universe. A test set of randomly selected domains that are not in the seed database was classified with over 97% accuracy, independent of sequence divergence. As an application of this novel fold signature, a PCAIN-based scoring scheme was developed for comparative (homology-based) structure prediction, with 1-2 angstroms (mean 1.61A) C(alpha) RMSD generally observed between computed structures and reference crystal structures. Our results are consistent across the full spectrum of test domains including those from recent CASP experiments and most notably in the 'twilight' and 'midnight' zones wherein <30% and <10% target-template sequence identity prevails (mean twilight RMSD of 1.69A). We further demonstrate the utility of the PCAIN protocol to derive biological insight into protein structure-function relationships, by modeling the structure of the YopM effector novel E3 ligase (NEL) domain from plague-causative bacterium Yersinia Pestis and discussing its implications for host adaptive and innate immune modulation by the pathogen. Considering the several high-throughput, sequence-identity-independent applications demonstrated in this work, we suggest that the PCAIN is a fundamental fold feature that could be a valuable addition to the arsenal of protein modeling and analysis tools.

Glycan topology determines human adaptation of avian H5N1 virus hemagglutinin.

A switch in specificity of avian influenza A viruses' hemagglutinin (HA) from avian-like (alpha2-3 sialylated glycans) to human-like (alpha2-6 sialylated glycans) receptors is believed to be associated with their adaptation to infect humans. We show that a characteristic structural topology--and not the alpha2-6 linkage itself--enables specific binding of HA to alpha2-6 sialylated glycans and that recognition of this topology may be critical for adaptation of HA to bind glycans in the upper respiratory tract of humans. An integrated biochemical, analytical and data mining approach demonstrates that HAs from the human-adapted H1N1 and H3N2 viruses, but not H5N1 (bird flu) viruses, specifically bind to long alpha2-6 sialylated glycans with this topology. This could explain why H5N1 viruses have not yet gained a foothold in the human population. Our findings will enable the development of additional strategies for effective surveillance and potential therapeutic interventions for H5N1 and possibly other influenza A viruses.

Advancing glycomics: implementation strategies at the consortium for functional glycomics.

Glycomics-an integrated approach to study structure-function relationships of complex carbohydrates (or glycans)-is an emerging field in this age of post-genomics. Realizing the importance of glycomics, many large scale research initiatives have been established to generate novel resources and technologies to advance glycomics. These initiatives are generating and cataloging diverse data sets necessitating the development of bioinformatic platforms to acquire, integrate, and disseminate these data sets in a meaningful fashion. With the consortium for functional glycomics (CFG) as the model system, this review discusses databases and the bioinformatics platform developed by this consortium to advance glycomics.

Glycomics: an integrated systems approach to structure-function relationships of glycans.

In comparison with genomics and proteomics, the advancement of glycomics has faced unique challenges in the pursuit of developing analytical and biochemical tools and biological readouts to investigate glycan structure-function relationships. Glycans are more diverse in terms of chemical structure and information density than are DNA and proteins. This diversity arises from glycans' complex nontemplate-based biosynthesis, which involves several enzymes and isoforms of these enzymes. Consequently, glycans are expressed as an 'ensemble' of structures that mediate function. Moreover, unlike protein-protein interactions, which can be generally viewed as 'digital' in regulating function, glycan-protein interactions impinge on biological functions in a more 'analog' fashion that can in turn 'fine-tune' a biological response. This fine-tuning by glycans is achieved through the graded affinity, avidity and multivalency of their interactions. Given the importance of glycomics, this review focuses on areas of technologies and the importance of developing a bioinformatics platform to integrate the diverse datasets generated using the different technologies to allow a systems approach to glycan structure-function relationships.