Comparison of the predicted and observed secondary structure of T4 phage lysozyme.
TL;DR: Although empirical predictions based on larger numbers of known protein structure tend to be more accurate than those based on a limited sample, the improvement in accuracy is not dramatic, suggesting that the accuracy of current empirical predictive methods will not be substantially increased simply by the inclusion of more data from additional protein structure determinations.
About: This article is published in Biochimica et Biophysica Acta.The article was published on 1975-10-20. It has received 4522 citations till now. The article focuses on the topics: Protein secondary structure & Protein structure.
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TL;DR: SignalP 4.0 was the best signal-peptide predictor for all three organism types but was not in all cases as good as SignalP 3.0 according to cleavage-site sensitivity or signal- peptide correlation when there are no transmembrane proteins present.
Abstract: We benchmarked SignalP 4.0 against SignalP 3.0 and ten other signal peptide prediction algorithms (Fig. 1). We compared prediction performance using the Matthews correlation coefficient16, for which each sequence was counted as a true or false positive or negative. To test SignalP 4.0 performance, we did not use data that had been used in training the networks or selecting the optimal architecture, and the test data did not contain homologs to the training and optimization data (Supplementary Methods). The test set for SignalP 3.0 was also independent of the training set because we removed sequences used to construct SignalP 3.0 and their homologs from the benchmark data. For other algorithms more recent than SignalP 3.0, the benchmark data may include data used to train the methods, possibly leading to slight overestimations of their performance. Our results show that SignalP 4.0 was the best signal-peptide predictor for all three organism types (Fig. 1). This comes at a price, however, because SignalP 4.0 was not in all cases as good as SignalP 3.0 according to cleavage-site sensitivity or signal-peptide correlation when there are no transmembrane proteins present (Supplementary Results). An ideal method would have the best SignalP 4.0: discriminating signal peptides from transmembrane regions
8,370 citations
TL;DR: Improvements of the currently most popular method for prediction of classically secreted proteins, SignalP, which consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated.
6,492 citations
TL;DR: The algorithm is shown to be at least as good as, and usually superior to, the reported prediction methods assessed in the same way and the implication in protein folding is discussed.
4,360 citations
TL;DR: A neural network-based tool, TargetP, for large-scale subcellular location prediction of newly identified proteins has been developed and it is estimated that 10% of all plant proteins are mitochondrial and 14% chloroplastic, and that the abundance of secretory proteins, in both Arabidopsis and Homo, is around 10%.
4,268 citations
Cites methods from "Comparison of the predicted and obs..."
...The Matthews correlation coefficient, MCC ( Matthews, 1975 ), defined as:...
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...MCC equals one for a perfect prediction, while it is zero for a completely random assignment....
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...Measured by the Matthews correlation coefficient, MCC ( Matthews, 1975 ), TargetP performed better than both PSORT and MitoProt on all the tested categories....
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...The Matthews correlation coef®cient, MCC (Matthews, 1975), de®ned as: MCC tp tnÿ fp fn tp fn tp fp tn fp tn fn p where tn true negatives, was used in the comparison of performances of different predictors....
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...Measured by the Matthews correlation coef®cient, MCC (Matthews, 1975), TargetP performed better than both PSORT and MitoProt on all the tested categories....
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TL;DR: In this article, exact dynamic programming algorithms can be used to compute ground states, base pairing probabilities, as well as thermodynamic properties of nucleic acids based on carefully measured thermodynamic parameters.
Abstract: Background
Secondary structure forms an important intermediate level of description of nucleic acids that encapsulates the dominating part of the folding energy, is often well conserved in evolution, and is routinely used as a basis to explain experimental findings. Based on carefully measured thermodynamic parameters, exact dynamic programming algorithms can be used to compute ground states, base pairing probabilities, as well as thermodynamic properties.
3,620 citations
References
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Book•
01 Jan 1925
TL;DR: The prime object of as discussed by the authors is to put into the hands of research workers, and especially of biologists, the means of applying statistical tests accurately to numerical data accumulated in their own laboratories or available in the literature.
Abstract: The prime object of this book is to put into the hands of research workers, and especially of biologists, the means of applying statistical tests accurately to numerical data accumulated in their own laboratories or available in the literature.
11,308 citations
TL;DR: The helix, s Applequist, 1963) in which the Zimm-Bragg parameters u and s are defined respectively as the cooperativity factor for helix initiation, and the equi- librium constant for converting a coil residue to a helical helix.
Abstract: The helix, s Applequist, 1963) in which the Zimm-Bragg parameters u and s are defined respectively as the cooperativity factor for helix initiation, and the equi- librium constant for converting a coil residue to a helical ~~~~
2,112 citations
TL;DR: Examples of hydrogen‐bonded, nonhelical conformations which occur in peptides and proteins are discussed—e.g., in cyclohexaglyeyl, an open tetrapeptide Gly‐L‐Pro‐ L‐Leu‐Gly, and in parts of the lysozyme chain.
Abstract: The general conformations of a system of three linked peptide units are studied, and it is found that there are three types of conformations which contain NH…O hydrogen bonding between the first and the third units. One of them is part of a 310-helix, while the other two arc nonhelical. The two nonhelical conformations are very similar, and in both the cases the peptide chain turns around, reversing the direction of progress. Such a conformation can therefore occur in the region where a polypeptide chain folds back on itself, as in the cross-β structure. The method of representing these interesting tripeptide conformations in a (ϕ,ψ) map is described. Examples of such hydrogen-bonded, nonhelical conformations which occur in peptides and proteins are discussed—e.g., in cyclohexaglyeyl, an open tetrapeptide Gly-L-Pro-L-Leu-Gly, and in parts of the lysozyme chain.
1,388 citations
513 citations