Here we present PREDDIMER, a web tool for prediction of dimer structure of transmembrane (TM) helices. PREDDIMER allows (i) reconstruction of a number of dimer structures for given sequence(s) of TM protein fragments; (ii) ranking and filtering of predicted structures according to respective values of a scoring function; (iii) visualization of predicted 3D dimer structures; (iv) visualization of surface hydrophobicity of TM helices and their contacting (interface) regions represented as 2D maps.
Results: We implemented on-line the original PREDDIMER algorithm and benchmarked the server on 11 TM sequences, whose 3D dimer conformations were obtained previously by NMR spectroscopy. In the most of tested cases backbone root-mean-square deviations (RMSD) of closest predicted conformations from the experimental reference are below 3 Å. A randomization test displays good anti-correlation (-0.82) between values of the scoring function and statistical significance of the prediction. Going beyond a single dimer conformation, our web tool predicts an ensemble of possible conformations, which may be useful for explanation of a functioning of bitopic membrane proteins, e.g. receptor-tyrosine kinases.
Genomics features with similar genome-wide distributions are generally hypothesized to be functionally related, for example, colocalization of histones and transcription start sites indicate chromatin regulation of transcription factor activity. Therefore, statistical algorithms to perform spatial, genome-wide correlation among genomic features are required.
Here, we propose a method, StereoGene, that rapidly estimates genome-wide correlation among pairs of genomic features. These features may represent high-throughput data mapped to reference genome or sets of genomic annotations in that reference genome. StereoGene enables correlation of continuous data directly, avoiding the data binarization and subsequent data loss. Correlations are computed among neighboring genomic positions using kernel correlation. Representing the correlation as a function of the genome position, StereoGene outputs the local correlation track as part of the analysis. StereoGene also accounts for confounders such as input DNA by partial correlation. We apply our method to numerous comparisons of ChIP-Seq datasets from the Human Epigenome Atlas and FANTOM CAGE to demonstrate its wide applicability. We observe the changes in the correlation between epigenomic features across developmental trajectories of several tissue types consistent with known biology and find a novel spatial correlation of CAGE clusters with donor splice sites and with poly(A) sites. These analyses provide examples for the broad applicability of StereoGene for regulatory genomics.