Identification of Natural Compounds against Neurodegenerative Diseases Using In Silico Techniques

Abstract

The aim of this study was to identify new potentially active compounds for three protein targets, tropomyosin receptor kinase A (TrkA), N-methyl-d-aspartate (NMDA) receptor, and leucine-rich repeat kinase 2 (LRRK2), that are related to various neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and neuropathic pain. We used a combination of machine learning methods including artificial neural networks and advanced multilinear techniques to develop quantitative structure–activity relationship (QSAR) models for all target proteins. The models were applied to screen more than 13,000 natural compounds from a public database to identify active molecules. The best candidate compounds were further confirmed by docking analysis and molecular dynamics simulations using the crystal structures of the proteins. Several compounds with novel scaffolds were predicted that could be used as the basis for development of novel drug inhibitors related to each target.

Recent Developments in New Therapeutic Agents against Alzheimer and Parkinson Diseases: In-Silico Approaches

“…On the oth Singh et al developed pharmacophore models based on the structure of ifenprod known NMDAR antagonist, performed virtual screening, studied the affinity of Nevertheless, over the years, numerous studies reported the design of novel active NMDAR antagonists using in silico methodologies [9]. For instance, Ivanova and co-workers used a virtual screening approach to discover new potential NMDAR antagonists [36]. Using a combination of various machine learning methods, including artificial neural networks and advanced multilinear techniques to build QSAR models, they screened over 13,000 natural compounds and ranked them based on their predicted affinity towards the target (PDB#5TP9) [36].…”

“…For instance, Ivanova and co-workers used a virtual screening approach to discover new potential NMDAR antagonists [36]. Using a combination of various machine learning methods, including artificial neural networks and advanced multilinear techniques to build QSAR models, they screened over 13,000 natural compounds and ranked them based on their predicted affinity towards the target (PDB#5TP9) [36]. The best candidates were also analysed by docking and molecular dynamics simulations to identify essential structural moieties that could serve as basis for the design and development of novel and improved NMDAR antagonists [36].…”

“…Using a combination of various machine learning methods, including artificial neural networks and advanced multilinear techniques to build QSAR models, they screened over 13,000 natural compounds and ranked them based on their predicted affinity towards the target (PDB#5TP9) [36]. The best candidates were also analysed by docking and molecular dynamics simulations to identify essential structural moieties that could serve as basis for the design and development of novel and improved NMDAR antagonists [36]. A distinct strategy was carried out by Sharma et al using pharmacophore modeling and a four-phased virtual screening study to identify potential drug candidates [37].…”

An in silico structural approach to characterize human and rainbow trout estrogenicity of mycotoxins: Proof of concept study using zearalenone and alternariol

“…(Cheron, Casciuc, Golebiowski, Antonczak, & Fiorucci, 2017;L. Dellafiora, Dall'Asta, Cruciani, Galaverna, & Cozzini, 2015;Ivanova, Karelson, & Dobchev, 2018;Lin, Zhang, Han, Xin, Meng, Gong, et al, 2018)). In addition, the estrogenic potential of alternariol (AOH), an emerging mycotoxin with estrogenic properties produced by Alternaria spp.…”

On the Mechanism of Action of Anti-Inflammatory Activity of Hypericin: An In Silico Study Pointing to the Relevance of Janus Kinases Inhibition

“…In this framework, the aptitude of Hyp and some structural analogues to behave as JAK inhibitors was assessed in silico via molecular modeling, a technique that is getting more and more consensus in the high-throughput molecular characterization of pharmacologically relevant compounds [21,22,23]. Given the complex network of molecular events likely underlying the anti-inflammatory activity of H. perforatum components, the existence of possible direct effects of Hyp and congeners on JAKs were assessed on the basis of: (i) the involvement of JAK-STAT pathway previously described in the anti-inflammatory activity of mixtures containing Hyp congeners [17]; (ii) the relevance of JAK inhibition in anti-inflammatory activity [24]; (iii) the known activity of Hyp as kinases inhibitor, also in the lack of light activation [25]; (iv) evidences reporting the ATP-competitive inhibition of kinases by Hyp-related polyaromatic phenols [26].…”