ORCID Profile
0000-0002-5569-6186
Current Organisation
Technische Universität München
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Publisher: Proceedings of the National Academy of Sciences
Date: 17-11-2015
Abstract: A key remaining frontier in our understanding of biological systems is the “dark proteome”—that is, the regions of proteins where molecular conformation is completely unknown. We systematically surveyed these regions, finding that nearly half of the proteome in eukaryotes is dark and that, surprisingly, most of the darkness cannot be accounted for. We also found that the dark proteome has unexpected features, including an association with secretory tissues, disulfide bonding, low evolutionary conservation, and very few known interactions with other proteins. This work will help future research shed light on the remaining dark proteome, thus revealing molecular processes of life that are currently unknown.
Publisher: Springer Science and Business Media LLC
Date: 29-01-2015
DOI: 10.1038/NMETH.3258
Publisher: CRC Press
Date: 13-06-2006
Publisher: Cold Spring Harbor Laboratory
Date: 17-07-2020
DOI: 10.1101/2020.07.16.207308
Abstract: 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 ( 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. Currently, 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.
Publisher: Springer Science and Business Media LLC
Date: 03-2010
DOI: 10.1038/NMETH.1427
Publisher: Oxford University Press (OUP)
Date: 2003
DOI: 10.1093/NAR/GKG110
Abstract: We introduce the PSSH ('Protein Sequence-to-Structure Homologies') database derived from HSSP2, an improved version of the HSSP ('Homology-derived Secondary Structure of Proteins') database [Dodge et al. (1998) Nucleic Acids Res., 26, 313-315]. Whereas each HSSP entry lists all protein sequences related to a given 3D structure, PSSH is the 'inverse', with each entry listing all structures related to a given sequence. In addition, we introduce two other derived databases: HSSPchain, in which each entry lists all sequences related to a given PDB chain, and HSSPalign, in which each entry gives details of one sequence aligned onto one PDB chain. This re-organization makes it easier to navigate from sequence to structure, and to map sequence features onto 3D structures. Currently (September 2002), PSSH provides structural information for over 400 000 protein sequences, covering 48% of SWALL and 61% of SWISS-PROT sequences HSSPchain provides sequence information for over 25 000 PDB chains, and HSSPalign gives over 14 million sequence-to-structure alignments. The databases can be accessed via SRS 3D, an extension to the SRS system, at srs3d.ebi.ac.uk/.
Publisher: Wiley
Date: 11-2018
Abstract: Despite substantial and successful projects for structural genomics, many proteins remain for which neither experimental structures nor homology-based models are known for any part of the amino acid sequence. These have been called "dark proteins," in contrast to non-dark proteins, in which at least part of the sequence has a known or inferred structure. It has been hypothesized that non-dark proteins may be more abundantly expressed than dark proteins, which are known to have much fewer sequence relatives. Surprisingly, the opposite has been observed: human dark and non-dark proteins had quite similar levels of expression, in terms of both mRNA and protein abundance. Such high levels of expression strongly indicate that dark proteins-as a group-are important for cellular function. This is remarkable, given how carefully structural biologists have focused on proteins crucial for function, and highlights the important challenge posed by dark proteins in future research.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: Cold Spring Harbor Laboratory
Date: 24-02-2021
DOI: 10.1101/2021.02.23.432527
Abstract: Since 1992 PredictProtein ( 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). PredictProtein’s 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. Freely accessible webserver PredictProtein.org Source and docker images: ostlab
Publisher: Springer Science and Business Media LLC
Date: 13-08-2015
Publisher: Cold Spring Harbor Laboratory
Date: 11-09-2021
DOI: 10.1101/2021.09.10.459756
Abstract: Variant 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. To 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 ex le, ‘ 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. Our resource is freely available at the project home page ( aquaria.app ). After peer review, the code will be openly available via a GPL version 2 license at github.com/ODonoghueLab/Aquaria . PSSH2, the database of sequence-to-structure alignments, is also freely available for download at ecord/4279164 . sean@odonoghuelab.org None.
Publisher: EMBO
Date: 09-2021
Publisher: Oxford University Press (OUP)
Date: 15-04-2004
DOI: 10.1093/BIOINFORMATICS/BTH260
Abstract: Summary: In this paper we present SRS 3D, a new service that allows users to easily and rapidly find all related structures for a given target sequence structures can then be viewed together with sequences, alignments and sequence features (currently from UniProt, InterPro and PDB). Extensive user feedback confirms that SRS 3D is intuitive and useful especially for those not expert in structures. Availability: An SRS 3D server is provided at srs3d.ebi.ac.uk/ Supplementary information: Complete documentation is available (click the ‘Help’ button after login). Product information can be found at www.lionbioscience.com/SRS3D roducts/srs/srs-3d
No related grants have been discovered for Andrea Schafferhans.