Changing the world, one paper at a time
Selected Publications (*equally contributed authors, †corresponding author)
D. Oldham, H. Wang, J. Mullen, E. Lietzke, K. G. Sprenger, P. Reigan, R. H. Eckel, K. D. Bruce†. Frontiers in Cardiovascular Medicine, 2022, 9:926631.
Lipoprotein lipase (LPL) plays a crucial role in preventing dyslipidemia by hydrolyzing triglycerides (TGs) in packaged lipoproteins. Since hypertriglyceridemia (HTG) is a major risk factor for cardiovascular disease (CVD), the leading cause of death worldwide, methods that accurately quantify the hydrolytic activity of LPL in clinical and pre-clinical samples are much needed. To date, the methods used to determine LPL activity vary considerably in their approach, in the LPL substrates used, and in the source of LPL activators and inhibitors used to quantify LPL-specific activity, rather than other lipases, e.g., hepatic lipase (HL) or endothelial lipase (EL) activity. Here, we describe methods recently optimized in our laboratory, using a synthetic ApoC-II peptide to activate LPL, and an n-terminal Angiopoietin-Like 4 fragment (nAngptl4) to inhibit LPL, presenting a cost-effective and reproducible method to measure LPL activity in human post-heparin plasma (PHP) and in LPL-enriched heparin released (HR) fractions from LPL secreting cells. We also describe a modified version of the triolein-based assay using human serum as a source of endogenous activators and inhibitors and to determine the relative abundance of circulating factors that regulate LPL activity. Finally, we describe how an ApoC-II peptide and nAngptl4 can be applied to high-throughput measurements of LPL activity using the EnzChek™ fluorescent TG analog substrate with PHP, bovine LPL, and HR LPL enriched fractions. In summary, this manuscript assesses the current methods of measuring LPL activity and makes new recommendations for measuring LPL-mediated hydrolysis in pre-clinical and clinical samples.
J. G. Faris, D. Orbidan, C. Wells, B. K. Petersen†, K. G. Sprenger†. Frontiers in Immunology, 2022, 13:6448.
Highly mutable infectious disease pathogens (hm-IDPs) such as HIV and influenza evolve faster than the human immune system can contain them, allowing them to circumvent traditional vaccination approaches and causing over one million deaths annually. Agent-based models can be used to simulate the complex interactions that occur between immune cells and hm-IDP-like proteins (antigens) during affinity maturation—the process by which antibodies evolve. Compared to existing experimental approaches, agent-based models offer a safe, low-cost, and rapid route to study the immune response to vaccines spanning a wide range of design variables. However, the highly stochastic nature of affinity maturation and vast sequence space of hm-IDPs render brute force searches intractable for exploring all pertinent vaccine design variables and the subset of immunization protocols encompassed therein. To address this challenge, we employed deep reinforcement learning to drive a recently developed agent-based model of affinity maturation to focus sampling on immunization protocols with greater potential to improve the chosen metrics of protection, namely the broadly neutralizing antibody (bnAb) titers or fraction of bnAbs produced. Using this approach, we were able to coarse-grain a wide range of vaccine design variables and explore the relevant design space. Our work offers new testable insights into how vaccines should be formulated to maximize protective immune responses to hm-IDPs and how they can be minimally tailored to account for major sources of heterogeneity in human immune responses and various socioeconomic factors. Our results indicate that the first 3 to 5 immunizations, depending on the metric of protection, should be specially tailored to achieve a robust protective immune response, but that beyond this point further immunizations require only subtle changes in formulation to sustain a durable bnAb response.
S. Conti, V. Ovchinnikov, J. G. Faris, A. K. Chakraborty, M. Karplus†, K. G. Sprenger†. PLOS Computational Biology, 2022, 18(4):e1009391.
The design of vaccines against highly mutable pathogens, such as HIV and influenza, requires a detailed understanding of how the adaptive immune system responds to encountering multiple variant antigens (Ags). Here, we describe a multiscale model of B cell receptor (BCR) affinity maturation that employs actual BCR nucleotide sequences and treats BCR/Ag interactions in atomistic detail. We apply the model to simulate the maturation of a broadly neutralizing Ab (bnAb) against HIV. Starting from a germline precursor sequence of the VRC01 anti-HIV Ab, we simulate BCR evolution in response to different vaccination protocols and different Ags, which were previously designed by us. The simulation results provide qualitative guidelines for future vaccine design and reveal unique insights into bnAb evolution against the CD4 binding site of HIV. Our model makes possible direct comparisons of simulated BCR populations with results of deep sequencing data, which will be explored in future applications.
With the flood of engineered antibodies, there is a heightened need to elucidate the structural features of antibodies that contribute to specificity, stability, and breadth. While antibody flexibility and interface angle have begun to be explored, design rules have yet to emerge, as their impact on the metrics above remains unclear. Furthermore, the purpose of framework mutations in mature antibodies is highly convoluted. To this end, a case study utilizing molecular dynamics (MD) simulations was undertaken to determine the impact framework mutations have on the VH-VL interface. We further sought to elucidate the governing mechanisms by which changes in the VH-VL interface angle impact structural elements of mature antibodies by looking at root mean squared deviations (RMSD), VH-VL interface angle, root mean squared fluctuations (RMSF) and solvent exposed surface area (SASA). Overall, our results suggest framework mutations can significantly shift the distribution of VH-VL interface angles which leads to increased flexibility by changing which portions of the antibody have a greater solvent exposed surface area. The data presented herein highlights the need to reject the dogma of static antibody crystal structures and exemplifies the dynamic nature of these proteins in solution. Findings from this work further demonstrate the importance of framework mutations on antibody structure and lay the foundation for establishing design principles to create antibodies with increased specificity, stability, and breadth.
Monoclonal antibodies (mAbs) are an important class of therapeutics used to treat cancer, inflammation, and infectious diseases. Identifying highly developable mAb sequences in silico could greatly reduce the time and cost required for therapeutic mAb development. Here, we present position-specific scoring matrices (PSSMs) for antibody framework mutations developed using baseline human antibody repertoire sequences. Our analysis shows that human antibody repertoire-based PSSMs are consistent across individuals and demonstrate high correlations between related germlines. We show that mutations in existing therapeutic antibodies can be accurately predicted solely from baseline human antibody sequence data. We find that mAbs developed using humanized mice had more human-like FR mutations than mAbs originally developed by hybridoma technology. A quantitative assessment of entire framework regions of therapeutic antibodies revealed that there may be potential for improving the properties of existing therapeutic antibodies by incorporating additional mutations of high frequency in baseline human antibody repertoires. In addition, high frequency mutations in baseline human antibody repertoires were predicted in silico to reduce immunogenicity in therapeutic mAbs due to the removal of T cell epitopes. Several therapeutic mAbs were identified to have common, universally high-scoring framework mutations, and molecular dynamics simulations revealed the mechanistic basis for the evolutionary selection of these mutations. Our results suggest that baseline human antibody repertoires may be useful as predictive tools to guide mAb development in the future.
K. G. Sprenger, S. J. Roeters, S. Mauri, R. Mertig, Y. Nishiyama, J. Pfaendtner†, T. Weidner†, Journal of Physical Chemistry Letters, 2021, 12(43):10684.
The conversion of biomass into green fuels and chemicals is of great societal interest. Engineers have been designing new cellulase enzymes for the breakdown of otherwise insoluble cellulose materials. A barrier to the rational design of new enzymes has been our lack of a molecular picture of how cellulase binding occurs. A critical factor is the attachment via the enzyme’s carbohydrate binding module (CBM). To elucidate the structural and mechanistic details of cellulase adsorption, we have combined experimental data from sum frequency generation spectroscopy with molecular dynamics simulations to probe the equilibrium structure and surface alignment of a 14-residue peptide mimicking the CBM. The data show that binding is driven by hydrogen bonding and that tyrosine side chains within the CBM align the cellulase with the registry of the cellulose surface. Such an alignment is favorable for the translocation and effective cellulose breakdown and is therefore likely an important parameter for the design of novel enzymes.
I. M Francino-Urdaniz, P. J. Steiner, M. B. Kirby, F. Zhao, C. M. Haas, S. Barman, E. R. Rhodes, A. C. Leonard, L. Peng, K. G. Sprenger, J. G. Jardine, T. A. Whitehead†. Cell Reports, 2021, 36(9):109627.
The potential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) escape mutants is a threat to the efficacy of existing vaccines and neutralizing antibody (nAb) therapies. An understanding of the antibody/S escape mutation landscape is urgently needed to preemptively address this threat. Here we describe a rapid method to identify escape mutants for nAbs targeting the S receptor binding site. We identified escape mutants for five nAbs, including three from the public germline class VH3-53 elicited by natural coronavirus disease 2019 (COVID-19) infection. Escape mutations predominantly mapped to the periphery of the angiotensin-converting enzyme 2 (ACE2) recognition site on the RBD with K417, D420, Y421, F486, and Q493 as notable hotspots. We provide libraries, methods, and software as an openly available community resource to accelerate new therapeutic strategies against SARS-CoV-2.