Journal ArticleDOI
Prediction of protein structural classes.
Kuo-Chen Chou,Chun-Ting Zhang +1 more
TLDR
The very high success rate for both the training- set proteins and the testing-set proteins, which has been further validated by a simulated analysis and a jackknife analysis, indicates that it is possible to predict the structural class of a protein according to its amino acid composition if an ideal and complete database can be established.Abstract:
A protein is usually classified into one of the following five struc- tural classes: a!, j3, a! +j3, a!/j3, and ( (irregular). The structural class of aprotein is correlated with its amino acid composition. However, given the amino acid composition of aprotein, how may one predict its structural class? Various efforts have been made in addressing this problem. This review addresses the progress in this field, with the focus on the state of the art, which is featured by a novel prediction algorithm and a recently developed database. The novel algorithm is characterized by a covariance matrix that takes into account the coupling effect among different amino acid components of a protein. The new database was established based on the requirement that the classes should have (1) as many nonhomologous structures as possible, (2) good quality structure, and (3) typical or distinguishable features for each of the structural classes concerned. The very high success rate for both the training-set proteins and the testing-set proteins, which has been further validated by a simulated analysis and a jackknife analysis, indicates that it is possible to predict the structural class of a protein according to its amino acid composition if an ideal and complete database can be established. It also suggests that the overall fold of a protein is basically determined by its amino acid composition.read more
Citations
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Journal ArticleDOI
Prediction of protein cellular attributes using pseudo‐amino acid composition
TL;DR: A remarkable improvement in prediction quality has been observed by using the pseudo‐amino acid composition and its mathematical framework and biochemical implication may also have a notable impact on improving the prediction quality of other protein features.
Journal ArticleDOI
Some remarks on protein attribute prediction and pseudo amino acid composition.
TL;DR: This review is to discuss each of the five procedures of the introduction of pseudo amino acid composition (PseAAC), its different modes and applications as well as its recent development, particularly in how to use the general formulation of PseAAC to reflect the core and essential features that are deeply hidden in complicated protein sequences.
Journal ArticleDOI
Multi-class protein fold recognition using support vector machines and neural networks.
Chris Ding,Inna Dubchak +1 more
TL;DR: This work investigated two new methods for protein fold prediction using the Support Vector Machine and the Neural Network learning methods as base classifiers, and examined many issues involved with large number of classes, including dependencies of prediction accuracy on the number of folds and on thenumber of representatives in a fold.
Journal ArticleDOI
Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms.
Kuo-Chen Chou,Hong-Bin Shen +1 more
TL;DR: This protocol is a step-by-step guide on how to use the Web-server predictors in the Cell-PLoc package, a package of Web servers developed recently by hybridizing the 'higher level' approach with the ab initio approach.
Journal ArticleDOI
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes
TL;DR: The success rates obtained by the new predictor are all significantly higher than those by the previous predictors, which implies that the distribution of hydrophobicity and hydrophilicity of the amino acid residues along a protein chain plays a very important role to its structure and function.
References
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Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Journal ArticleDOI
Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features
Wolfgang Kabsch,Chris Sander +1 more
TL;DR: A set of simple and physically motivated criteria for secondary structure, programmed as a pattern‐recognition process of hydrogen‐bonded and geometrical features extracted from x‐ray coordinates is developed.
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Amino acid substitution matrices from protein blocks
TL;DR: This work has derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins, leading to marked improvements in alignments and in searches using queries from each of the groups.