A novel artificial intelligence protocol to investigate potential leads for Parkinson's disease

Previous studies have shown that small molecule inhibitors of NLRP3 may be a potential treatment for Parkinson's disease (PD). NACHT, LRR and PYD domains-containing protein 3 (NLRP3), heat shock protein HSP 90-beta (HSP90AB1), caspase-1 (CASP1) and cellular tumor antigen p53 (TP53) have significant involvement in the pathogenesis pathway of PD. Molecular docking was used to screen the traditional Chinese medicine database TCM Database@Taiwan. Top traditional Chinese medicine (TCM) compounds with high affinities based on Dock Score were selected to form the drug–target interaction network to investigate potential candidates targeting NLRP3, HSP90AB1, CASP1, and TP53 proteins. Artificial intelligence model, 3D-Quantitative Structure–Activity Relationship (3D-QSAR) were constructed respectively utilizing training sets of inhibitors against the four proteins with known inhibitory activities (pIC50). The results showed that 2007_22057 (an indole derivative), 2007_22325 (a valine anhydride) and 2007_15317 (an indole derivative) might be a potential medicine formula for the treatment of PD. Then there are three candidate compounds identified by the result of molecular dynamics.

[1]  Rémi Legay,et al.  Rational design of novel benzisoxazole derivatives with acetylcholinesterase inhibitory and serotoninergic 5-HT4 receptors activities for the treatment of Alzheimer’s disease , 2020, Scientific Reports.

[2]  Hye-Young Jung,et al.  Ridge Fuzzy Regression Model , 2019, International Journal of Fuzzy Systems.

[3]  Vinicius Gonçalves Maltarollo,et al.  HQSAR and random forest-based QSAR models for anti-T. vaginalis activities of nitroimidazoles derivatives. , 2019, Journal of molecular graphics & modelling.

[4]  Adriano D Andricopulo,et al.  ADMET modeling approaches in drug discovery. , 2019, Drug discovery today.

[5]  G. Hessler,et al.  Artificial Intelligence in Drug Design , 2018, Molecules.

[6]  Michael Wainberg,et al.  Deep learning in biomedicine , 2018, Nature Biotechnology.

[7]  Ming Lu,et al.  Inhibition of the hepatic Nlrp3 protects dopaminergic neurons via attenuating systemic inflammation in a MPTP/p mouse model of Parkinson’s disease , 2018, Journal of Neuroinflammation.

[8]  A. Abbate,et al.  Structural Insights of Benzenesulfonamide Analogues as NLRP3 Inflammasome Inhibitors: Design, Synthesis, and Biological Characterization. , 2018, Journal of medicinal chemistry.

[9]  Xiaofeng Zhu,et al.  Efficient kNN Classification With Different Numbers of Nearest Neighbors , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Amir Abbas Najafi,et al.  Support vector machine with Dirichlet feature mapping , 2018, Neural Networks.

[11]  Zukang Feng,et al.  RCSB Protein Data Bank: Sustaining a living digital data resource that enables breakthroughs in scientific research and biomedical education , 2017, Protein science : a publication of the Protein Society.

[12]  I. Richard,et al.  Parkinson's disease psychosis: presentation, diagnosis and management. , 2017, Neurodegenerative disease management.

[13]  Charles L. Brooks,et al.  CHARMM‐GUI ligand reader and modeler for CHARMM force field generation of small molecules , 2017, J. Comput. Chem..

[14]  Alexander D. MacKerell,et al.  CHARMM‐GUI 10 years for biomolecular modeling and simulation , 2017, J. Comput. Chem..

[15]  Qingmei Yang,et al.  The NLRP3 Inflammasome is Involved in the Pathogenesis of Parkinson’s Disease in Rats , 2017, Neurochemical Research.

[16]  Damian Szklarczyk,et al.  The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible , 2016, Nucleic Acids Res..

[17]  The UniProt Consortium.  UniProt: the universal protein knowledgebase , 2016, Nucleic Acids Res..

[18]  H. Hong,et al.  Drug Repositioning Through Network Pharmacology. , 2016, Current topics in medicinal chemistry.

[19]  D. Agard,et al.  Atomic structure of Hsp90-Cdc37-Cdk4 reveals that Hsp90 traps and stabilizes an unfolded kinase , 2016, Science.

[20]  K. Roy,et al.  Be aware of error measures. Further studies on validation of predictive QSAR models , 2016 .

[21]  Martin Krzywinski,et al.  Points of Significance: Multiple linear regression , 2015, Nature Methods.

[22]  A. Lang,et al.  Parkinson's disease , 2015, The Lancet.

[23]  Yu-chian Chen.  Beware of docking! , 2015, Trends in pharmacological sciences.

[24]  Bo Zhang,et al.  Traditional Chinese medicine network pharmacology: theory, methodology and application: Traditional Chinese medicine network pharmacology: theory, methodology and application , 2014 .

[25]  Yang Zhang,et al.  The I-TASSER Suite: protein structure and function prediction , 2014, Nature Methods.

[26]  Peter M. Kasson,et al.  GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit , 2013, Bioinform..

[27]  Shao Li,et al.  Traditional Chinese medicine network pharmacology: theory, methodology and application. , 2013, Chinese journal of natural medicines.

[28]  K. Roy,et al.  QSAR of phytochemicals for the design of better drugs , 2012, Expert opinion on drug discovery.

[29]  Robert P. Sheridan,et al.  Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest , 2012, J. Chem. Inf. Model..

[30]  R. Olshen,et al.  Points of Significance: Classification and regression trees , 2017, Nature Methods.

[31]  VINCENT ZOETE,et al.  SwissParam: A fast force field generation tool for small organic molecules , 2011, J. Comput. Chem..

[32]  Kai-Wei Chang,et al.  iScreen: world’s first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan , 2011, J. Comput. Aided Mol. Des..

[33]  Calvin Yu-Chian Chen,et al.  TCM Database@Taiwan: The World's Largest Traditional Chinese Medicine Database for Drug Screening In Silico , 2011, PloS one.

[34]  Yang Zhang,et al.  I-TASSER: a unified platform for automated protein structure and function prediction , 2010, Nature Protocols.

[35]  Jitender Verma,et al.  3D-QSAR in drug design--a review. , 2010, Current topics in medicinal chemistry.

[36]  G. Morris,et al.  Molecular docking. , 2008, Methods in molecular biology.

[37]  Yang Zhang,et al.  I-TASSER server for protein 3D structure prediction , 2008, BMC Bioinformatics.

[38]  Petra Fromme,et al.  A monovalent mutant of cyanovirin-N provides insight into the role of multiple interactions with gp120 for antiviral activity. , 2007, Biochemistry.

[39]  A. Hopkins.  Network pharmacology , 2007, Nature Biotechnology.

[40]  Joni W. Lam,et al.  Tethering identifies fragment that yields potent inhibitors of human caspase-1. , 2006, Bioorganic & medicinal chemistry letters.

[41]  Gerrit Groenhof,et al.  GROMACS: Fast, flexible, and free , 2005, J. Comput. Chem..

[42]  H. Zou,et al.  Addendum: Regularization and variable selection via the elastic net , 2005 .

[43]  C. Venkatachalam,et al.  LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. , 2003, Journal of molecular graphics & modelling.