Tobias Olenyi

ProtSpace: Protein Universe in Your Browser

ProtSpace visualizes protein language model embeddings interactively, enabling hypothesis generation across biological scales without installation.

biocentral: embedding-based protein predictions

Biocentral provides an open embedding-based service for protein predictions, demonstrating its utility with a virus database analysis and training module.

FlatProt: 2D visualization eases protein structure comparison

FlatProt provides 2D visualizations for comparing protein structures, enabling scalable and standardized analysis of large protein sets.

Critical assessment of missense variant effect predictors on disease-relevant variant data

The paper evaluates missense variant effect predictors using CAGI6 data, finding strong performance across settings and highlighting biases in some predictors.

TMVisDB: Annotation and 3D-visualization of transmembrane proteins

TMVisDB provides a database and visualization tool for transmembrane proteins, enabling large-scale analysis of predicted structures and topologies without requiring costly AlphaFold3-style predictions.

ProtSpace: a tool for visualizing protein space

ProtSpace visualizes protein embeddings in 2D/3D to reveal functional patterns and challenges existing classifications in phage and venom protein analyses.

LambdaPP: Fast and accessible protein-specific phenotype predictions

LambdaPP provides fast, accessible protein feature predictions including structure and function, using AI models and embeddings from protein Language Models.

Refining Embedding-Based Binding Predictions by Leveraging AlphaFold2 Structures

The paper introduces bindAdjust, a method that improves binding predictions by incorporating structural information from AlphaFold2, enhancing recall for small molecule binding and expanding predictions to larger binding sites.

PredictProtein - Predicting Protein Structure and Function for 29 Years

PredictProtein provides protein sequence analysis tools, offering structure and function predictions with updates including deep learning and DNA/RNA/protein binding predictions.

Embeddings from deep learning transfer GO annotations beyond homology

This paper proposes using deep learning embeddings to transfer GO annotations based on protein sequence proximity rather than homology, achieving performance comparable to existing methods.