A database for human Y chromosome protein data

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Abstract

The human Y chromosome is the sex determining chromosome. The number of proteins associated with this chromosome is 196 and 107 of the 196 proteins have yet not been characterised. Here, we describe the analysis of these 107 proteins by computing various physicochemical properties using sequence and predicted structural data to elucidate molecular function. We present the derived data in the form a form a database made freely available for download, review, refinement and update.

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Can correct protein models be identified?

Björn Wallner, Arne Elofsson (2003)

The ability to separate correct models of protein structures from less correct models is of the greatest importance for protein structure prediction methods. Several studies have examined the ability of different types of energy function to detect the native, or native-like, protein structure from a large set of decoys. In contrast to earlier studies, we examine here the ability to detect models that only show limited structural similarity to the native structure. These correct models are defined by the existence of a fragment that shows significant similarity between this model and the native structure. It has been shown that the existence of such fragments is useful for comparing the performance between different fold recognition methods and that this performance correlates well with performance in fold recognition. We have developed ProQ, a neural-network-based method to predict the quality of a protein model that extracts structural features, such as frequency of atom-atom contacts, and predicts the quality of a model, as measured either by LGscore or MaxSub. We show that ProQ performs at least as well as other measures when identifying the native structure and is better at the detection of correct models. This performance is maintained over several different test sets. ProQ can also be combined with the Pcons fold recognition predictor (Pmodeller) to increase its performance, with the main advantage being the elimination of a few high-scoring incorrect models. Pmodeller was successful in CASP5 and results from the latest LiveBench, LiveBench-6, indicating that Pmodeller has a higher specificity than Pcons alone.

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SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments.

C Geourjon, G. Deleage (1995)

Recently a new method called the self-optimized prediction method (SOPM) has been described to improve the success rate in the prediction of the secondary structure of proteins. In this paper we report improvements brought about by predicting all the sequences of a set of aligned proteins belonging to the same family. This improved SOPM method (SOPMA) correctly predicts 69.5% of amino acids for a three-state description of the secondary structure (alpha-helix, beta-sheet and coil) in a whole database containing 126 chains of non-homologous (less than 25% identity) proteins. Joint prediction with SOPMA and a neural networks method (PHD) correctly predicts 82.2% of residues for 74% of co-predicted amino acids. Predictions are available by Email to deleage@ibcp.fr or on a Web page (http:@www.ibcp.fr/predict.html).

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GOR method for predicting protein secondary structure from amino acid sequence.

J-F Gibrat, B Robson, J Garnier (1995)

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Author and article information

Journal

Journal ID (nlm-ta): Bioinformation

Journal ID (publisher-id): Bioinformation

Title: Bioinformation

Publisher: Biomedical Informatics Publishing Group

ISSN (Electronic): 0973-2063

Publication date Collection: 2009

Publication date (Electronic): 24 October 2009

Volume: 4

Issue: 5

Pages: 184-186

Affiliations

[1 ]Phytomatics Laboratory, Bioinformatics Division, Bharathiar University, Coimbatore, India

[2 ]Bioinformatics division, School of Biosciences and Technology, VIT University, Vellore, India

[3 ]Department of Biotechnology, School of Biotechnology & Health Science, Karunya University, Coimbatore

Author notes

[* ]Pallipalayam Periyasamy Karthikeyan: ppkarthikeyan@ 123456gmail.com

Article

Publisher ID: 004400042009

PMC ID: 2859573

PubMed ID: 20461156

SO-VID: 3a6b192c-795b-45b9-a868-51c66e2acb11

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This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.

History

Date received : 06 October 2009

Date revision received : 16 October 2009

Date accepted : 20 October 2009

A database for human Y chromosome protein data

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Microbial Genomics

Most cited references 19

Can correct protein models be identified?

SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments.

GOR method for predicting protein secondary structure from amino acid sequence.

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