scholarly journals Distinct REV-ERBα Conformational State Predicted by GaMD Simulations Leads to the Structure-Based Discovery of Novel REV-ERBα Antagonist

2021 ◽  
Author(s):  
LAMEES HEGAZY ◽  
Aurore-Cecile Valfort ◽  
Thomas P. Burris ◽  
Bahaa Elgendy
Keyword(s):  
Ligand Binding ◽  
Conformational Changes ◽  
Nuclear Hormone Receptor ◽  
Conformational State ◽  
Accelerated Molecular Dynamics ◽  
Physiological Processes ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Synthetic Ligand ◽  
Specific Orientation

REV-ERBα is a nuclear hormone receptor that plays important role in the regulation of many physiological processes such as circadian clock regulation, inflammation, and metabolism. Despite its importance, few chemical tools are available to study this receptor. In addition, there is no available X-ray crystal structures of REV-ERB bound with synthetic ligands, hampering the development of targeted therapeutics. SR8278 is the only identified synthetic antagonist of REV-ERB. We have performed Gaussian accelerated molecular dynamics (GaMD) simulations to sample the binding pathway of SR8278 and associated conformational changes to REV-ERBα. The simulations revealed a novel and more energetically favorable conformational state than the starting conformation. The new conformation allows ligand binding to the orthosteric binding site in a specific orientation. This state is reached after a tryptophan (Trp436) rotameric switch coupled with H3-H6 distance change. We used the newly identified GaMD conformational state in structure-based virtual screening of one million compounds library which led to the identification of novel REV-ERBα antagonist. This study is the first that demonstrates a synthetic ligand binding pathway to REV-ERBα, which provided important insights into the REV-ERBα functional mechanism and lead to the discovery of novel REV-ERBα antagonists. This study further emphasizes the power of computational chemistry methods in advancing drug discovery research.

scholarly journals Capturing Peptide–GPCR Interactions and Their Dynamics

Molecules ◽  
2020 ◽  
Vol 25 (20) ◽  
pp. 4724
Author(s):  
Anette Kaiser ◽  
Irene Coin
Keyword(s):  
Ligand Binding ◽  
Conformational Changes ◽  
Structural Changes ◽  
Structural Heterogeneity ◽  
Receptor Interaction ◽  
Spectroscopic Techniques ◽  
Peptide Receptors ◽  
Physiological Processes ◽  
High Dynamics ◽  
G Protein Coupled

Many biological functions of peptides are mediated through G protein-coupled receptors (GPCRs). Upon ligand binding, GPCRs undergo conformational changes that facilitate the binding and activation of multiple effectors. GPCRs regulate nearly all physiological processes and are a favorite pharmacological target. In particular, drugs are sought after that elicit the recruitment of selected effectors only (biased ligands). Understanding how ligands bind to GPCRs and which conformational changes they induce is a fundamental step toward the development of more efficient and specific drugs. Moreover, it is emerging that the dynamic of the ligand–receptor interaction contributes to the specificity of both ligand recognition and effector recruitment, an aspect that is missing in structural snapshots from crystallography. We describe here biochemical and biophysical techniques to address ligand–receptor interactions in their structural and dynamic aspects, which include mutagenesis, crosslinking, spectroscopic techniques, and mass-spectrometry profiling. With a main focus on peptide receptors, we present methods to unveil the ligand–receptor contact interface and methods that address conformational changes both in the ligand and the GPCR. The presented studies highlight a wide structural heterogeneity among peptide receptors, reveal distinct structural changes occurring during ligand binding and a surprisingly high dynamics of the ligand–GPCR complexes.

DLSCORE: A Deep Learning Model for Predicting Protein-Ligand Binding Affinities

2018 ◽  
Author(s):  
Mahmudulla Hassan ◽  
Daniel Castaneda Mogollon ◽  
Olac Fuentes ◽  
suman sirimulla
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ligand Binding ◽  
Scoring Function ◽  
Learning Approaches ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Fully Connected

<p>In recent years, the cheminformatics community has seen an increased success with machine learning-based scoring functions for estimating binding affinities and pose predictions. The prediction of protein-ligand binding affinities is crucial for drug discovery research. Many physics-based scoring functions have been developed over the years. Lately, machine learning approaches are proven to boost the performance of traditional scoring functions. In this study, a novel deep learning based scoring function (DLSCORE) was developed and trained on the refined PDBBind v.2016 dataset using 348 BINding ANAlyzer (BINANA) descriptors. The neural networks of the DLSCORE model have different number of fully connected hidden layers. Our model, an ensemble of 10 networks, yielded a Pearson R2 of 0.82, a Spearman Rho R2 of 0.90, Kendall Tau R2 of 0.74, an RMSE of 1.15 kcal=mol, and an MAE of 0.86 kcal=mol for our test set. This software is available on Github at https://github.com/sirimullalab/dlscore.git</p><p><br></p>

DLSCORE: A Deep Learning Model for Predicting Protein-Ligand Binding Affinities

2018 ◽  
Author(s):  
Mahmudulla Hassan ◽  
Daniel Castaneda Mogollon ◽  
Olac Fuentes ◽  
suman sirimulla
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ligand Binding ◽  
Scoring Function ◽  
Learning Approaches ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Fully Connected

<p>In recent years, the cheminformatics community has seen an increased success with machine learning-based scoring functions for estimating binding affinities and pose predictions. The prediction of protein-ligand binding affinities is crucial for drug discovery research. Many physics-based scoring functions have been developed over the years. Lately, machine learning approaches are proven to boost the performance of traditional scoring functions. In this study, a novel deep learning based scoring function (DLSCORE) was developed and trained on the refined PDBBind v.2016 dataset using 348 BINding ANAlyzer (BINANA) descriptors. The neural networks of the DLSCORE model have different number of fully connected hidden layers. Our model, an ensemble of 10 networks, yielded a Pearson R2 of 0.82, a Spearman Rho R2 of 0.90, Kendall Tau R2 of 0.74, an RMSE of 1.15 kcal=mol, and an MAE of 0.86 kcal=mol for our test set. This software is available on Github at https://github.com/sirimullalab/dlscore.git</p><p><br></p>

CarR, a MarR-family regulator from Corynebacterium glutamicum, modulated antibiotic and aromatic compound resistance

2019 ◽  
Vol 476 (21) ◽  
pp. 3141-3159 ◽  
Author(s):  
Meiru Si ◽  
Can Chen ◽  
Zengfan Wei ◽  
Zhijin Gong ◽  
GuiZhi Li ◽  
...  
Keyword(s):  
Stress Response ◽  
Ligand Binding ◽  
Corynebacterium Glutamicum ◽  
Conformational Changes ◽  
Cysteine Oxidation ◽  
Transcriptional Regulatory ◽  
Ligand Free ◽  
Redox Sensing

Abstract MarR (multiple antibiotic resistance regulator) proteins are a family of transcriptional regulators that is prevalent in Corynebacterium glutamicum. Understanding the physiological and biochemical function of MarR homologs in C. glutamicum has focused on cysteine oxidation-based redox-sensing and substrate metabolism-involving regulators. In this study, we characterized the stress-related ligand-binding functions of the C. glutamicum MarR-type regulator CarR (C. glutamicum antibiotic-responding regulator). We demonstrate that CarR negatively regulates the expression of the carR (ncgl2886)–uspA (ncgl2887) operon and the adjacent, oppositely oriented gene ncgl2885, encoding the hypothetical deacylase DecE. We also show that CarR directly activates transcription of the ncgl2882–ncgl2884 operon, encoding the peptidoglycan synthesis operon (PSO) located upstream of carR in the opposite orientation. The addition of stress-associated ligands such as penicillin and streptomycin induced carR, uspA, decE, and PSO expression in vivo, as well as attenuated binding of CarR to operator DNA in vitro. Importantly, stress response-induced up-regulation of carR, uspA, and PSO gene expression correlated with cell resistance to β-lactam antibiotics and aromatic compounds. Six highly conserved residues in CarR were found to strongly influence its ligand binding and transcriptional regulatory properties. Collectively, the results indicate that the ligand binding of CarR induces its dissociation from the carR–uspA promoter to derepress carR and uspA transcription. Ligand-free CarR also activates PSO expression, which in turn contributes to C. glutamicum stress resistance. The outcomes indicate that the stress response mechanism of CarR in C. glutamicum occurs via ligand-induced conformational changes to the protein, not via cysteine oxidation-based thiol modifications.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

scholarly journals iPSC technology-based regenerative medicine for kidney diseases

2021 ◽  
Author(s):  
Kenji Osafune
Keyword(s):  
Regenerative Medicine ◽  
Cell Therapy ◽  
Kidney Development ◽  
Disease Modeling ◽  
Kidney Diseases ◽  
Collecting Duct ◽  
Current Status ◽  
Renal Tubules ◽  
Discovery Research ◽  
Drug Discovery Research

AbstractWith few curative treatments for kidney diseases, increasing attention has been paid to regenerative medicine as a new therapeutic option. Recent progress in kidney regeneration using human-induced pluripotent stem cells (hiPSCs) is noteworthy. Based on the knowledge of kidney development, the directed differentiation of hiPSCs into two embryonic kidney progenitors, nephron progenitor cells (NPCs) and ureteric bud (UB), has been established, enabling the generation of nephron and collecting duct organoids. Furthermore, human kidney tissues can be generated from these hiPSC-derived progenitors, in which NPC-derived glomeruli and renal tubules and UB-derived collecting ducts are interconnected. The induced kidney tissues are further vascularized when transplanted into immunodeficient mice. In addition to the kidney reconstruction for use in transplantation, it has been demonstrated that cell therapy using hiPSC-derived NPCs ameliorates acute kidney injury (AKI) in mice. Disease modeling and drug discovery research using disease-specific hiPSCs has also been vigorously conducted for intractable kidney disorders, such as autosomal dominant polycystic kidney disease (ADPKD). In an attempt to address the complications associated with kidney diseases, hiPSC-derived erythropoietin (EPO)-producing cells were successfully generated to discover drugs and develop cell therapy for renal anemia. This review summarizes the current status and future perspectives of developmental biology of kidney and iPSC technology-based regenerative medicine for kidney diseases.

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