RADPOP: A new modelling framework for radiation protection

Input datasets (train and test) and the generated models which can be used to reproduce the results presented in the current study

Checking the STEP-Associated Trafficking and Internalization of Glutamate Receptors for Reduced Cognitive Deficits: A Machine Learning Approach-Based Cheminformatics Study and Its Application for Drug Repurposing

<div><p>Background</p><p>Alzheimer’s disease, a lethal neurodegenerative disorder that leads to progressive memory loss, is the most common form of dementia. Owing to the complexity of the disease, its root cause still remains unclear. The existing anti-Alzheimer’s drugs are unable to cure the disease while the current therapeutic options have provided only limited help in restoring moderate memory and remain ineffective at restricting the disease’s progression. The striatal-enriched protein tyrosine phosphatase (STEP) has been shown to be involved in the internalization of the receptor, N-methyl D-aspartate (NMDR) and thus is associated with the disease. The present study was performed using machine learning algorithms, docking protocol and molecular dynamics (MD) simulations to develop STEP inhibitors, which could be novel anti-Alzheimer’s molecules.</p><p>Methods</p><p>The present study deals with the generation of computational predictive models based on chemical descriptors of compounds using machine learning approaches followed by substructure fragment analysis. To perform this analysis, the 2D molecular descriptors were generated and machine learning algorithms (Naïve Bayes, Random Forest and Sequential Minimization Optimization) were utilized. The binding mechanisms and the molecular interactions between the predicted active compounds and the target protein were modelled using docking methods. Further, the stability of the protein-ligand complex was evaluated using MD simulation studies. The substructure fragment analysis was performed using Substructure fingerprint (SubFp), which was further explored using a predefined dictionary.</p><p>Results</p><p>The present study demonstrates that the computational methodology used can be employed to examine the biological activities of small molecules and prioritize them for experimental screening. Large unscreened chemical libraries can be screened to identify potential novel hits and accelerate the drug discovery process. Additionally, the chemical libraries can be searched for significant substructure patterns as reported in the present study, thus possibly contributing to the activity of these molecules.</p></div

Evaluation of generated models based on 10 BestFirst attributes obtained using CfsSubsetEval module.

<p>Evaluation of generated models based on 10 BestFirst attributes obtained using CfsSubsetEval module.</p

ROC plot for the three machine learning models generated using (A) 154 descriptors.

<p>(B) 10 BestFirst descriptors.</p

Additional file 2: Figure S1. of Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes

Shows the percent variation explained by. the first two principal components

Additional file 8: Figure S2. of Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes

depicts the interaction patterns of the ligands within the active site of the novel candidate Alzheimer protein targets, Cadherin, CARD8, JAK2 and NFKBIA

shows the hydrogen and hydrophobic interactions between the ligand and the protein.

<p>shows the hydrogen and hydrophobic interactions between the ligand and the protein.</p

RMSD trajectory of the backbone obtained after MD simulation study.

<p>RMSD trajectory of the backbone obtained after MD simulation study.</p

Superimposition of the structures of the complexes corresponding to various frames, 7, 9, 11, 13 and 15, during the trajectory analysis.

<p>Superimposition of the structures of the complexes corresponding to various frames, 7, 9, 11, 13 and 15, during the trajectory analysis.</p

Docked conformations of the lead compounds with STEP protein (A) H-bond interactions of Ligand_7 (B) H-bond interactions of Ligand_5 (C) H-bond interactions of ATP (D) H-bond interactions of Folic acid.

<p>Docked conformations of the lead compounds with STEP protein (A) H-bond interactions of Ligand_7 (B) H-bond interactions of Ligand_5 (C) H-bond interactions of ATP (D) H-bond interactions of Folic acid.</p