Computational Prediction of Propeller Performance in Icing Conditions

2010 ◽  
Author(s):  
Greg Busch ◽  
Michael Bragg
Keyword(s):  
Computational Prediction ◽  
Propeller Performance

scholarly journals COMPUTATIONAL PREDICTION OF A PROPELLER PERFORMANCE IN OPEN WATER CONDITION

SINERGI ◽  
2020 ◽  
Vol 24 (2) ◽  
pp. 163
Author(s):  
Ahmad Fitriadhy ◽  
N. Amira Adam ◽  
CJ. Quah
Keyword(s):  
Cfd Simulation ◽  
Open Water ◽  
Computational Prediction ◽  
Maximum Efficiency ◽  
Cfd Simulations ◽  
Water Condition ◽  
Pitch Ratio ◽  
Computational Fluid Dynamics Cfd ◽  
Preliminary Study ◽  
Propeller Performance

In presence of hydrodynamics phenomena occur surrounding propeller evidently affects on accuracy’s prediction of thrust, torque and its efficiency. To achieve the objective, a Computational Fluid Dynamics (CFD) simulations approach is then proposed to obtain a reliable prediction of the thrust (KT), torque (KQ) and efficiency (η) coefficients in open water condition. The effect of various blade numbers associated with constant propeller revolution (RPM=1320) and pitch ratio (P/D=1.0); are performed within the range of advance ratio from 0.1J1.0. The results revealed that the increase of blade number from Z=3 to 5 was proportional with the increase of thrust (KT) and torque (KQ) coefficients; meanwhile, it was reduced the maximum efficiency (η) that possibly lead to downgrade the propeller performance. It should be noted here, the propeller with three blade numbers (Z=3) provide the highest efficiency (η) up to 78.8% at J=0.9. These CFD simulation results are very useful as a preliminary study of propeller characteristics.

Covalent-Fragment Screening of Brd4 Identifies a Ligandable Site Orthogonal to the Acetyl-Lysine Binding Sites

2019 ◽  
Author(s):  
Michael Olp ◽  
Daniel Sprague ◽  
Stefan Kathman ◽  
Ziyang Xu ◽  
Alexandar Statsyuk ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Binding Site ◽  
Binding Sites ◽  
Computational Prediction ◽  
Chemical Probes ◽  
Computational Docking ◽  
Fragment Screening ◽  
Covalent Inhibitors ◽  
Bet Protein ◽  
Proof Of Principle

<p>Brd4, a member of the bromodomain and extraterminal domain (BET) family, has emerged as a promising epigenetic target in cancer and inflammatory disorders. All reported BET family ligands bind within the bromodomain acetyl-lysine binding sites and competitively inhibit BET protein interaction with acetylated chromatin. Alternative chemical probes that act orthogonally to the highly-conserved acetyl-lysine binding sites may exhibit selectivity within the BET family and avoid recently reported toxicity in clinical trials of BET bromodomain inhibitors. Here, we report the first identification of a ligandable site on a bromodomain outside the acetyl-lysine binding site. Inspired by our computational prediction of hotspots adjacent to non-homologous cysteine residues within the <i>C</i>-terminal Brd4 bromodomain (Brd4-BD2), we performed a mid-throughput mass spectrometry screen to identify cysteine-reactive fragments that covalently and selectively modify Brd4. Subsequent mass spectrometry, NMR and computational docking analyses of electrophilic fragment hits revealed a novel ligandable site near Cys356 that is unique to Brd4 among all human bromodomains. This site is orthogonal to the Brd4-BD2 acetyl-lysine binding site as Cys356 modification did not impact binding of the pan-BET bromodomain inhibitor JQ1 in fluorescence polarization assays. Finally, we tethered covalent fragments to JQ1 and performed NanoBRET assays to provide proof of principle that this orthogonal site can be covalently targeted in intact human cells. Overall, we demonstrate the potential of targeting sites orthogonal to bromodomain acetyl-lysine binding sites to develop bivalent and covalent inhibitors that displace Brd4 from chromatin.</p>

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

2020 ◽  
Vol 27 (5) ◽  
pp. 385-391
Author(s):  
Lin Zhong ◽  
Zhong Ming ◽  
Guobo Xie ◽  
Chunlong Fan ◽  
Xue Piao
Keyword(s):  
Supervised Learning ◽  
Protein Interactions ◽  
Computational Models ◽  
Prediction Models ◽  
Chromatin Modification ◽  
Computational Prediction ◽  
Human Diseases ◽  
Future Research ◽  
Non Coding Rna ◽  
Long Non Coding Rna

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

Computational Prediction of Protein Hot Spot Residues

2012 ◽  
Vol 18 (9) ◽  
pp. 1255-1265 ◽  
Author(s):  
John Kenneth Morrow ◽  
Shuxing Zhang
Keyword(s):  
Hot Spot ◽  
Computational Prediction
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