A Computational Perspective on Molecular Recognition by Galectins

2021 ◽  
Vol 28 ◽  
Author(s):  
Reyes Núñez-Franco ◽  
Francesca Peccati ◽  
Gonzalo Jiménez-Osés
Keyword(s):  
Binding Energies ◽  
Computational Studies ◽  
Computational Results ◽  
Future Perspectives ◽  
Computational Approaches ◽  
Simulation Techniques ◽  
Small Molecule Ligands ◽  
Ligand Interactions ◽  
Entropy Effects ◽  
Mature Field

: This article presents an overview of recent computational studies dedicated to the analysis of binding between galectins and small-molecule ligands. We first present a summary of the most popular simulation techniques adopted for calculating binding poses and binding energies, and then discuss relevant examples reported in the literature for the three main classes of galectins (dimeric, tandem and chimera). We show that simulation of galectin-ligand interactions is a mature field which has proven invaluable for completing and unraveling experimental observations. Future perspectives to further improve the accuracy and cost-effectiveness of existing computational approaches will involve the development of new schemes to account for solvation and entropy effects, which represent the main current limitations to the accuracy of computational results.

EHAI: Enhanced Human Microbe-Disease Association Identification

2020 ◽  
Vol 21 (11) ◽  
pp. 1078-1084
Author(s):  
Ruizhi Fan ◽  
Chenhua Dong ◽  
Hu Song ◽  
Yixin Xu ◽  
Linsen Shi ◽  
...  
Keyword(s):  
Microbial Community ◽  
Human Health ◽  
Complex Diseases ◽  
Disease Association ◽  
Computational Results ◽  
Computational Approaches ◽  
Disease Diagnostics ◽  
Disease Associations ◽  
Association Discovery ◽  
Biological Tool

: Recently, an increasing number of biological and clinical reports have demonstrated that imbalance of microbial community has the ability to play important roles among several complex diseases concerning human health. Having a good knowledge of discovering potential of microbe-disease relationships, which provides the ability to having a better understanding of some issues, including disease pathology, further boosts disease diagnostics and prognostics, has been taken into account. Nevertheless, a few computational approaches can meet the need of huge scale of microbe-disease association discovery. In this work, we proposed the EHAI model, which is Enhanced Human microbe- disease Association Identification. EHAI employed the microbe-disease associations, and then Gaussian interaction profile kernel similarity has been utilized to enhance the basic microbe-disease association. Actually, some known microbe-disease associations and a large amount of associations are still unavailable among the datasets. The ‘super-microbe’ and ‘super-disease’ were employed to enhance the model. Computational results demonstrated that such super-classes have the ability to be helpful to the performance of EHAI. Therefore, it is anticipated that EHAI can be treated as an important biological tool in this field.

Using experimental and computational approaches to probe an unusual carbon–carbon bond cleavage observed in the synthesis of benzimidazole N-oxides

2021 ◽  
Vol 19 (1) ◽  
pp. 208-215
Author(s):  
Fabrizio Politano ◽  
Arturo León Sandoval ◽  
Jorge G. Uranga ◽  
Elba I. Buján ◽  
Nicholas E. Leadbeater
Keyword(s):  
Bond Cleavage ◽  
Computational Studies ◽  
Computational Approaches ◽  
Carbon Bond ◽  
Carbon Carbon Bond

An unusual carbon–carbon bond-cleavage is explored using a combination of experimental and computational studies.

scholarly journals LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Emily J. Ha ◽  
Cara T. Lwin ◽  
Jacob D. Durrant
Keyword(s):  
Virtual Screening ◽  
Open Source ◽  
Experimental Testing ◽  
Molecular Binding ◽  
Protein Receptor ◽  
Source Program ◽  
Small Molecule Ligands ◽  
Ligand Interactions ◽  
Hit Rates ◽  
Related Proteins

Abstract Structure-based virtual screening (VS) uses computer docking to prioritize candidate small-molecule ligands for subsequent experimental testing. Docking programs evaluate molecular binding in part by predicting the geometry with which a given compound might bind a target receptor (e.g., the docked “pose” relative to a protein target). Candidate ligands predicted to participate in the same intermolecular interactions typical of known ligands (or ligands that bind related proteins) are arguably more likely to be true binders. Some docking programs allow users to apply constraints during the docking process with the goal of prioritizing these critical interactions. But these programs often have restrictive and/or expensive licenses, and many popular open-source docking programs (e.g., AutoDock Vina) lack this important functionality. We present LigGrep, a free, open-source program that addresses this limitation. As input, LigGrep accepts a protein receptor file, a directory containing many docked-compound files, and a list of user-specified filters describing critical receptor/ligand interactions. LigGrep evaluates each docked pose and outputs the names of the compounds with poses that pass all filters. To demonstrate utility, we show that LigGrep can improve the hit rates of test VS targeting H. sapiens poly(ADPribose) polymerase 1 (HsPARP1), H. sapiens peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (HsPin1p), and S. cerevisiae hexokinase-2 (ScHxk2p). We hope that LigGrep will be a useful tool for the computational biology community. A copy is available free of charge at http://durrantlab.com/liggrep/.

scholarly journals Prediction of Binding Stability of Pu(IV) and PuO2(VI) by Nitrogen Tridentate Ligands in Aqueous Solution

2020 ◽  
Vol 21 (8) ◽  
pp. 2791 ◽  
Author(s):  
Keunhong Jeong ◽  
Hye Jin Jeong ◽  
Seung Min Woo ◽  
Sungchul Bae
Keyword(s):  
Chemical Sensors ◽  
Nuclear Power ◽  
Power Plants ◽  
Chemical Sensor ◽  
Structural Information ◽  
Population Analysis ◽  
Binding Energies ◽  
Tridentate Ligands ◽  
Ligand Interactions ◽  
Bonding Characteristics

Plutonium has potential applications in energy production in well-controlled nuclear reactors. Since nuclear power plants have great merit as environmentally friendly energy sources with a recyclable system, a recycling system for extracting Pu from spent fuels using suitable extractants has been proposed. Pu leakage is a potential environmental hazard, hence the need for chemical sensor development. Both extractants and chemical sensors involve metal–ligand interactions and to develop efficient extractants and chemical sensors, structural information about Pu ligands must be obtained by quantum calculations. Herein, six representative nitrogen tridentate ligands were introduced, and their binding stabilities were evaluated. The tridentate L6, which contains tri-pyridine chelate with benzene connectors, showed the highest binding energies for Pu(IV) and PuO2(VI) in water. Analysis based on the quantum theory of atoms in molecular analysis, including natural population analysis and electron density studies, provided insight into the bonding characteristics for each structure. We propose that differences in ionic bonding characteristics account for the Pu-ligand stability differences. These results form a basis for designing novel extractants and organic Pu sensors.

Computational Modeling and Validation of the Encapsulation of Plastic Packages by Transfer Molding

1999 ◽  
Vol 122 (2) ◽  
pp. 138-146 ◽  
Author(s):  
L. Nguyen ◽  
C. Quentin ◽  
W. Lee ◽  
S. Bayyuk ◽  
S. A. Bidstrup-Allen ◽  
...  
Keyword(s):  
Computational Modeling ◽  
Experimental Measurements ◽  
Computational Studies ◽  
Computational Results ◽  
Flow Front ◽  
Transfer Molding ◽  
Plastic Packages ◽  
Encapsulation Process ◽  
Computational Predictions ◽  
Good Agreement

This paper presents, discusses, and compares results from experimental and computational studies of the plastic encapsulation process for a 144-lead TQFP package. The experimental results were obtained using an instrumented molding press, while the computational predictions were obtained using a newly-developed software for modeling transfer molding processes. Validation of the software is emphasized, and this was done mainly by comparing the computational results with the corresponding experimental measurements for pressure, temperature, and flow front advancement in the cavities and runners. The experimental and computational results were found to be in good agreement, especially for the flow-front shapes and locations. [S1043-7398(00)00502-8]

IN SILICO PREDICTION OF PROTEIN-LIGAND INTERACTIONS

2020 ◽  
pp. 254-256
Author(s):  
D. Filimonov ◽  
B. Sobolev ◽  
A. Lagunin
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
High Efficiency ◽  
Amino Acid Sequences ◽  
In Silico Prediction ◽  
Computer Prediction ◽  
Protein Ligand Interactions ◽  
Small Molecule Ligands ◽  
Ligand Interactions ◽  
Structural Descriptions

The method for computer prediction of protein-ligand interactions was developed. The amino acid sequences of target proteins and structural descriptions of small molecule ligands are used as the input data. The method was tested on protein families representing perspective drug targets. The developed approach allows one to predict ligand-protein interactions with high efficiency.

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