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Identifying polymer-forming SAM domains.

ABSTRACT: Sterile alpha motif (SAM) domains are common protein modules in eukaryotic cells. It has not been possible to assign functions to uncharacterized SAM domains because they have been found to participate in diverse functions ranging from protein-protein interactions to RNA binding. Here we computationally identify likely members of the subclass of SAM domains that form polymers. Sequences were virtually threaded onto known polymer structures and then evaluated for compatibility with the polymer. We find that known SAM polymers score better than the vast majority of known nonpolymers: 100% (7 of 7) of known polymers and only 8% of known nonpolymers (1 of 12) score above a defined threshold value. Of 2901 SAM family members, we find 694 that score above the threshold and are likely polymers, including SAM domains from the proteins Lethal Malignant Brain Tumor, Bicaudal-C, Liprin-beta, Adenylate Cyclase, and Atherin.

SUBMITTER: Meruelo AD

PROVIDER: S-EPMC2605191 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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Identifying polymer-forming SAM domains.

Meruelo Alejandro D AD Bowie James U JU

Proteins 20090101 1

Sterile alpha motif (SAM) domains are common protein modules in eukaryotic cells. It has not been possible to assign functions to uncharacterized SAM domains because they have been found to participate in diverse functions ranging from protein-protein interactions to RNA binding. Here we computationally identify likely members of the subclass of SAM domains that form polymers. Sequences were virtually threaded onto known polymer structures and then evaluated for compatibility with the polymer. W ...[more]

PMID: 18831011

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Project description:Chromatin is a polymer complex of DNA and proteins that regulates gene expression. The three-dimensional (3D) structure and organization of chromatin controls DNA transcription and replication. High-throughput chromatin conformation capture techniques generate Hi-C maps that can provide insight into the 3D structure of chromatin. Hi-C maps can be represented as a symmetric matrix [Formula: see text], where each element represents the average contact probability or number of contacts between chromatin loci i and j. Previous studies have detected topologically associating domains (TADs), or self-interacting regions in [Formula: see text] within which the contact probability is greater than that outside the region. Many algorithms have been developed to identify TADs within Hi-C maps. However, most TAD identification algorithms are unable to identify nested or overlapping TADs and for a given Hi-C map there is significant variation in the location and number of TADs identified by different methods. We develop a novel method to identify TADs, KerTAD, using a kernel-based technique from computer vision and image processing that is able to accurately identify nested and overlapping TADs. We benchmark this method against state-of-the-art TAD identification methods on both synthetic and experimental data sets. We find that the new method consistently has higher true positive rates (TPR) and lower false discovery rates (FDR) than all tested methods for both synthetic and manually annotated experimental Hi-C maps. The TPR for KerTAD is also largely insensitive to increasing noise and sparsity, in contrast to the other methods. We also find that KerTAD is consistent in the number and size of TADs identified across replicate experimental Hi-C maps for several organisms. Thus, KerTAD will improve automated TAD identification and enable researchers to better correlate changes in TADs to biological phenomena, such as enhancer-promoter interactions and disease states.

Dataset Information

Identifying polymer-forming SAM domains.

Publications

Identifying polymer-forming SAM domains.

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