ø—ψconformational pattern clustering of protein amino acid residues using the potential function method

1994 ◽  
Vol 10 (2) ◽  
pp. 163-169
Author(s):  
Motokazu Kamimura ◽  
Yoshimasa Takahashi
Keyword(s):  
Amino Acid ◽  
Potential Function ◽  
Function Method ◽  
Amino Acid Residues ◽  
Protein Amino Acid ◽  
Pattern Clustering ◽  
Potential Function Method

Classifying Large Chemical Data Sets: Using A Regularized Potential Function Method

2010 ◽  
Vol 51 (1) ◽  
pp. 4-14 ◽  
Author(s):  
Hamse Y. Mussa ◽  
Lezan Hawizy ◽  
Florian Nigsch ◽  
Robert C. Glen
Keyword(s):  
Potential Function ◽  
Function Method ◽  
Chemical Data ◽  
Data Sets ◽  
Potential Function Method

On different parameterisation methods to analyse spacecraft attitude manoeuvres in the presence of attitude constraints

2007 ◽  
Vol 111 (1119) ◽  
pp. 335-342 ◽  
Author(s):  
G. Radice ◽  
M. Casasco
Keyword(s):  
Potential Function ◽  
Function Method ◽  
Potential Functions ◽  
Spacecraft Attitude ◽  
Modified Rodrigues Parameters ◽  
Current Attitude ◽  
Definition Of ◽  
Potential Function Method

Abstract This paper analyses and compares two different attitude representations, using quaternions and modified Rodrigues parameters, in the context of the potential function method applied to autonomously control constrained attitude slew manoeuvres. This method hinges on the definition of novel Lyapunov potential functions in terms of the attitude parameters representing the current attitude, the goal attitude and any pointing constraints, which may be present. It proves to be successful in forcing the satellite to achieve the desired attitude while at the same time avoiding the pointing constraints. A linearised version of the modified Rodrigues parameterisation is also introduced and analysed. Finally advantages and drawbacks of all attitude representations are discussed.

scholarly journals iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou’s 5-steps Rule and Pseudo Components

2019 ◽  
Vol 20 (4) ◽  
pp. 306-320 ◽  
Author(s):  
Omar Barukab ◽  
Yaser Daanial Khan ◽  
Sher Afzal Khan ◽  
Kuo-Chen Chou
Keyword(s):  
Amino Acid ◽  
Computational Model ◽  
Amino Acid Sequences ◽  
Site Directed Mutagenesis ◽  
Statistical Moments ◽  
Amino Acid Residues ◽  
Tyrosine Sulfation ◽  
Protein Amino Acid ◽  
Jackknife Test ◽  
Self Consistency

Background: The amino acid residues, in protein, undergo post-translation modification (PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn alters behavioral properties of proteins. Tyrosine sulfation is a ubiquitous posttranslational modification which is known to be associated with regulation of various biological functions and pathological processes. Thus its identification is necessary to understand its mechanism. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of sulfotyrosine sites. Methodology: In this paper, we present a computational model for the prediction of the sulfotyrosine sites named iSulfoTyr-PseAAC in which feature vectors are constructed using statistical moments of protein amino acid sequences and various position/composition relative features. These features are incorporated into PseAAC. The model is validated by jackknife, cross-validation, self-consistency and independent testing. Results: Accuracy determined through validation was 93.93% for jackknife test, 95.16% for crossvalidation, 94.3% for self-consistency and 94.3% for independent testing. Conclusion: The proposed model has better performance as compared to the existing predictors, however, the accuracy can be improved further, in future, due to increasing number of sulfotyrosine sites in proteins.

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