From Dynamics to Diagnosis and Therapy: A Multiscale Computational Framework for MALT1-Targeted Cancer Theranostics
Abstract
Cancer is one of the leading causes of death worldwide, making it a major concern in modern society. Therefore, the proposal of strategies against this illness is of major importance and has been widely studied by the scientific community in the past decades. In this sense, these strategies mainly focus on achieving improved therapy and diagnosis, with some proposals focusing on developing chemical agents capable of both treating and diagnosing cancer through targeting a cancer-related biomarker. In the role of cancer-related targets, MALT1 shows great potential as a cancer biomarker, being related to the nuclear factor-κB (NF-κB) signaling activation, an important biochemical process that regulates immune responses and inflammatory events in the human body, and its malfunction is related to the development and survival of several types of cancer. Despite MALT1's importance as a cancer target, to the best of our knowledge, there is a lack of literature proposing chemical agents capable of treating and diagnosing cancer through this biomarker. Hence, the present work's main goal was to propose a theranostic agent for this task, for which a combination of biased MD simulations, convolutional variational autoencoders (CVAEs), and quantum calculations was used. From this, it was possible to propose and optimize a compound to achieve a good MALT1 allosteric inhibition ratio while also presenting different fluorescence in different environments, crucial for signaling purposes.…
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