Relation between amino acid composition and cellular location of proteins 1 1Edited by F. E. Cohen

Cedano, Juan; Aloy, Patrick; Pérez-Pons, Josep A.; Querol, Enrique

doi:10.1006/jmbi.1996.0804

Cited by 383 publications

(269 citation statements)

References 23 publications

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“…The second part is a summary of the network-based approaches from the work of Lee et al to the present. (Nakai & Kanehisa, 1991) Expert Systems SignalMotif 4 X X (Nakai & Kanehisa, 1992) Expert Systems AA, SingalMotif 14 X X (Nakashima & Nishikawa, 1994) Scoring System AA, diAA 2 X X (Cedano et al, 1997) LDA using Mahalanobis distance AA 5 X X (Reinhardt & Hubbard, 1998 Table 4. Summary of previous methods for prediction of protein subcellular location.…”

Section: Network-based Localization Predictionmentioning

confidence: 99%

Protein Interactome and Its Application to Protein Function Prediction

Jung¹,

Jeong²,

Lee³

2012

Protein-Protein Interactions - Computational and Experimental Tools

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Section: Network-based Localization Predictionmentioning

confidence: 99%

Protein Interactome and Its Application to Protein Function Prediction

Jung¹,

Jeong²,

Lee³

2012

Protein-Protein Interactions - Computational and Experimental Tools

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“…Cedano et al (1997) recently reported a single-residue statistical algorithm with 76% accuracy that used the protein's amino acid composition for classifying a protein sequence according to its cellular location using five classes -extracellular (E), intracellular (I), nuclear (N), membrane integral (M), and membrane anchored (A).…”

Section: 1mentioning

confidence: 99%

“…Adjacent-Pair-Statistics Cedano et al (1997) acknowledged that their single-residue statistical algorithm "is an apparently simple relationship between amino acid composition and protein class (location)" and that it alone cannot pinpoint the cellular location of a protein. For example, it is from known that augmenting single-residue percentages with tworesidue percentages can improve the accuracy of classification (Nakashima and Nishikawa 1994).…”

Section: 2mentioning

confidence: 99%

“…Thus, fitness ranges between 0.0 and +1.0, so that lower values of fitness are better than higher values and a value of 0 corresponds to the best. Cedano et al (1997) used 1,000 proteins (200 from each of five classes) as their insample fitness cases. We reduced computer time by about three-fourths by randomly choosing 50% of these proteins that were of length less than 600 (i.e., about twice the length of an average-sized protein) as our in-sample fitness cases.…”

Section: 3mentioning

confidence: 99%

“…The results were first cross-validated using the remaining proteins of length less than 600. Then, the results were further crossvalidated using the same 200 out-of-sample fitness cases as in Cedano et al (1997) (specifically, 41 membrane, 46 nuclear, 55 intracellular, 33 extracellular, and 25 anchored proteins) with 100,029 residues.…”

Section: 3mentioning

confidence: 99%

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Using programmatic motifs and genetic programming to classify protein sequences as to cellular location

Koza

Bennett²,

Debenath

1998

Lecture Notes in Computer Science

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As newly sequenced proteins are deposited into the world's evergrowing archives, they are typically immediately tested by various algorithms for clues as to their biological structure and function. One question about a new protein involves its cellular location -that is, where the protein resides in a living organism (e.g., extracellular, membrane, nuclear). A human-created five-way algorithm for cellular location using statistical techniques with 76% accuracy was recently reported. This paper describes a two-way algorithm that was evolved using genetic programming with 83% accuracy for determining whether a protein is an extracellular protein, 84% for nuclear proteins, 89% for membrane proteins, and 83% for anchored membrane proteins. Unlike the statistical calculation, the genetically evolved programs employ a large and varied arsenal of computational capabilities, including arithmetic functions, conditional operations, subroutines, iterations, named memory, indexed memory, set-creating operations, and look-ahead.The genetically evolved classification program can be viewed as an extension (which we call a programmatic motif) of the conventional notion of a protein motif.

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Prediction and classification of domain structural classes

Chou¹,

Liu

Maggiora³

et al. 1998

Proteins

103

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Can the coupling effect among different amino acid components be used to improve the prediction of protein structural classes? The answer is yes according to the study by Chou and Zhang (Crit. Rev. Biochem. Mol. Biol. 30:275-349, 1995), but a completely opposite conclusion was drawn by Eisenhaber et al. when using a different dataset constructed by themselves (Proteins 25:169-179, 1996). To resolve such a perplexing problem, predictions were performed by various approaches for the datasets from an objective database, the SCOP database (Murzin, Brenner, Hubbard, and Chothia. J. Mol. Biol. 247:536-540, 1995). According to SCOP, the classification of structural classes for protein domains is based on the evolutionary relationship and on the principles that govern the 3D structure of proteins, and hence is more natural and reliable. The results from both resubstitution tests and jackknife tests indicate that the overall rates of correct prediction by the algorithm incorporated with the coupling effect among different amino acid components are significantly higher than those by the algorithms without using such an effect. It is elucidated through an analysis that the main reasons for Eisenhaber et al. to have reached an opposite conclusion are the result of (1) misusing the component-coupled algorithm, and (2) using a conceptually incorrect rule to classify protein structural classes. The formulation and analysis presented in this article are conducive to clarify these problems, helping correctly to apply the prediction algorithm and interpret the results.

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Relation between amino acid composition and cellular location of proteins 1 1Edited by F. E. Cohen

Cited by 383 publications

References 23 publications

Protein Interactome and Its Application to Protein Function Prediction

Protein Interactome and Its Application to Protein Function Prediction

Using programmatic motifs and genetic programming to classify protein sequences as to cellular location

Prediction and classification of domain structural classes

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