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Incorporating Deep Learning and Molecular Dynamics to Identify Immune Drug Modulators

초록/요약

Cells dynamically respond to diverse stimuli, both endogenous and exogenous, with the potential to cause tissue damage. This prompts the innate immune system to activate toll-like receptors (TLRs), crucial for detecting danger signals. TLR activation initiates signaling pathways involving cytosolic NOD-like receptors, ultimately leading to inflammasome formation. This cascade results in pyroptotic cell death mediated by caspase-1 and gasdermin-D, releasing pro-inflammatory cytokines IL-1β and IL-18. A comprehensive molecular understanding of these processes is imperative for identifying effective drug modulators to counter immune responses. TLRs, fundamental in host defense, extend their sentinel function to peripheral tissues. TLR signaling pathways interact not only with pathogens but also with the complement system and NLRP3 inflammasome, influencing immune and inflammatory responses. This intricate interplay is critical for maintaining the delicate balance of immune processes in both physiological and pathological contexts. Anomalies in NLRP3 inflammasome responses significantly contribute to various health challenges, including atherosclerosis, diabetes, cardiovascular diseases, and neurodegenerative disorders. Moreover, TLR signaling emerges as a pivotal factor in autoimmune conditions such as systemic lupus erythematosus, experimental autoimmune encephalitis, rheumatoid arthritis, type 1 diabetes mellitus, and neurodegenerative disorders. The versatile involvement of TLRs in immune regulation, their connections with the complement system and NLRP3 inflammasome, and their impact on various health conditions emphasize their pivotal role in orchestrating immune responses and maintaining a delicate balance between protective and pathological outcomes. In response to these insights, recent years have witnessed a growing interest in targeting the NLRP3 inflammasome and modulating its immune response for the development of anti-inflammatory drugs. This approach holds promise, considering the association of NLRP3 inflammasome dysfunction with diverse health issues. Moreover, anomalies in NLRP3 inflammasome responses are implicated in various health issues, from atherosclerosis to neurodegenerative diseases. Targeting NLRP3 for immune modulation is a promising strategy for anti-inflammatory drug development. Our computational approach integrates deep learning, utilizing an LSTM-based neural network in a recurrent neural network (RNN) to design peptides targeting NLRP3 inflammasomes. Molecular dynamics simulations guide peptide selection based on circular dichroism spectra and physicochemical features. Experimentally tested sequences demonstrate 60% inhibition of NLRP3-mediated IL-1β and IL-18, with one peptide selectively inhibiting NLRP3-mediated IL-1β, showcasing the potential of deep learning and molecular dynamics in NLRP3 inhibitor discovery. Simultaneously, Tomaralimab, a humanized monoclonal antibody against TLR2, addresses inflammation stemming from innate immune activation. A homology model, combined with deep learning, elucidates Tomaralimab's behavior and predicts a novel epitope. A geometric deep learning algorithm assesses Tomaralimab's binding affinity changes upon mutation, uncovering significant differences in epitope-mutated alanine substitutions. This molecular understanding informs the design of structure-based mimics or bispecific antibodies inhibiting both lipopeptide-binding and TLR2 dimerization, underscoring the potential of this approach in anti-inflammatory drug development. Expanding the scope to TLR signaling, pivotal in autoimmune and neurodegenerative diseases, our computational-driven approach investigates the TLR4 antibody-binding epitope. In silico mutagenesis identifies key residues affecting antibody interaction and TLR4 structural integrity. A predicted novel epitope at the TLR4-MD2 interface opens avenues for therapeutic antibodies and small molecule exploration. This comprehensive technique offers detailed insights into antibody–antigen interactions, providing valuable information for the development of monoclonal antibodies. Coupling computational techniques with experimental methods promises to expedite the rational design and development of antibody therapeutics and other immune-modulating drugs, revolutionizing our approach to immune system modulation. In conclusion, the synergistic integration of deep learning and molecular dynamics emerges as a powerful and transformative approach, expediting the identification and design of immune drug modulators. This innovative methodology, demonstrated in our investigations targeting NLRP3 inflammasomes and TLR signaling, promises novel solutions for targeted interventions in complex immunological pathways. As we advance towards precision medicine, the amalgamation of computational techniques with experimental methods not only accelerates drug discovery but also revolutionizes our understanding and strategic development of immune-modulating therapeutics.

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목차

CHAPTER 1 1
Introduction 1
1.1. Unveiling immunological dynamics and inflammation 2
1.2. Molecular architecture and activation mechanisms of the NLRP3 inflammasome 4
1.3. Unveiling Toll like receptors in innate immunity: From recognition to therapeutic prospects 6
1.4.Interplay of Toll like receptors, NLRP3 inflammasome, and complement system in inflammation 7
1.5. Leveraging computational approaches for immunomodulatory strategies 8
CHAPTER 2 10
Methods 10
2.1. Data acquisition and preparation for NLRP3 inflammasome peptides 11
2.2. Training recurrent neural networks for peptide generation 11
2.3. Computational evaluation of peptide binding to NLRP3 and ASC 14
2.4. Peptide selection, fusion strategy, and in vitro screening 14
2.5. Cell culturing, proliferation, and viability assessment 15
2.6. In vitro inflammasome activation assay: experimental setup and procedures 15
2.7. Quantification of proteins and western blot analysis 16
2.8. Quantitative assay for assessing cell death through lactate dehydrogenase release 17
2.9. Statistical analysis 17
2.10. Antibody sequence retrieval, homology modeling, and CDR assignment 17
2.11. Molecular docking, clustering, and molecular dynamics simulations in antigen-antibody interaction analysis 18
2.12. Computational analysis of binding energy and decomposition in TLR2/TLR4-mAb complexes through mmpbsa methodology 19
2.13. Deep learning-based prediction of binding affinity changes induced by mutations 20
2.14. Construction and analysis of residue interaction network (RIN) for protein structure dyn-amics 20
2.15. PCA and free-energy landscape analysis of TLR4 and variants 21
CHAPTER 3 22
Results and Discussion (1) 22
CHAPTER 4 39
Results and Discussion (2) 39
CHAPTER 5 59
Results and Discussion (3) 59
CHAPTER 6 78
6.1. References 78
Appendix 91

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