ORIGINAL RESEARCH

Focusing on a novel upstream target for Alzheimer’s Disease therapy: Identification of potential BACE1 inhibitors Using In Silico Methods
Matteo Bulgini and Jack A. Tuszynski​

Alzheimer’s Disease (AD) is the most common form of dementia and one of the most prevalent neurodegenerative diseases in the world. Currently, there’s no definitive cure for this disease. The most widely accepted hypothesis involves the accumulation of β-amyloid into plaques and of the MAP τ protein into neurofibrillary tangles located between and inside the neurons, respectively. These processes cause neuronal degeneration and a disruption of the functionality of the brain. The β-amyloid plaques are due to the action of the β -secretase protein, which cleaves the APP protein in the Aβ region, resulting in β -amyloid fragments that aggregate, as opposed to the production of α-amyloid, which can stimulate neuroplasticity and neuroregenerative capabilities. Previous research showed that mutations in the sequence of the APP protein can inhibit the formation of these fragments, but this route is not conducive to therapeutic approaches. Several trials have been conducted into developing pharmaceutical compounds that can inhibit the cleavage site and in particular the catalytic dyad (Asp32−Asp228). Using the data from these trials, the aim of the research reported in this paper was to widen the possibilities of drug discovery by parsing a wide database of bioactive molecules, simulating their interactions with the BACE1 protein. By forming bonds with the pocket responsible for the cleavage of the APP protein, these ligands may be putative inhibitors. Here, we report the identification of several bioactive compounds predicted to be active inhibitors of the β-secretase 1, which is the main enzyme responsible for the formation of the β amyloid plaques in the pathogenesis of AD. To this end we performed database parsing, selecting clinical-stage drugs and similar compounds, which then were computationally docked to a mature chain of BACE1 protein using Boltz2 AI-based software. From the resulting compounds the top 27, ranked for confidence, affinity and accounting for the wider base possible, were then investigate for binding affinity to the target using the GROMACS suite to predict their docking poses to the protein. This was done by calculating free energy applying the MM-GBSA algorithm for the resulting structures. The highest ranked compounds can be validated by in vitro and in vivo studies and potentially represent drug candidates for AD therapies.

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