scholarly journals Suitable Docking Protocol for the Design of Novel Coumarin Derivatives with Selective MAO-B Effects

2021 ◽  
Vol 1 (1) ◽  
pp. 40-47
Author(s):  
Emilio Viktorov Mateev ◽  
Iva Valkova ◽  
Maya Georgieva ◽  
Alexander Zlatkov
Keyword(s):  
Correlation Coefficient ◽  
Scoring Functions ◽  
Pearson’S Correlation ◽  
Chain Flexibility ◽  
Pearson’S Correlation Coefficient ◽  
Pairwise Correlation ◽  
Mao B ◽  
Docking Protocol ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

Recently, the application of molecular docking is drastically increasing due to the rapid growth of resolved crystallographic receptors with co-crystallized ligands. However, the inability of docking softwares to correctly score the occurred interactions between ligands and receptors is still a relevant issue. This study examined the Pearson’s correlation coefficient between the experimental monoamine oxidase-B (MAO-B) inhibitory activity of 44 novel coumarins and the obtained GOLD 5.3 docking scores. Subsequently, optimization of the docking protocol was carried out to achieve the best possible pairwise correlation. Numerous modifications in the docking settings such as alteration in the scoring functions, size of the grid space, presence of active waters, and side-chain flexibility were conducted. Furthermore, ensemble docking simulations into two superimposed complexes were performed. The model was validated with a test set. A significant Pearson’s correlation coefficient of 0.8217 was obtained for the latter. In the final stage of our work, we observed the major interactions between the top-scored ligands and the active site of 1S3B.

scholarly journals Learning Protein-Ligand Binding Affinity with Atomic Environment Vectors

2020 ◽  
Author(s):  
Rocco Meli ◽  
Andrew Anighoro ◽  
Mike Bodkin ◽  
Garrett Morris ◽  
Philip Biggin
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Correlation Coefficient ◽  
Scoring Function ◽  
Scoring Functions ◽  
Autodock Vina ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

<div> <div> <div> <p>Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we explore the use of atomic environment vectors (AEVs) and feed-forward neural networks, the building blocks of several neural network potentials, for the prediction of protein-ligand binding affinity. The AEV-based scoring function, which we term AEScore, is shown to perform as well or better than other state-of-the-art scoring functions on binding affinity prediction, with an RMSE of 1.22 pK units and a Pearson’s correlation coefficient of 0.83 for the CASF-2016 benchmark. However, AEScore does not perform as well in docking and virtual screening tasks. We therefore show that the model can be combined with the classical scoring function AutoDock Vina in the context of ∆-learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. Combined with AutoDock Vina, ∆-AEScore has an RMSE of 1.32 pK units and a Pearson’s correlation coefficient of 0.80 on the CASF-2016 benchmark, while retaining the good docking and screening power of the underlying classical scoring function. </p> </div> </div> </div>

scholarly journals Learning Protein-Ligand Binding Affinity with Atomic Environment Vectors

2020 ◽  
Author(s):  
Rocco Meli ◽  
Andrew Anighoro ◽  
Mike Bodkin ◽  
Garrett Morris ◽  
Philip Biggin
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Correlation Coefficient ◽  
Scoring Function ◽  
Scoring Functions ◽  
Autodock Vina ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

<div> <div> <div> <p>Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we explore the use of atomic environment vectors (AEVs) and feed-forward neural networks, the building blocks of several neural network potentials, for the prediction of protein-ligand binding affinity. The AEV-based scoring function, which we term AEScore, is shown to perform as well or better than other state-of-the-art scoring functions on binding affinity prediction, with an RMSE of 1.22 pK units and a Pearson’s correlation coefficient of 0.83 for the CASF-2016 benchmark. However, AEScore does not perform as well in docking and virtual screening tasks. We therefore show that the model can be combined with the classical scoring function AutoDock Vina in the context of ∆-learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. Combined with AutoDock Vina, ∆-AEScore has an RMSE of 1.32 pK units and a Pearson’s correlation coefficient of 0.80 on the CASF-2016 benchmark, while retaining the good docking and screening power of the underlying classical scoring function. </p> </div> </div> </div>

scholarly journals Case Study of Anaerobic Digestion Process Stability Detected by Dissolved Hydrogen Concentration

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 106
Author(s):  
Daniela Platošová ◽  
Jiří Rusín ◽  
Jan Platoš ◽  
Kateřina Smutná ◽  
Roman Buryjan
Keyword(s):  
Anaerobic Digestion ◽  
Correlation Coefficient ◽  
Hydrogen Concentration ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Digestion Process ◽  
Short Service Life ◽  
Pearson's Correlation ◽  
Dissolved Hydrogen ◽  
Pearson's Correlation Coefficient

The paper presents the results of a laboratory experiment of mesophilic single-stage anaerobic digestion performed to verify the possibility of early detection of process instability and reactor overload by evaluating the course of dissolved hydrogen concentration of the main intermediate. The digestion process was run in a Terrafors IS rotary drum bioreactor for 230 days. The substrate dosed on weekdays was food leftovers from the university canteen. At an average temperature of 37 °C, an organic loading of volatiles of 0.858 kg m−3 day−1 and a theoretical retention time of 259 days, biogas production of 0.617 Nm3 kg VS−1 was achieved with a CH4 content of 51.7 vol. %. The values of the established FOS/TAC stability indicator ranged from 0.26 to 11.4. The highest value was reached when the reactor was overloaded. The dissolved hydrogen concentration measured by the amperometric microsensor ranged from 0.039–0.425 mg dm−3. Data were statistically processed using Pearson’s correlation coefficient. The correlation of the hydrogen concentration with other parameters such as the concentration of organic acids was evaluated. The value of Pearson’s correlation coefficient was 0.331 and corresponded to a p-value of 0. The results confirmed a very low limit of the hydrogen concentration at which the microbial culture, especially methanogens, was already overloaded. The amperometric microsensor proved to be rather unsuitable for operational applications due to insufficient sensitivity and short service life. The newly designed ratio of dissolved hydrogen concentration to neutralizing capacity was tested but did not work significantly better than the established FOS/TAC stability indicator.

Italian version of the cornell musculoskeletal discomfort questionnaire (CMDQ-I): Translation, cultural adaptation and validation

Work ◽  
2021 ◽  
pp. 1-7
Author(s):  
F. Magnifica ◽  
F. Colagrossi ◽  
A. Aloisi ◽  
S. Politi ◽  
A. Peretti ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Cultural Adaptation ◽  
Italian Version ◽  
Cronbach’S Alpha ◽  
Musculoskeletal Discomfort ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Cronbach's Alpha ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

BACKGROUND: Almost 25%of workers in the European Union suffer from back pain, and 23%complain of muscle pain. Sixty-two percent of workers carry out repetitive operations with their hands or arms, 46%work in painful or tired positions and 35%carry or handle loads. OBJECTIVE: This study aimed to translate, culturally adapt and validate the Italian version of the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ-I). METHODS: Translation and cultural adaptation procedures followed international guidelines. Participants were recruited from among the personnel components of the Italian Air Force, who were between 18 and 65 years old. Cronbach’s alpha and the intraclass correlation coefficient (ICC) were calculated to assess internal consistency and stability, respectively. The CDMQ-I was administered together with the Visual Analogic Scale (VAS), and the validity was evaluated using Pearson’s correlation coefficient. RESULTS: All CDMQ-I items were either identical or similar in meaning to the original version’s items. The scale was administered twice with a retest after seven to 10 days to 66 participants. Cronbach’s alpha was higher than 0.761, and the ICC ranged between 0.737 and 0.952. Pearson’s correlation coefficient showed positive and significant correlations (p >  0.01). CONCLUSIONS: The study produced an Italian version of the CMDQ with good reliability and validity. This scale is a useful tool to investigate the frequency and intensity of musculoskeletal disorders in various categories of workers.

scholarly journals Validation of a Water Vapor Micropulse Differential Absorption Lidar (DIAL)

2016 ◽  
Vol 33 (11) ◽  
pp. 2353-2372 ◽  
Author(s):  
Tammy M. Weckwerth ◽  
Kristy J. Weber ◽  
David D. Turner ◽  
Scott M. Spuler
Keyword(s):  
Water Vapor ◽  
Correlation Coefficient ◽  
Microwave Radiometer ◽  
Temporal Trends ◽  
Differential Absorption ◽  
Differential Absorption Lidar ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

AbstractA water vapor micropulse differential absorption lidar (DIAL) instrument was developed collaboratively by the National Center for Atmospheric Research (NCAR) and Montana State University (MSU). This innovative, eye-safe, low-power, diode-laser-based system has demonstrated the ability to obtain unattended continuous observations in both day and night. Data comparisons with well-established water vapor observing systems, including radiosondes, Atmospheric Emitted Radiance Interferometers (AERIs), microwave radiometer profilers (MWRPs), and ground-based global positioning system (GPS) receivers, show excellent agreement. The Pearson’s correlation coefficient for the DIAL and radiosondes is consistently greater than 0.6 from 300 m up to 4.5 km AGL at night and up to 3.5 km AGL during the day. The Pearson’s correlation coefficient for the DIAL and AERI is greater than 0.6 from 300 m up to 2.25 km at night and from 300 m up to 2.0 km during the day. Further comparison with the continuously operating GPS instrumentation illustrates consistent temporal trends when integrating the DIAL measurements up to 6 km AGL.

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