scholarly journals Evidence for the preferential reuse of sub‐domain motifs in primordial protein folds

2021 ◽  
Author(s):  
Leonhard Heizinger ◽  
Rainer Merkl
Keyword(s):  
Protein Folds

Protein Folds Prediction with Hierarchical Structured SVM

2016 ◽  
Vol 13 (2) ◽  
pp. 79-85 ◽  
Author(s):  
Dapeng Li ◽  
Ying Ju ◽  
Quan Zou
Keyword(s):  
Protein Folds

Protein folds recognized by an intelligent predictor based-on evolutionary and structural information

2015 ◽  
Vol 37 (4) ◽  
pp. 426-436 ◽  
Author(s):  
Ngaam J. Cheung ◽  
Xue-Ming Ding ◽  
Hong-Bin Shen
Keyword(s):  
Structural Information ◽  
Protein Folds

scholarly journals A "slow" protein folds quickly in the end

2013 ◽  
Vol 110 (15) ◽  
pp. 5744-5745 ◽  
Author(s):  
R. B. Best
Keyword(s):  
Protein Folds

FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds

2013 ◽  
Vol 43 (9) ◽  
pp. 1182-1191 ◽  
Author(s):  
Elham Abbasi ◽  
Mehdi Ghatee ◽  
M.E. Shiri
Keyword(s):  
Protein Folds

Predicting novel protein folds by using FRAGFOLD

2001 ◽  
Vol 45 (S5) ◽  
pp. 127-132 ◽  
Author(s):  
David T. Jones
Keyword(s):  
Protein Folds ◽  
Novel Protein

scholarly journals Sialidase-like Asp-boxes: Sequence-similar structures within different protein folds

2001 ◽  
Vol 10 (2) ◽  
pp. 285-292 ◽  
Author(s):  
Richard R. Copley ◽  
Robert B. Russell ◽  
Chris P. Ponting
Keyword(s):  
Protein Folds

scholarly journals Protein folds and functions

Structure ◽  
1998 ◽  
Vol 6 (7) ◽  
pp. 875-884 ◽  
Author(s):  
Andrew CR Martin ◽  
Christine A Orengo ◽  
E Gail Hutchinson ◽  
Susan Jones ◽  
Maria Karmirantzou ◽  
...  
Keyword(s):  
Protein Folds

PROTEIN FOLD CLASSIFICATION WITH GENETIC ALGORITHMS AND FEATURE SELECTION

2009 ◽  
Vol 07 (05) ◽  
pp. 773-788 ◽  
Author(s):  
PENG CHEN ◽  
CHUNMEI LIU ◽  
LEGAND BURGE ◽  
MOHAMMAD MAHMOOD ◽  
WILLIAM SOUTHERLAND ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Training Dataset ◽  
Support Vector ◽  
Protein Fold ◽  
Classification Rate ◽  
Test Dataset ◽  
Feature Vectors ◽  
Protein Folds

Protein fold classification is a key step to predicting protein tertiary structures. This paper proposes a novel approach based on genetic algorithms and feature selection to classifying protein folds. Our dataset is divided into a training dataset and a test dataset. Each individual for the genetic algorithms represents a selection function of the feature vectors of the training dataset. A support vector machine is applied to each individual to evaluate the fitness value (fold classification rate) of each individual. The aim of the genetic algorithms is to search for the best individual that produces the highest fold classification rate. The best individual is then applied to the feature vectors of the test dataset and a support vector machine is built to classify protein folds based on selected features. Our experimental results on Ding and Dubchak's benchmark dataset of 27-class folds show that our approach achieves an accuracy of 71.28%, which outperforms current state-of-the-art protein fold predictors.

The Disulphide β-Cross: From Cystine Geometry and Clustering to Classification of Small Disulphide-rich Protein Folds

1996 ◽  
Vol 264 (3) ◽  
pp. 603-623 ◽  
Author(s):  
Paul M. Harrison ◽  
Michael J.E. Sternberg
Keyword(s):  
Protein Folds
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