Changing the world, one paper at a time
Selected Publications (*equally contributed authors, †corresponding author)
The flexible stalk region of sTREM2 modulates its interactions with phospholipids in the brain
D. Saeb, E. Lietzke, D. I. Fuchs, E. C. Aldrich, K. D. Bruce, K. G. Sprenger†, Under Review.
The microglial surface protein Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) plays a critical role in mediating brain homeostasis and inflammatory responses in Alzheimer’s disease (AD). The soluble form of TREM2 (sTREM2) exhibits neuroprotective effects in AD, though the underlying mechanisms remain elusive. Moreover, differences in ligand binding between TREM2 and sTREM2, which have major implications for their roles in AD pathology, remain unexplained. To address these knowledge gaps, we conducted the most computationally intensive molecular dynamics simulations to date of (s)TREM2, exploring their interactions with key damage- and lipoprotein-associated phospholipids and the impact of the AD-risk mutation R47H. Our results demonstrate that the flexible stalk domain of sTREM2 serves as the molecular basis for differential ligand binding between sTREM2 and TREM2, facilitated by its role in stabilizing the Ig-like domain and altering the accessibility of canonical ligand binding sites. We identified a novel ligand binding site on sTREM2, termed the ‘Expanded Surface 2’, which emerges due to competitive binding of the stalk with the Ig-like domain. Additionally, we observed that the stalk domain itself functions as a site for ligand binding, with increased binding in the presence of R47H. This suggests that sTREM2’s neuroprotective role in AD may, at least in part, arise from the stalk domain’s ability to rescue dysfunctional ligand binding caused by AD-risk mutations. Lastly, our findings indicate that R47H-induced dysfunction in membrane-bound TREM2 may result from both diminished ligand binding due to restricted complementarity-determining region 2 loop motions and an impaired ability to differentiate between ligands, proposing a novel mechanism for loss-of-function. In summary, these results provide valuable insights into the role of sTREM2 in AD pathology, laying the groundwork for the design of new therapeutic approaches targeting (s)TREM2 in AD.
Unveiling a novel binding site on P-glycoprotein as a crucial factor in small-molecule discrimination and inhibition by HIV-1 antiretrovirals
D. I. Fuchs, L. D. Serio, S. Balaji, K. G. Sprenger†, Under Review.
Upon initial infection, HIV-1 can rapidly infect the brain and establish a latent reservoir, inducing neuronal damage and/or death and resulting in HIV-Associated Neurocognitive Disorder. Though HIV-1 antiretrovirals suppress viral load, the blood-brain barrier limits drug access to the brain, in large part because of highly expressed efflux proteins like P-glycoprotein (P-gp). While no FDA- approved P-gp inhibitor exists due to side effects, HIV-1 protease inhibitors show promise as partial P-gp inhibitors, potentially enhancing drug delivery to the brain. Herein, we employed docking and molecular dynamics simulations to elucidate differences in the interactions between P-gp and several antiretrovirals, including protease inhibitors, with known inhibitory or substrate- like behaviors towards P-gp. Our results revealed a new binding pocket adjacent to the canonical substrate binding site in the transmembrane domain of P-gp, which we hypothesize is the primary initial site for small-molecule binding. Furthermore, our results reinforce the understanding that both binding energetics and their impacts on protein dynamics are crucial in discerning small molecules as non-substrates, substrates, or inhibitors of P-gp. Specifically, our findings indicate that interactions between P-gp and ARV inhibitors induce bridging of transmembrane domain helices, impeding P-gp conformational changes and contributing, at least in part, to the inhibitory behavior of these ARVs. These discoveries led us to propose a novel mechanism for P-gp efflux of, and inhibition by, small-molecule drugs. Overall, insights gained in this study could serve as a guide to the design of future P-gp-targeting therapeutics for a wide range of pathological conditions and diseases, including HIV-1.
I. R. Campbell, Z. Dong, P. Grandgeorge, A. M. Jimenez, E. R. Rhodes, E. Lee, S. Edmundson, C. Subban, K. G. Sprenger, Eleftheria Roumeli†. In Revision, Available at SSRN: http://dx.doi.org/10.2139/ssrn.4734573.
Unaltered biological matter (biomatter) can be harnessed to fabricate cohesive, sustainable bioplastics. However, controlling the material properties of these bioplastics is challenging as the contributions of different macromolecular building blocks to processability and performance are unknown. To deconvolute the roles of different classes of biomolecules, we developed experimental and computational methods to construct and analyze biomatter analogues comprised of carbohydrates, proteins, and lipids. Spectroscopic analyses suggest that biomatter cohesion depends on protein aggregation occurring during thermomechanical processing. Molecular dynamics simulations confirm that alterations to both protein conformation and inter- and intramolecular hydrogen bonding are likely the primary mechanisms underlying the formation of a cohesive, proteinaceous matrix. Simulations also corroborate experimental measurements highlighting the importance of small molecules by illustrating that the extent of intermolecular hydrogen bonding differs for various molecular species. These conclusions may enable the rational design of next-generation, sustainable biomatter plastics with tunable properties.
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J. G. Faris, C. F. Hayes, A. R. Goncalves, K. G. Sprenger, D. Faissol, B. K. Petersen, M. Landajuela, F. L. da Silva†.
Although Symbolic Optimization (SO) solutions have successfully been used in applications ranging from Neural Architecture Search to Antibody Therapeutics Optimization, current SO algorithms are typically limited to using a single quality measure to search for optimal solutions. However, for many applications, solutions are more naturally described by multiple measures, e.g., a solar panel must be designed to maximize power generation while minimizing heat generation. Herein, we propose Pareto Front Training (PFT), a SO algorithm that searches for token sequences by training a Recurrent Neural Network on the Pareto front of explored solutions. We evaluate PFT in an antibody optimization scenario using a real SARS-CoV-2 viral strain and show that PFT outperforms the baselines in terms of antibody binding quality, stability, and humanness. We hope PFT will inspire a new family of multi-objective SO algorithms and will help SO achieve varied new applications.
E. R. Rhodes, J. G. Faris, B. M. Petersen, K. G. Sprenger†. Frontiers in Immunology, 2023, 14:1120582.
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.
E.Z.L. Zhong-Johnson, Z. Dong, C. Canova, F. Destro, M. Cañellas, M. C. Hoffman, J. Maréchal, T. M. Johnson, G. S. Schlau-Cohen, M. F. Lucas, R. D. Braatz, K. G. Sprenger, C. A. Voigt, A. J. Sinskey†. Journal of Biological Chemistry, 2024, 300(3):105783.
Poly(ethylene terephthalate) (PET) is a major plastic polymer utilized in the single-use and textile industries. The discovery of PET-degrading enzymes (PETases) has led to an increased interest in the biological recycling of PET in addition to mechanical recycling. IsPETase from Ideonella sakaiensis is a candidate catalyst, but little is understood about its structure-function relationships with regards to PET degradation. To understand the effects of mutations on IsPETase productivity, we develop a directed evolution assay to identify mutations beneficial to PET film degradation at 30 °C. IsPETase also displays enzyme concentration-dependent inhibition effects, and surface crowding has been proposed as a causal phenomenon. Based on total internal reflectance fluorescence microscopy and adsorption experiments, IsPETase is likely experiencing crowded conditions on PET films. Molecular dynamics simulations of IsPETase variants reveal a decrease in active site flexibility in free enzymes and reduced probability of productive active site formation in substrate-bound enzymes under crowding. Hence, we develop a surface crowding model to analyze the biochemical effects of three hit mutations (T116P, S238N, S290P) that enhanced ambient temperature activity and/or thermostability. We find that T116P decreases susceptibility to crowding, resulting in higher PET degradation product accumulation despite no change in intrinsic catalytic rate. In conclusion, we show that a macromolecular crowding-based biochemical model can be used to analyze the effects of mutations on properties of PETases and that crowding behavior is a major property to be targeted for enzyme engineering for improved PET degradation.
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.
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B. M. Petersen, S. A. Ulmer, E. R. Rhodes, M. F. Gutierrez-Gonzalez, B. J. Dekosky, K. G. Sprenger†, T. A. Whitehead†. Frontiers in Immunology, 2021, 12:728694.
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.