Prof. Dr. Markus A. Lill Department of Pharmaceutical Sciences Profiles & Affiliations OverviewResearch Publications Projects & Collaborations Projects & Collaborations OverviewResearch Publications Projects & Collaborations Profiles & Affiliations Projects & Collaborations 7 foundShow per page10 10 20 50 Helostasin - A Novel Complement Pathway Modulator Research Project | 4 Project MembersImported from Grants Tool 4702675 Advancing Protein-Ligand Co-Folding through Physics-Informed Machine Learning Research Project | 2 Project MembersCurrent 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. Design of antimicrobial peptides through active learning Research Project | 2 Project MembersNo Description available Development of brain-penetrant MNK inhibitors Research Project | 3 Project MembersMAP-kinase interacting kinases (MNKs) are a novel target for the treatment of autism spectrum disorders. We aim to develop a brain-penetrant, small molecule inhibitor that selectively targets MNKs and alleviates the core symptoms of autism. Inspired by Nature: New Therapeutic Modalities to Control Adverse Complement & Coagulation Reactions in Immune and Thrombo-Inflammatory Conditions Research Project | 3 Project MembersThe therapeutic strategy is based on the biological features of a parasitic protein, which we have termed 'leech inhibitor of host defence responses' (LIHDR). Its activity against initiating SP of complement and coagulation (incl. C1s, MASP2, FXIIa) renders LIHDR attractive for early, upstream inhibition of adverse defence responses. Despite its structural complexity, we managed to produce highly active LIHDR analogues in prokaryotic expression systems, which greatly facilitates structure-activity-relationship (SAR) studies and protein engineering. In in vitro studies, recombinant LIHDR analogues showed favourable efficacy profiles. Using experimental and computational methods, we are elucidating molecular determinants of target activity/selectivity/specificity and already generated LIHDR analogues with shifted SP selectivity, indicating a potential to produce inhibitors with broad or narrow SP-inhibitory activity. Our extensive SAR is also expected to identify common hot-spot areas on host SP that may be targeted by antibodies or other entities. Testing multi- and monospecific analogues of the same inhibitor in relevant disease models may provide an ideal platform for evaluating therapeutic strategies in defence-triggered disorders. Specificity, selectivity and pharmacokinetics of compstatin: a comprehensive multidisciplinary analysis Research Project | 6 Project MembersThe 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. Instantaneous Flexible and Coarse-Grained Docking based on Novel Deep Neural Network Approaches Research Project | 4 Project MembersThe goal of this proposal is to develop novel computational methods for flexible docking combining modern deep neural network approaches with detailed physicochemical models of protein-ligand complexes with consideration of hydration phenomena. The methodology includes completely novel concepts such as the prediction of intermolecular distance matrices based on graph-convolutional methods, efficient coarse-grained docking to implicitly model protein flexibility, and on-the-spot prediction of peptide configurations binding to protein surfaces. The methodology will be retrospectively validated and prospectively applied together with our experimental collaborator. The developed concepts have broad impact to drug discovery, for example for the design of peptides as protein-protein interaction modulators or for rapid structure-based vaccine design, a task of crucial need in context of the current COVID-19 pandemic. 1 1 OverviewResearch Publications Projects & Collaborations
Projects & Collaborations 7 foundShow per page10 10 20 50 Helostasin - A Novel Complement Pathway Modulator Research Project | 4 Project MembersImported from Grants Tool 4702675 Advancing Protein-Ligand Co-Folding through Physics-Informed Machine Learning Research Project | 2 Project MembersCurrent 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. Design of antimicrobial peptides through active learning Research Project | 2 Project MembersNo Description available Development of brain-penetrant MNK inhibitors Research Project | 3 Project MembersMAP-kinase interacting kinases (MNKs) are a novel target for the treatment of autism spectrum disorders. We aim to develop a brain-penetrant, small molecule inhibitor that selectively targets MNKs and alleviates the core symptoms of autism. Inspired by Nature: New Therapeutic Modalities to Control Adverse Complement & Coagulation Reactions in Immune and Thrombo-Inflammatory Conditions Research Project | 3 Project MembersThe therapeutic strategy is based on the biological features of a parasitic protein, which we have termed 'leech inhibitor of host defence responses' (LIHDR). Its activity against initiating SP of complement and coagulation (incl. C1s, MASP2, FXIIa) renders LIHDR attractive for early, upstream inhibition of adverse defence responses. Despite its structural complexity, we managed to produce highly active LIHDR analogues in prokaryotic expression systems, which greatly facilitates structure-activity-relationship (SAR) studies and protein engineering. In in vitro studies, recombinant LIHDR analogues showed favourable efficacy profiles. Using experimental and computational methods, we are elucidating molecular determinants of target activity/selectivity/specificity and already generated LIHDR analogues with shifted SP selectivity, indicating a potential to produce inhibitors with broad or narrow SP-inhibitory activity. Our extensive SAR is also expected to identify common hot-spot areas on host SP that may be targeted by antibodies or other entities. Testing multi- and monospecific analogues of the same inhibitor in relevant disease models may provide an ideal platform for evaluating therapeutic strategies in defence-triggered disorders. Specificity, selectivity and pharmacokinetics of compstatin: a comprehensive multidisciplinary analysis Research Project | 6 Project MembersThe 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. Instantaneous Flexible and Coarse-Grained Docking based on Novel Deep Neural Network Approaches Research Project | 4 Project MembersThe goal of this proposal is to develop novel computational methods for flexible docking combining modern deep neural network approaches with detailed physicochemical models of protein-ligand complexes with consideration of hydration phenomena. The methodology includes completely novel concepts such as the prediction of intermolecular distance matrices based on graph-convolutional methods, efficient coarse-grained docking to implicitly model protein flexibility, and on-the-spot prediction of peptide configurations binding to protein surfaces. The methodology will be retrospectively validated and prospectively applied together with our experimental collaborator. The developed concepts have broad impact to drug discovery, for example for the design of peptides as protein-protein interaction modulators or for rapid structure-based vaccine design, a task of crucial need in context of the current COVID-19 pandemic. 1 1
Helostasin - A Novel Complement Pathway Modulator Research Project | 4 Project MembersImported from Grants Tool 4702675
Advancing Protein-Ligand Co-Folding through Physics-Informed Machine Learning Research Project | 2 Project MembersCurrent 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.
Design of antimicrobial peptides through active learning Research Project | 2 Project MembersNo Description available
Development of brain-penetrant MNK inhibitors Research Project | 3 Project MembersMAP-kinase interacting kinases (MNKs) are a novel target for the treatment of autism spectrum disorders. We aim to develop a brain-penetrant, small molecule inhibitor that selectively targets MNKs and alleviates the core symptoms of autism.
Inspired by Nature: New Therapeutic Modalities to Control Adverse Complement & Coagulation Reactions in Immune and Thrombo-Inflammatory Conditions Research Project | 3 Project MembersThe therapeutic strategy is based on the biological features of a parasitic protein, which we have termed 'leech inhibitor of host defence responses' (LIHDR). Its activity against initiating SP of complement and coagulation (incl. C1s, MASP2, FXIIa) renders LIHDR attractive for early, upstream inhibition of adverse defence responses. Despite its structural complexity, we managed to produce highly active LIHDR analogues in prokaryotic expression systems, which greatly facilitates structure-activity-relationship (SAR) studies and protein engineering. In in vitro studies, recombinant LIHDR analogues showed favourable efficacy profiles. Using experimental and computational methods, we are elucidating molecular determinants of target activity/selectivity/specificity and already generated LIHDR analogues with shifted SP selectivity, indicating a potential to produce inhibitors with broad or narrow SP-inhibitory activity. Our extensive SAR is also expected to identify common hot-spot areas on host SP that may be targeted by antibodies or other entities. Testing multi- and monospecific analogues of the same inhibitor in relevant disease models may provide an ideal platform for evaluating therapeutic strategies in defence-triggered disorders.
Specificity, selectivity and pharmacokinetics of compstatin: a comprehensive multidisciplinary analysis Research Project | 6 Project MembersThe 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.
Instantaneous Flexible and Coarse-Grained Docking based on Novel Deep Neural Network Approaches Research Project | 4 Project MembersThe goal of this proposal is to develop novel computational methods for flexible docking combining modern deep neural network approaches with detailed physicochemical models of protein-ligand complexes with consideration of hydration phenomena. The methodology includes completely novel concepts such as the prediction of intermolecular distance matrices based on graph-convolutional methods, efficient coarse-grained docking to implicitly model protein flexibility, and on-the-spot prediction of peptide configurations binding to protein surfaces. The methodology will be retrospectively validated and prospectively applied together with our experimental collaborator. The developed concepts have broad impact to drug discovery, for example for the design of peptides as protein-protein interaction modulators or for rapid structure-based vaccine design, a task of crucial need in context of the current COVID-19 pandemic.
