Review: Protein Secondary Structure Prediction Continues to Rise

Rost, Burkhard

doi:10.1006/jsbi.2001.4336

Cited by 480 publications

(384 citation statements)

References 112 publications

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“…For comparison we also list the results reported by Frishman and Argos [16] (FpA pro ) and the performance obtained by Jones' PSIpred method [17] (Jones Xpro with extended pro¢les taken from EVA [18]), which is similar to other recent results [19,20]. The exploitation of evolutionary information in the form of sequence pro¢les has raised the prediction performance by 5^10%.…”

Section: Prediction Performancesupporting

confidence: 62%

Protein secondary structure: category assignment and predictability

2001

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In the last decade, the prediction of protein secondary structure has been optimized using essentially one and the same assignment scheme known as DSSP. We present here a different scheme, which is more predictable. This scheme predicts directly the hydrogen bonds, which stabilize the secondary structures. Single sequence prediction of the new three category assignment gives an overall prediction improvement of 3.1% and 5.1% compared to the DSSP assignment and schemes where the helix category consists of K K-helix and 3 10 -helix, respectively. These results were achieved using a standard feed-forward neural network with one hidden layer on a data set identical to the one used in earlier work. ß

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Section: Prediction Performancesupporting

confidence: 62%

Protein secondary structure: category assignment and predictability

2001

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“…2). However, this is not a phenomenon unique to protein disorder prediction, but seems to be intrinsic to all aspects of protein structure and function predictions 59 . Given an out -of-sample protein, the problem is how to assess the prediction quality without the actual structural information.…”

Section: Discussionmentioning

confidence: 99%

Optimizing Long Intrinsic Disorder Predictors With Protein Evolutionary Information

Peng

Vučetić

Radivojac

et al. 2005

J. Bioinform. Comput. Biol.

463

428

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Protein existing as an ensemble of structures, called intrinsically disordered, has been shown to be res ponsible for a wide variety of biological functions and to be common in nature. Here we focus on improving sequence-based predictions of long (> 30 amino acid residues) regions lacking specific 3-D structure by means of four new neural -network-based Predictors Of Natural Disordered Regions (PONDRs): VL3, VL3H, VL3P, and VL3E. PONDR VL3 used several features from a previously introduced PONDR VL2, but benefitted from optimized predictor models and a slightly larger (152 versus 145) set of disordered proteins that were cleaned of mislabeling errors found in the smaller set. PONDR VL3H utilized homologues of the disordered proteins in the training stage, while PONDR VL3P used attributes derived from sequence profiles obtained by PSI-BLAST searches. The measure of accuracy was the average between accuracies on disordered and ordered protein regions. By this measure, the 30-fold cross-validation accuracies of VL3, VL3H, and VL3P were, respectively, 83.6±1.4%, 85.3±1.4%, and 85.2±1.5%. By combining VL3H and VL3P, t he resulting PONDR VL3E achieved an accuracy of 86.7±1.4%. This is a significant improvement over our previous PONDRs VLXT (71.6±1.3%) and VL2 (80.9±1.4% ). The new disorder predictors with the corresponding datasets are freely accessible through the web server at www.ist.temple.edu/disprot.

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“…Numerous research teams have analyzed the sequence -structure relationship for a better understanding of protein fold and to perform structural prediction from sequence. At a local level, secondary structure predictions have been a tremendous research area during the last three decades [3] the prediction rates reaching now 80% [4,5]. Nonetheless, protein secondary structure prediction progress attains a plateau and prediction rates seem now close to their optimal limit.…”

Section: Introductionmentioning

confidence: 99%

Protein contacts, inter-residue interactions and side-chain modelling

Faure¹,

Bornot²,

Brevern³

2008

Biochimie

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To cite this version:Guilhem Faure, Aurélie Bornot, Alexandre De Brevern. Protein contacts, inter-residue interactions and side-chain modelling.: protein contacts. Biochimie, Elsevier, 2008, 90 (4) AbstractThree-dimensional structures of proteins are the support of their biological functions.Their folds are stabilized by contacts between residues. Inner protein contacts are generally described through direct atomic contacts, i.e. interactions between side-chain atoms, while contact prediction methods mainly used inter-C distances. In this paper, we have analyzed the protein contacts on a recent high quality non-redundant databank using different criteria.First, we have studied the average number of contacts depending on the distance threshold to define a contact. Preferential contacts between types of amino acids have been highlighted.Detailed analyses have been done concerning the proximity of contacts in the sequence, the size of the proteins and fold classes. The strongest differences have been extracted, highlighting important residues.Then, we studied the influence of five different side-chain conformation prediction methods (SCWRL, IRECS, SCAP, SCATD and SSCOMP) on the distribution of contacts.The prediction rates of these different methods are quite similar. However, using a distance criterion between side-chains, the results are quite different, e.g. SCAP predicts 50% more contacts than observed, unlike other methods that predict fewer contacts than observed.Contacts deduced are quite distinct from one method to another with at most 75% contacts in common. Moreover, distributions of amino acid preferential contacts present unexpected behaviors distinct from previously observed in the X-ray structures, especially at the surface of proteins. For instance, the interactions involving Tryptophan greatly decrease.

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Review: Protein Secondary Structure Prediction Continues to Rise

Cited by 480 publications

References 112 publications

Protein secondary structure: category assignment and predictability

Protein secondary structure: category assignment and predictability

Optimizing Long Intrinsic Disorder Predictors With Protein Evolutionary Information

Protein contacts, inter-residue interactions and side-chain modelling

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