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Computational Pharmacy (Lill)

Projects & Collaborations

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Design, synthesis, and evaluation of collectin-11 inhibitors for biomedical research and therapeutic development

Research Project  | 2 Project Members

The complement system is an important innate immune pathway that plays critical roles as a first line of defense against invading organisms, damaged tissues, and the maintenance of healthy cell populations. However, its uncontrolled activation or insufficient regulation contributes to diverse clinical conditions affecting millions of patients worldwide, such as ischemia-reperfusion injury (IRI; during myocardial infarction, stroke, or organ transplantation), age-related macular degeneration, and several neurodegenerative disorders. Therefore, complement has steadily moved into the focus of drug development efforts. Whereas most therapeutic strategies focus on blocking individual effector functions, preventing the initial activation of complement could offer distinct advantages.


Collectin-11 (CL-11), a soluble C-type lectin that recognizes mannose (Man)- and fucose (Fuc)-containing glycans at the surface of pathogens or damaged cells is one such target of interest. CL-11 was recently implicated in the pathology of both acute and chronic kidney injury. In IRI-mediated acute kidney injury associated with transplantation, the expression of CL-11 is significantly elevated and co-localizes with Fuc-rich cell-surface glycans to initiate complement-dependent destruction of the kidney and loss of organ function. In chronic kidney injury, CL-11 binds to Man-based structures and acts as a leukocyte chemoattractant and enhancer of fibroblast proliferation, resulting in renal fibrosis. CL-11 also activates complement in retinal stem cells, restricting the use of stem cell transplantation for patients with age-related macular degeneration, the leading cause of blindness in the developed world. In both cell and animal models, these harmful immune responses were suppressed: (i) in CL-11 knockout mice; (ii) through enzymatic cleavage of cell-surface Man/Fuc; or (iii) by treatment with high concentrations of soluble Man/Fuc. While high-dose Fuc therapy largely prevented complement-mediated tissue damage in mice, this treatment option has real-world limitations concerning specificity, dose requirements, and pharmacokinetic (PK) profile. Therefore, the availability of CL-11-specific inhibitors with improved efficacy and drug-like properties is of therapeutic interest and would provide an important tool for delineating CL-11’s (patho)physiological role in disease.


This joint proposal between lead investigators Dr. Rachel Hevey and Prof. Martin Smieško combines their strong and synergistic expertises in computational modeling, glycomimetic drug design, chemical synthesis, and functional evaluation, to develop a novel class of specific CL-11 inhibitors. Based on the emerging role of CL-11 in renal disease and IRI, we envision that the development of carbohydrate-based, selective inhibitors for CL-11 could eventually provide a promising therapy for preventing or reducing tissue damage. To our knowledge, there are no active development programs for glycomimetic inhibitors of CL-11, and therefore the development of molecules with high affinity, selectivity, and suitable PK properties constitutes an innovative project with a potential for real-world impact.

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Mucin O-glycans as regulators of pathogen virulence: glycan synthesis, mechanistic investigation, and therapeutic potential

Research Project  | 3 Project Members

Current estimates suggest that nearly 1.3 million lives are lost annually to antibiotic-resistant infections, with this number rising to 10 million by 2050. Antibiotic resistance results in failed treatment, prolonged hospital stays, and increases the financial burden placed on medical systems and society. Therefore, novel approaches to antibiotic use are urgently needed.


Recently, a novel class of anti-virulence compounds based on mucosal glycans were discovered, which are active against a range of pathogenic cross-kingdom species, including fungal, Gram-positive, and Gram-negative bacterial pathogens. These mucin O-glycans do not directly affect pathogen survival but instead repress virulence, thereby increasing susceptibility to host immune pathways and indirectly reducing infection load. By exerting their activity through virulence attenuation instead of broad-spectrum elimination, these compounds can promote the reestablishment of a healthy microbiome and are expected to be at a reduced risk of developing drug resistance. Although this exciting new class of compounds has demonstrated broad activity, the mechanism(s) by which these glycans act has not yet been elucidated. By identifying their discrete molecular target(s), specific binding interactions can be analyzed and more drug-like glycomimetic therapies can be developed.


Mucins contain hundreds of different O-glycans which are not commercially available, cannot be purified as single structures from native sources, and are not readily amenable to solid-phase synthesis. Therefore, in ongoing work we have been developing a platform to generate individual mucin glycan structures and evaluate their ability to regulate pathogen virulence. In the further development and application of this platform, specific aims of the current proposal are focused on: (i) the recombinant expression of predicted virulence-regulating proteins; (ii) the investigation of mucin glycan binding to virulence-regulating proteins through parallel techniques; and (iii) further expansion of the mucin glycan library and the development of next-generation glycomimetic molecules.


The proposed project will improve our understanding of mucus–pathogen relationships, provide insights into virulence-attenuating mechanism(s), and provide the next steps to development of an innovative therapeutic.

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Advancing Protein-Ligand Co-Folding through Physics-Informed Machine Learning

Research Project  | 2 Project Members

Current machine learning approaches for protein-ligand complexes often depend on limited and potentially biased data. Recently, it has been demonstrated that integrating physical aspects, such as hydration thermodynamics, can result in more robust models while requiring only a small portion of the available protein-ligand complex data. This strategy has been validated for rigid docking, but recent advancements in generative modeling have paved the way for extending this approach to more versatile forms, accommodating flexibility and blind docking. We aim to develop a robust protein-ligand cofolding framework by integrating physics-based elements with cutting-edge techniques in generative modeling and reinforcement learning, such as diffusion and GFlowNets. The main objectives are: (1) creating a cascading resolution model that can generate ensembles of protein-ligand complex structures by modeling their probability distributions. This hybrid data and physics-driven model leverages informative priors and physics-based projection; (2) enhancing robustness against skewed data by incorporating physical principles, particularly hydration data and thermodynamics, which can be predicted efficiently using machine learning; (3) exploring the generative potential of GFlowNets and their explorative nature that fosters the sampling of unvisited states. The integration of accurate physics into generative modeling is expected to open numerous opportunities in structure-based drug design.

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Specificity, selectivity and pharmacokinetics of compstatin: a comprehensive multidisciplinary analysis

Research Project  | 6 Project Members

The complement system plays a major role in innate immunity as it confers immune surveillance and first-line defense against non- or altered-self entities such as microbes or apoptotic cells. Yet, misguided complement activation may trigger or contribute to severe clinical conditions or complications, including autoimmune, hemolytic, inflammatory and age-related disorders and transplant rejection (PMID) [1]. Owing to its cascade organization, involving ~50 plasma proteins, receptors and enzymes, complement provides multiple points for novel pharmacological intervention [2]. However, few complement-targeted drugs have reached the clinic, and the available options primarily target peripheral steps in cascade initiation or effector generation. For many acute-phase or multifactorial complement disorders, blocking the activation of the central complement component C3 is considered important [3]. Derivatives of compstatin, a peptidic inhibitor of C3 activation [4], are the most advanced compounds in this class, with two candidate drugs being evaluated in clinical trials. However, its narrow species-specificity for primate C3 currently restricts a broader exploration of potential benefits of C3 inhibition in various established animal models of complement disorders. Furthermore, despite considerable progress in structure optimization, some pharmacokinetic and physicochemical properties of the compstatin class remain to be improved to fully unleash its unique therapeutic potential. The main objective of this project is to understand target binding and complement inhibition by compstatin in the human system at the atomic level and identify key determinants of its narrow species specificity. We will utilize this knowledge for designing compstatin analogs that recognize non-primate C3 and, for example, inhibit mouse, rat or pig complement. Simultaneously, we will assess compstatin's target selectivity for C3 over the orthologous C4 and C5 proteins and explore options for achieving C4-, C5- or pan-specific inhibitors for research or clinical applications. Finally, we will analyze and optimize the pharmacokinetic properties of compstatin with special emphasis on solubility and bioavailability. The proposed rationalization and optimization efforts will be driven by well-established in silico simulation techniques such as molecular dynamics simulations, free energy methods, homology modeling, and post-MD analyses, supported by novel approaches based on deep learning (Prof. Markus Lill, Computational Pharmacy). Thanks to project collaborations with strong experimental groups, in silico findings will be experimentally verified by employing peptide synthesis and characterization, chemical modification and labeling, and target binding and functional assays in vitro (Prof. Daniel Ricklin, Molecular Pharmacy) as well as pre-clinical assessments of cellular permeability in vitro and in vivo (Prof. Henriette Meyer zu Schwabedissen, Biopharmacy; all at University of Basel). Our studies are expected to extend preclinical evaluation options of compstatin-based drugs in animal models and enhance their pharmacokinetic profile, thereby facilitating clinical development of this important inhibitor class. Selectivity studies with C4/C5 may provide insight into complement activation and potentially reveal novel inhibitors. Finally, atomic level insight into the structure-activity/property relationships of cyclic peptides may be used for the design of this compound type in general. [1] Ricklin, D.; Reis, E. S.; Lambris, J. D. Complement in Disease: A Defence System Turning Offensive. Nat. Rev. Nephrol. 2016, 12 (7), 383-401. https://doi.org/10.1038/nrneph.2016.70. [2] Mastellos, D.C., Ricklin, D. & Lambris, J.D. Clinical promise of next-generation complement therapeutics. Nat Rev Drug Discov 18, 707-729 (2019). https://doi.org/10.1038/s41573-019-0031-6 [3] Mastellos, D. C.; Reis, E. S.; Ricklin, D.; Smith, R. J.; Lambris, J. D. Complement C3-Targeted Therapy: Replacing Long-Held Assertions with Evidence-Based Discovery. Trends Immunol. 2017, 38 (6), 383-394. https://doi.org/10.1016/j.it.2017.03.003. [4] Mastellos, D. C.; Yancopoulou, D.; Kokkinos, P.; Huber-Lang, M.; Hajishengallis, G.; Biglarnia, A. R.; Lupu, F.; Nilsson, B.; Risitano, A. M.; Ricklin, D.; Lambris, J. D. Compstatin: A C3-Targeted Complement Inhibitor Reaching Its Prime for Bedside Intervention. Eur. J. Clin. Invest. 2015, 45 (4), 423-440. https://doi.org/10.1111/eci.12419.