Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs. We introduce SignalP 6.0, a machine learning model that detects all five SP types and is applicable to metagenomic data.

Transmembrane proteins span the lipid bilayer and are divided into two major structural classes, namely alpha helical and beta barrels. We introduce DeepTMHMM, a deep learning protein language model-based algorithm that can detect and predict the topology of both alpha helical and beta barrels proteins with unprecedented accuracy. DeepTMHMM ( https://dtu.biolib.com/DeepTMHMM ) scales to proteomes and covers all domains of life, which makes it ideal for metagenomics analyses.

Motivation: The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only. Results: Here we present a prediction algorithm using deep neural networks to predict protein subcellular localization relying only on sequence information. At its core the prediction model uses a recurrent neural network that processes the entire protein sequence and an attention mechanism identifying protein regions important for the subcellular localization. The model was trained and tested on a protein dataset extracted from one of the latest UniProt releases, in which experimentally annotated proteins follow more stringent criteria than previously. We demonstrate that our model achieves a good accuracy (78% for 10 categories; 92% for membrane-bound or soluble), outperforming current state-of-the-art algorithms, including those relying on homology information. Availability: The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc Example code is available at https://github.com/JJAlmagro/subcellular_localization The dataset is available at http://www.cbs.dtu.dk/services/DeepLoc/data.php Contact:jjalma@dtu.dk

In bioinformatics, machine learning methods have been used to predict features embedded in the sequences. In contrast to what is generally assumed, machine learning approaches can also provide new insights into the underlying biology. Here, we demonstrate this by presenting TargetP 2.0, a novel state-of-the-art method to identify N-terminal sorting signals, which direct proteins to the secretory pathway, mitochondria, and chloroplasts or other plastids. By examining the strongest signals from the attention layer in the network, we find that the second residue in the protein, that is, the one following the initial methionine, has a strong influence on the classification. We observe that two-thirds of chloroplast and thylakoid transit peptides have an alanine in position 2, compared with 20% in other plant proteins. We also note that in fungi and single-celled eukaryotes, less than 30% of the targeting peptides have an amino acid that allows the removal of the N-terminal methionine compared with 60% for the proteins without targeting peptide. The importance of this feature for predictions has not been highlighted before.

JASPAR is a popular open-access database for matrix models describing DNA-binding preferences for transcription factors and other DNA patterns. With its third major release, JASPAR has been expanded and equipped with additional functions aimed at both casual and power users. The heart of the JASPAR database—the JASPAR CORE sub-database—has increased by 12% in size, and three new specialized sub-databases have been added. New functions include clustering of matrix models by similarity, generation of random matrices by sampling from selected sets of existing models and a language-independent Web Service applications programming interface for matrix retrieval. JASPAR is available at http://jaspar.genereg.net .