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Fast and accurate protein substructure searching with simulated annealing and... Related Articles

Fast and accurate protein substructure searching with simulated annealing and GPUs.

BMC Bioinformatics. 2010 Sep 3;11(1):446

Authors: Stivala AD, Stuckey PJ, Wirth AI

ABSTRACT: BACKGROUND: Searching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif) searching. RESULTS: We developed an improved heuristic for tableau-based protein structure and substructure searching using simulated annealing, that is as fast or faster, and comparable in accuracy, with some widely used existing methods. Furthermore, we created a parallel implementation on a modern graphics processing unit (GPU). CONCLUSIONS: The GPU implementation achieves up to 34 times speedup over the CPU implementation of tableau-based structure search with simulated annealing, making it one of the fastest available methods. To the best of our knowledge, this is the first application of a GPU to the protein structural search problem.

PMID: 20813068 [PubMed - as supplied by publisher]

An algorithm to discover gene signatures with predictive potential.

J Exp Clin Cancer Res. 2010 Sep 2;29(1):120

Authors: Hallett RM, Dvorkin A, Gabardo CM, Hassell JA

ABSTRACT: BACKGROUND: The advent of global gene expression profiling has generated unprecedented insight into our molecular understanding of cancer, including breast cancer. For example, human breast cancer patients display significant diversity in terms of their survival, recurrence, metastasis as well as response to treatment. These patient outcomes can be predicted by the transcriptional programs of their individual breast tumors. Predictive gene signatures allow us to correctly classify human breast tumors into various risk groups as well as to more accurately target therapy to ensure more durable cancer treatment. RESULTS: Here we present a novel algorithm to generate gene signatures with predictive potential. The method first classifies the expression intensity for each gene as determined by global gene expression profiling as low, average or high. The matrix containing the classified data for each gene is then used to score the expression of each gene based its individual ability to predict the patient characteristic of interest. Finally, all examined genes are ranked based on their predictive ability and the most highly ranked genes are included in the master gene signature, which is then ready for use as a predictor. This method was used to accurately predict the survival outcomes in a cohort of human breast cancer patients. CONCLUSIONS: We confirmed the capacity of our algorithm to generate gene signatures with bona fide predictive ability. The simplicity of our algorithm will enable biological researchers to quickly generate valuable gene signatures without specialized software or extensive bioinformatics training.

PMID: 20813028 [PubMed - as supplied by publisher]

Redox Biology: Computational Approaches to the Investigation of Functional Cysteine Residues.

Antioxid Redox Signal. 2010 Sep 2;

Authors: Marino SM, Gladyshev V

Cysteine (Cys) residues serve many functions, such as catalysis, stabilization of protein structure through disulfides, metal binding, and regulation of protein function. Cys residues are also subject to numerous posttranslational modifications. In recent years, various computational tools aiming at classifying and predicting different functional categories of Cys have been developed, particularly for structural and catalytic Cys. On the other hand, given complexity of the subject, bioinformatics approaches have been less successful for the investigation of regulatory Cys sites. In this review, we introduce different functional categories of Cys residues. For each category, an overview of state-of-the-art bioinformatics methods and tools is provided, along with examples of successful applications and potential limitations associated with each approach. Finally, we discuss Cys-based redox switches, which modify the view of distinct functional categories of Cys in proteins.

PMID: 20812876 [PubMed - as supplied by publisher]