scholarly journals Hermes III endpoint energy calculation from photonuclear activation of 197Au and 58Ni foils

2014 ◽  
Author(s):  
Christopher Thomas Parzyck
Keyword(s):  
Energy Calculation ◽  
Endpoint Energy

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

2020 ◽  
Author(s):  
Christina Schindler ◽  
Hannah Baumann ◽  
Andreas Blum ◽  
Dietrich Böse ◽  
Hans-Peter Buchstaller ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Active Drug ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculation ◽  
Large Scale Assessment ◽  
New Public ◽  
Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

Application of the ESMACS Binding Free Energy Protocol to a Highly Varied Ligand Dataset: Lactate Dehydogenase A

2019 ◽  
Author(s):  
David Wright ◽  
Fouad Husseini ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
...  
Keyword(s):  
Free Energy ◽  
Surface Area ◽  
Binding Free Energy ◽  
Normal Mode Analysis ◽  
Binding Mode ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Mode Analysis ◽  
Energy Calculation ◽  
Accessible Surface

<div>Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) for use in fragment based drug design scenarios. ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in the lactate dehydogenase A target protein, which vary in size, charge and binding mode. When comparing to experimental results, we obtain excellent statistical rankings across this highly diverse set of ligands. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. </div>

Free Energy Calculation Methods of Biomolecules

2011 ◽  
Vol 27 (5) ◽  
pp. 395-402 ◽  
Author(s):  
Changjun CHEN ◽  
Yanzhao HUANG ◽  
Yi XIAO
Keyword(s):  
Free Energy ◽  
Free Energy Calculation ◽  
Energy Calculation ◽  
Calculation Methods

Recent Advances of In-Silico Modeling of Potent Antagonists for the Adenosine Receptors

2019 ◽  
Vol 25 (7) ◽  
pp. 750-773 ◽  
Author(s):  
Pabitra Narayan Samanta ◽  
Supratik Kar ◽  
Jerzy Leszczynski
Keyword(s):  
Drug Targets ◽  
Biological Activities ◽  
Scoring Function ◽  
Md Simulations ◽  
Energy Calculation ◽  
Binding Modes ◽  
Macromolecular Systems ◽  
In Silico Modeling ◽  
Mathematical Algorithms ◽  
The Impact

The rapid advancement of computer architectures and development of mathematical algorithms offer a unique opportunity to leverage the simulation of macromolecular systems at physiologically relevant timescales. Herein, we discuss the impact of diverse structure-based and ligand-based molecular modeling techniques in designing potent and selective antagonists against each adenosine receptor (AR) subtype that constitutes multitude of drug targets. The efficiency and robustness of high-throughput empirical scoring function-based approaches for hit discovery and lead optimization in the AR family are assessed with the help of illustrative examples that have led to nanomolar to sub-micromolar inhibition activities. Recent progress in computer-aided drug discovery through homology modeling, quantitative structure-activity relation, pharmacophore models, and molecular docking coupled with more accurate free energy calculation methods are reported and critically analyzed within the framework of structure-based virtual screening of AR antagonists. Later, the potency and applicability of integrated molecular dynamics (MD) methods are addressed in the context of diligent inspection of intricated AR-antagonist binding processes. MD simulations are exposed to be competent for studying the role of the membrane as well as the receptor flexibility toward the precise evaluation of the biological activities of antagonistbound AR complexes such as ligand binding modes, inhibition affinity, and associated thermodynamic and kinetic parameters.

Machine Learning-based Virtual Screening Strategy Reveals Some Natural Compounds As Potential PAK4 Inhibitors In Triple Negative Breast Cancer

2020 ◽  
Vol 18 ◽  
Author(s):  
Opeyemi Iwaloye ◽  
Olusola Olalekan Elekofehinti ◽  
Babatomiwa Kikiowo ◽  
Emmanuel Ayo Oluwarotimi ◽  
Toyin Mary Fadipe
Keyword(s):  
Breast Cancer ◽  
Virtual Screening ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Binding Free Energy ◽  
Natural Compounds ◽  
Energy Calculation ◽  
Reference Drug ◽  
Predictive Quality

Background: P-21 activating kinase 4 (PAK4) is implicated in poor prognosis of many cancers, especially in the progression of Triple Negative Breast Cancer (TNBC). The present study was aimed at designing some potential drug candidates as PAK4 inhibitors for breast cancer therapy. Objective: This study aimed to finding novel inhibitors of PAK4 from natural compounds using computational approach. Methods: An e-pharmacophore model was developed from docked PAK4-coligand complex and used to screen over a thousand natural compounds downloaded from BIOFACQUIM and NPASS databases to match a minimum of 5 sites for selected (ADDDHRR) hypothesis. The robustness of the virtual screening method was accessed by well-established methods including EF, ROC, BEDROC, AUAC, and the RIE. Compounds with fitness score greater than one were filtered by applying molecular docking (HTVS, SP, XP and Induced fit docking) and ADME prediction. Using Machine learningbased approach QSAR model was generated using Automated QSAR. The computed top model kpls_des_17 (R2= 0.8028, RMSE = 0.4884 and Q2 = 0.7661) was used to predict the pIC50 of the lead compounds. Internal and external validations were accessed to determine the predictive quality of the model. Finally the binding free energy calculation was computed. Results: The robustness/predictive quality of the models were affirmed. The hits had better binding affinity than the reference drug and interacted with key amino acids for PAK4 inhibition. Overall, the present analysis yielded three potential inhibitors that are predicted to bind with PAK4 better than reference drug tamoxifen. The three potent novel inhibitors vitexin, emodin and ziganein recorded IFD score of -621.97 kcal/mol, -616.31 kcal/mol and -614.95 kcal/mol, respectively while showing moderation for ADME properties and inhibition constant. Conclusion: It is expected that the findings reported in this study may provide insight for designing effective and less toxic PAK4 inhibitors for triple negative breast cancer.

scholarly journals Predicting gas selectivity in organic ionic plastic crystals by free energy calculations

RSC Advances ◽  
2021 ◽  
Vol 11 (32) ◽  
pp. 19623-19629
Author(s):  
Vinay S. Kandagal ◽  
Jennifer M. Pringle ◽  
Maria Forsyth ◽  
Fangfang Chen
Keyword(s):  
Free Energy ◽  
Gas Separation ◽  
Free Energy Calculations ◽  
Free Energy Calculation ◽  
Energy Calculation ◽  
Plastic Crystal ◽  
Plastic Crystals ◽  
New Type ◽  
Gas Molecules ◽  
Gas Selectivity

The free energy calculation shows the different free energy changes of the adsorption and absorption of gas molecules into an organic ionic plastic crystal, successfully predicting the gas selectivity of this new type of gas separation material.

scholarly journals Scaffold Hopping of α-Rubromycin Enables Direct Access to FDA-Approved Cromoglicic Acid as a SARS-CoV-2 MPro Inhibitor

2021 ◽  
Vol 14 (6) ◽  
pp. 541
Author(s):  
Hani A. Alhadrami ◽  
Ahmed M. Sayed ◽  
Heba Al-Khatabi ◽  
Nabil A. Alhakamy ◽  
Mostafa E. Rateb
Keyword(s):  
Binding Free Energy ◽  
Human Fibroblasts ◽  
Critical Time ◽  
Dynamics Simulation ◽  
Direct Access ◽  
Energy Calculation ◽  
Wide Range ◽  
Normal Human ◽  
Novel Scaffold

The COVID-19 pandemic is still active around the globe despite the newly introduced vaccines. Hence, finding effective medications or repurposing available ones could offer great help during this serious situation. During our anti-COVID-19 investigation of microbial natural products (MNPs), we came across α-rubromycin, an antibiotic derived from Streptomyces collinus ATCC19743, which was able to suppress the catalytic activity (IC50 = 5.4 µM and Ki = 3.22 µM) of one of the viral key enzymes (i.e., MPro). However, it showed high cytotoxicity toward normal human fibroblasts (CC50 = 16.7 µM). To reduce the cytotoxicity of this microbial metabolite, we utilized a number of in silico tools (ensemble docking, molecular dynamics simulation, binding free energy calculation) to propose a novel scaffold having the main pharmacophoric features to inhibit MPro with better drug-like properties and reduced/minimal toxicity. Nevertheless, reaching this novel scaffold synthetically is a time-consuming process, particularly at this critical time. Instead, this scaffold was used as a template to explore similar molecules among the FDA-approved medications that share its main pharmacophoric features with the aid of pharmacophore-based virtual screening software. As a result, cromoglicic acid (aka cromolyn) was found to be the best hit, which, upon in vitro MPro testing, was 4.5 times more potent (IC50 = 1.1 µM and Ki = 0.68 µM) than α-rubromycin, with minimal cytotoxicity toward normal human fibroblasts (CC50 > 100 µM). This report highlights the potential of MNPs in providing unprecedented scaffolds with a wide range of therapeutic efficacy. It also revealed the importance of cheminformatics tools in speeding up the drug discovery process, which is extremely important in such a critical situation.

Stoichiometric and Off-Stoichiometric Alloying in Silicide Compound Ti5Si3 By Nb or Cr Additions

1998 ◽  
Vol 552 ◽  
Author(s):  
Lanting Zhang ◽  
Jiansheng Wu
Keyword(s):  
Total Energy ◽  
Melting Temperature ◽  
Stoichiometric Composition ◽  
Primary Phase ◽  
Titanium Silicide ◽  
Energy Calculation ◽  
Alloying Element ◽  
Total Energy Calculation ◽  
Element Addition ◽  
Cr Addition

ABSTRACTTitanium silicide Ti5Si3 whose melting temperature is 2130°C bears the potential for very hightemperature application. This paper reports our results on the alloying behaviour of Nb or Cr addition to this compound. Total energy calculation shows that the substitution of Ti by Nb or Cr atoms in Ti5Si3 crystal stiffens the bonding between the atoms. In experiment, two means of alloying are considered: stoichiometric and off-stoichiometric alloying. Stoichiometric alloying in Ti5Si3 results in compounds consisting of single Ti5Si3 phase while off-stoichiometric alloying yields hypereutectic microstructure with Ti5Si3 being the primary phase. The Ti5Si3 phase in both cases dissolves certain amount of Nb or Cr alloying element and its composition agrees with the stoichiometric composition of (Ti,Nb) 5Si3 or (Ti,Cr) 5Si3 The moduli of the stoichiometric alloys increase with the increase of alloying element addition, indicating an enhancement in Ti5Si3crystal.

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