scholarly journals Alzheimer’s protection effect of A673T mutation may be driven by lower Aβ oligomer binding affinity

2020 ◽  
Author(s):  
Colleen S. Limegrover ◽  
Harry LeVine ◽  
Nicholas J. Izzo ◽  
Raymond Yurko ◽  
Kelsie Mozzoni ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Protection Effect ◽  
Aβ Oligomer

Antiproliferative and antiviral effects of mutated IFN-alpha with increased IFN receptor binding affinity

2011 ◽  
Vol 49 (01) ◽  
Author(s):  
MF Sprinzl ◽  
L Bührer ◽  
D Strand ◽  
G Schreiber ◽  
PR Galle ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Receptor Binding ◽  
Receptor Binding Affinity ◽  
Ifn Alpha ◽  
Antiviral Effects

Enhancement of Plasminogen Binding to U937 Cells and Fibrin by Complestatin

1997 ◽  
Vol 77 (01) ◽  
pp. 137-142 ◽  
Author(s):  
Kiyoshi Tachikawa ◽  
Keiji Hasurni ◽  
Akira Endo
Keyword(s):  
Binding Site ◽  
Binding Affinity ◽  
Blood Cells ◽  
Aminocaproic Acid ◽  
U937 Cells ◽  
Biochemical Basis ◽  
Equilibrium Binding ◽  
Pharmacological Stimulation ◽  
Endogenous Fibrinolysis ◽  
Stimulation Of

SummaryPlasminogen binds to endothelial and blood cells as well as to fibrin, where the zymogen is efficiently activated and protected from inhibition by α2-antiplasmin. In the present study we have found that complestatin, a peptide-like metabolite of a streptomyces, enhances binding of plasminogen to cells and fibrin. Complestatin, at concentrations ranging from 1 to 5 μM, doubled 125I-plasminogen binding to U937 cells both in the absence and presence of lipoprotein(a), a putative physiological competitor of plasminogen. The binding of 125I-plasminogen in the presence of complestatin was abolished by e-aminocaproic acid, suggesting that the lysine binding site(s) of the plasminogen molecule are involved in the binding. Equilibrium binding analyses indicated that complestatin increased the maximum binding of 125I-plasminogen to U937 cells without affecting the binding affinity. Complestatin was also effective in increasing 125I-plasminogen binding to fibrin, causing 2-fold elevation of the binding at ~1 μM. Along with the potentiation of plasminogen binding, complestatin enhanced plasmin formation, and thereby increased fibrinolysis. These results would provide a biochemical basis for a pharmacological stimulation of endogenous fibrinolysis through a promotion of plasminogen binding to cells and fibrin.

The Role of the Initiator System in the Synthesis of Acidic Multifunctional Nanoparticles Designed for Molecular Imprinting of Proteins

2020 ◽  
Vol 65 (1) ◽  
pp. 28-41
Author(s):  
Marwa Aly Ahmed ◽  
Júlia Erdőssy ◽  
Viola Horváth
Keyword(s):  
Acrylic Acid ◽  
Binding Affinity ◽  
Molecular Imprinting ◽  
Low Temperatures ◽  
Ammonium Persulfate ◽  
Sodium Bisulfite ◽  
Multifunctional Nanoparticles ◽  
High Affinity ◽  
Initiator System

Multifunctional nanoparticles have been shown earlier to bind certain proteins with high affinity and the binding affinity could be enhanced by molecular imprinting of the target protein. In this work different initiator systems were used and compared during the synthesis of poly (N-isopropylacrylamide-co-acrylic acid-co-N-tert-butylacrylamide) nanoparticles with respect to their future applicability in molecular imprinting of lysozyme. The decomposition of ammonium persulfate initiator was initiated either thermally at 60 °C or by using redox activators, namely tetramethylethylenediamine or sodium bisulfite at low temperatures. Morphology differences in the resulting nanoparticles have been revealed using scanning electron microscopy and dynamic light scattering. During polymerization the conversion of each monomer was followed in time. Striking differences were demonstrated in the incorporation rate of acrylic acid between the tetramethylethylenediamine catalyzed initiation and the other systems. This led to a completely different nanoparticle microstructure the consequence of which was the distinctly lower lysozyme binding affinity. On the contrary, the use of sodium bisulfite activation resulted in similar nanoparticle structural homogeneity and protein binding affinity as the thermal initiation.

Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free Energy Calculations using Lambda Dynamics

2020 ◽  
Author(s):  
E. Prabhu Raman ◽  
Thomas J. Paul ◽  
Ryan L. Hayes ◽  
Charles L. Brooks III
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Combinatorial Libraries ◽  
Free Energy Calculations ◽  
Lead Optimization ◽  
Efficient Estimation ◽  
Lead Compound

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>

Crosslinker Chemistry Determines the Uptake Potential of Perfluorinated Alkyl Substances by β-Cyclodextrin Polymers

2018 ◽  
Author(s):  
Leilei Xiao ◽  
Casey Ching ◽  
Yuhan Ling ◽  
Mohammadreza Nasiri ◽  
Max Justin Klemes ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Polymer Networks ◽  
Cyclodextrin Polymer ◽  
Perfluorinated Alkyl Substances ◽  
Cyclodextrin Polymers

This work describes several crosslinked β-cyclodextrin polymer networks and correlates the crosslinker chemistry with binding affinity for per- and polyfluorinated alkyl substances (PFASs), including PFOA and PFOS.

GRAM: A True Null Model for Relative Binding Affinity Predictions

2019 ◽  
Author(s):  
Guanglei Cui ◽  
Alan P. Graves ◽  
Eric S. Manas
Keyword(s):  
Binding Affinity ◽  
Performance Metrics ◽  
Null Model ◽  
Performance Measure ◽  
Interval Estimate ◽  
Empirical Observation ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Relative Binding Affinity ◽  
Relative Binding

Relative binding affinity prediction is a critical component in computer aided drug design. Significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of random in an objective manner. Although many performance metrics, such as correlation coefficient (r2), mean unsigned error (MUE), and room mean square error (RMSE), are frequently used in the literature, a true and non-trivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are 1) it provides the uncertainty range in the predicted activities, which is important in prospective applications; 2) a true null model with well-defined PI can be established. We provide one such example termed Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, s). Having an analytically defined PI that only depends on the variation in the activities, GRAM should in principle allow us to compare the performance of relative binding affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.<br>

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

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