Biology subjects

Vriend, G.

Publications and source records attributed to Vriend, G..

4 recordsLinked to original sources

bioRxiv2022

Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells

Several haematologic diseases, including malaria, diabetes, and sickle cell anaemia, result in a reduced red blood cell deformability. This deformability can be measured using a microfluidic device with channels of varying width. Nevertheless, it is challenging to algorithmically recognise large numbers of red blood cells and quantify their deformability from image data. Deep learning has become the method of choice to handle noisy and complex image data. However, it requires a significant amount of labelled data to train the neural networks. By creating images of cells and mimicking noise and plasticity in those images, we generate synthetic data to train a network to detect and segment red blood cells from video-recordings, without the need for manually annotated labels. Using this new method, we uncover significant differences between the deformability of RBCs infected with different strains of Plasmodium falciparum, providing clues to the variation in virulence of these strains.

microbiology↗

bioRxiv2021

PredictProtein - Predicting Protein Structure and Function for 29 Years

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProteins infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold; user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings. AvailabilityFreely accessible webserver PredictProtein.org; Source and docker images: github.com/rostlab

bioinformatics↗

bioRxiv2019

Class A GPCRs use the membrane potential to increase their sensitivity and selectivity

The human genome contains about 700 genes of G protein-coupled receptors (GPCRs) of class A; these seven-helical membrane proteins are the targets of almost half of all known drugs. In the middle of the helix bundle, crystal structures revealed a highly conserved sodium-binding site, which is connected with the extracellular side by a water-filled tunnel. Sodium ions are observed in GPCRs crystallized in their inactive conformations, but not in GPCRs that were trapped in agonist-bound active conformations. The escape route of the sodium ion upon the inactive-to-active transition and its very direction, either into the cytoplasm or back outside the cell, hitherto remained obscure. We modeled sodium-binding GPCRs as electrogenic carriers of sodium ions. In this model the sodium gradient over the cell membrane would increase the sensitivity of GPCRs if their activation is thermodynamically coupled to the translocation of the sodium ion into the cytoplasm, while decreasing it if the sodium ion retreats into the extracellular space upon receptor activation. The model quantitatively describes the available data on both activation and suppression of distinct GPCRs by membrane voltage. The model also predicts selective amplification of the signal from (endogenous) agonists if only they, but not their (partial) analogs, could induce sodium translocation. Comparative structure and sequence analyses of sodium-binding GPCRs indicate a key role for the conserved leucine residue in the second transmembrane helix (Leu2.46) in coupling sodium translocation to receptor activation. Hence, class A GPCRs appear to utilize the energy of the transmembrane sodium potential to increase their sensitivity and selectivity.

biophysics↗

bioRxiv2019

MetaDome: Pathogenicity analysis of genetic variants through aggregation of homologous human protein domains

The growing availability of human genetic variation has given rise to novel methods of measuring genetic tolerance that better interpret variants of unknown significance. We recently developed a novel concept based on protein domain homology in the human genome to improve variant interpretation. For this purpose we mapped population variation from the Exome Aggregation Consortium (ExAC) and pathogenic mutations from the Human Gene Mutation Database (HGMD) onto Pfam protein domains. The aggregation of these variation data across homologous domains into meta-domains allowed us to generate base-pair resolution of genetic intolerance profiles for human protein domains.\n\nHere we developed MetaDome, a fast and easy-to-use web service that visualizes meta-domain information and gene-wide profiles of genetic tolerance. We updated the underlying data of MetaDome to contain information from 56,319 human transcripts, 71,419 protein domains, 12,164,292 genetic variants from gnomAD, and 34,076 pathogenic mutations from ClinVar. MetaDome allows researchers to easily investigate their variants of interest for the presence or absence of variation at corresponding positions within homologous domains. We illustrate the added value of MetaDome by an example that highlights how it may help in the interpretation of variants of unknown significance. The MetaDome web server is freely accessible at https://stuart.radboudumc.nl/metadome.

bioinformatics↗