mgr Kamil Kamiński
Research Interests
- Development and analysis of new techniques for protein structural representation
- Machine learning
- Bioinformatics
Publications
Winski, Aleksander; Ludwiczak, Jan; Orlowska, Malgorzata; Madaj, Rafal; Kaminski, Kamil; Dunin-Horkawicz, Stanislaw
AlphaFold2 captures the conformational landscape of the HAMP signaling domain Journal Article
In: Protein Science, vol. 33, no. 1, pp. e4846, 2024.
@article{https://doi.org/10.1002/pro.4846,
title = {AlphaFold2 captures the conformational landscape of the HAMP signaling domain},
author = {Aleksander Winski and Jan Ludwiczak and Malgorzata Orlowska and Rafal Madaj and Kamil Kaminski and Stanislaw Dunin-Horkawicz},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.4846},
doi = {https://doi.org/10.1002/pro.4846},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Protein Science},
volume = {33},
number = {1},
pages = {e4846},
abstract = {Abstract In this study, we present a conformational landscape of 5000 AlphaFold2 models of the Histidine kinases, Adenyl cyclases, Methyl-accepting proteins and Phosphatases (HAMP) domain, a short helical bundle that transduces signals from sensors to effectors in two-component signaling proteins such as sensory histidine kinases and chemoreceptors. The landscape reveals the conformational variability of the HAMP domain, including rotations, shifts, displacements, and tilts of helices, many combinations of which have not been observed in experimental structures. HAMP domains belonging to a single family tend to occupy a defined region of the landscape, even when their sequence similarity is low, suggesting that individual HAMP families have evolved to operate in a specific conformational range. The functional importance of this structural conservation is illustrated by poly-HAMP arrays, in which HAMP domains from families with opposite conformational preferences alternate, consistent with the rotational model of signal transduction. The only poly-HAMP arrays that violate this rule are predicted to be of recent evolutionary origin and structurally unstable. Finally, we identify a family of HAMP domains that are likely to be dynamic due to the presence of a conserved pi-helical bulge. All code associated with this work, including a tool for rapid sequence-based prediction of the rotational state in HAMP domains, is deposited at https://github.com/labstructbioinf/HAMPpred.},
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pubstate = {published},
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Abstract In this study, we present a conformational landscape of 5000 AlphaFold2 models of the Histidine kinases, Adenyl cyclases, Methyl-accepting proteins and Phosphatases (HAMP) domain, a short helical bundle that transduces signals from sensors to effectors in two-component signaling proteins such as sensory histidine kinases and chemoreceptors. The landscape reveals the conformational variability of the HAMP domain, including rotations, shifts, displacements, and tilts of helices, many combinations of which have not been observed in experimental structures. HAMP domains belonging to a single family tend to occupy a defined region of the landscape, even when their sequence similarity is low, suggesting that individual HAMP families have evolved to operate in a specific conformational range. The functional importance of this structural conservation is illustrated by poly-HAMP arrays, in which HAMP domains from families with opposite conformational preferences alternate, consistent with the rotational model of signal transduction. The only poly-HAMP arrays that violate this rule are predicted to be of recent evolutionary origin and structurally unstable. Finally, we identify a family of HAMP domains that are likely to be dynamic due to the presence of a conserved pi-helical bulge. All code associated with this work, including a tool for rapid sequence-based prediction of the rotational state in HAMP domains, is deposited at https://github.com/labstructbioinf/HAMPpred.
Kaminski, Kamil; Ludwiczak, Jan; Pawlicki, Kamil; Alva, Vikram; Dunin-Horkawicz, Stanislaw
pLM-BLAST: distant homology detection based on direct comparison of sequence representations from protein language models Journal Article
In: Bioinformatics, vol. 39, no. 10, pp. btad579, 2023, ISSN: 1367-4811.
@article{10.1093/bioinformatics/btad579b,
title = {pLM-BLAST: distant homology detection based on direct comparison of sequence representations from protein language models},
author = {Kamil Kaminski and Jan Ludwiczak and Kamil Pawlicki and Vikram Alva and Stanislaw Dunin-Horkawicz},
url = {https://doi.org/10.1093/bioinformatics/btad579},
doi = {10.1093/bioinformatics/btad579},
issn = {1367-4811},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Bioinformatics},
volume = {39},
number = {10},
pages = {btad579},
abstract = {The detection of homology through sequence comparison is a typical first step in the study of protein function and evolution. In this work, we explore the applicability of protein language models to this task.We introduce pLM-BLAST, a tool inspired by BLAST, that detects distant homology by comparing single-sequence representations (embeddings) derived from a protein language model, ProtT5. Our benchmarks reveal that pLM-BLAST maintains a level of accuracy on par with HHsearch for both highly similar sequences (with >50% identity) and markedly divergent sequences (with <30% identity), while being significantly faster. Additionally, pLM-BLAST stands out among other embedding-based tools due to its ability to compute local alignments. We show that these local alignments, produced by pLM-BLAST, often connect highly divergent proteins, thereby highlighting its potential to uncover previously undiscovered homologous relationships and improve protein annotation.pLM-BLAST is accessible via the MPI Bioinformatics Toolkit as a web server for searching precomputed databases (https://toolkit.tuebingen.mpg.de/tools/plmblast). It is also available as a standalone tool for building custom databases and performing batch searches (https://github.com/labstructbioinf/pLM-BLAST).},
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pubstate = {published},
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The detection of homology through sequence comparison is a typical first step in the study of protein function and evolution. In this work, we explore the applicability of protein language models to this task.We introduce pLM-BLAST, a tool inspired by BLAST, that detects distant homology by comparing single-sequence representations (embeddings) derived from a protein language model, ProtT5. Our benchmarks reveal that pLM-BLAST maintains a level of accuracy on par with HHsearch for both highly similar sequences (with >50% identity) and markedly divergent sequences (with <30% identity), while being significantly faster. Additionally, pLM-BLAST stands out among other embedding-based tools due to its ability to compute local alignments. We show that these local alignments, produced by pLM-BLAST, often connect highly divergent proteins, thereby highlighting its potential to uncover previously undiscovered homologous relationships and improve protein annotation.pLM-BLAST is accessible via the MPI Bioinformatics Toolkit as a web server for searching precomputed databases (https://toolkit.tuebingen.mpg.de/tools/plmblast). It is also available as a standalone tool for building custom databases and performing batch searches (https://github.com/labstructbioinf/pLM-BLAST).
Kamiński, Kamil; Ludwiczak, Jan; Jasiński, Maciej; Bukala, Adriana; Madaj, Rafal; Szczepaniak, Krzysztof; Dunin-Horkawicz, Stanisław
Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins Journal Article
In: Briefings in Bioinformatics, vol. 23, 2022, ISSN: 1467-5463.
@article{SDH5,
title = {Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins},
author = {Kamil Kamiński and Jan Ludwiczak and Maciej Jasiński and Adriana Bukala and Rafal Madaj and Krzysztof Szczepaniak and Stanisław Dunin-Horkawicz},
doi = {10.1093/bib/bbab371},
issn = {1467-5463},
year = {2022},
date = {2022-01-01},
journal = {Briefings in Bioinformatics},
volume = {23},
abstract = {The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved βαβ motif shared by all Rossmann fold proteins. While Rossmann methyltransferases recognize only a single cofactor type, the S-adenosylmethionine, the oxidoreductases, depending on the family, bind nicotinamide (nicotinamide adenine dinucleotide, nicotinamide adenine dinucleotide phosphate) or flavin-based (flavin adenine dinucleotide) cofactors. In this study, we showed that despite its short length, the βαβ motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the βαβ motif. A benchmark on two independent test sets, one containing βαβ motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of the cofactor specificity, revealed the nearly perfect performance of the two methods. The Rossmann-toolbox protocols can be accessed via the webserver at https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved βαβ motif shared by all Rossmann fold proteins. While Rossmann methyltransferases recognize only a single cofactor type, the S-adenosylmethionine, the oxidoreductases, depending on the family, bind nicotinamide (nicotinamide adenine dinucleotide, nicotinamide adenine dinucleotide phosphate) or flavin-based (flavin adenine dinucleotide) cofactors. In this study, we showed that despite its short length, the βαβ motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the βαβ motif. A benchmark on two independent test sets, one containing βαβ motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of the cofactor specificity, revealed the nearly perfect performance of the two methods. The Rossmann-toolbox protocols can be accessed via the webserver at https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox.
Antosiewicz, Jan M; Kamiński, Kamil; Długosz, Maciej
Hydrodynamic Steering in Protein Association Revisited: Surprisingly Minuscule Effects of Considerable Torques Journal Article
In: The Journal of Physical Chemistry B, vol. 121, pp. 8475-8491, 2017, ISSN: 1520-6106.
@article{KKa2,
title = {Hydrodynamic Steering in Protein Association Revisited: Surprisingly Minuscule Effects of Considerable Torques},
author = {Jan M Antosiewicz and Kamil Kamiński and Maciej Długosz},
doi = {10.1021/acs.jpcb.7b06053},
issn = {1520-6106},
year = {2017},
date = {2017-01-01},
journal = {The Journal of Physical Chemistry B},
volume = {121},
pages = {8475-8491},
abstract = {We investigate the previously postulated hydrodynamic steering phenomenon, resulting from complication of molecular shapes, its magnitude and possible relevance for protein–ligand and protein–protein diffusional encounters, and the kinetics of diffusion-controlled association. We consider effects of hydrodynamic interactions in a prototypical model system consisting of a cleft enzyme and an elongated substrate, and real protein–protein complexes, that of barnase and barstar, and human growth hormone and its binding protein. The kinetics of diffusional encounters is evaluated on the basis of rigid-body Brownian dynamics simulations in which hydrodynamic interactions between molecules are modeled using the bead-shell method for detailed description of molecular surfaces, and the first-passage-time approach. We show that magnitudes of steering torques resulting from the hydrodynamic coupling of associating molecules, evaluated for the studied systems on the basis of the Stokes–Einstein type relations for arbitrarily shaped rigid bodies, are comparable with magnitudes of torques resulting from electrostatic interactions of binding partners. Surprisingly, however, unlike in the case of electrostatic torques that strongly affect the diffusional encounter, overall effects of hydrodynamic steering torques on the association kinetics, while clearly discernible in Brownian dynamics simulations, are rather minute. We explain this result as a consequence of the thermal agitation of the binding partners. Our finding is relevant for the general understanding of a wide spectrum of molecular processes in solution but there is also a more practical aspect to it if one considers the low level of shape detail of models that are usually employed to evaluate hydrodynamic interactions in particle-based Stokesian and Brownian dynamics simulations of multicomponent biomolecular systems. Results described in the current work justify, in part at least, such a low-resolution description.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We investigate the previously postulated hydrodynamic steering phenomenon, resulting from complication of molecular shapes, its magnitude and possible relevance for protein–ligand and protein–protein diffusional encounters, and the kinetics of diffusion-controlled association. We consider effects of hydrodynamic interactions in a prototypical model system consisting of a cleft enzyme and an elongated substrate, and real protein–protein complexes, that of barnase and barstar, and human growth hormone and its binding protein. The kinetics of diffusional encounters is evaluated on the basis of rigid-body Brownian dynamics simulations in which hydrodynamic interactions between molecules are modeled using the bead-shell method for detailed description of molecular surfaces, and the first-passage-time approach. We show that magnitudes of steering torques resulting from the hydrodynamic coupling of associating molecules, evaluated for the studied systems on the basis of the Stokes–Einstein type relations for arbitrarily shaped rigid bodies, are comparable with magnitudes of torques resulting from electrostatic interactions of binding partners. Surprisingly, however, unlike in the case of electrostatic torques that strongly affect the diffusional encounter, overall effects of hydrodynamic steering torques on the association kinetics, while clearly discernible in Brownian dynamics simulations, are rather minute. We explain this result as a consequence of the thermal agitation of the binding partners. Our finding is relevant for the general understanding of a wide spectrum of molecular processes in solution but there is also a more practical aspect to it if one considers the low level of shape detail of models that are usually employed to evaluate hydrodynamic interactions in particle-based Stokesian and Brownian dynamics simulations of multicomponent biomolecular systems. Results described in the current work justify, in part at least, such a low-resolution description.