Recognition of remote protein homologies using three-dimensional information to generate a position specific scoring matrix in the program 3D-PSSM

1999 ◽  
Author(s):  
Lawrence A. Kelley ◽  
Robert M. MacCallum ◽  
Michael J. E. Sternberg
Keyword(s):  
Three Dimensional ◽  
Position Specific Scoring Matrix ◽  
Scoring Matrix

scholarly journals Using position specific scoring matrix and auto covariance to predict protein subnuclear localization

2009 ◽  
Vol 02 (01) ◽  
pp. 51-56 ◽  
Author(s):  
Rong-Quan Xiao ◽  
Yan-Zhi Guo ◽  
Yu-Hong Zeng ◽  
Hai-Feng Tan ◽  
Hai-Feng Tan ◽  
...  
Keyword(s):  
Position Specific Scoring Matrix ◽  
Subnuclear Localization ◽  
Scoring Matrix ◽  
Auto Covariance

scholarly journals LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Wenzheng Bao ◽  
Bin Yang ◽  
Rong Bao ◽  
Yuehui Chen
Keyword(s):  
Machine Learning ◽  
Posttranslational Modification ◽  
Protein Level ◽  
Cellular Level ◽  
Position Specific Scoring Matrix ◽  
Computational Approaches ◽  
Tree Algorithm ◽  
Feature Description ◽  
Scoring Matrix ◽  
The Relationship

Lysine lipoylation is a special type of posttranslational modification in both prokaryotes’ and eukaryotes’ proteomics researches. Such a modification takes part in several significant biological processions and plays a key role in the cellular level. In order to construct and design an accurate classification algorithm for identifying lipoylation sites in the protein level, the computational approaches should be taken into account in this field. Meanwhile, several factors plays different role in the identification of modification sites. Considering such a situation, the foundational elements of the effective identification of modification sites are the available feature description and the high effective classification. With these two elements, the distinguishing between the lipoylation samples and the nonlipoylation samples can be treated as a typical classification issue in the field of machine learning. In this work, we have proposed a method named LipoFNT, which employed the two featuring sets, including the Position-Specific Scoring Matrix and bi-profile Bayesian, as the classification features. And then, the flexible neural tree algorithm is utilized to deal with the imbalance classification issue in lipoylation modification sample dataset. The proposed method can achieve 81.07% in sn%, 80.29% in sp, 80.68% in Acc, 0.8076 in F1, and 0.6136 in MCC, respectively. Meanwhile, we have demonstrated the relationship between the lengths of peptide and identification of modification sites.

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