Konrad Krawczyk

PDV provides a lightweight, browser-based molecular visualization tool with validated quantitative structural analysis and antibody-specific features.

KyDab is a database of antibody discovery selection campaigns that provides full-funnel selection data for AI model development and evaluation.

NAStructuralDB provides a processed dataset of antibody, Nanobody, and protein structures with molecular contact information and annotations to aid in predictive tasks.

RePaRank predicts antibody-antigen interfaces using a proximity ranking task, outperforming benchmarks in paratope prediction and being competitive in epitope prediction without external resources.

The paper develops the ASD database to aggregate antibody-antigen interaction data from multiple sources, providing a standardized resource for computational model development.

This study benchmarks inverse folding methods for antibody design to assess their effectiveness in predicting binder sequences given a structure.

The paper curates a database of therapeutic antibodies linked to clinical trial outcomes, identifies poor performance of existing tools in predicting immunogenicity, and highlights the need for systems immunology approaches.

The paper creates a dataset of antibody-antigen complexes with mutations and evaluates affinity prediction methods, finding that current models poorly predict binding effects compared to empirical methods.

PairedAbNGS pools 58 single-cell bioprojects of natural paired human and mouse antibodies, revealing heavy/light V gene pairing biases beyond usage frequencies and conserved CDR3-FWR2 inter-chain contacts.

The paper introduces nanoFOLD, an inverse folding method for designing nanobodies, and applies it to binder enrichment and novel design.

RIOT annotates immunoglobulin sequences by handling nucleotide and amino acid data, using an open germline database, and improving speed and accuracy over existing tools.

The paper extends antibody databases for mass spectrometry by mining millions of sequences from the OAS database to improve antibody peptide identification in SARS-CoV-2 plasma samples.

The paper quantifies the plasticity of antibody developability by analyzing sequence and structure-based parameters in natural and human-engineered antibodies.

The paper introduces nanoBERT, a transformer model for predicting amino acids in nanobody sequences, showing its advantage over general models and gene-specific statistics.

The paper shows that pre-training deep learning models on physically accurate antibody structures improves the prediction of physically plausible CDR-H3 loop geometries.

The paper analyzes variance sources in LC-MS data and evaluates transfer learning's impact on ML performance across public datasets.

AbDiver enables comparison of therapeutic antibody sequences to natural antibody diversity, aiding in rational design by identifying mutation patterns and relevant sequence matches.

The paper analyzes antibody sequences from patents to assess their utility for therapeutic antibody engineering and finds they reflect clinical sequences.

The paper evaluates contact prediction methods by comparing predicted contacts to non-predicted ones, finding predicted contacts have more physico-chemical bonds and are more conserved.

The paper compares therapeutic antibodies to NGS-derived antibody sequences to identify sequence matches and convergence between natural and artificial antibody development.

SciRide Finder searches biomedical literature by focusing on cited statements to help researchers find evidence-based information for their manuscripts.