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Decoding Allosteric Inhibition in MALT1: The Hidden Role of Conformational Plasticity in Metastable States via Biased MD and Deep Learning

dc.contributor.authorSantos, Rodrigo M
dc.contributor.authorSantos, Tainah M. R
dc.contributor.authorde Castro Ramalho, Teodorico
dc.date.accessioned2026-08-17T07:23:46Z
dc.date.issued2026
dc.description.abstractThe 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-kappa B (NF-kappa B) 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.eng
dc.formatp. 1182-1196
dc.identifier.doi10.1021/acs.jpcb.5c07665
dc.identifier.issn1520-6106
dc.identifier.obd43882778
dc.identifier.scopus2-s2.0-105028937916
dc.identifier.urihttps://hdl.handle.net/20.500.12603/2869
dc.identifier.wos001665979100001
dc.language.isoeng
dc.noteAutor chybně uvádí financování navázané na nevykazovanou fakultu Long-Term Development Plan of the University of Hradec Kralove (UHK, Faculty of Science, VT2019-2021). Není v popisu financování uváděno.eng
dc.peerreviewedyes
dc.project.IDLong-Term Development Plan of the University of Hradec Kralove
dc.publicationstatuspostprint
dc.publisherAmerican chemical society
dc.relation.ispartofJournal of physical chemistry B, volume 130, issue: 4eng
dc.relation.publisherversionhttps://pubs.acs.org/doi/10.1021/acs.jpcb.5c07665
dc.rights.accessOpen Access
dc.rights.licenseCC BY 4.0
dc.source.urlhttps://pubs.acs.org/doi/10.1021/acs.jpcb.5c07665
dc.subjectproteineng
dc.subjectparameterseng
dc.subjectnmreng
dc.titleDecoding Allosteric Inhibition in MALT1: The Hidden Role of Conformational Plasticity in Metastable States via Biased MD and Deep Learningeng
dc.typearticle

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