SignalP 6.0 predicts all five types of signal peptides using protein language models
Signal peptides (SPs) are short amino acid sequences that control protein secretion and
translocation in all living organisms. SPs can be predicted from sequence data, but existing …
translocation in all living organisms. SPs can be predicted from sequence data, but existing …
PredictProtein-predicting protein structure and function for 29 years
Abstract 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 …
for protein sequence analysis with its main site hosted at the Luxembourg Centre for …
[HTML][HTML] Deep learning for intrinsically disordered proteins: From improved predictions to deciphering conformational ensembles
Highlights•Intrinsically disordered proteins (IDPs) defy traditional structure-based prediction
methods, pushing for novel approaches in protein research.•Recent advances in deep …
methods, pushing for novel approaches in protein research.•Recent advances in deep …
Proteinnpt: Improving protein property prediction and design with non-parametric transformers
Protein design holds immense potential for optimizing naturally occurring proteins, with
broad applications in drug discovery, material design, and sustainability. However …
broad applications in drug discovery, material design, and sustainability. However …
Protein embeddings and deep learning predict binding residues for various ligand classes
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 …
molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of …
Embeddings from protein language models predict conservation and variant effects
The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret
the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational …
the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational …
Transfer learning in proteins: evaluating novel protein learned representations for bioinformatics tasks
A representation method is an algorithm that calculates numerical feature vectors for
samples in a dataset. Such vectors, also known as embeddings, define a relatively low …
samples in a dataset. Such vectors, also known as embeddings, define a relatively low …
Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction
Advanced protein structure prediction requires evolutionary information from multiple
sequence alignments (MSAs) from evolutionary couplings that are not always available …
sequence alignments (MSAs) from evolutionary couplings that are not always available …
UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity
Identification of potent peptides through model prediction can reduce benchwork in wet
experiments. However, the conventional process of model buildings can be complex and …
experiments. However, the conventional process of model buildings can be complex and …
Contrastive learning on protein embeddings enlightens midnight zone
Experimental structures are leveraged through multiple sequence alignments, or more
generally through homology-based inference (HBI), facilitating the transfer of information …
generally through homology-based inference (HBI), facilitating the transfer of information …