Discovering promising drug candidates for Parkinson’s disease: integrating miRNA and DEG analysis with molecular dynamics and MMPBSA

Ball N et al (2019) Parkinson’s disease and the environment. Front Neurol 10:218

Article  PubMed  PubMed Central  Google Scholar 

Simon DK, Tanner CM, Brundin P (2020) Parkinson disease epidemiology, pathology, genetics, and pathophysiology. Clin Geriat Med 36(1):1–12

Article  Google Scholar 

DeMaagd G, Philip A (2015) Parkinson’s disease and its management: part 1: disease entity, risk factors, pathophysiology, clinical presentation, and diagnosis. Pharm Ther 40(8):504

Google Scholar 

Thal DR, Tredici KD, Braak H (2004) Neurodegeneration in normal brain aging and disease. Sci Aging Knowl Environ 2004(23):26

Article  Google Scholar 

Risiglione P et al (2021) Alpha-synuclein and mitochondrial dysfunction in Parkinson’s disease: the emerging role of VDAC. Biomolecules 11(5):718

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chang K-H, Chen C-M (2020) The role of oxidative stress in Parkinson’s disease. Antioxidants 9(7):597

Article  CAS  PubMed  PubMed Central  Google Scholar 

Dong-Chen Xu et al (2023) Signaling pathways in Parkinson’s disease: molecu- lar mechanisms and therapeutic interventions. Signal Transduct Target Ther 8(1):73

Article  PubMed  PubMed Central  Google Scholar 

Yuan H et al (2010) Treatment strategies for Parkinson’s disease. Neuro-sci Bullet 26(1):66

Article  CAS  Google Scholar 

Bonifati V (2013) Genetics of Parkinson’s disease–state of the art. Parkinsonism Relat Disord 20(2014):S23–S28

Google Scholar 

Dong Na, Zhang X, Liu Q (2017) Identification of therapeutic tar- gets for Parkinson’s disease via bioinformatics analysis. Mol Med Rep 15(2):731–735

Article  CAS  PubMed  Google Scholar 

Yin Xi et al (2021) Identification of potential miRNA-mRNA regulatory network contributing to Parkinson’s disease. Parkinson’s Dis 2022:2877728

Google Scholar 

Celorrio M et al (2017) GPR55: a therapeutic target for Parkinson’s disease? Neuropharmacology 125:319–332

Article  CAS  PubMed  Google Scholar 

Stoker TB, Barker RA (2020) Recent developments in the treatment of Parkinson’s disease. F1000Research. https://doi.org/10.12688/f1000research.25634.1

Article  PubMed  PubMed Central  Google Scholar 

Gandolfo LC, Speed TP (2018) RLE plots: visualizing unwanted variation in high dimensional data. PloS one 13(2):e0191629

Article  PubMed  PubMed Central  Google Scholar 

Gatto L et al (2015) Visualization of proteomics data using R and bioconductor. Proteomics 15(8):1375–1389

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pradervand S et al (2009) Impact of normalization on miRNA microarray expression profiling. RNA 15(3):493–501

Article  CAS  PubMed  PubMed Central  Google Scholar 

Klaus B (2016) An end to end workflow for differential gene expression using Affymetrix microarrays. F1000Research. https://doi.org/10.12688/f1000research.8967.2

Article  PubMed  Google Scholar 

KBaSRaM Lewis et al (2020) EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling. R package version 1.8.0

Chen S et al (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34(17):i884–i890

Article  PubMed  PubMed Central  Google Scholar 

Blankenberg D, Hillman-Jackson J (2014) Analysis of next-generation sequencing data using Galaxy. Stem Cell Transcript Netw Methods Protocols 2014:21–43

Article  Google Scholar 

Pertea M et al (2015) StringTie enables improved reconstruction of a tran- scriptome from RNA-seq reads. Nat Biotechnol 33(3):290–295

Article  CAS  PubMed  PubMed Central  Google Scholar 

Frazee AC et al (2015) Ballgown bridges the gap between transcriptome as- sembly and expression analysis. Nat Biotechnol 33(3):243–246

Article  CAS  PubMed  PubMed Central  Google Scholar 

Friedlander MR et al (2012) miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucl Acids Res 40(1):37–52

Article  PubMed  Google Scholar 

Rifqi Rafsanjani M (2021) Analysing and identifying miRNAs from RNA-seq data using miRDeep2 tool in Galaxy, a practical guide. bioRxiv 2021–10

Doyle M et al (2023) 2: RNA-seq counts to genes

Abdullah T (2018) Algorithm and workflow of miRDB. Bioinform Rev 4(9):9–13

Google Scholar 

Sherman BT et al (2022) DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucl Acids Res 50(W1):W216–W221

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhou Z et al (2007) Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharma-ceutically relevant targets. J Chem Inf Model 47(4):1599–1608

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bordoli L et al (2009) Protein structure homology modeling using SWISS- MODEL workspace. Nat Protoc 4(1):1–13

Article  CAS  PubMed  Google Scholar 

Madhavi Sastry G et al (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aid Mol Design 27:221–234

Article  CAS  Google Scholar 

Halgren TA (2009) Identifying and characterizing binding sites and assessing druggability. J Chem Inform Model 49(2):377–389

Article  CAS  Google Scholar 

Bhachoo J, Beuming T (2017) Investigating protein–peptide interactions using the Schr¨odinger computational suite. Model Peptide-protein Inter- Actions Methods Protocols. https://doi.org/10.1007/978-1-4939-6798-8_14

Article  Google Scholar 

Vemula V et al (2023) Fragment-based design and MD simulations of human papilloma virus-16 E6 protein inhibitors. J Biomol Struct Dyn 42:1–10

Google Scholar 

Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7(1):42717

Article  PubMed  PubMed Central  Google Scholar 

James Abraham M et al (2015) GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1:19–25

Article  Google Scholar 

Zhu X, Lopes PEM, MacKerell Jr AD (2012) Recent developments and applications of the CHARMM force fields. Wiley Interdiscipl Rev Comput Mol Sci 2(1):167–185

Article  CAS  Google Scholar 

Onufriev AV, Izadi S (2018) Water models for biomolecular simulations. Wiley Interdiscipl Rev Comput Mol Sci 8(2):e1347

Article  Google Scholar 

Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10(5):449–461

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kumari R et al (2014) g mmpbsa A GROMACS tool for high-throughput MM- PBSA calculations. J Chem Inf Model 54(7):1951–1962

Article  CAS  PubMed  Google Scholar 

Comments (0)

No login
gif