Maria Littmann
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View article: Varicella-zoster virus reactivation and the risk of dementia
Varicella-zoster virus reactivation and the risk of dementia Open
Varicella-zoster virus (VZV) is a neurotropic virus that establishes lifelong latency in humans. VZV reactivation is associated with a wide range of symptoms, including herpes zoster (HZ; also known as shingles), and has been implicated in…
View article: Toxin data quality: a critical examination of bacterial exotoxins and animal toxins
Toxin data quality: a critical examination of bacterial exotoxins and animal toxins Open
Objective Existing toxins datasets include a mixture of proteins and toxin peptides. In this study we present two curated datasets of toxic proteins free of associated proteins: bacterial exotoxins and animal toxins. Our stringent selectio…
View article: Recombinant zoster vaccine and reduced risk of dementia: matched‐cohort study using large‐scale electronic health records and machine learning methodology
Recombinant zoster vaccine and reduced risk of dementia: matched‐cohort study using large‐scale electronic health records and machine learning methodology Open
Background With the world population aging, the number of individuals living with dementia is expected to increase significantly. Vaccination against herpes zoster (HZ) with the live‐attenuated zoster vaccine (ZVL) was associated with a lo…
View article: Toxin Data Quality: A Critical Examination of Bacterial Exotoxins and Animal Toxins
Toxin Data Quality: A Critical Examination of Bacterial Exotoxins and Animal Toxins Open
Objective Existing toxins data sets include a mixture of proteins and toxin peptides. In this study we present two curated data sets of toxic proteins free of associated proteins: bacterial exotoxins and animal toxins. Our stringent select…
View article: CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins
CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins Open
Intrinsic disorder (ID) in proteins is well-established in structural biology, with increasing evidence for its involvement in essential biological processes. As measuring dynamic ID behavior experimentally on a large scale remains difficu…
View article: AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms
AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms Open
Deep-learning (DL) methods like DeepMind’s AlphaFold2 (AF2) have led to substantial improvements in protein structure prediction. We analyse confident AF2 models from 21 model organisms using a new classification protocol (CATH-Assign) whi…
View article: CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models
CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models Open
Motivation CATH is a protein domain classification resource that exploits an automated workflow of structure and sequence comparison alongside expert manual curation to construct a hierarchical classification of evolutionary and structural…
View article: Novel machine learning approaches revolutionize protein knowledge
Novel machine learning approaches revolutionize protein knowledge Open
Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large sc…
View article: <scp>LambdaPP</scp> : Fast and accessible protein‐specific phenotype predictions
<span>LambdaPP</span> : Fast and accessible protein‐specific phenotype predictions Open
The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, t…
View article: Refining Embedding-Based Binding Predictions by Leveraging AlphaFold2 Structures
Refining Embedding-Based Binding Predictions by Leveraging AlphaFold2 Structures Open
Background Identifying residues in a protein involved in ligand binding is important for understanding its function. bindEmbed21DL is a Machine Learning method which predicts protein-ligand binding on a per-residue level using embeddings d…
View article: LambdaPP: Fast and accessible protein-specific phenotype predictions
LambdaPP: Fast and accessible protein-specific phenotype predictions Open
The availability of accurate and fast Artificial Intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, t…
View article: CATH Structural domains in AlphaFold2 models for 21 model organisms
CATH Structural domains in AlphaFold2 models for 21 model organisms Open
CATH structural domain assignments for AlphaFold2 models in 21 model organisms. The table cath-v4_3_0.alphafold-v2.2022-11-22.tsv contains the domain assignments with information on model quality, CATH superfamily and Class, organism, aver…
View article: CATH Structural domains in AlphaFold2 models for 21 model organisms
CATH Structural domains in AlphaFold2 models for 21 model organisms Open
CATH structural domain assignments for AlphaFold2 models in 21 model organisms. The table cath-v4_3_0.alphafold-v2.2022-11-22.tsv contains the domain assignments with information on model quality, CATH superfamily and Class, organism, aver…
View article: AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms
AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms Open
Over the last year, there have been substantial improvements in protein structure prediction, particularly in methods like DeepMind’s AlphaFold2 (AF2) that exploit deep learning strategies. Here we report a new CATH-Assign protocol which i…
View article: Contrastive learning on protein embeddings enlightens midnight zone
Contrastive learning on protein embeddings enlightens midnight zone Open
Experimental structures are leveraged through multiple sequence alignments, or more generally through homology-based inference (HBI), facilitating the transfer of information from a protein with known annotation to a query without any anno…
View article: CATHe: Detection of remote homologues for CATH superfamilies using embeddings from protein language models
CATHe: Detection of remote homologues for CATH superfamilies using embeddings from protein language models Open
1. Abstract CATH is a protein domain classification resource that combines an automated workflow of structure and sequence comparison alongside expert manual curation to construct a hierarchical classification of evolutionary and structura…
View article: CATHe Dataset and Weights
CATHe Dataset and Weights Open
This dataset consists of the training, optimization, and testing sets used for developing the CATHe model, which is a deep learning framework capable of detecting extremely remote homologues (< 20% sequence identity) for CATH superfamilies…
View article: CATHe Dataset and Weights
CATHe Dataset and Weights Open
This dataset consists of the training, optimization, and testing sets used for developing the CATHe model, which is a deep learning framework capable of detecting extremely remote homologues (< 20% sequence identity) for CATH superfamilies…
View article: Protein embeddings and deep learning predict binding residues for various ligand classes
Protein embeddings and deep learning predict binding residues for various ligand classes Open
View article: Contrastive learning on protein embeddings enlightens midnight zone
Contrastive learning on protein embeddings enlightens midnight zone Open
Experimental structures are leveraged through multiple sequence alignments, or more generally through homology-based inference (HBI), facilitating the transfer of information from a protein with known annotation to a query without any anno…
View article: Protein embeddings and deep learning predict binding residues for various ligand classes
Protein embeddings and deep learning predict binding residues for various ligand classes Open
One important aspect of protein function is the binding of proteins to ligands, including small molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of experimental progress many binding sites remain obscure. Here,…
View article: Clustering FunFams using sequence embeddings improves EC purity
Clustering FunFams using sequence embeddings improves EC purity Open
Motivation Classifying proteins into functional families can improve our understanding of protein function and can allow transferring annotations within one family. For this, functional families need to be ‘pure’, i.e., contain only protei…
View article: PredictProtein - Predicting Protein Structure and Function for 29 Years
PredictProtein - Predicting Protein Structure and Function for 29 Years Open
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…
View article: PredictProtein – Predicting Protein Structure and Function for 29 Years
PredictProtein – Predicting Protein Structure and Function for 29 Years Open
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 …
View article: Clustering FunFams using sequence embeddings improves EC purity
Clustering FunFams using sequence embeddings improves EC purity Open
Motivation Classifying proteins into functional families can improve our understanding of protein function and can allow transferring annotations within one family. For this, functional families need to be “pure”, i.e., contain only protei…
View article: Embeddings from deep learning transfer GO annotations beyond homology
Embeddings from deep learning transfer GO annotations beyond homology Open
View article: Embeddings from deep learning transfer GO annotations beyond homology
Embeddings from deep learning transfer GO annotations beyond homology Open
Knowing protein function is crucial to advance molecular and medical biology, yet experimental function annotations through the Gene Ontology (GO) exist for fewer than 0.5% of all known proteins. Computational methods bridge this sequence-…
View article: Validity of machine learning in biology and medicine increased through collaborations across fields of expertise
Validity of machine learning in biology and medicine increased through collaborations across fields of expertise Open
View article: Correction to: Detailed prediction of protein sub-nuclear localization
Correction to: Detailed prediction of protein sub-nuclear localization Open
View article: FunFam protein families improve residue level molecular function prediction
FunFam protein families improve residue level molecular function prediction Open
Background The CATH database provides a hierarchical classification of protein domain structures including a sub-classification of superfamilies into functional families (FunFams). We analyzed the similarity of binding site annotations in …