Biology subjects

Nielsen, H.

Publications and source records attributed to Nielsen, H..

At least 19 recordsLinked to original sources

bioRxiv2026

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

Protein secretion is essential for cell function and begins when signal peptides direct nascent proteins into the secretory pathway, where they are cleaved by the signal peptidase complex (SPC). Although this pathway is highly conserved, signal peptides do not always function efficiently across species, and the molecular basis for this incompatibility remains unclear. Here, we show that human signal peptides with longer hydrophobic cores efficiently target and translocate nascent proteins in both yeast and human but are frequently left uncleaved in yeast, identifying signal peptide cleavage as a major barrier to cross-species compatibility. Molecular dynamics (MD) simulations reveal that, despite their highly conserved architectures, yeast and human SPCs generate distinct membrane-thinning profiles near the signal peptide-binding region. Our results support a model in which SPC-lipid interactions tune the local membrane environment to accommodate signal peptides of distinct hydrophobic core length, providing a biophysical mechanism for species-specific signal peptide recognition. These findings offer a mechanistic framework for understanding and potentially engineering protein secretion across diverse expression hosts.

cell biology↗

bioRxiv2026

DeepTMHMM2 enables accurate prediction of transmembrane protein topology and subcellular location

Transmembrane -helical and {beta}-barrel proteins are a ubiquitous component of proteomes. Topology prediction infers how proteins are embedded in lipid bilayers, identifying membrane-spanning segments and their orientation. While recent methods achieve high performance for membrane-spanning segments, they cannot predict re-entrant regions and interfacial helices - membrane-associated segments that partially insert but do not cross the bilayer - nor identify which biological membrane a protein resides in. Here, we present DeepTMHMM2, the first predictor to include re-entrant regions and interfacial helices in its topologies and jointly predict localization across 17 biological membranes. Benchmark results show that DeepTMHMM2 successfully learns to predict the additional elements, while achieving strong performance on canonical -helical and {beta}-barrel topology prediction. Applying DeepTMHMM2 to Swiss-Prot reveals that non-crossing segments are a ubiquitous feature of the transmembrane proteome, with interfacial helices present in nearly a quarter of all -helical transmembrane proteins.

bioinformatics↗

bioRxiv2026

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

The family of human G-patch proteins comprises more than 20 members, each characterized by a glycine-rich G-patch implicated in mediating interactions with RNA helicases. Here, we systematically identify the cognate RNA helicase of each G-patch protein, highlighting the association of DHX15 with a network of 20 G-patch cofactors. DHX35 and GPATCH1 represent a unique G-patch protein-RNA helicase pair, and we uncover a regulatory circuit between these partners. Comprehensive in vitro analyses of ATPase activity and RNA binding identify distinguishing features of DHX15- and non-DHX15-associated G-patch proteins, and demonstrate the roles of most G-patch proteins as bona fide stimulatory cofactors of DHX15. RNA interactome analyses of each G-patch protein and complementary transcriptome-wide alternative splicing analyses in cells lacking a G-patch protein reveal distinct modes of regulation of mRNA maturation by different G-patch proteins. For example, ZGPAT affects splicing indirectly through its requirement for efficient 2'-O-methylation of snRNAs, GPATCH8 exemplifies DHX15-associated alternative splicing modulation, whereas SUGP2 suppresses splicing in an RNA helicase-independent manner via direct binding to pre-mRNA introns.

molecular biology↗

bioRxiv2026

DeepCDS: Ab initio coding sequence prediction in prokaryotic short reads

Accurate coding sequence prediction in short prokaryotic metagenomic sequences, such as Illumina reads, remains challenging due to sequence fragmentation, unknown sequence origins, and sequencing errors. Here we introduce DeepCDS, a deep learning-based ab initio coding sequence predictor trained on short prokaryotic sequences with and without simulated Illumina-like sequencing errors. DeepCDS integrates ESM-2 protein language model embeddings with nucleotide-level information to predict complete and fragmented coding sequence regions. Benchmarking on 215 phylogenetically diverse prokaryotic organisms demonstrates that DeepCDS consistently outperforms current state-of-the-art methods in coding sequence detection across sequence lengths from 75bp to 1000bp, including improved identification of both canonical and non-canonical start codons as well as stop codons, and greater robustness to different sequencing error profiles. DeepCDS also remains operational on sequences shorter than existing tools support. We additionally evaluate DeepCDS on a simulated metagenomic dataset from the Critical Assessment of Metagenome Interpretation initiative, where it likewise achieves the strongest performance among tested tools. These findings demonstrate that protein language models capture distinct signals relevant for nucleotide-level coding sequence detection, especially at very short lengths. Ultimately, DeepCDS may help uncover the functional potential of the vast microbial diversity that remains genomically uncharacterized.

bioinformatics↗

bioRxiv2026

Adaptive immunity shapes baseline physiology of M. tuberculosis in high-dose versus low-dose infection BALB/c mouse drug treatment models

Preclinical tuberculosis (TB) drug evaluation relies heavily on mouse infection models. Drug efficacy varies depending on inoculum and the timing of treatment initiation. These differences reflect, in part, physiological adaptations of Mycobacterium tuberculosis (Mtb) to host immune pressure. We used novel molecular markers of pathogen health (RS ratio and SEARCH-TB) to contrast Mtb phenotypes in the BALB/c high dose aerosol (HDA) subacute TB and low dose aerosol (LDA) chronic TB infection models, focusing on the transition from innate to adaptive immunity. We found that the onset of adaptive immunity coincided with a rapid reprogramming of Mtb physiology, characterized by suppressed respiration, metabolism, and biosynthesis, together with induction of stress and nutrient acquisition pathways. Changes in key Mtb processes were concordant with changes in host expression of canonical features of adaptive immunity. Our results also explain key model-specific differences in drug efficacy: when used for drug evaluation, the HDA model begins treatment during the innate immune phase when Mtb are metabolically active, whereas the LDA model initiates therapy after host adaptive immunity has already activated and the bacterial population is immune-constrained. The HDA model is shown to be a mixed model, spanning immune phases with initial treatment acting on replicating bacteria and later treatment acting on immune-constrained populations. Together, the two models offer complementary perspectives on therapeutic activity across the spectrum of bacterial states and provide a framework for designing regimens that are effective against both active and immune-constrained Mtb.

genomics↗

bioRxiv2025

Acinetobacter enrichment shapes composition and function of the bacterial microbiota of field-grown tomato plants

Tomato is a staple crop and an excellent model to study host-microbiota interactions in the plant food chain. In this study we describe a lab-in-the-field approach to investigate the microbiota of field-grown tomato plants. High-throughput amplicon sequencing revealed a three-microhabitat partition, phyllosphere, rhizosphere and root interior, differentiating host-associated communities from the environmental microbiota. An individual bacterium, classified as Acinetobacter sp., emerged as a dominant member of the microbiota at the plant-soil continuum. To gain insights into the functional significance of this enrichment, we subjected rhizosphere specimens to shotgun metagenomics. Similar to the amplicon sequencing survey, a microhabitat effect defined by a set of rhizosphere-enriched functions was identified. Mobilisation of mineral nutrients, as well as adaptation to salinity and polymicrobial communities, including antimicrobial resistance genes (ARGs), emerged as a functional requirement sustaining metagenomic diversification. A metagenome-assembled genome (MAG) representative of Acinetobacter calcoaceticus was retrieved and metagenomic reads associated to this species identified a functional specialisation for plant-growth promotion traits (PGPTs), such as phosphate solubilization, siderophores production and reactive oxygen species detoxification, which were similarly represented in across tomato genotype-independent fashion. Our results revealed that the enrichment of a beneficial bacterium capable of alleviating plants abiotic stresses appears decoupled from ARGs facilitating microbiota persistence at the root-soil interface. IMPORTANCETomatoes are at centre-stage in global food security due to their high nutritional value, widespread cultivation, and versatility. Tomatoes provide essential vitamins and minerals, contribute to diverse diets, and support farmer livelihoods, making them a cornerstone of sustainable food systems. Beyond direct dietary benefits, the intricate relationship between tomatoes, their associated microbiota, and ARG is increasingly recognised. Tomato plants host diverse microbial communities in association with their organs, which influence plant health and productivity. Crop management impacts on composition and function of these communities, contributing to the prevalence of ARG in the soil and on the plants themselves. These genes can potentially transfer to human pathogens, posing a food safety and public health risk. Understanding these complex interactions is critical for developing sustainable agricultural practices capable of mitigating the impact of climatic modifications and the global threat of antimicrobial resistance.

plant biology↗

bioRxiv2025

Whole-genome prediction of bacterial pathogenic capacity on novel bacteria using protein language models, with PathogenFinder2

Infectious diseases continue to be a leading cause of mortality and pose a significant global health threat. Thus the development of tools for surveillance and early detection of emerging pathogens is needed. In this study, we introduce PathogenFinder2, a novel predictor of bacterial pathogenic capacity in humans, available through an online server (http://genepi.food.dtu.dk/pathogenfinder2), or as a standalone program (https://github.com/genomicepidemiology/PathogenFinder2). The model, using protein language models for whole-genome phenotype prediction, surpasses the performance of previous methods, especially for novel bacterial taxa, while being taxonomy-agnostic and alignment-free. At the same time, it predicts the importance of each protein for the pathogenic capacity. This output might aid in characterizing potential pathogens, it readily identifies new candidates for virulence factors and vaccine targets, and offers insights into infection metabolic pathways. Furthermore, we introduce the Bacterial Pathogenic Landscape, revealing distributions related to the host conditions, antagonist bacteria, infection site, or habitat.

bioinformatics↗

bioRxiv2024

Predicting the subcellular location of prokaryotic proteins with DeepLocPro

Protein subcellular location prediction is a widely explored task in bioinformatics because of its importance in proteomics research. We propose DeepLocPro, an extension to the popular method DeepLoc, tailored specifically to archaeal and bacterial organisms. DeepLocPro is a multiclass subcellular location prediction tool for prokaryotic proteins, trained on experimentally verified data curated from UniProt and PSORTdb. DeepLocPro compares favorably to the PSORTb 3.0 ensemble method, surpassing its performance across multiple metrics on our benchmark experiment. The DeepLocPro prediction tool is available online at https://ku.biolib.com/deeplocpro and https://services.healthtech.dtu.dk/services/DeepLocPro-1.0/.

bioinformatics↗

bioRxiv2023

GraphPart: Homology partitioning for biological sequence analysis

When splitting biological sequence data for the development and testing of predictive models, it is necessary to avoid too closely related pairs of sequences ending up in different partitions. If this is ignored, performance estimates of prediction methods will tend to be exaggerated. Several algorithms have been proposed for homology reduction, where sequences are removed until no too closely related pairs remain. We present GraphPart, an algorithm for homology partitioning, where as many sequences as possible are kept in the dataset, but partitions are defined such that closely related sequences always end up in the same partition. Evaluation of GraphPart on Protein, DNA and RNA datasets shows that it is capable of retaining a larger number of sequences per dataset, while providing homology separation quality on par with reduction approaches.

bioinformatics↗

bioRxiv2022

Ribosomal RNA 2'-O-methylation dynamics impact cell fate decisions

Translational regulation impacts both pluripotency maintenance and cell differentiation. To what degree the ribosome itself exerts control over this process remains unanswered. Accumulating evidence has demonstrated heterogeneity in ribosome composition in various organisms. 2-O-methylation of rRNA represents an important source of heterogeneity, where site-specific alteration of methylation levels can modulate translation. Here we explore changes in rRNA 2-O-methylation during mouse brain development and during tri-lineage differentiation of human embryonic stem cells. We find distinct alterations between brain regions, as well as clear dynamics during cortex development and germ layer differentiation. We identify a methylation site which impacts neuronal differentiation. Modulation of its methylation levels affects ribosome association of the Fragile X Mental Retardation Protein and translation of WNT pathway-related mRNAs. Together, the data reveals ribosome heterogeneity through rRNA 2-O-methylation during early development and differentiation and suggests a direct role for ribosomes in regulating translation during cell fate acquisition.

developmental biology↗

bioRxiv2022

DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks

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.

bioinformatics↗

bioRxiv2021

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

Solubility and expression levels of proteins can be a limiting factor for large-scale studies and industrial production. By determining the solubility and expression directly from the protein sequence, the success rate of wet-lab experiments can be increased. In this study, we focus on predicting the solubility and usability for purification of proteins expressed in Escherichia coli directly from the sequence. Our model NetSolP is based on deep learning protein language models called transformers and we show that it achieves state-of-the-art performance and improves extrapolation across datasets. As we find current methods are built on biased datasets, we curate existing datasets by using strict sequence-identity partitioning and ensure that there is minimal bias in the sequences. The predictor is available at https://services.healthtech.dtu.dk/service.php?NetSolP-1.0

bioinformatics↗

bioRxiv2021

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

Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. As experimental characterization of SPs is costly, prediction algorithms are applied to predict them from sequence data. However, existing methods are unable to detect all known types of SPs. We introduce SignalP 6.0, the first model capable of detecting all five SP types. Additionally, the model accurately identifies the positions of regions within SPs, revealing the defining biochemical properties that underlie the function of SPs in vivo. Results show that SignalP 6.0 has improved prediction performance, and is the first model to be applicable to metagenomic data. SignalP 6.0 is available at https://services.healthtech.dtu.dk/service.php?SignalP-6.0

bioinformatics↗

bioRxiv2021

Deep protein representations enable recombinant protein expression prediction

A crucial process in the production of industrial enzymes is recombinant gene expression, which aims to induce enzyme overexpression of the genes in a host microbe. Current approaches for securing overexpression rely on molecular tools such as adjusting the recombinant expression vector, adjusting cultivation conditions, or performing codon optimizations. However, such strategies are time-consuming, and an alternative strategy would be to select genes for better compatibility with the recombinant host. Several methods for predicting soluble expression are available; however, they are all optimized for the expression host Escherichia coli and do not consider the possibility of an expressed protein not being soluble. We show that these tools are not suited for predicting expression potential in the industrially important host Bacillus subtilis. Instead, we build a B. subtilis-specific machine learning model for expressibility prediction. Given millions of unlabelled proteins and a small labeled dataset, we can successfully train such a predictive model. The unlabeled proteins provide a performance boost relative to using amino acid frequencies of the labeled proteins as input. On average, we obtain a modest performance of 0.64 area-under-the-curve (AUC) and 0.2 Matthews correlation coefficient (MCC). However, we find that this is sufficient for the prioritization of expression candidates for high-throughput studies. Moreover, the predicted class probabilities are correlated with expression levels. A number of features related to protein expression, including base frequencies and solubility, are captured by the model.

bioinformatics↗

bioRxiv2020

Language modelling for biological sequences - curated datasets and baselines

MotivationLanguage modelling (LM) on biological sequences is an emergent topic in the field of bioinformatics. Current research has shown that language modelling of proteins can create context-dependent representations that can be applied to improve performance on different protein prediction tasks. However, little effort has been directed towards analyzing the properties of the datasets used to train language models. Additionally, only the performance of cherry-picked downstream tasks are used to assess the capacity of LMs. ResultsWe analyze the entire UniProt database and investigate the different properties that can bias or hinder the performance of LMs such as homology, domain of origin, quality of the data, and completeness of the sequence. We evaluate n-gram and Recurrent Neural Network (RNN) LMs to assess the impact of these properties on performance. To our knowledge, this is the first protein dataset with an emphasis on language modelling. Our inclusion of properties specific to proteins gives a detailed analysis of how well natural language processing methods work on biological sequences. We find that organism domain and quality of data have an impact on the performance, while the completeness of the proteins has little influence. The RNN based LM can learn to model Bacteria, Eukarya, and Archaea; but struggles with Viruses. By using the LM we can also generate novel proteins that are shown to be similar to real proteins. Availability and implementationhttps://github.com/alrojo/UniLanguage

bioinformatics↗

bioRxiv2019

Prediction of GPI-Anchored proteins with pointer neural networks

GPI-anchors constitute a very important post-translational modification, linking many proteins to the outer face of the plasma membrane in eukaryotic cells. Since experimental validation of GPI-anchoring signals is slow and costly, computational approaches for predicting them from amino acid sequences are needed. However, the most recent GPI predictor is more than a decade old and considerable progress has been made in machine learning since then. We present a new dataset and a novel method, NetGPI, for GPI signal prediction. NetGPI is based on recurrent neural networks, incorporating an attention mechanism that simultaneously detects GPI-anchoring signals and points out the location of their{omega} -sites. The performance of NetGPI is superior to existing methods with regards to discrimination between GPI-anchored proteins and other secretory proteins and approximate ({+/-}1 position) placement of the{omega} -site. NetGPI is available at: https://services.healthtech.dtu.dk/service.php?NetGPI The code repository is available at: https://github.com/mhgislason/netgpi-1.1

bioinformatics↗

bioRxiv2019

RRP7A links primary microcephaly to radial glial cells and dysfunction of ribosomal biogenesis, neurogenesis and ciliary resorption

Introductory paragraphPrimary microcephaly (MCPH) is characterized by reduced brain size and intellectual disability1. The exact pathophysiological mechanism underlying MCPH remains to be elucidated, but dysfunction of neuronal progenitors in the developing neocortex plays a major role1. Using homozygosity mapping and whole exome sequencing, we identified a homozygous missense mutation (p.W155C) in Ribosomal RNA Processing 7 Homolog A, RRP7A, which segregated with MCPH in a consanguineous family with 10 affected individuals. RRP7A is expressed in neural stem cells/radial glial cells of the developing human forebrain, and targeted mutation of Rrp7a leads to defects in both neurogenesis and proliferation in a mouse stem cell model. RRP7A localizes to centrosomes, cilia and nucleoli, and patient-derived fibroblasts display defects in processing of ribosomal RNA, resorption of primary cilia and cell cycle progression. Finally, analysis of zebrafish embryos with loss-of-function mutation in rrp7a confirmed that RRP7A depletion causes reduced brain size, impaired neurogenesis and cell proliferation as well as defective ribosomal RNA processing. These findings provide novel insight into human brain development and MCPH.

neuroscience↗

bioRxiv2019

Detecting Novel Sequence Signals in Targeting Peptides Using Deep Learning

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 art method to identify N-terminal sorting signals, which direct proteins to the secretory pathway, mitochondria and chloroplasts or other plastids.\n\nBy examining the strongest signals from the attention layer in the network, we find that the second residue in the protein, i.e. the one following the initial methionine, has a strong influence on the classification. When subsequently examining all targeting peptides, we observe that two-thirds of chloroplast and thylakoid transit peptides have an alanine in position two, but only 20% of other plant proteins. Further highlighting the importance of the second residue, 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.\n\nTargetP 2.0 is available at http://www.cbs.dtu.dk/services/TargetP-2.0/index.php

bioinformatics↗