Jiřı́ Damborský

The paper benchmarks variant effect predictors using a leakage-controlled dataset and develops a gradient-boosting consensus model that outperforms existing tools.

Unattended droplet-microfluidic Bayesian optimization of a glycerol detection cascade matched design of experiments (DoE) with one third of the conditions; a second cascade used DoE with Raman readout.

This study uses ultrahigh-throughput screening to identify mutations in dehalogenases that modulate substrate access and binding, revealing distal functional effects on enzyme dynamics.

This paper designs next-generation thrombolytic proteases with improved catalytic efficiency, fibrin selectivity, and inhibition resistance through multi-objective enzyme engineering.

This study reports co-crystal structures of a bioluminescent energy-transfer complex in Renilla reniformis, revealing the spatial arrangement of donor-acceptor chromophores and their role in efficient radiationless energy transfer.

This study characterizes the conformational states of human plasminogen using structural biology techniques, revealing closed and open conformations in recombinant and plasma-derived variants.

The paper compares biochemical and immunological properties of engineered low-immunogenic staphylokinase variants to identify the most promising for thrombolytic therapy.

ProtXAI combines molecular dynamics and explainable AI to identify residue-level determinants of protein conformational changes across diverse systems.

The paper evaluates deep learning models for predicting protein melting temperatures using proteomics and biophysics datasets, finding that fine-tuned embeddings improve performance on diverse validation sets.

This study shows taurine inhibits ApoE4 aggregation and reduces its pathophysiological effects, similar to tramiprosate and 3-sulfopropanoic acid.

This study investigates the conformational flexibility of staphylokinase variants to explain their reduced immunogenicity and altered properties.

The paper introduces SoluProtMut, a Siamese deep learning model for predicting solubility effects of protein mutations, validated experimentally on 277 mutants.

This paper shows that replacing histidine with phenylalanine in haloalkane dehalogenase reverses the SN2 mechanism by remodeling the active site, enabling reversible reactions for sustainable transhalogenation.

This study identifies conformational dynamics as a key determinant of luciferase catalytic efficiency and light-emission stability, revealing a trade-off between oxygen affinity and enzyme longevity.

This study determines the crystal structures and dynamical behavior of an ancestral ABH-fold enzyme, revealing molecular features that enabled the transition from hydrolytic to oxygenolytic catalysis.

This paper enhances fluorinase catalytic efficiency by engineering mutants and optimizing reaction conditions, achieving a 60-fold increase in turnover rate.

The paper identifies the origin of a luciferase gene from haloalkane dehalogenases in brittle stars and shows parallel evolution of bioluminescence across distant species.

The paper introduces a computational method to predict protein evolution by reconstructing a protein's evolutionary history and suggesting future amino acid substitutions to improve desired properties.

PredictONCO is a web tool that predicts the impact of mutations in cancer-related proteins using structural and sequence-based features, achieving high accuracy in variant classification.

The paper develops a computational pipeline to annotate functional tunnels in enzyme-ligand complexes and validates it using molecular systems, showing it matches molecular dynamics simulations in speed and accuracy.

This paper presents a bioinformatics pipeline for analyzing missense mutations in oncogenic proteins, providing a probability score for functional impact and suggesting potential FDA/EMA-approved drugs.

This paper develops a deep neural network predictor, AggreProt, to identify aggregation-prone regions in proteins and demonstrates its utility in designing mutations to suppress aggregation.

The paper analyzes ganglioside profiles in rat brains and fluids across age and regions, finding age-related shifts in a-series to b-series ratios and region-specific changes.

The paper investigates how the C112R mutation in ApoE4 causes aggregation and shows that tramiprosate reduces this aggregation.

The paper identifies a novel archaeal fluorinase with the highest reported fluorination rate and demonstrates its application in enhancing fluoronucleotide production in bacterial hosts.