Scientific publications authored and co-authored by the members of our lab
Gront, D.;Syed, K.;Nelson, D.R.
Exploring P450 superfamily diversity with P450Atlas - Online tool for automated subfamily assignment..
Protein science : a publication of the Protein Society.
2025,
34
(3),
e70057.
https://doi.org/10.1002/pro.70057
Kryś, J.D. et al.
deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction..
Biomolecules.
2024,
14
(11),
1448.
https://doi.org/10.3390/biom14111448
Kryś, J.D.;Gront, D.
Coarse-grained potential for hydrogen bond interactions..
Journal of molecular graphics & modelling.
2023,
124
(),
108507.
https://doi.org/10.1016/j.jmgm.2023.108507
Macnar, J.M. et al.
Analysis of protein structures containing HEPES and MES molecules.
Protein Science.
2022,
(),
e4415.
Malinga, N.A. et al.
An Unprecedented Number of Cytochrome P450s Are Involved in Secondary Metabolism in Salinispora Species..
Microorganisms.
2022,
10
(5),
871.
https://doi.org/10.3390/microorganisms10050871
Saqib, M.N.;Kryś, J.D.;Gront, D.
Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks..
Biomolecules.
2022,
12
(6),
841.
https://doi.org/10.3390/biom12060841
Msweli, S. et al.
Lifestyles Shape the Cytochrome P450 Repertoire of the Bacterial Phylum Proteobacteria..
International journal of molecular sciences.
2022,
23
(10),
5821.
https://doi.org/10.3390/ijms23105821
Nkosi, B.V.Z. et al.
Contrasting Health Effects of Bacteroidetes and Firmicutes Lies in Their Genomes: Analysis of P450s, Ferredoxins, and Secondary Metabolite Clusters..
International journal of molecular sciences.
2022,
23
(9),
5057.
https://doi.org/10.3390/ijms23095057
Akapo, O.O. et al.
In Silico Structural Modeling and Analysis of Interactions of Tremellomycetes Cytochrome P450 Monooxygenases CYP51s with Substrates and Azoles..
International journal of molecular sciences.
2021,
22
(15),
.
https://doi.org/10.3390/ijms22157811
Koehler Leman, J. et al.
Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks..
Nature communications.
2021,
12
(1),
6947.
https://doi.org/10.1038/s41467-021-27222-7
Msomi, N.N. et al.
In Silico Analysis of P450s and Their Role in Secondary Metabolism in the Bacterial Class Gammaproteobacteria..
Molecules (Basel, Switzerland).
2021,
26
(6),
.
https://doi.org/10.3390/molecules26061538
Nzuza, N. et al.
Ancient Bacterial Class Alphaproteobacteria Cytochrome P450 Monooxygenases Can Be Found in Other Bacterial Species..
International journal of molecular sciences.
2021,
22
(11),
5542.
https://doi.org/10.3390/ijms22115542
Nzuza, N. et al.
Diversification of Ferredoxins across Living Organisms..
Current issues in molecular biology.
2021,
43
(3),
1374-1390.
https://doi.org/10.3390/cimb43030098
Kryś, J.D.;Gront, D.
VisuaLife: library for interactive visualization in rich web applications..
Bioinformatics (Oxford, England).
2021,
37
(20),
3662-3663.
https://doi.org/10.1093/bioinformatics/btab251
Kim, D.N.;Gront, D.;Sanbonmatsu, K.Y.
Practical Considerations for Atomistic Structure Modeling with Cryo-EM Maps..
Journal of chemical information and modeling.
2020,
60
(5),
2436-2442.
https://doi.org/10.1021/acs.jcim.0c00090
Leman, J.K. et al.
Macromolecular modeling and design in Rosetta: recent methods and frameworks..
Nature methods.
2020,
17
(7),
665-680.
https://doi.org/10.1038/s41592-020-0848-2
Koehler Leman, J. et al.
Better together: Elements of successful scientific software development in a distributed collaborative community..
PLoS computational biology.
2020,
16
(5),
e1007507.
https://doi.org/10.1371/journal.pcbi.1007507
Macnar, J.M. et al.
BioShell 3.0: Library for Processing Structural Biology Data..
Biomolecules.
2020,
10
(3),
461.
https://doi.org/10.3390/biom10030461
Mnguni, F.C. et al.
More P450s Are Involved in Secondary Metabolite Biosynthesis in Streptomyces Compared to Bacillus, Cyanobacteria, and Mycobacterium..
International journal of molecular sciences.
2020,
21
(13),
4814.
https://doi.org/10.3390/ijms21134814
Migacz, S. et al.
Parallel Implementation of a Sequential Markov Chain in Monte Carlo Simulations of Physical Systems with Pairwise Interactions..
Journal of chemical theory and computation.
2019,
15
(5),
2797-2806.
https://doi.org/10.1021/acs.jctc.8b01168
Kopeć, K. et al.
Comparison of α-Helix and β-Sheet Structure Adaptation to a Quantum Dot Geometry: Toward the Identification of an Optimal Motif for a Protein Nanoparticle Cover..
ACS omega.
2019,
4
(8),
13086-13099.
https://doi.org/10.1021/acsomega.9b00505
Dawid, A.E.;Gront, D.;Kolinski, A.
Coarse-Grained Modeling of the Interplay between Secondary Structure Propensities and Protein Fold Assembly..
Journal of chemical theory and computation.
2018,
14
(4),
2277-2287.
https://doi.org/10.1021/acs.jctc.7b01242
Sicinska, W.;Gront, D.;Sicinski, K.
Mutation goals in the vitamin D receptor predicted by computational methods..
The Journal of steroid biochemistry and molecular biology.
2018,
183
(),
210-220.
https://doi.org/10.1016/j.jsbmb.2018.06.016
Matowane, R.G. et al.
In silico analysis of cytochrome P450 monooxygenases in chronic granulomatous infectious fungus Sporothrix schenckii: Special focus on CYP51..
Biochimica et biophysica acta. Proteins and proteomics.
2018,
1866
(1),
166-177.
https://doi.org/10.1016/j.bbapap.2017.10.003
Dawid, A.E.;Gront, D.;Kolinski, A.
SURPASS Low-Resolution Coarse-Grained Protein Modeling..
Journal of chemical theory and computation.
2017,
13
(11),
5766-5779.
https://doi.org/10.1021/acs.jctc.7b00642
Janna Olmos, J.D. et al.
Biofunctionalisation of p-doped silicon with cytochrome c553 minimises charge recombination and enhances photovoltaic performance of the all-solid-state photosystem I-based biophotoelectrode.
RSC Advances.
2017,
(),
47854-47866.
Wieteska, L. et al.
Improving thermal stability of thermophilic L-threonine aldolase from Thermotoga maritima..
Journal of biotechnology.
2015,
199
(),
69-76.
https://doi.org/10.1016/j.jbiotec.2015.02.013
Gniewek, P. et al.
BioShell-Threading: versatile Monte Carlo package for protein 3D threading..
BMC bioinformatics.
2014,
15
(),
22.
https://doi.org/10.1186/1471-2105-15-22
Wabik, J. et al.
Combining coarse-grained protein models with replica-exchange all-atom molecular dynamics..
International journal of molecular sciences.
2013,
14
(5),
9893-905.
https://doi.org/10.3390/ijms14059893
Gront, D. et al.
Assessing the accuracy of template-based structure prediction metaservers by comparison with structural genomics structures..
Journal of structural and functional genomics.
2012,
13
(4),
213-25.
https://doi.org/10.1007/s10969-012-9146-2
Gront, D. et al.
BioShell Threader: protein homology detection based on sequence profiles and secondary structure profiles..
Nucleic acids research.
2012,
40
(Web Server issue),
W257-62.
https://doi.org/10.1093/nar/gks555
Gniewek, P.;Kolinski, A.;Gront, D.
Optimization of profile-to-profile alignment parameters for one-dimensional threading..
Journal of computational biology : a journal of computational molecular cell.
2012,
19
(7),
879-86.
https://doi.org/10.1089/cmb.2011.0307
Kmiecik, S. et al.
From coarse-grained to atomic-level characterization of protein dynamics: transition state for the folding of B domain of protein A..
The journal of physical chemistry. B.
2012,
116
(23),
7026-32.
https://doi.org/10.1021/jp301720w
Gront, D. et al.
Optimization of protein models.
.
2012,
(),
479-493.
Wang, S. et al.
The crystal structure of the AF2331 protein from Archaeoglobus fulgidus DSM 4304 forms an unusual interdigitated dimer with a new type of alpha + beta fold..
Protein science : a publication of the Protein Society.
2009,
18
(11),
2410-9.
https://doi.org/10.1002/pro.251
Gront, D.;Kolinski, A.
Fast and accurate methods for predicting short-range constraints in protein models..
Journal of computer-aided molecular design.
2008,
22
(11),
783-8.
https://doi.org/10.1007/s10822-008-9213-8
Gront, D.;Kolinski, A.
T-Pile--a package for thermodynamic calculations for biomolecules..
Bioinformatics (Oxford, England).
2007,
23
(14),
1840-2.
https://doi.org/10.1093/bioinformatics/btm259
Ibryashkina, E.M. et al.
Type II restriction endonuclease R.Eco29kI is a member of the GIY-YIG nuclease superfamily..
BMC structural biology.
2007,
7
(),
48.
https://doi.org/10.1186/1472-6807-7-48
Kmiecik, S.;Gront, D.;Kolinski, A.
Towards the high-resolution protein structure prediction. Fast refinement of reduced models with all-atom force field..
BMC structural biology.
2007,
7
(),
43.
https://doi.org/10.1186/1472-6807-7-43
Gront, D.;Kolinski, A.
Efficient scheme for optimization of parallel tempering Monte Carlo method.
.
2007,
(),
036225.
Kolinski, A.;Gront, D.
Comparative modeling without implicit sequence alignments..
Bioinformatics (Oxford, England).
2007,
23
(19),
2522-7.
https://doi.org/10.1093/bioinformatics/btm380
Gront, D.;Kurcinski, M.;Kolinski, A.
Clustering as a supporting tool for structural drug design..
Acta poloniae pharmaceutica.
2006,
63
(5),
436-8.
Kmiecik, S. et al.
Denatured proteins and early folding intermediates simulated in a reduced conformational space..
Acta biochimica Polonica.
2006,
53
(1),
131-44.
Gront, D.;Kolinski, A.
HCPM--program for hierarchical clustering of protein models..
Bioinformatics (Oxford, England).
2005,
21
(14),
3179-80.
https://doi.org/10.1093/bioinformatics/bti450
Gront, D.;Hansmann, U.H.E.;Kolinski, A.
Exploring protein energy landscapes with hierarchical clustering..
International journal of quantum chemistry.
2005,
105
(6),
826-830.
https://doi.org/10.1002/qua.20741
Gront, D.;Kolinski, A.;Hansmann, U.H.E.
Protein structure prediction by tempering spatial constraints..
Journal of computer-aided molecular design.
2005,
19
(8),
603-8.
https://doi.org/10.1007/s10822-005-9016-0
Gront, D.;Kolinski, A.
A new approach to prediction of short-range conformational propensities in proteins..
Bioinformatics (Oxford, England).
2005,
21
(7),
981-7.
https://doi.org/10.1093/bioinformatics/bti080
Kolinski, A. et al.
A simple lattice model that exhibits a protein-like cooperative all-or-none folding transition..
Biopolymers.
2003,
69
(3),
399-405.
https://doi.org/10.1002/bip.10385