Biology subjects

Almagro Armenteros, J. J.

Publications and source records attributed to Almagro Armenteros, J. J..

7 recordsLinked to original sources

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↗

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

Temporal dynamics of the multi-omic response to endurance exercise training across tissues

Regular exercise promotes whole-body health and prevents disease, yet the underlying molecular mechanisms throughout a whole organism are incompletely understood. Here, the Molecular Transducers of Physical Activity Consortium (MoTrPAC) profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome, and immunome in whole blood, plasma, and 18 solid tissues in Rattus norvegicus over 8 weeks of endurance exercise training. The resulting data compendium encompasses 9466 assays across 19 tissues, 25 molecular platforms, and 4 training time points in young adult male and female rats. We identified thousands of shared and tissue- and sex-specific molecular alterations. Temporal multi-omic and multi-tissue analyses demonstrated distinct patterns of tissue remodeling, with widespread regulation of immune, metabolism, heat shock stress response, and mitochondrial pathways. These patterns provide biological insights into the adaptive responses to endurance training over time. For example, exercise training induced heart remodeling via altered activity of the Mef2 family of transcription factors and tyrosine kinases. Translational analyses revealed changes that are consistent with human endurance training data and negatively correlated with disease, including increased phospholipids and decreased triacylglycerols in the liver. Sex differences in training adaptation were widespread, including those in the brain, adrenal gland, lung, and adipose tissue. Integrative analyses generated novel hypotheses of disease relevance, including candidate mechanisms that link training adaptation to non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health, and tissue injury and recovery. The data and analysis results presented in this study will serve as valuable resources for the broader community and are provided in an easily accessible public repository (https://motrpac-data.org/). HighlightsO_LIMulti-tissue resource identifies 35,439 analytes regulated by endurance exercise training at 5% FDR across 211 combinations of tissues and molecular platforms. C_LIO_LIInterpretation of systemic and tissue-specific molecular adaptations produced hypotheses to help describe the health benefits induced by exercise. C_LIO_LIRobust sex-specific responses to endurance exercise training are observed across multiple organs at the molecular level. C_LIO_LIDeep multi-omic profiling of six tissues defines regulatory signals for tissue adaptation to endurance exercise training. C_LIO_LIAll data are available in a public repository, and processed data, analysis results, and code to reproduce major analyses are additionally available in convenient R packages. C_LI

molecular 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↗

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

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↗