Thousands of protein linear motif classes may still be undiscovered

Exploiting the endogenous yeast nuclear proteome to identify short linear motifs in vivo

Peptide-domain interactions mediated by short linear motifs (SLiMs) play crucial roles in cellular biology. The simplicity of SLiMs poses challenges in their computational identification. Existing high-throughput methods for discovering SLiMs lack cellular context as they are typically performed in vitro. We developed a functional selection method using yeast to identify peptides that interact with the endogenous yeast nuclear proteome. Remarkably, peptides selected for in yeast also mediated nuclear import in human cells. Notably, the identified peptides did not resemble classical nuclear localization sequences. This platform has the potential to identify and investigate motifs that interact with the nuclear proteome of yeast and human and to aid in the identification and understanding of alternative protein nuclear import mechanisms.

Multi-dimensional feature recognition model based on capsule network for ubiquitination site prediction

Ubiquitination is an important post-translational modification of proteins that regulates many cellular activities. Traditional experimental methods for identification are costly and time-consuming, so many researchers have proposed computational methods for ubiquitination site prediction in recent years. However, traditional machine learning methods focus on feature engineering and are not suitable for large-scale proteomic data. In addition, deep learning methods are mostly based on convolutional neural networks and fuse multiple coding approaches to achieve classification prediction. This cannot effectively identify potential fine-grained features of the input data and has limitations in the representation of dependencies between low-level features and high-level features. A multi-dimensional feature recognition model based on a capsule network (MDCapsUbi) was proposed to predict protein ubiquitination sites. The proposed module consisting of convolution operations and channel attention was used to recognize coarse-grained features in the sequence dimension and the feature map dimension. The capsule network module consisting of capsule vectors was used to identify fine-grained features and classify ubiquitinated sites. With ten-fold cross-validation, the MDCapsUbi achieved 91.82% accuracy, 91.39% sensitivity, 92.24% specificity, 0.837 MCC, 0.918 F-Score and 0.97 AUC. Experimental results indicated that the proposed method outperformed other ubiquitination site prediction technologies.

Transcription factor specificity limits the number of DNA-binding motifs

We study the limits imposed by transcription factor specificity on the maximum number of binding motifs that can coexist in a gene regulatory network, using the SwissRegulon Fantom5 collection of 684 human transcription factor binding sites as a model. We describe transcription factor specificity using regular expressions and find that most human transcription factor binding site motifs are separated in sequence space by one to three motif-discriminating positions. We apply theorems based on the pigeonhole principle to calculate the maximum number of transcription factors that can coexist given this degree of specificity, which is in the order of ten thousand and would fully utilize the space of DNA subsequences. Taking into account an expanded DNA alphabet with modified bases can further raise this limit by several orders of magnitude, at a lower level of sequence space usage. Our results may guide the design of transcription factors at both the molecular and system scale.