AbPredict 2: a server for accurate and unstrained structure prediction of antibody variable domains

Methods for antibody structure prediction rely on sequence homology to experimentally determined structures. Resulting models may be accurate but are often stereochemically strained, limiting their usefulness in modeling and design workflows. We present the AbPredict 2 web-server, which instead of using sequence homology, conducts a Monte Carlo-based search for low-energy combinations of backbone conformations to yield accurate and unstrained antibody structures.We introduce several important improvements over the previous AbPredict implementation: (i) backbones and sidechains are now modeled using ideal bond lengths and angles, substantially reducing stereochemical strain, (ii) sampling of the rigid-body orientation at the light-heavy chain interface is improved, increasing model accuracy and (iii) runtime is reduced 20-fold without compromising accuracy, enabling the implementation of AbPredict 2 as a fully automated web-server (http://abpredict.weizmann.ac.il). Accurate and unstrained antibody model structures may in some cases obviate the need for experimental structures in antibody optimization workflows

Definition of immunoglobulin germline genes by next generation sequencing for studies of antigen-specific b cell responses

Immunoglobulins play a critical role in the adaptive immune system, existing as cell surface-expressed B cell receptors and secreted antibodies. Circulating antibodies are the main correlate of protective immunity for most vaccines. An improved understanding of the germline genes that rearrange to encode the vast repertoire of antibodies is therefore of central interest. Despite this, current databases of immunoglobulin germline gene variation are incomplete, both for humans and research animal models, limiting studies of antigen-specific B cell responses. In Paper I, we developed a computational tool, IgDiscover, which infers germline immunoglobulin V alleles from the repertoire of expressed antibodies in a given individual. We validated IgDiscover for the identification of human, mouse and rhesus macaque IGHV alleles and described novel IGHV alleles in all three species. Our results highlighted a high degree of inter-individual allelic diversity in rhesus macaques. In Paper II, we optimized and compared two major immunoglobulin library production methods based on 5′RACE and 5′multiplex PCR, respectively. We observed that, despite 5′RACE being unbiased in terms of amplification and having the advantage of not requiring 5′ end IGHV genomic information, current limitations on high-throughput sequence read length resulted in the 5′ multiplex method delivering a higher quality output due to its shorter amplicon size. In Paper III, we inferred germline immunoglobulin alleles in 45 macaques from four sub-populations of the two most common species used in biomedical research, rhesus and cynomolgus macaques. We confirmed and extended our observations concerning high inter-individual diversity, demonstrating that it was highest among Indonesian cynomolgus macaques and lowest among Mauritian cynomolgus macaques in the sub-populations studied. We compiled comprehensive IGHV, D and J allele databases and used several methods to independently validate novel alleles. In conclusion, the work presented in this thesis establishes a road map to generate individualized immunoglobulin germline gene databases from diverse species, even if genomic immunoglobulin loci information is limited. This thesis also examines the advantages and disadvantages of commonly used next generation sequencing library preparation methods. Finally, it reports novel inferred immunoglobulin alleles in humans and macaques and illustrates a high degree of inter-individual immunoglobulin allelic diversity in primates, underlining the utility of generating individualized immunoglobulin databases for studies of immune repertoires