Henrik Nielsen

DeepCDS: Ab initio coding sequence prediction in prokaryotic short nucleotide sequences

DeepCDS improves coding sequence prediction in short prokaryotic sequences using deep learning and protein language model embeddings, outperforming existing methods across various sequence lengths and error profiles.

Signal peptidase complexes set species-specific rules for signal peptide cleavage

The paper shows that human signal peptides are inefficiently cleaved by yeast SPCs due to differences in SPC-lipid interactions and membrane thinning profiles.

DeepTMHMM2 enables accurate prediction of transmembrane protein topology and subcellular location

DeepTMHMM2 predicts transmembrane protein topology and subcellular location, including re-entrant regions and interfacial helices, and identifies localization across 17 membranes.

Regulation of RNA maturation by the family of human G-patch proteins

The paper identifies RNA helicase partners of G-patch proteins and reveals distinct regulatory mechanisms by which they influence mRNA maturation.

Predicting the subcellular location of prokaryotic proteins with DeepLocPro

DeepLocPro is a subcellular location predictor for prokaryotic proteins, outperforming PSORTb 3.0 on benchmark metrics using experimentally verified data.

GraphPart: Homology partitioning for biological sequence analysis

GraphPart partitions biological sequences to avoid closely related sequences in different partitions, retaining more sequences than reduction methods while maintaining homology separation quality.

DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks

Combining an ESM-1b encoder with a conditional random field decoder, DeepTMHMM classifies alpha-helical and beta-barrel transmembrane proteins and their topology, outperforming most compared tools, including on signal-peptide cleavage sites.

SignalP 6.0 achieves signal peptide prediction across all types using protein language models

SignalP 6.0 predicts all five types of signal peptides with improved accuracy and applies to metagenomic data.

NetSolP: predicting protein solubility in E. coli using language models

NetSolP predicts protein solubility in E. coli from sequence using transformers, achieving state-of-the-art performance with reduced dataset bias.

Deep protein representations enable recombinant protein expression prediction

The paper develops a machine learning model to predict recombinant protein expression in Bacillus subtilis using deep protein representations, achieving moderate performance for prioritizing expression candidates.

Prediction of GPI-Anchored proteins with pointer neural networks

The paper introduces NetGPI, a new method for predicting GPI-anchored proteins using recurrent neural networks with attention mechanisms, achieving better performance than existing methods.

Language modelling for biological sequences - curated datasets and baselines

The paper analyzes properties of protein datasets for language modelling and evaluates n-gram and RNN models, finding that organism domain and data quality affect performance while completeness has little impact.

Detecting Novel Sequence Signals in Targeting Peptides Using Deep Learning

The paper introduces TargetP 2.0, a deep learning method to identify N-terminal sorting signals in proteins, revealing insights into the importance of the second residue in targeting peptides.

NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning

NetSurfP-2.0 improves prediction of protein structural features with higher accuracy and faster runtime than previous versions.