Biology subjects

Schafferhans, A.

Publications and source records attributed to Schafferhans, A..

3 recordsLinked to original sources

bioRxiv2021

Streamlined use of protein structures in variant analysis

MotivationVariant analysis is a core task in bioinformatics that requires integrating data from many sources. This process can be helped by using 3D structures of proteins, which can provide a spatial context that can provide insight into how variants affect function. Many available tools can help with mapping variants onto structures; but each has specific restrictions, with the result that many researchers fail to benefit from valuable insights that could be gained from structural data. ResultsTo address this, we have created a streamlined system for incorporating 3D structures into variant analysis. Variants can be easily specified via URLs that are easily readable and writable, and use the notation recommended by the Human Genome Variation Society (HGVS). For example, https://aquaria.app/SARS-CoV-2/S/?N501Y specifies the N501Y variant of SARS-CoV-2 S protein. In addition to mapping variants onto structures, our system provides summary information from multiple external resources, including COSMIC, CATH-FunVar, and PredictProtein. Furthermore, our system identifies and summarizes structures containing the variant, as well as the variant-position. Our system supports essentially any mutation for any well-studied protein, and uses all available structural data -- including models inferred via very remote homology -- integrated into a system that is fast and simple to use. By giving researchers easy, streamlined access to a wealth of structural information during variant analysis, our system will help in revealing novel insights into the molecular mechanisms underlying protein function in health and disease. AvailabilityOur resource is freely available at the project home page (https://aquaria.app). After peer review, the code will be openly available via a GPL version 2 license at https://github.com/ODonoghueLab/Aquaria. PSSH2, the database of sequence-to-structure alignments, is also freely available for download at https://zenodo.org/record/4279164. Contactsean@odonoghuelab.org Supplementary informationNone.

bioinformatics↗

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

Systematic modeling of SARS-CoV-2 protein structures

In response to the COVID-19 pandemic, many life scientists are focused on SARS-CoV-2. To help them use available structural data, we systematically modeled all viral proteins using all related 3D structures, generating 872 models that provide detail not available elsewhere. To organise these models, we created a structural coverage map: a novel, one-stop visualization summarizing what is -- and is not -- known about the 3D structure of the viral proteome. The map highlights structural evidence for viral protein interactions, mimicry, and hijacking; it also helps researchers find 3D models of interest, which can then be mapped with UniProt, PredictProtein, or CATH features. The resulting Aquaria-COVID resource (https://aquaria.ws/covid) helps scientists understand molecular mechanisms underlying coronavirus infection. Based on insights gained using our resource, we propose mechanisms by which the virus may enter immune cells, sense the cell type, then switch focus from viral reproduction to disrupting host immune responses. SignificanceCurrently, much of the COVID-19 viral proteome has unknown molecular structure. To improve this, we generated [~]1,000 structural models, designed to capture multiple states for each viral protein. To organise these models, we created a structure coverage map: a novel, one-stop visualization summarizing what is -- and is not -- known about viral protein structure. We used these data to create an online resource, designed to help COVID-19 researchers gain insight into the key molecular processes that drive infection. Based on insights gained using our resource, we speculate that the virus may sense the type of cells it infects and, within certain cells, it may switch from reproduction to disruption of the immune system.

bioinformatics↗