Biology subjects

Gawron, P.

Publications and source records attributed to Gawron, P..

3 recordsLinked to original sources

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↗

bioRxiv2020

Targeted biomass degradation by the higher termite gut system - integrative omics applied to host and its gut microbiome

Miscanthus sp. is regarded as suitable biomass for different biorefinery value chains. However, due to high recalcitrance, its wide use is yet untapped. Termite is the most efficient lignocellulose degrading insect, and its success results from synergistic cooperation with its gut microbiome. Here, we investigated at holobiont level the dynamic adaptation of a higher termite Cortaritermes sp. to imposed Miscanthus diet, with a long-term objective of overcoming lignocellulose recalcitrance. We used an integrative omics approach, comprising amplicon sequencing, metagenomics and metatranscriptomics that we combined with enzymatic characterisation of carbohydrate active enzymes from termite gut Fibrobacteres and Spirochaetae. Adaptation to the new diet was evidenced by reduced gut bacterial diversity and modified gene expression profiles, further suggesting a shift towards utilisation of cellulose and arabinoxylan, two main components of Miscanthus lignocellulose. Low identity of reconstructed microbial genomes to microbes from closely related termite species, supported the hypothesis of a strong phylogenetic relationship between host and its gut microbiome. Application-wise, this makes each termite gut system an endless source of enzymes that are potentially industrially relevant. This study provides a framework for better understanding the complex lignocellulose degradation by the higher termite gut system and paves a road towards its future bioprospecting.

systems biology↗

bioRxiv2018

The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease

A multitude of factors contribute to complex diseases and can be measured with \"omics\" methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, http://vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources \"Human metabolism\", \"Gut microbiome\", \"Disease\", \"Nutrition\", and \"ReconMaps\". The VMH captures 5,180 unique metabolites, 17,730 unique reactions, 3,288 human genes, 255 Mendelian diseases, 818 microbes, 632,685 microbial genes, and 8,790 food items. The VMHs unique features are i) the hosting the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; ii) seven human metabolic maps for data visualization; iii) a nutrition designer; iv) a user-friendly webpage and application-programming interface to access its content; and v) user feedback option for community engagement. We demonstrate with four examples the VMHs utility. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community.

systems biology↗