Slx5/Slx8-dependent ubiquitin hotspots on chromatin contribute to stress tolerance.

Chromatin is a highly regulated environment, and protein association with chromatin is often controlled by post-translational modifications and the corresponding enzymatic machinery. Specifically, SUMO-targeted ubiquitin ligases (STUbLs) have emerged as key players in nuclear quality control, genome maintenance, and transcription. However, how STUbLs select specific substrates among myriads of SUMOylated proteins on chromatin remains unclear. Here, we reveal a remarkable co-localization of the budding yeast STUbL Slx5/Slx8 and ubiquitin at seven genomic loci that we term "ubiquitin hotspots". Ubiquitylation at these sites depends on Slx5/Slx8 and protein turnover on the Cdc48 segregase. We identify the transcription factor-like Ymr111c/Euc1 to associate with these sites and to be a critical determinant of ubiquitylation. Euc1 specifically targets Slx5/Slx8 to ubiquitin hotspots via bipartite binding of Slx5 that involves the Slx5 SUMO-interacting motifs and an additional, novel substrate recognition domain. Interestingly, the Euc1-ubiquitin hotspot pathway acts redundantly with chromatin modifiers of the H2A.Z and Rpd3L pathways in specific stress responses. Thus, our data suggest that STUbL-dependent ubiquitin hotspots shape chromatin during stress adaptation.

HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons.

Short linear motifs (SLiMs) in proteins are self-sufficient functional sequences that specify interaction sites for other molecules and thus mediate a multitude of functions. Computational, as well as experimental biological research would significantly benefit, if SLiMs in proteins could be correctly predicted de novo with high sensitivity. However, de novo SLiM prediction is a difficult computational task. When considering recall and precision, the performances of published methods indicate remaining challenges in SLiM discovery. We have developed HH-MOTiF, a web-based method for SLiM discovery in sets of mainly unrelated proteins. HH-MOTiF makes use of evolutionary information by creating Hidden Markov Models (HMMs) for each input sequence and its closely related orthologs. HMMs are compared against each other to retrieve short stretches of homology that represent potential SLiMs. These are transformed to hierarchical structures, which we refer to as motif trees, for further processing and evaluation. Our approach allows us to identify degenerate SLiMs, while still maintaining a reasonably high precision. When considering a balanced measure for recall and precision, HH-MOTiF performs better on test data compared to other SLiM discovery methods. HH-MOTiF is freely available as a web-server at http://hh-motif.biochem.mpg.de.

SLALOM, a flexible method for the identification and statistical analysis of overlapping continuous sequence elements in sequence- and time-series data.

BACKGROUND: Protein or nucleic acid sequences contain a multitude of associated annotations representing continuous sequence elements (CSEs). Comparing these CSEs is needed, whenever we want to match identical annotations or integrate distinctive ones. Currently, there is no ready-to-use software available that provides comprehensive statistical readout for comparing two annotations of the same type with each other, which can be adapted to the application logic of the scientific question. RESULTS: We have developed a method, SLALOM (for StatisticaL Analysis of Locus Overlap Method), to perform comparative analysis of sequence annotations in a highly flexible way. SLALOM implements six major operation modes and a number of additional options that can answer a variety of statistical questions about a pair of input annotations of a given sequence collection. We demonstrate the results of SLALOM on three different examples from biology and economics and compare our method to already existing software. We discuss the importance of carefully choosing the application logic to address specific scientific questions. CONCLUSION: SLALOM is a highly versatile, command-line based method for comparing annotations in a collection of sequences, with a statistical read-out for performance evaluation and benchmarking of predictors and gene annotation pipelines. Abstraction from sequence content even allows SLALOM to compare other kinds of positional data including, for example, data coming from time series.