Decoding Allosteric Inhibition in MALT1: The Hidden Role of Conformational Plasticity in Metastable States via Biased MD and Deep Learning
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články
PubMed
41557786
PubMed Central
PMC12862816
DOI
10.1021/acs.jpcb.5c07665
Knihovny.cz E-zdroje
- MeSH
- alosterická regulace účinky léků MeSH
- deep learning * MeSH
- konformace proteinů MeSH
- lidé MeSH
- protein MALT1 * chemie antagonisté a inhibitory metabolismus MeSH
- simulace molekulární dynamiky * MeSH
- simulace molekulového dockingu MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- MALT1 protein, human MeSH Prohlížeč
- protein MALT1 * MeSH
The mucosa-associated lymphoid tissue lymphoma-translocation protein 1 (MALT1) is a key protein in the adaptive immune response system in humans. This protein is widely expressed in the human body and is related to nuclear factor-κB (NF-κB) signaling activation in response to T-cell receptors. Due to this, MALT1 is key in the regulation of inflammatory events in a variety of tissues, where its dysregulation is associated with several types of cancer, especially hematological cancers. In this sense, its relevance makes MALT1 a valuable target to treat many diseases, drawing the attention of many researchers with the aim of proposing new MALT1 inhibitors. However, there is a lack of literature describing its complex dynamical behavior and allosteric inhibition, which considerably hampers the computational design of new MALT1 allosteric inhibitors. In that regard, the present work investigated the complex conformational behavior of MALT1 protein during its allosteric inhibition. For this, biased molecular dynamics simulations, sophisticated machine learning techniques such as neural networks, and docking calculations were used. From the performed investigation, it was observed that through allosteric inhibition, Loop 1 and 3 movements were crucial to reduce the catalytic site cavity volume, keeping cysteine unavailable for substrate mimic binding. In addition, statistical information over the explored ensemble showed that the great majority of the inhibited conformations presented an unavailable catalytic cysteine for substrate binding. Hence, the presented results can be used as an objective criterion for the computational proposal of new MALT1 allosteric inhibitors. However, despite mouse MALT1 and human MALT1 presenting 93% homology, the generalization of the findings to human MALT1 protein should be taken with care, and the obtained results apply specifically to the mouse MALT1 construct.
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Sefer A. P., Abolhassani H., Ober F., Kayaoglu B., Bilgic Eltan S., Kara A., Erman B., Surucu Yilmaz N., Aydogmus C., Aydemir S., Charbonnier L.-M., Kolukisa B., Azizi G., Delavari S., Momen T., Aliyeva S., Kendir Demirkol Y., Tekin S., Kiykim A., Baser O. F., Cokugras H., Gursel M., Karakoc-Aydiner E., Ozen A., Krappmann D., Chatila T. A., Rezaei N., Baris S.. Expanding the Clinical and Immunological Phenotypes and Natural History of MALT1 Deficiency. J. Clin. Immunol. 2022;42:634–652. doi: 10.1007/s10875-021-01191-4. PubMed DOI
Ruland J., Hartjes L.. CARD–BCL-10–MALT1 signalling in protective and pathological immunity. Nat. Rev. Immunol. 2019;19:118–134. doi: 10.1038/s41577-018-0087-2. PubMed DOI
Brvar M., O’Neill T. J., Plettenburg O., Krappmann D.. An updated patent review of MALT1 inhibitors (2021-present) Expert Opin. Ther. Pat. 2025;35:639–656. doi: 10.1080/13543776.2025.2484371. PubMed DOI
Asaba K. N., Adachi Y., Tokumaru K., Watanabe A., Goto Y., Aoki T.. Structure-activity relationship studies of 3-substituted pyrazoles as novel allosteric inhibitors of MALT1 protease. Bioorg. Med. Chem. Lett. 2021;41:127996. doi: 10.1016/j.bmcl.2021.127996. PubMed DOI
Liang X., Cao Y., Li C., Yu H., Yang C., Liu H.. MALT1 as a promising target to treat lymphoma and other diseases related to MALT1 anomalies. Medicinal Research Reviews. 2021;41:2388–2422. doi: 10.1002/med.21799. PubMed DOI
Gomez Solsona B., Schmitt A., Schulze-Osthoff K., Hailfinger S.. The Paracaspase MALT1 in Cancer. Biomedicines. 2022;10:344. doi: 10.3390/biomedicines10020344. PubMed DOI PMC
Isay S. E., Vornholz L., Schnalzger T., Groll T., Magg T., Loll P., Weirich G., Steiger K., Hauck F., Ruland J.. Enforced CARD11/MALT1 signaling in dendritic cells triggers hemophagocytic lymphohistiocytosis. Proc. Natl. Acad. Sci. U. S. A. 2024;121:e2413162121. doi: 10.1073/pnas.2413162121. PubMed DOI PMC
Seshadri M. R., Melnick A. M.. Targeting MALT1 for the treatment of diffuse large B-cell lymphoma. Leuk. Lymphoma. 2022;63:789–798. doi: 10.1080/10428194.2021.1999444. PubMed DOI
Mempel T. R., Krappmann D.. Combining precision oncology and immunotherapy by targeting the MALT1 protease. J. Immunother. Cancer. 2022;10:e005442. doi: 10.1136/jitc-2022-005442. PubMed DOI PMC
Kurden-Pekmezci A., Cakiroglu E., Eris S., Mazi F. A., Coskun-Deniz O. S., Dalgic E., Oz O., Senturk S.. MALT1 paracaspase is overexpressed in hepatocellular carcinoma and promotes cancer cell survival and growth. Life Sciences. 2023;323:121690. doi: 10.1016/j.lfs.2023.121690. PubMed DOI
Rosenbaum M., Gewies A., Pechloff K., Heuser C., Engleitner T., Gehring T., Hartjes L., Krebs S., Krappmann D., Kriegsmann M., Weichert W., Rad R., Kurts C., Ruland J.. Bcl10-controlled Malt1 paracaspase activity is key for the immune suppressive function of regulatory T cells. Nat. Commun. 2019;10:2352. doi: 10.1038/s41467-019-10203-2. PubMed DOI PMC
Di Pilato M., Kim E. Y., Cadilha B. L., Prüßmann J. N., Nasrallah M. N., Seruggia D., Usmani S. M., Misale S., Zappulli V., Carrizosa E., Mani V., Ligorio M., Warner R. D., Medoff B. D., Marangoni F., Villani A.-C., Mempel T. R.. Targeting the CBM complex causes Treg cells to prime tumours for immune checkpoint therapy. Nature. 2019;570:112–116. doi: 10.1038/s41586-019-1215-2. PubMed DOI PMC
Flynn S. M., Chen C., Artan M., Barratt S., Crisp A., Nelson G. M., Peak-Chew S.-Y., Begum F., Skehel M., de Bono M.. MALT-1 mediates IL-17 neural signaling to regulate C. elegans behavior, immunity and longevity. Nat. Commun. 2020;11:2099. doi: 10.1038/s41467-020-15872-y. PubMed DOI PMC
Kip E., Staal J., Tima H. G., Verstrepen L., Romano M., Lemeire K., Suin V., Hamouda A., Baens M., Libert C., Kalai M., Van Gucht S., Beyaert R.. Inhibition of MALT1 Decreases Neuroinflammation and Pathogenicity of Virulent Rabies Virus in Mic. Journal of Virology. 2018;92:n/a. doi: 10.1128/JVI.00720-18. PubMed DOI PMC
Zhang R.-Y., Wang Z.-X., Zhang M.-Y., Wang Y.-F., Zhou S.-L., Xu J.-L., Lin W.-X., Ji T.-R., Chen Y.-D., Lu T., Li N.-G., Shi Z.-H.. MALT1 Inhibitors and Degraders: Strategies for NF-κB-Driven Malignancies. J. Med. Chem. 2025;68:5075–5096. doi: 10.1021/acs.jmedchem.4c02873. PubMed DOI
Unnerståle S., Nowakowski M., Baraznenok V., Stenberg G., Lindberg J., Mayzel M., Orekhov V., Agback T.. Backbone Assignment of the MALT1 Paracaspase by Solution NMR. PLoS One. 2016;11:e0146496. doi: 10.1371/journal.pone.0146496. PubMed DOI PMC
Trivedi R., Nagarajaram H. A.. Intrinsically Disordered Proteins: An Overview. International Journal of Molecular Sciences. 2022;23:14050. doi: 10.3390/ijms232214050. PubMed DOI PMC
Babu M. M.. The contribution of intrinsically disordered regions to protein function, cellular complexity, and human disease. Biochem. Soc. Trans. 2016;44:1185–1200. doi: 10.1042/BST20160172. PubMed DOI PMC
Kletzien O. A., Wuttke D. S., Batey R. T.. J. Mol. Biol. 2024;436:168702. doi: 10.1016/j.jmb.2024.168702. PubMed DOI PMC
Jones A. N., Graß C., Meininger I., Geerlof A., Klostermann M., Zarnack K., Krappmann D., Sattler M.. Modulation of pre-mRNA structure by hnRNP proteins regulates alternative splicing of MALT1. Science Advances. 2022;8:eabp9153. doi: 10.1126/sciadv.abp9153. PubMed DOI PMC
Guo H.-B., Huntington B., Perminov A., Smith K., Hastings N., Dennis P., Kelley-Loughnane N., Berry R.. AlphaFold2 modeling and molecular dynamics simulations of an intrinsically disordered protein. PLoS One. 2024;19:e0301866. doi: 10.1371/journal.pone.0301866. PubMed DOI PMC
Aupič J., Pokorná P., Ruthstein S., Magistrato A.. Predicting Conformational Ensembles of Intrinsically Disordered Proteins: From Molecular Dynamics to Machine Learning. J. Phys. Chem. Lett. 2024;15:8177–8186. doi: 10.1021/acs.jpclett.4c01544. PubMed DOI
Coskuner-Weber O.. Structures prediction and replica exchange molecular dynamics simulations of α-synuclein: A case study for intrinsically disordered proteins. Int. J. Biol. Macromol. 2024;276:133813. doi: 10.1016/j.ijbiomac.2024.133813. PubMed DOI
Rebeaud F., Hailfinger S., Posevitz-Fejfar A., Tapernoux M., Moser R., Rueda D., Gaide O., Guzzardi M., Iancu E. M., Rufer N., Fasel N., Thome M.. The proteolytic activity of the paracaspase MALT1 is key in T cell activation. Nat. Immunol. 2008;9:272–281. doi: 10.1038/ni1568. PubMed DOI
Wiesmann C., Leder L., Blank J., Bernardi A., Melkko S., Decock A., D’Arcy A., Villard F., Erbel P., Hughes N., Freuler F., Nikolay R., Alves J., Bornancin F., Renatus M.. Structural Determinants of MALT1 Protease Activity. J. Mol. Biol. 2012;419:4–21. doi: 10.1016/j.jmb.2012.02.018. PubMed DOI
Schiesser S., Hajek P., Pople H. E., Käck H., Öster L., Cox R. J.. Discovery and optimization of cyclohexane-1,4-diamines as allosteric MALT1 inhibitors. Eur. J. Med. Chem. 2022;227:113925. doi: 10.1016/j.ejmech.2021.113925. PubMed DOI
Hughes N., Erbel P., Bornancin F., Wiesmann C., Schiering N., Villard F., Decock A., Rubi B., Melkko S., Spanka C., Buschmann N., Pissot-Soldermann C., Simic O., Beerli R., Sorge M., Tintelnot-Blomley M., Beltz K., Régnier C. H., Quancard J., Schlapbach A., Langlois J.-B., Renatus M.. Stabilizing Inactive Conformations of MALT1 as an Effective Approach to Inhibit Its Protease Activity. Adv. Ther. (Weinh.) 2020;3:2000078. doi: 10.1002/adtp.202000078. DOI
Zhang J., Ren L., Wang Y., Fang X.. In silico study on identification of novel MALT1 allosteric inhibitors. RSC Adv. 2019;9:39338–39347. doi: 10.1039/C9RA07036B. PubMed DOI PMC
Quancard J., Klein T., Fung S.-Y., Renatus M., Hughes N., Israël L., Priatel J. J., Kang S., Blank M. A., Viner R. I., Blank J., Schlapbach A., Erbel P., Kizhakkedathu J., Villard F., Hersperger R., Turvey S. E., Eder J., Bornancin F., Overall C. M.. An allosteric MALT1 inhibitor is a molecular corrector rescuing function in an immunodeficient patient. Nat. Chem. Biol. 2019;15:304–313. doi: 10.1038/s41589-018-0222-1. PubMed DOI
Wallerstein J., Han X., Levkovets M., Lesovoy D., Malmodin D., Mirabello C., Wallner B., Sun R., Sandalova T., Agback P., Karlsson G., Achour A., Agback T., Orekhov V.. Insights into mechanisms of MALT1 allostery from NMR and AlphaFold dynamic analyses. Commun. Biol. 2024;7:868. doi: 10.1038/s42003-024-06558-y. PubMed DOI PMC
Schlauderer F., Seeholzer T., Desfosses A., Gehring T., Strauss M., Hopfner K.-P., Gutsche I., Krappmann D., Lammens K.. Molecular architecture and regulation of BCL10-MALT1 filaments. Nat. Commun. 2018;9:4041. doi: 10.1038/s41467-018-06573-8. PubMed DOI PMC
Zhang G.. Advancing DFT predictions in Cu-chalcogenides with full-yet-shallow 3d-orbitals: Meta-GGA plus Hubbard-like U correction. J. Chem. Phys. 2024;161:174109. doi: 10.1063/5.0232711. PubMed DOI
Sisk T. R., Robustelli P.. Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model. Proceedings of the National Academy of Sciences. 2024;121:e2313360121. doi: 10.1073/pnas.2313360121. PubMed DOI PMC
Liu J., Liu X., Zhang T., Huang J., Tang B. Z., Chau Y.. Metastable biological matter in liquid phase separation. Applied Research. 2024;3:e202300071. doi: 10.1002/appl.202300071. DOI
Andolpho G. A., Ramalho T. C.. Pnictogen bond-driven control of the molecular interaction between organophosphorus and acetylcholinesterase enzyme. J. Comput. Chem. 2024;45:1303–1315. doi: 10.1002/jcc.27328. PubMed DOI
Tavares C. A., Santos T. M. R., da Cunha E. F. F., Ramalho T. C.. Molecular Dynamics-Assisted Interaction of Vanadium Complex–AMPK: From Force Field Development to Biological Application for Alzheimer’s Treatment. J. Phys. Chem. B. 2023;127:495–504. doi: 10.1021/acs.jpcb.2c07147. PubMed DOI
Gonçalves M. A., Gonçalves A. S., Franca T. C. C., Santana M. S., da Cunha E. F. F., Ramalho T. C.. Improved Protocol for the Selection of Structures from Molecular Dynamics of Organic Systems in Solution: The Value of Investigating Different Wavelet Families. J. Chem. Theory Comput. 2022;18:5810–5818. doi: 10.1021/acs.jctc.2c00593. PubMed DOI
Ramalho T. C., Bühl M.. Probing NMR parameters, structure and dynamics of 5-nitroimidazole derivatives. Density functional study of prototypical radiosensitizers. Magn. Reson. Chem. 2005;43:139–146. doi: 10.1002/mrc.1514. PubMed DOI
Ramalho T. C., Taft C. A.. Thermal and solvent effects on the NMR and UV parameters of some bioreductive drugs. J. Chem. Phys. 2005;123:054319. doi: 10.1063/1.1996577. PubMed DOI
Invernizzi M., Piaggi P. M., Parrinello M.. Unified Approach to Enhanced Sampling. Phys. Rev. X. 2020;10:041034. doi: 10.1103/PhysRevX.10.041034. DOI
Novelli P., Bonati L., Pontil M., Parrinello M.. Characterizing Metastable States with the Help of Machine Learning. J. Chem. Theory Comput. 2022;18:5195–5202. doi: 10.1021/acs.jctc.2c00393. PubMed DOI
Invernizzi M., Parrinello M.. Rethinking Metadynamics: From Bias Potentials to Probability Distributions. J. Phys. Chem. Lett. 2020;11:2731–2736. doi: 10.1021/acs.jpclett.0c00497. PubMed DOI
Rizzi V., Aureli S., Ansari N., Gervasio F. L.. OneOPES, a Combined Enhanced Sampling Method to Rule Them All. J. Chem. Theory Comput. 2023;19:5731–5742. doi: 10.1021/acs.jctc.3c00254. PubMed DOI PMC
Gangwal A., Lavecchia A.. AI-Driven Drug Discovery for Rare Diseases. J. Chem. Inf. Model. 2025;65:2214–2231. doi: 10.1021/acs.jcim.4c01966. PubMed DOI
Rizzi V., Bonati L., Ansari N., Parrinello M.. The role of water in host-guest interaction. Nat. Commun. 2021;12:93. doi: 10.1038/s41467-020-20310-0. PubMed DOI PMC
Ansari N., Rizzi V., Parrinello M.. Water regulates the residence time of Benzamidine in Trypsin. Nat. Commun. 2022;13:5438. doi: 10.1038/s41467-022-33104-3. PubMed DOI PMC
Santos R. M., Ramalho T. C.. Molecular Dynamic-Assisted Interaction Between HABT and PI3K Enzyme: Exploring Metastable States for Promising Cancer Diagnosis Applications. J. Comput. Chem. 2025;46:e70080. doi: 10.1002/jcc.70080. PubMed DOI PMC
Hanwell M. D., Curtis D. E., Lonie D. C., Vandermeersch T., Zurek E., Hutchison G. R.. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J. Cheminform. 2012;4:17. doi: 10.1186/1758-2946-4-17. PubMed DOI PMC
Vanommeslaeghe K., Hatcher E., Acharya C., Kundu S., Zhong S., Shim J., Darian E., Guvench O., Lopes P., Vorobyov I., Mackerell A. D.. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 2010;31:671–690. doi: 10.1002/jcc.21367. PubMed DOI PMC
Abraham, M. ; Alekseenko, A. ; Bergh, C. ; Blau, C. ; Briand, E. ; Doijade, M. ; Fleischmann, S. ; Gapsys, V. ; Garg, G. ; Gorelov, S. ; Gouaillardet, G. ; Gray, A. ; Eric Irrgang, M. ; Jalalypour, F. ; Jordan, J. ; Junghans, C. ; Kanduri, P. ; Keller, S. ; Kutzner, C. ; Lemkul, J. A. ; Lundborg, M. ; Merz, P. ; Miletić, V. ; Morozov, D. ; Páll, S. ; Schulz, R. ; Shirts, M. ; Shvetsov, A. ; Soproni, B. ; van der Spoel, D. ; Turner, P. ; Uphoff, C. ; Villa, A. ; Wingbermühle, S. ; Zhmurov, A. ; Bauer, P. ; Hess, B. ; Lindahl, E. . GROMACS 2023 Source code, 2023.
Huang J., Rauscher S., Nawrocki G., Ran T., Feig M., de Groot B. L., Grubmüller H., MacKerell A. D.. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods. 2017;14:71–73. doi: 10.1038/nmeth.4067. PubMed DOI PMC
Tribello G. A., Bonomi M., Branduardi D., Camilloni C., Bussi G.. PLUMED 2: New feathers for an old bird. Comput. Phys. Commun. 2014;185:604–613. doi: 10.1016/j.cpc.2013.09.018. DOI
Bonati L., Piccini G., Parrinello M.. Deep learning the slow modes for rare events sampling. Proceedings of the National Academy of Sciences. 2021;118:e2113533118. doi: 10.1073/pnas.2113533118. PubMed DOI PMC
Pérez-Hernández G., Paul F., Giorgino T., De Fabritiis G., Noé F.. Identification of slow molecular order parameters for Markov model construction. J. Chem. Phys. 2013;139:015102. doi: 10.1063/1.4811489. PubMed DOI
Noé F., Clementi C.. Collective variables for the study of long-time kinetics from molecular trajectories: theory and methods. Curr. Opin. Struct. Biol. 2017;43:141–147. doi: 10.1016/j.sbi.2017.02.006. PubMed DOI
Schwantes C. R., Pande V. S.. Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9. J. Chem. Theory Comput. 2013;9:2000–2009. doi: 10.1021/ct300878a. PubMed DOI PMC
Bonati L., Trizio E., Rizzi A., Parrinello M.. A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar. J. Chem. Phys. 2023;159:014801. doi: 10.1063/5.0156343. PubMed DOI
Paszke, A. ; Gross, S. ; Massa, F. ; Lerer, A. ; Bradbury, J. ; Chanan, G. ; Killeen, T. ; Lin, Z. ; Gimelshein, N. ; Antiga, L. ; Desmaison, A. ; Köpf, A. ; Yang, E. ; DeVito, Z. ; Raison, M. ; Tejani, A. ; Chilamkurthy, S. ; Steiner, B. ; Fang, L. ; Bai, J. ; Chintala, S. . PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Proceedings of the 33rd International Conference on Neural Information Processing Systems; Curran Associates Inc.: Red Hook, NY, USA, 2019.
Falcon, W. ; Borovec, J. ; Wälchli, A. ; Eggert, N. ; Schock, J. ; Jordan, J. ; Skafte, N. ; Bereznyuk, V. ; Harris, E. ; Murrell, T. ; Yu, P. ; Præsius, S. ; Addair, T. ; Zhong, J. ; Lipin, D. ; Uchida, S. ; Bapat, S. ; Schröter, H. ; Dayma, B. ; Karnachev, A. ; Kulkarni, A. ; Komatsu, S. ; Martin, B. ; Schiratti, J.-B. ; Mary, H. ; Byrne, D. ; Eyzaguirre, C. ; cinjon; Bakhtin, A. . PyTorchLightning/pytorch-lightning: Version 0.7.6; Zenodo, 2020. 10.5281/zenodo.3828935. DOI
Kozakov D., Hall D. R., Xia B., Porter K. A., Padhorny D., Yueh C., Beglov D., Vajda S.. The ClusPro web server for protein–protein docking. Nat. Protoc. 2017;12:255–278. doi: 10.1038/nprot.2016.169. PubMed DOI PMC
Jones G., Jindal A., Ghani U., Kotelnikov S., Egbert M., Hashemi N., Vajda S., Padhorny D., Kozakov D.. Elucidation of protein function using computational docking and hotspot analysis by ClusPro and FTMap. Acta Crystallographica Section D. 2022;78:690–697. doi: 10.1107/S2059798322002741. PubMed DOI PMC
Ravindranath P. A., Forli S., Goodsell D. S., Olson A. J., Sanner M. F.. AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility. PLoS Comput. Biol. 2015;11:e1004586. doi: 10.1371/journal.pcbi.1004586. PubMed DOI PMC
Abadi, M. , et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, 2015. https://www.tensorflow.org/ (accessed 2025-12-11).
Berman H., Henrick K., Nakamura H.. Announcing the worldwide Protein Data Bank. Nature Structural & Molecular Biology. 2003;10:n/a. doi: 10.1038/nsb1203-980. PubMed DOI
Xu Y., Wang S., Hu Q., Gao S., Ma X., Zhang W., Shen Y., Chen F., Lai L., Pei J.. CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction. Nucleic Acids Res. 2018;46:W374–W379. doi: 10.1093/nar/gky380. PubMed DOI PMC
Wang S., Xie J., Pei J., Lai L.. CavityPlus 2022 Update: An Integrated Platform for Comprehensive Protein Cavity Detection and Property Analyses with User-friendly Tools and Cavity Databases. J. Mol. Biol. 2023;435:168141. doi: 10.1016/j.jmb.2023.168141. PubMed DOI