scholarly journals Exploration of the selective binding mechanism of protein kinase Aurora A selectivity via a comprehensive molecular modeling study

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7832 ◽  
Author(s):  
Zhe Zhang ◽  
Yafei Xu ◽  
Jian Wu ◽  
Ying Shen ◽  
Hao Cheng ◽  
...  
Keyword(s):  
Protein Dynamics ◽  
Umbrella Sampling ◽  
Potential Of Mean Force ◽  
Aurora A ◽  
Enhanced Sampling ◽  
Selective Inhibitors ◽  
Altered Expression ◽  
Promising Therapeutic Target ◽  
Calculation Results ◽  
Underlying Mechanisms

Background The kinase of Aurora A has been regarded as a promising therapeutic target due to its altered expression in various human cancers. However, given the high similarity of the active binding site of Aurora A to other kinases, designing highly selective inhibitors towards Aurora A remains a challenge. Recently, two potential small-molecule inhibitors named AT9283 and Danusertib were reported to exhibit significant selectivity to Aurora A, but not to Gleevec. It was argued that protein dynamics is crucial for drug selectivity to Aurora A. However, little computational research has been conducted to shed light on the underlying mechanisms. Methods In this study, MM/GBSA calculations based on conventional molecular dynamics (cMD) simulations and enhanced sampling simulations including Gaussian accelerated MD (GaMD) simulations and umbrella sampling were carried out to illustrate the selectivity of inhibitors to Aurora A. Results The calculation results from cMD simulation showed that the binding specificity is primarily controlled by conformational change of the kinase hinge. The protein dynamics and energetic differences were further supported by the GaMD simulations. Umbrella sampling further proved that AT9283 and Danusertib have similar potential of mean force (PMF) profiles toward Aurora A in terms of PMF depth. Compared with AT9283 and Danusertib, Gleevec has much lower PMF depth, indicating that Gleevec is more easily dissociated from Aurora A than AT9283 and Danusertib. These results not only show the selective determinants of Aurora A, but also provide valuable clues for the further development of novel potent Aurora A selective inhibitors.

scholarly journals An Efficient GaMD Multi-Level Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

2021 ◽  
Author(s):  
Fréderic Célerse ◽  
Theo Jaffrelot-Inizan ◽  
Louis Lagardère ◽  
Olivier Adjoua ◽  
Pierre Monmarché ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Adaptive Sampling ◽  
Sampling Strategy ◽  
Umbrella Sampling ◽  
Potential Of Mean Force ◽  
Enhanced Sampling ◽  
Polarizable Force Field ◽  
Accelerated Molecular Dynamics ◽  
Polarizable Force Fields ◽  
Multi Level

We detail a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We then introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple--timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual-water approach is tested on the 1D Potential of Mean Force (PMF) of the CD2-CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling--US--GaMD (ASUS--GaMD) scheme. The highly parallel ASUS--GaMD setup decreases time to convergence by respectively 10 and 20 compared to GaMD--US and US.

scholarly journals An Efficient GaMD Multi-Level Enhanced Sampling Strategy for Polarizable Force Fields Simulations of Large Biological Systems

2021 ◽  
Author(s):  
Fréderic Célerse ◽  
Theo Jaffrelot-Inizan ◽  
Louis Lagardère ◽  
Olivier Adjoua ◽  
Pierre Monmarché ◽  
...  
Keyword(s):  
Adaptive Sampling ◽  
Sampling Strategy ◽  
Umbrella Sampling ◽  
Potential Of Mean Force ◽  
Enhanced Sampling ◽  
Polarizable Force Field ◽  
Accelerated Molecular Dynamics ◽  
Polarizable Force Fields ◽  
Solute Interactions ◽  
Multi Level

We introduce a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs -accelerated implementation within Tinker-HP. For the specific use with the flexible AMOEBA polarizable force field (PFF), we introduce the new "dual–water" GaMD mode. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups with fast multiple–timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual–water approach is tested on the 1D Potential of Mean Force (PMF) of the CD2–CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS–GaMD capabilities but also the introduction of the new Adaptive Sampling–US–GaMD (ASUS–GaMD) scheme. The highly parallel ASUS–GaMD setup decreases time to convergence by respectively 10 and 20 compared to GaMD–US and US.

scholarly journals An Efficient GaMD Multi-Level Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

2021 ◽  
Author(s):  
Fréderic Célerse ◽  
Theo Jaffrelot-Inizan ◽  
Louis Lagardère ◽  
Olivier Adjoua ◽  
Pierre Monmarché ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Adaptive Sampling ◽  
Force Fields ◽  
Biological Systems ◽  
Sampling Strategy ◽  
Umbrella Sampling ◽  
Potential Of Mean Force ◽  
Enhanced Sampling ◽  
Polarizable Force Fields ◽  
Multi Level

We introduce a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field.By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple--timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual--water approach is tested on the 1D Potential of Mean Force (PMF) of the solvated CD2--CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling--US--GaMD (ASUS-GaMD) scheme. The highly parallel ASUS--GaMD setup decreases time to convergence by respectively 10 and 20 times compared to GaMD-US and US. Overall, beside the acceleration of PMF computations, Tinker-HP now allows for the simultaneous use of Adaptive Sampling and GaMD-"dual water" enhanced sampling approaches increasing the applicability of polarizable force fields to large scale simulations of biological systems.

Capturing the Flexibility of a Protein-Ligand Complex with Enhanced Sampling Techniques: Binding Free Energies from Umbrella Sampling, REST2, and Bias Exchange

2019 ◽  
Author(s):  
Sebastian Wingbermühle ◽  
Lars V. Schäfer
Keyword(s):  
Degrees Of Freedom ◽  
Large Scale ◽  
Statistical Error ◽  
Umbrella Sampling ◽  
Potential Of Mean Force ◽  
Sampling Techniques ◽  
Enhanced Sampling ◽  
Ligand Complex ◽  
Mhc I ◽  
Binding Groove

The performance of the three popular enhanced sampling techniques Umbrella Sampling (US), Replica Exchange with Solute Tempering 2 (REST2), and Bias Exchange (BE) is tested on Major Histocompatibility Complex I (MHC I) binding an antigenic peptide. The configurational flexibility of peptide-MHC I complexes (pMHC I) is key to their immunological function and must therefore be captured thoroughly by the sampling techniques used to yield accurate thermodynamics of pMHC I. Here, we calculate the Potential of Mean Force (PMF) for the dissociation of the peptide N-terminus from the MHC I binding groove. We carefully analyze the statistical error of the resulting PMF and the sampling of the orthogonal degrees of freedom to assess how well the three sampling techniques used can handle large-scale configurational flexibility.<br>

An Efficient GaMD Multi-Level Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

2021 ◽  
Author(s):  
Fréderic Célerse ◽  
Theo Jaffrelot-Inizan ◽  
Louis Lagardère ◽  
Olivier Adjoua ◽  
Pierre Monmarché ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Adaptive Sampling ◽  
Sampling Strategy ◽  
Umbrella Sampling ◽  
Potential Of Mean Force ◽  
Enhanced Sampling ◽  
Polarizable Force Field ◽  
Accelerated Molecular Dynamics ◽  
Polarizable Force Fields ◽  
Multi Level

We detail a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We then introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple--timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual-water approach is tested on the 1D Potential of Mean Force (PMF) of the CD2-CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling--US--GaMD (ASUS--GaMD) scheme. The highly parallel ASUS--GaMD setup decreases time to convergence by respectively 10 and 20 compared to GaMD--US and US.

scholarly journals Insulin secretion in health and disease: nutrients dictate the pace

2015 ◽  
Vol 75 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Romano Regazzi ◽  
Adriana Rodriguez-Trejo ◽  
Cécile Jacovetti
Keyword(s):  
Fatty Acids ◽  
Insulin Secretion ◽  
Cell Mass ◽  
Insulin Biosynthesis ◽  
Β Cells ◽  
Β Cell ◽  
Altered Expression ◽  
Cell Dysfunction ◽  
Dietary Nutrients ◽  
Underlying Mechanisms

Insulin is a key hormone controlling metabolic homeostasis. Loss or dysfunction of pancreatic β-cells lead to the release of insufficient insulin to cover the organism needs, promoting diabetes development. Since dietary nutrients influence the activity of β-cells, their inadequate intake, absorption and/or utilisation can be detrimental. This review will highlight the physiological and pathological effects of nutrients on insulin secretion and discuss the underlying mechanisms. Glucose uptake and metabolism in β-cells trigger insulin secretion. This effect of glucose is potentiated by amino acids and fatty acids, as well as by entero-endocrine hormones and neuropeptides released by the digestive tract in response to nutrients. Glucose controls also basal and compensatory β-cell proliferation and, along with fatty acids, regulates insulin biosynthesis. If in the short-term nutrients promote β-cell activities, chronic exposure to nutrients can be detrimental to β-cells and causes reduced insulin transcription, increased basal secretion and impaired insulin release in response to stimulatory glucose concentrations, with a consequent increase in diabetes risk. Likewise, suboptimal early-life nutrition (e.g. parental high-fat or low-protein diet) causes altered β-cell mass and function in adulthood. The mechanisms mediating nutrient-induced β-cell dysfunction include transcriptional, post-transcriptional and translational modifications of genes involved in insulin biosynthesis and secretion, carbohydrate and lipid metabolism, cell differentiation, proliferation and survival. Altered expression of these genes is partly caused by changes in non-coding RNA transcripts induced by unbalanced nutrient uptake. A better understanding of the mechanisms leading to β-cell dysfunction will be critical to improve treatment and find a cure for diabetes.

scholarly journals Identification of rare Lewis oligosaccharide conformers in aqueous solution using enhanced sampling molecular dynamics

2017 ◽  
Author(s):  
Irfan Alibay ◽  
Kepa K. Burusco ◽  
Neil J. Bruce ◽  
Richard A. Bryce
Keyword(s):  
Molecular Dynamics ◽  
Aqueous Solution ◽  
Md Simulations ◽  
Umbrella Sampling ◽  
Biological Action ◽  
Sialyl Lewis X ◽  
Enhanced Sampling ◽  
Sialyl Lewis ◽  
Aqueous Solvent ◽  
A Minor

<p>Determining the conformations accessible to carbohydrate ligands in aqueous solution is important for understanding their biological action. In this work, we evaluate the conformational free energy surfaces of Lewis oligosaccharides in explicit aqueous solvent using a multidimensional variant of the swarm-enhanced sampling molecular dynamics (msesMD) method; we compare with multi-microsecond unbiased MD simulations, umbrella sampling and accelerated MD approaches. For the sialyl Lewis A tetrasaccharide, msesMD simulations in aqueous solution predict conformer landscapes in general agreement with the other biased methods and with triplicate unbiased 10 ms trajectories; these simulations find a predominance of closed conformer and a range of low occupancy open forms. The msesMD simulations also suggest closed-to-open transitions in the tetrasaccharide are facilitated by changes in ring puckering of its GlcNAc residue away from the <sup>4</sup>C<sub>1</sub> form, in line with previous work. For sialyl Lewis X tetrasaccharide, msesMD simulations predict a minor population of an open form in solution, corresponding to a rare lectin-bound pose observed crystallographically. Overall, from comparison with biased MD calculations, we find that triplicate 10 ms unbiased MD simulations may not be enough to fully sample glycan conformations in aqueous solution. However, the computational efficiency and intuitive approach of the msesMD method suggest potential for its application in glycomics as a tool for analysis of oligosaccharide conformation.</p>

scholarly journals What turns CREB on? And off? And why does it matter?

2020 ◽  
Vol 77 (20) ◽  
pp. 4049-4067 ◽  
Author(s):  
André Steven ◽  
Michael Friedrich ◽  
Paul Jank ◽  
Nadine Heimer ◽  
Jan Budczies ◽  
...  
Keyword(s):  
Overall Survival ◽  
Transcription Factor ◽  
Cyclic Amp ◽  
Therapeutic Target ◽  
Therapy Response ◽  
Predictive Marker ◽  
Tumor Patients ◽  
Altered Expression ◽  
Underlying Mechanisms ◽  
And Function

Abstract Altered expression and function of the transcription factor cyclic AMP response-binding protein (CREB) has been identified to play an important role in cancer and is associated with the overall survival and therapy response of tumor patients. This review focuses on the expression and activation of CREB under physiologic conditions and in tumors of distinct origin as well as the underlying mechanisms of CREB regulation by diverse stimuli and inhibitors. In addition, the clinical relevance of CREB is summarized, including its use as a prognostic and/or predictive marker as well as a therapeutic target.

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