Structural binding site comparisons reveal Crizotinib as a novel LRRK2 inhibitor

AI-Powered Virtual Screening of Large Compound Libraries Leads to the Discovery of Novel Inhibitors of Sirtuin-1

The discovery of new scaffolds and chemotypes via high-throughput screening is tedious and resource intensive. Yet, there are millions of small molecules commercially available, rendering comprehensive in vitro tests intractable. We show how smart algorithms reduce large screening collections to target-specific sets of just a few hundred small molecules, allowing for a much faster and more cost-effective hit discovery process. We showcase the application of this virtual screening strategy by preselecting 434 compounds for Sirtuin-1 inhibition from a library of 2.6 million compounds, corresponding to 0.02% of the original library. Multistage in vitro validation ultimately confirmed nine chemically novel inhibitors. When compared to a competitive benchmark study for Sirtuin-1, our method shows a 12-fold higher hit rate. The results demonstrate how AI-driven preselection from large screening libraries allows for a massive reduction in the number of small molecules to be tested in vitro while still retaining a large number of hits.

Repurposing of neprilysin inhibitor 'sacubitrilat' as an anti-cancer drug by modulating epigenetic and apoptotic regulators

Modifications in the epigenetic landscape have been considered a hallmark of cancer. Histone deacetylation is one of the crucial epigenetic modulations associated with the aggressive progression of various cancer subtypes. Herein, we have repurposed the neprilysin inhibitor sacubitrilat as a potent anticancer agent using in-silico protein-ligand interaction profiler (PLIP) analysis, molecular docking, and in vitro studies. The screening of PLIP profiles between vorinostat/panobinostat and HDACs/LTA4H followed by molecular docking resulted in five (Sacubitrilat, B65, BDS, BIR, and NPV) FDA-approved, experimental and investigational drugs. Sacubitrilat has demonstrated promising anticancer activity against colorectal cancer (SW-480) and triple-negative breast cancer (MDA-MB-231) cells, with IC₅₀ values of 14.07 μg/mL and 23.02 μg/mL, respectively. FACS analysis revealed that sacubitrilat arrests the cell cycle at the G0/G1 phase and induces apoptotic-mediated cell death in SW-480 cells. In addition, sacubitrilat inhibited HDAC isoforms at the transcriptomic level by 0.7-0.9 fold and at the proteomic level by 0.5-0.6 fold as compared to the control. Sacubitrilat increased the protein expression of tumor-suppressor (p53) and pro-apoptotic makers (Bax and Bid) by 0.2-2.5 fold while decreasing the expression of anti-apoptotic Bcl2 and Nrf2 proteins by 0.2-0.5 fold with respect to control. The observed cleaved PARP product indicates that sacubitrilat induces apoptotic-mediated cell death. This study may pave the way to identify the anticancer potential of sacubitrilat and can be explored in human clinical trials.

Identification of LRRK2 Inhibitors through Computational Drug Repurposing

Parkinson's disease (PD) is the second most common neurodegenerative disorder that affects more than ten million people worldwide. However, the current PD treatments are still limited and alternative treatment strategies are urgently required. Leucine-rich repeat kinase 2 (LRRK2) has been recognized as a promising target for PD treatment. However, there are no approved LRRK2 inhibitors on the market. To rapidly identify potential drug repurposing candidates that inhibit LRRK2 kinase, we report a structure-based drug repurposing workflow that combines molecular docking, recursive partitioning model, molecular dynamics (MD) simulation, and molecular mechanics-generalized Born surface area (MM-GBSA) calculation. Thirteen compounds screened from our drug repurposing workflow were further evaluated through the experiment. The experimental results showed six drugs (Abivertinib, Aumolertinib, Encorafenib, Bosutinib, Rilzabrutinib, and Mobocertinib) with IC₅₀ less than 5 μM that were identified as potential LRRK2 kinase inhibitors. The most potent compound Abivertinib showed potent inhibitions with IC₅₀ toward G2019S mutation and wild-type LRRK2 of 410.3 nM and 177.0 nM, respectively. Our combination screening strategy had a 53% hit rate in this repurposing task. MD simulations and MM-GBSA free energy analysis further revealed the atomic binding mechanism between the identified drugs and G2019S LRRK2. In summary, the results showed that our drug repurposing workflow could be used to identify potent compounds for LRRK2. The potent inhibitors discovered in our work can be a starting point to develop more effective LRRK2 inhibitors.

FDA-Approved Kinase Inhibitors in Preclinical and Clinical Trials for Neurological Disorders

Cancers and neurological disorders are two major types of diseases. We previously developed a new concept termed "Aberrant Cell Cycle Diseases" (ACCD), revealing that these two diseases share a common mechanism of aberrant cell cycle re-entry. The aberrant cell cycle re-entry is manifested as kinase/oncogene activation and tumor suppressor inactivation, which are hallmarks of both tumor growth in cancers and neuronal death in neurological disorders. Therefore, some cancer therapies (e.g., kinase inhibition, tumor suppressor elevation) can be leveraged for neurological treatments. The United States Food and Drug Administration (US FDA) has so far approved 74 kinase inhibitors, with numerous other kinase inhibitors in clinical trials, mostly for the treatment of cancers. In contrast, there are dire unmet needs of FDA-approved drugs for neurological treatments, such as Alzheimer's disease (AD), intracerebral hemorrhage (ICH), ischemic stroke (IS), traumatic brain injury (TBI), and others. In this review, we list these 74 FDA-approved kinase-targeted drugs and identify those that have been reported in preclinical and/or clinical trials for neurological disorders, with a purpose of discussing the feasibility and applicability of leveraging these cancer drugs (FDA-approved kinase inhibitors) for neurological treatments.

Possible association between the lrrk2 gene and anxiety behavior: a systematic literature review

Alterations to the LRRK2 gene have been associated with Parkinson's disease and alcohol consumption in animals and humans. Furthermore, these disorders are strongly related to anxiety disorders (ADs). Thus, we investigated how the LRRK2 gene might influence anxiety in humans and mice. We elaborated a systematic review based on the PRISMA Statement of studies that investigated levels of anxiety in animal or human models with alterations in the LRRK2 gene. The search was conducted in the PubMed, Scopus, and Web of Science databases, and in reference lists with descriptors related to ADs and the LRRK2. From the 62 articles assessed for eligibility, 16 were included: 11 conducted in humans and seven, in mice. Lrrk2 KO mice and the LRRK2 G2019S, LRRK2 R1441G, and LRRK2 R1441C variants were addressed. Five articles reported an increase in anxiety levels concerning the LRRK2 variants. Decreased anxiety levels were observed in two articles, one focusing on the LRRK2 G2019S and the other, on the Lrrk2 KO mice. Eight other articles reported no differences in anxiety levels in individuals with Lrrk2 alterations compared to their healthy controls. This study discusses a possible influence between the LRRK2 gene and anxiety, adding information to the existing knowledge respecting the influence of genetics on anxiety.

The Double-Faceted Role of Leucine-Rich Repeat Kinase 2 in the Immunopathogenesis of Parkinson’s Disease

Leucine-rich repeat kinase 2 (LRRK2) is one of the most common causative genes in Parkinson’s disease (PD). The complex structure of this multiple domains’ protein determines its versatile functions in multiple physiological processes, including migration, autophagy, phagocytosis, and mitochondrial function, among others. Mounting studies have also demonstrated the role of LRRK2 in mediating neuroinflammation, the prominent hallmark of PD, and intricate functions in immune cells, such as microglia, macrophages, and astrocytes. Of those, microglia were extensively studied in PD, which serves as the resident immune cell of the central nervous system that is rapidly activated upon neuronal injury and pathogenic insult. Moreover, the activation and function of immune cells can be achieved by modulating their intracellular metabolic profiles, in which LRRK2 plays an emerging role. Here, we provide an updated review focusing on the double-faceted role of LRRK2 in regulating various cellular physiology and immune functions especially in microglia. Moreover, we will summarize the latest discovery of the three-dimensional structure of LRRK2, as well as the function and dysfunction of LRRK2 in immune cell-related pathways.

Decomposing compounds enables reconstruction of interaction fingerprints for structure-based drug screening

Background

Structure-based drug repositioning has emerged as a promising alternative to conventional drug development. Regardless of the many success stories reported over the past years and the novel breakthroughs on the AI-based system AlphaFold for structure prediction, the availability of structural data for protein–drug complexes remains very limited. Whereas the chemical libraries contain millions of drug compounds, the vast majority of them do not have structures to crystallized targets,and it is, therefore, impossible to characterize their binding to targets from a structural view. However, the concept of building blocks offers a novel perspective on the structural problem. A drug compound is considered a complex of small chemical blocks or fragments, which confer the relevant properties to the drug and have a high proportion of functional groups involved in protein binding. Based on this, we propose a novel approach to expand the scope of structure-based repositioning approaches by transferring the structural knowledge from a fragment to a compound level.

Results

We fragmented over 100,000 compounds in the Protein Data Bank (PDB) and characterized the structural binding mode of 153,000 fragments to their crystallized targets. Using the fragment’s data, we were able to artificially reconstruct the binding mode of over 7,800 complexes between ChEMBL compounds and their known targets, for which no structural data is available. We proved that the conserved binding tendency of fragments, when binding to the same targets, highly influences the drug’s binding specificity and carries the key information to reconstruct full drugs binding mode. Furthermore, our approach was able to reconstruct multiple compound-target pairs at optimal thresholds and high similarity to the actual binding mode.

Conclusions

Such reconstructions are of great value and benefit structure-based drug repositioning since they automatically enlarge the technique’s scope and allow exploring the so far ‘unexplored compounds’ from a structural perspective. In general, the transfer of structural information is a promising technique that could be applied to any chemical library, to any compound that has no crystal structure available in PDB, and even to transfer any other feature that may be relevant for the drug discovery process and that due to data limitations is not yet fully available. In that sense, the results of this work document the full potential of structure-based screening even beyond PDB.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13321-022-00592-w.