J Chem Inf Model - Protein secondary structure prediction with SPARROW.

Tópicos

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Resumo

A first step toward predicting the structure of a protein is to determine its secondary structure. The secondary structure information is generally used as starting point to solve protein crystal structures. In the present study, a machine learning approach based on a complete set of two-class scoring functions was used. Such functions discriminate between two specific structural classes or between a single specific class and the rest. The approach uses a hierarchical scheme of scoring functions and a neural network. The parameters are determined by optimizing the recall of learning data. Quality control is performed by predicting separate independent test data. A first set of scoring functions is trained to correlate the secondary structures of residues with profiles of sequence windows of width 15, centered at these residues. The sequence profiles are obtained by multiple sequence alignment with PSI-BLAST. A second set of scoring functions is trained to correlate the secondary structures of the center residues with the secondary structures of all other residues in the sequence windows used in the first step. Finally, a neural network is trained using the results from the second set of scoring functions as input to make a decision on the secondary structure class of the residue in the center of the sequence window. Here, we consider the three-class problem of helix, strand, and other secondary structures. The corresponding prediction scheme "SPARROW" was trained with the ASTRAL40 database, which contains protein domain structures with less than 40% sequence identity. The secondary structures were determined with DSSP. In a loose assignment, the helix class contains all DSSP helix types (a, 3-10, p), the strand class contains ?-strand and ?-bridge, and the third class contains the other structures. In a tight assignment, the helix and strand classes contain only a-helix and ?-strand classes, respectively. A 10-fold cross validation showed less than 0.8% deviation in the fraction of correct structure assignments between true prediction and recall of data used for training. Using sequences of 140,000 residues as a test data set, 80.46% ? 0.35% of secondary structures are predicted correctly in the loose assignment, a prediction performance, which is very close to the best results in the field. Most applications are done with the loose assignment. However, the tight assignment yields 2.25% better prediction performance. With each individual prediction, we also provide a confidence measure providing the probability that the prediction is correct. The SPARROW software can be used and downloaded on the Web page http://agknapp.chemie.fu-berlin.de/sparrow/ .

Resumo Limpo

first step toward predict structur protein determin secondari structur secondari structur inform general use start point solv protein crystal structur present studi machin learn approach base complet set twoclass score function use function discrimin two specif structur class singl specif class rest approach use hierarch scheme score function neural network paramet determin optim recal learn data qualiti control perform predict separ independ test data first set score function train correl secondari structur residu profil sequenc window width center residu sequenc profil obtain multipl sequenc align psiblast second set score function train correl secondari structur center residu secondari structur residu sequenc window use first step final neural network train use result second set score function input make decis secondari structur class residu center sequenc window consid threeclass problem helix strand secondari structur correspond predict scheme sparrow train astral databas contain protein domain structur less sequenc ident secondari structur determin dssp loos assign helix class contain dssp helix type p strand class contain strand bridg third class contain structur tight assign helix strand class contain ahelix strand class respect fold cross valid show less deviat fraction correct structur assign true predict recal data use train use sequenc residu test data set secondari structur predict correct loos assign predict perform close best result field applic done loos assign howev tight assign yield better predict perform individu predict also provid confid measur provid probabl predict correct sparrow softwar can use download web page httpagknappchemiefuberlindesparrow

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