Fast databank searching with a reduced amino-acid alphabet

1994 ◽  
Vol 10 (4) ◽  
pp. 453-454 ◽  
Author(s):  
Claudine Landès ◽  
Jean-Loup Risler
Keyword(s):  
Amino Acid ◽  
Reduced Amino Acid Alphabet ◽  
Amino Acid Alphabet

scholarly journals A method for computing the inter-residue interaction potentials for reduced amino acid alphabet

2007 ◽  
Vol 32 (S1) ◽  
pp. 883-889 ◽  
Author(s):  
Abhinav Luthra ◽  
Anupam Nath Jha ◽  
G. K. Ananthasuresh ◽  
Saraswathi Vishveswara
Keyword(s):  
Amino Acid ◽  
Interaction Potentials ◽  
Reduced Amino Acid Alphabet ◽  
Residue Interaction ◽  
Amino Acid Alphabet

scholarly journals RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou’s five-step rule

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Lei Zheng ◽  
Shenghui Huang ◽  
Nengjiang Mu ◽  
Haoyue Zhang ◽  
Jiayu Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Sequence Analysis ◽  
Protein Sequence ◽  
Web Server ◽  
Protein Sequence Analysis ◽  
Evaluation Indexes ◽  
Reduced Amino Acid Alphabet ◽  
Correlation Parameters ◽  
Amino Acid Alphabet

Abstract By reducing amino acid alphabet, the protein complexity can be significantly simplified, which could improve computational efficiency, decrease information redundancy and reduce chance of overfitting. Although some reduced alphabets have been proposed, different classification rules could produce distinctive results for protein sequence analysis. Thus, it is urgent to construct a systematical frame for reduced alphabets. In this work, we constructed a comprehensive web server called RAACBook for protein sequence analysis and machine learning application by integrating reduction alphabets. The web server contains three parts: (i) 74 types of reduced amino acid alphabet were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with unique protein problems. It is easy for users to select desired RAACs from a multilayer browser tool. (ii) An online tool was developed to analyze primary sequence of protein. The tool could produce K-tuple reduced amino acid composition by defining three correlation parameters (K-tuple, g-gap, λ-correlation). The results are visualized as sequence alignment, mergence of RAA composition, feature distribution and logo of reduced sequence. (iii) The machine learning server is provided to train the model of protein classification based on K-tuple RAAC. The optimal model could be selected according to the evaluation indexes (ROC, AUC, MCC, etc.). In conclusion, RAACBook presents a powerful and user-friendly service in protein sequence analysis and computational proteomics. RAACBook can be freely available at http://bioinfor.imu.edu.cn/raacbook. Database URL: http://bioinfor.imu.edu.cn/raacbook

Characterization and Prediction of Presynaptic and Postsynaptic Neurotoxin by Reduced Amino Acid and Biological Property.

2020 ◽  
Vol 15 ◽  
Author(s):  
Yiyin Cao ◽  
Chunlu Yu ◽  
Shenghui Huang ◽  
Shiyuan Wang ◽  
Yongchun Zuo ◽  
...  
Keyword(s):  
Amino Acids ◽  
Support Vector Machine ◽  
Amino Acid ◽  
Biological Properties ◽  
Statistical Test ◽  
Support Vector ◽  
Reduced Amino Acid Alphabet ◽  
The Difference ◽  
Amino Acid Alphabet

Background: Presynaptic and postsynaptic neurotoxins are two important neurotoxins. Due to the important role of presynaptic and postsynaptic neurotoxins in pharmacology and neuroscience, identification of them becomes very important in biology. Method: In this study, the statistical test and F-score were used to calculate the difference between amino acids and biological properties. The support vector machine was used to predict the presynaptic and postsynaptic neurotoxins by using the reduced amino acid alphabet types. Results: By using the reduced amino acid alphabet as the input parameters of support vector machine, the overall accuracy of our classifier had increased to 91.07%, which was the highest overall accuracy in this study. When compared with the other published methods, better predictive results were obtained by our classifier. Conclusion: In summary, we analyzed the differences between two neurotoxins in amino acids and biological properties, and constructed a classifier that could predict these two neurotoxins by using the reduced amino acid alphabet.

scholarly journals A reduced amino acid alphabet for understanding and designing protein adaptation to mutation

2007 ◽  
Vol 36 (8) ◽  
pp. 1059-1069 ◽  
Author(s):  
C. Etchebest ◽  
C. Benros ◽  
A. Bornot ◽  
A.-C. Camproux ◽  
A. G. de Brevern
Keyword(s):  
Amino Acid ◽  
Reduced Amino Acid Alphabet ◽  
Amino Acid Alphabet

scholarly journals Reduced amino acid alphabet is sufficient to accurately recognize intrinsically disordered protein

FEBS Letters ◽  
2004 ◽  
Vol 576 (3) ◽  
pp. 348-352 ◽  
Author(s):  
Edward A. Weathers ◽  
Michael E. Paulaitis ◽  
Thomas B. Woolf ◽  
Jan H. Hoh
Keyword(s):  
Amino Acid ◽  
Intrinsically Disordered Protein ◽  
Intrinsically Disordered ◽  
Disordered Protein ◽  
Reduced Amino Acid Alphabet ◽  
Amino Acid Alphabet
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