Predicting protein secondary structure content

Muskal, Steven M.; Kim, Sung‐Hou

doi:10.1016/0022-2836(92)90396-2

Cited by 96 publications

(63 citation statements)

References 33 publications

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“…Efforts at de novo folding class prediction have met with limited success. Although overall helix and sheet content can be predicted with less than 10% error (Muskal & Kim, 1992;Rost & Sander, 1993b), this margin of error is too large to identify proteins in the all-a and all-@ classes. One difficulty in identifying protein classes is that there are no clear-cut boundaries in secondary structure content between proteins from the 4 classes (Rost & Sander, 1994).…”

Section: Reprint Requests To: Martin Karplus Department Of Chemistrymentioning

confidence: 99%

Neural networks for secondary structure and structural class predictions

1995

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A pair of neural network-based algorithms is presented for predicting the tertiary structural class and the secondary structure of proteins. Each algorithm realizes improvements in accuracy based on information provided by the other. Structural class prediction of proteins nonhomologous to any in the training set is improved significantly, from 62.3% to 73.9%, and secondary structure prediction accuracy improves slightly, from 62.26% to 62.64%. A number of aspects of neural network optimization and testing are examined. They include network overtraining and an output filter based on a rolling average. Secondary structure prediction results vary greatly depending on the particular proteins chosen for the training and test sets; consequently, an appropriate measure of accuracy reflects the more unbiased approach of "jackknife" cross-validation (testing each protein in the database individually).

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Section: Reprint Requests To: Martin Karplus Department Of Chemistrymentioning

confidence: 99%

Neural networks for secondary structure and structural class predictions

1995

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“…19). This tool has been successful in the prediction of protein structural features (17,(20)(21)(22)(23). Three-layer feed forward NNs with weights adjusted by conjugate gradient minimization using the computer program BIOPROP (22) were utilized in our study.…”

Section: Data Base and Nnsmentioning

confidence: 99%

Prediction of protein folding class using global description of amino acid sequence.

Dubchak

Muchnik²,

Holbrook³

et al. 1995

Proc. Natl. Acad. Sci. U.S.A.

515

371

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We present a method for predicting protein folding class based on global protein chain description and a voting process. Selection of the best descriptors was achieved by a computer-simulated neural network trained on a data base consisting of 83 folding classes. Protein-chain descriptors include overall composition, transition, and distribution of amino acid attributes, such as relative hydrophobicity, predicted secondary structure, and predicted solvent exposure. Cross-validation testing was performed on 15 of the largest classes. The test shows that proteins were assigned to the correct class (correct positive prediction) with an average accuracy of 71.7%, whereas the inverse prediction of proteins as not belonging to a particular class (correct negative prediction) was 90-95% accurate. When tested on 254 structures used in this study, the top two predictions contained the correct class in 91% of the cases.Examination of three-dimensional (3D) structures of proteins determined by x-ray diffraction and NMR has shown that the variety of folding patterns of proteins is significantly restricted (1, 2). Since protein sequence information grows significantly faster than information on protein 3D structure, the need for predicting the folding pattern of a given protein sequence naturally arises. Since the first relatively full classification of folding patterns of globular proteins (3), researchers have developed various schemes for classification of protein 3D structures (4-6) that are essentially based on the same spatial motifs.If the prediction is restricted to a small number of structural classes (less than five), a prediction performance >70% can be easily achieved by using various methods based on a simple representation of sequences as vectors of a small number of general parameters. In the simplest classification, proteins are usually described in terms of the following "tertiary super classes:" all a (proteins have only a-helix secondary structure), all 13 (mainly 3-sheet secondary structure), a+0 (a-helix and {3-strand secondary structure segments that do not mix), a/13 (mixed or alternating segments of a-helical and 13-strand secondary structure), and irregular (7-9). Several statistical methods were developed to predict whether a protein belongs to one of these classes (10)(11)(12)(13)(14)(15)(16)(17). In a recent study on predicting protein structural class (all a, all 1, or composed of a and 1 elements) from amino acid composition and hydrophobic pattern frequency information using computer-simulated neural networks (NNs) and statistical clustering, Metfessel et al.(18) obtained a prediction accuracy of 80.2%. Consideration of specific features of folding classes in the form of so-called hidden Markov models or probabilistic grammars allows a >2-fold increase in the number of classes of recognition (9). This method accurately predicts 12 classes; however, the study gives test results only for 16 sequences.It is obvious that difficulty of folding pattern prediction grows rapidly with the number...

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“…In this problem, we use ANN as classification and recognition methods. Some calculation using ANN for Secondary structure prediction can be found in some papers [1,2,3,4,5,6,7,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

A Method of Classification and Recognition of Blue Copper Protein

Purqon¹,

Sugiyama²,

Takamatsu³

et al. 2006

AIP Conference Proceedings

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Abstract. Some proteins in blue copper proteins have similar properties. In some cases it is not easy to distinguish the proteins each other. The study to recognize and classify in blue copper proteins has important roles to recognize the difference of similar properties, for examples, structures and residue sequences in blue copper proteins. There are many methods being developed to predict protein structure from many approachs, which one still not satisfactory yet. Therefore it is a challenge for scientists to develop or improve their methods. One of promising method is artificial neural networks (ANN). ANN is learning machine methods consisted of input, hidden and output layer. ANN is tested to recognize secondary structure in blue copper protein. It is found that ANN can distinguish for 7-type of secondary structure and recognize 72% secondary structure in blue copper protein.

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Predicting protein secondary structure content

Cited by 96 publications

References 33 publications

Neural networks for secondary structure and structural class predictions

Neural networks for secondary structure and structural class predictions

Prediction of protein folding class using global description of amino acid sequence.

A Method of Classification and Recognition of Blue Copper Protein

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