Lukasz Kurgan

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Robert J. Mattauch Endowed Professor

Virginia Commonwealth University

United States

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About

Dr. Lukasz Kurgan is the Robert J. Mattauch Endowed Professor at Virginia Commonwealth University in the United States. His research focuses on binding residue predictions in intrinsically disordered regions, nucleic acid binding residues, and phase separation. He has published extensively on sequence-based predictions and structural assessments, contributing significantly to the fields of protein analysis and bioinformatics.

Recent Grants

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Collaborative Research: Identification and Structural Modeling of Intrinsically Disordered Protein-Protein and Protein-Nucleic Acids Interactions

Open Date: 2022-03-15

Close Date: 2025-02-28

Grant: Close

III: Small: Integrated prediction of intrinsic disorder and disorder functions with modular multi-label deep learning

Open Date: 2021-10-01

Close Date: 2024-09-30

Grant: Close

III: Small: High-Throughput Annotation of Cellular Functions of Intrinsic Disorder in Proteins

Open Date: 2016-10-01

Close Date: 2020-09-30

Grant: Close

III: Small: High-Throughput Annotation of Cellular Functions of Intrinsic Disorder in Proteins

Open Date: 2016-10-01

Close Date: 2019-09-30

Grant: Close

"High-throughput characterization, prediction, and applications of protein disorder"

Open Date: 2012-04-01

Close Date: 2016-03-31

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Collaborators (2)

Robin B. Gasser

Redmond Barry Distinguished Professor

University of Melbourne

AUSTRALIA

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Geoffrey Webb

MONASH UNIVERSITY

AUSTRALIA

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Lukasz Kurgan

Research Interests

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About

Recent Grants

Articles (27)

SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations

Comparative assessment of binding residue predictions in intrinsically disordered regions

Prediction of nucleic acid binding residues in protein sequences: Recent advances and future prospects

Empirical Assessment of Sequence-Based Predictions of Intrinsically Disordered Regions Involved in Phase Separation

Two decades of advances in sequence-based prediction of MoRFs, disorder-to-order transitioning binding regions

Comprehensive assessment of AlphaFold’s predictions of secondary structure and solvent accessibility at the amino acid-level in eukaryotic, bacterial and archaeal proteins

Evaluation of predictions of disordered binding regions in the CAID2 experiment

flDPnn2: Accurate and Fast Predictor of Intrinsic Disorder in Proteins

HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins

Assessment of Disordered Linker Predictions in the CAID2 Experiment

Assessment of Disordered Linker Predictions in the CAID2 Experiment

Taxonomy-specific assessment of intrinsic disorder predictions at residue and region levels in higher eukaryotes, protists, archaea, bacteria and viruses

Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences

CoMemMoRFPred: sequence-based prediction of MemMoRFs by combining predictors of intrinsic disorder, MoRFs and disordered lipid-binding regions

DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction

DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction

A comprehensive assessment and comparison of tools for HLA class I peptide-binding prediction

HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins

HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins

CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co-evolutionary information

qNABpredict: Quick, accurate, and taxonomy‐aware sequence‐based prediction of content of nucleic acid binding amino acids

qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids

CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins

HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins

CoMemMoRFPred: Sequence-based Prediction of MemMoRFs by Combining Predictors of Intrinsic Disorder, MoRFs and Disordered Lipid-binding Regions

Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins

Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins

Collaborators (2)

Robin B. Gasser

Geoffrey Webb

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