Laboratory of Structural Bioinformatics
Research
The amount of available biological data, originating from various experimental procedures (genome and transcriptome sequencing, structure determination, functional assays), is vast. In our group, we employ computational techniques, such as deep learning, molecular dynamics simulations, and sequence analysis to make use of this data. We are particularly interested in understanding how protein folds have emerged and how protein structures and functions are encoded by the alphabet of 20 amino acids.
Research projects
- A systems biology approach to study the role and evolution of molecular pathways related to multicellularity (NCN OPUS 2020/37/B/NZ2/03268)
- Development and validation of bioinformatic tools for high-throughput structure analysis of coiled-coil domains, with particular emphasis on the AlphaFold2 method (IDUB BOB-IDUB-622-314/2022)
Computational resources
Theses in progress
- Kamil Pawlicki, “Application of different language model architectures to the task of protein sequence classification“, Master’s thesis, Faculty of Biology
Completed theses
- Julia Gołębiowska, „Predicting absorption wavelengths of rhodopsins using machine learning-based methods”, Master’s thesis, Faculty of Mathematics, Informatics and Mechanics (supervised also by Anna Karnkowska, PhD, Faculty of Biology)
- Kamil Kamiński, „Re-engineering cofactor specificity of the Rossmann fold proteins using graph neural networks”, 2020, Master’s thesis, Faculty of Physics
- Adriana Bukała, „Ancestral state reconstruction approach in investigating Rossmann fold evolution”, 2020, bachelor thesis, Faculty of Mathematics, Informatics and Mechanics
- Aleksander Wiński, „The use of deep learning methods to predict protein π helix secondary structure based on sequence and evolutionary information”, 2018, bachelor thesis, Faculty of Physics
- Bartosz Szymański, „Modelling and substrate spectra characteristics of laccase from Trametes versicolor”, Bachelor’s thesis, IISMNS
- Jędrzej Kubica, “Sequence analysis and structure modeling of bacterial receptor proteins of unknown function“, Master’s thesis, Department of Chemistry (supervised also by prof. Dominik Gront, Department of Chemistry)
Selected 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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.
Smug, Bogna J.; Szczepaniak, Krzysztof; Rocha, Eduardo P. C.; Dunin-Horkawicz, Stanislaw; Mostowy, Rafał J.
Ongoing shuffling of protein fragments diversifies core viral functions linked to interactions with bacterial hosts Journal Article
In: Nature Communications, vol. 14, no. 1, pp. 7460, 2023, ISSN: 2041-1723.
@article{Smug2023,
title = {Ongoing shuffling of protein fragments diversifies core viral functions linked to interactions with bacterial hosts},
author = {Bogna J. Smug and Krzysztof Szczepaniak and Eduardo P. C. Rocha and Stanislaw Dunin-Horkawicz and Rafał J. Mostowy},
url = {https://doi.org/10.1038/s41467-023-43236-9},
doi = {10.1038/s41467-023-43236-9},
issn = {2041-1723},
year = {2023},
date = {2023-11-28},
urldate = {2023-11-28},
journal = {Nature Communications},
volume = {14},
number = {1},
pages = {7460},
abstract = {Biological modularity enhances evolutionary adaptability. This principle is vividly exemplified by bacterial viruses (phages), which display extensive genomic modularity. Phage genomes are composed of independent functional modules that evolve separately and recombine in various configurations. While genomic modularity in phages has been extensively studied, less attention has been paid to protein modularity—proteins consisting of distinct building blocks that can evolve and recombine, enhancing functional and genetic diversity. Here, we use a set of 133,574 representative phage proteins and highly sensitive homology detection to capture instances of domain mosaicism, defined as fragment sharing between two otherwise unrelated proteins, and to understand its relationship with functional diversity in phage genomes. We discover that unrelated proteins from diverse functional classes frequently share homologous domains. This phenomenon is particularly pronounced within receptor-binding proteins, endolysins, and DNA polymerases. We also identify multiple instances of recent diversification via domain shuffling in receptor-binding proteins, neck passage structures, endolysins and some members of the core replication machinery, often transcending distant taxonomic and ecological boundaries. Our findings suggest that ongoing diversification via domain shuffling is reflective of a co-evolutionary arms race, driven by the need to overcome various bacterial resistance mechanisms against phages.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Biological modularity enhances evolutionary adaptability. This principle is vividly exemplified by bacterial viruses (phages), which display extensive genomic modularity. Phage genomes are composed of independent functional modules that evolve separately and recombine in various configurations. While genomic modularity in phages has been extensively studied, less attention has been paid to protein modularity—proteins consisting of distinct building blocks that can evolve and recombine, enhancing functional and genetic diversity. Here, we use a set of 133,574 representative phage proteins and highly sensitive homology detection to capture instances of domain mosaicism, defined as fragment sharing between two otherwise unrelated proteins, and to understand its relationship with functional diversity in phage genomes. We discover that unrelated proteins from diverse functional classes frequently share homologous domains. This phenomenon is particularly pronounced within receptor-binding proteins, endolysins, and DNA polymerases. We also identify multiple instances of recent diversification via domain shuffling in receptor-binding proteins, neck passage structures, endolysins and some members of the core replication machinery, often transcending distant taxonomic and ecological boundaries. Our findings suggest that ongoing diversification via domain shuffling is reflective of a co-evolutionary arms race, driven by the need to overcome various bacterial resistance mechanisms against phages.
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).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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).
Ludwiczak, Jan; Winski, Aleksander; Dunin-Horkawicz, Stanislaw
localpdb —a Python package to manage protein structures and their annotations Journal Article
In: Bioinformatics, 2022, ISSN: 1367-4803.
@article{SDH2h,
title = {\textit{localpdb} —a Python package to manage protein structures and their annotations},
author = {Jan Ludwiczak and Aleksander Winski and Stanislaw Dunin-Horkawicz},
doi = {10.1093/bioinformatics/btac121},
issn = {1367-4803},
year = {2022},
date = {2022-01-01},
journal = {Bioinformatics},
abstract = {The wealth of protein structures collected in the Protein Data Bank enabled large-scale studies of their
function and evolution. Such studies, however, require the generation of customized datasets combining the struc-
tural data with miscellaneous accessory resources providing functional, taxonomic and other annotations.
Unfortunately, the functionality of currently available tools for the creation of such datasets is limited and their usage
frequently requires laborious surveying of various data sources and resolving inconsistencies between their
versions.
To address this problem, we developed localpdb, a versatile Python library for the management of protein
structures and their annotations. The library features a flexible plugin system enabling seamless unification of the
structural data with diverse auxiliary resources, full version control and powerful functionality of creating highly cus-
tomized datasets. The localpdb can be used in a wide range of bioinformatic tasks, in particular those involving
large-scale protein structural analyses and machine learning.
Availability and implementation: localpdb is freely available at https://github.com/labstructbioinf/localpdb.
Documentation along with the usage examples can be accessed at https://labstructbioinf.github.io/localpdb/.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The wealth of protein structures collected in the Protein Data Bank enabled large-scale studies of their
function and evolution. Such studies, however, require the generation of customized datasets combining the struc-
tural data with miscellaneous accessory resources providing functional, taxonomic and other annotations.
Unfortunately, the functionality of currently available tools for the creation of such datasets is limited and their usage
frequently requires laborious surveying of various data sources and resolving inconsistencies between their
versions.
To address this problem, we developed localpdb, a versatile Python library for the management of protein
structures and their annotations. The library features a flexible plugin system enabling seamless unification of the
structural data with diverse auxiliary resources, full version control and powerful functionality of creating highly cus-
tomized datasets. The localpdb can be used in a wide range of bioinformatic tasks, in particular those involving
large-scale protein structural analyses and machine learning.
Availability and implementation: localpdb is freely available at https://github.com/labstructbioinf/localpdb.
Documentation along with the usage examples can be accessed at https://labstructbioinf.github.io/localpdb/.
function and evolution. Such studies, however, require the generation of customized datasets combining the struc-
tural data with miscellaneous accessory resources providing functional, taxonomic and other annotations.
Unfortunately, the functionality of currently available tools for the creation of such datasets is limited and their usage
frequently requires laborious surveying of various data sources and resolving inconsistencies between their
versions.
To address this problem, we developed localpdb, a versatile Python library for the management of protein
structures and their annotations. The library features a flexible plugin system enabling seamless unification of the
structural data with diverse auxiliary resources, full version control and powerful functionality of creating highly cus-
tomized datasets. The localpdb can be used in a wide range of bioinformatic tasks, in particular those involving
large-scale protein structural analyses and machine learning.
Availability and implementation: localpdb is freely available at https://github.com/labstructbioinf/localpdb.
Documentation along with the usage examples can be accessed at https://labstructbioinf.github.io/localpdb/.
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}
}
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.
Latoszek, Ewelina; Wiweger, Małgorzata; Ludwiczak, Jan; Dunin-Horkawicz, Stanisław; Kuznicki, Jacek; Czeredys, Magdalena
Siah-1-interacting protein regulates mutated huntingtin protein aggregation in Huntington’s disease models Journal Article
In: Cell & Bioscience, vol. 12, pp. 34, 2022, ISSN: 2045-3701.
@article{SDH1,
title = {Siah-1-interacting protein regulates mutated huntingtin protein aggregation in Huntington’s disease models},
author = {Ewelina Latoszek and Małgorzata Wiweger and Jan Ludwiczak and Stanisław Dunin-Horkawicz and Jacek Kuznicki and Magdalena Czeredys},
doi = {10.1186/s13578-022-00755-0},
issn = {2045-3701},
year = {2022},
date = {2022-01-01},
journal = {Cell & Bioscience},
volume = {12},
pages = {34},
abstract = {Background
Huntington’s disease (HD) is a neurodegenerative disorder whereby mutated huntingtin protein (mHTT) aggregates when polyglutamine repeats in the N-terminal of mHTT exceeds 36 glutamines (Q). However, the mechanism of this pathology is unknown. Siah1-interacting protein (SIP) acts as an adaptor protein in the ubiquitination complex and mediates degradation of other proteins. We hypothesized that mHTT aggregation depends on the dysregulation of SIP activity in this pathway in HD.
Results
A higher SIP dimer/monomer ratio was observed in the striatum in young YAC128 mice, which overexpress mHTT. We found that SIP interacted with HTT. In a cellular HD model, we found that wildtype SIP increased mHTT ubiquitination, attenuated mHTT protein levels, and decreased HTT aggregation. We predicted mutations that should stabilize SIP dimerization and found that SIP mutant-overexpressing cells formed more stable dimers and had lower activity in facilitating mHTT ubiquitination and preventing exon 1 mHTT aggregation compared with wildtype SIP.
Conclusions
Our data suggest that an increase in SIP dimerization in HD medium spiny neurons leads to a decrease in SIP function in the degradation of mHTT through a ubiquitin–proteasome pathway and consequently an increase in mHTT aggregation. Therefore, SIP could be considered a potential target for anti-HD therapy during the early stage of HD pathology.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
Huntington’s disease (HD) is a neurodegenerative disorder whereby mutated huntingtin protein (mHTT) aggregates when polyglutamine repeats in the N-terminal of mHTT exceeds 36 glutamines (Q). However, the mechanism of this pathology is unknown. Siah1-interacting protein (SIP) acts as an adaptor protein in the ubiquitination complex and mediates degradation of other proteins. We hypothesized that mHTT aggregation depends on the dysregulation of SIP activity in this pathway in HD.
Results
A higher SIP dimer/monomer ratio was observed in the striatum in young YAC128 mice, which overexpress mHTT. We found that SIP interacted with HTT. In a cellular HD model, we found that wildtype SIP increased mHTT ubiquitination, attenuated mHTT protein levels, and decreased HTT aggregation. We predicted mutations that should stabilize SIP dimerization and found that SIP mutant-overexpressing cells formed more stable dimers and had lower activity in facilitating mHTT ubiquitination and preventing exon 1 mHTT aggregation compared with wildtype SIP.
Conclusions
Our data suggest that an increase in SIP dimerization in HD medium spiny neurons leads to a decrease in SIP function in the degradation of mHTT through a ubiquitin–proteasome pathway and consequently an increase in mHTT aggregation. Therefore, SIP could be considered a potential target for anti-HD therapy during the early stage of HD pathology.
Huntington’s disease (HD) is a neurodegenerative disorder whereby mutated huntingtin protein (mHTT) aggregates when polyglutamine repeats in the N-terminal of mHTT exceeds 36 glutamines (Q). However, the mechanism of this pathology is unknown. Siah1-interacting protein (SIP) acts as an adaptor protein in the ubiquitination complex and mediates degradation of other proteins. We hypothesized that mHTT aggregation depends on the dysregulation of SIP activity in this pathway in HD.
Results
A higher SIP dimer/monomer ratio was observed in the striatum in young YAC128 mice, which overexpress mHTT. We found that SIP interacted with HTT. In a cellular HD model, we found that wildtype SIP increased mHTT ubiquitination, attenuated mHTT protein levels, and decreased HTT aggregation. We predicted mutations that should stabilize SIP dimerization and found that SIP mutant-overexpressing cells formed more stable dimers and had lower activity in facilitating mHTT ubiquitination and preventing exon 1 mHTT aggregation compared with wildtype SIP.
Conclusions
Our data suggest that an increase in SIP dimerization in HD medium spiny neurons leads to a decrease in SIP function in the degradation of mHTT through a ubiquitin–proteasome pathway and consequently an increase in mHTT aggregation. Therefore, SIP could be considered a potential target for anti-HD therapy during the early stage of HD pathology.
Szczepaniak, Krzysztof; Bukala, Adriana; da Neto, Antonio Marinho Silva; Ludwiczak, Jan; Dunin-Horkawicz, Stanislaw
A library of coiled-coil domains: from regular bundles to peculiar twists Journal Article
In: Bioinformatics, vol. 36, pp. 5368-5376, 2021, ISSN: 1367-4803.
@article{SDH7,
title = {A library of coiled-coil domains: from regular bundles to peculiar twists},
author = {Krzysztof Szczepaniak and Adriana Bukala and Antonio Marinho Silva da Neto and Jan Ludwiczak and Stanislaw Dunin-Horkawicz},
doi = {10.1093/bioinformatics/btaa1041},
issn = {1367-4803},
year = {2021},
date = {2021-01-01},
journal = {Bioinformatics},
volume = {36},
pages = {5368-5376},
abstract = {Motivation
Coiled coils are widespread protein domains involved in diverse processes ranging from providing structural rigidity to the transduction of conformational changes. They comprise two or more α-helices that are wound around each other to form a regular supercoiled bundle. Owing to this regularity, coiled-coil structures can be described with parametric equations, thus enabling the numerical representation of their properties, such as the degree and handedness of supercoiling, rotational state of the helices, and the offset between them. These descriptors are invaluable in understanding the function of coiled coils and designing new structures of this type. The existing tools for such calculations require manual preparation of input and are therefore not suitable for the high-throughput analyses.
Results
To address this problem, we developed SamCC-Turbo, a software for fully automated, per-residue measurement of coiled coils. By surveying Protein Data Bank with SamCC-Turbo, we generated a comprehensive atlas of ∼50 000 coiled-coil regions. This machine learning-ready dataset features precise measurements as well as decomposes coiled-coil structures into fragments characterized by various degrees of supercoiling. The potential applications of SamCC-Turbo are exemplified by analyses in which we reveal general structural features of coiled coils involved in functions requiring conformational plasticity. Finally, we discuss further directions in the prediction and modeling of coiled coils.
Availability and implementation
SamCC-Turbo is available as a web server (https://lbs.cent.uw.edu.pl/samcc_turbo) and as a Python library (https://github.com/labstructbioinf/samcc_turbo), whereas the results of the Protein Data Bank scan can be browsed and downloaded at https://lbs.cent.uw.edu.pl/ccdb.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Motivation
Coiled coils are widespread protein domains involved in diverse processes ranging from providing structural rigidity to the transduction of conformational changes. They comprise two or more α-helices that are wound around each other to form a regular supercoiled bundle. Owing to this regularity, coiled-coil structures can be described with parametric equations, thus enabling the numerical representation of their properties, such as the degree and handedness of supercoiling, rotational state of the helices, and the offset between them. These descriptors are invaluable in understanding the function of coiled coils and designing new structures of this type. The existing tools for such calculations require manual preparation of input and are therefore not suitable for the high-throughput analyses.
Results
To address this problem, we developed SamCC-Turbo, a software for fully automated, per-residue measurement of coiled coils. By surveying Protein Data Bank with SamCC-Turbo, we generated a comprehensive atlas of ∼50 000 coiled-coil regions. This machine learning-ready dataset features precise measurements as well as decomposes coiled-coil structures into fragments characterized by various degrees of supercoiling. The potential applications of SamCC-Turbo are exemplified by analyses in which we reveal general structural features of coiled coils involved in functions requiring conformational plasticity. Finally, we discuss further directions in the prediction and modeling of coiled coils.
Availability and implementation
SamCC-Turbo is available as a web server (https://lbs.cent.uw.edu.pl/samcc_turbo) and as a Python library (https://github.com/labstructbioinf/samcc_turbo), whereas the results of the Protein Data Bank scan can be browsed and downloaded at https://lbs.cent.uw.edu.pl/ccdb.
Coiled coils are widespread protein domains involved in diverse processes ranging from providing structural rigidity to the transduction of conformational changes. They comprise two or more α-helices that are wound around each other to form a regular supercoiled bundle. Owing to this regularity, coiled-coil structures can be described with parametric equations, thus enabling the numerical representation of their properties, such as the degree and handedness of supercoiling, rotational state of the helices, and the offset between them. These descriptors are invaluable in understanding the function of coiled coils and designing new structures of this type. The existing tools for such calculations require manual preparation of input and are therefore not suitable for the high-throughput analyses.
Results
To address this problem, we developed SamCC-Turbo, a software for fully automated, per-residue measurement of coiled coils. By surveying Protein Data Bank with SamCC-Turbo, we generated a comprehensive atlas of ∼50 000 coiled-coil regions. This machine learning-ready dataset features precise measurements as well as decomposes coiled-coil structures into fragments characterized by various degrees of supercoiling. The potential applications of SamCC-Turbo are exemplified by analyses in which we reveal general structural features of coiled coils involved in functions requiring conformational plasticity. Finally, we discuss further directions in the prediction and modeling of coiled coils.
Availability and implementation
SamCC-Turbo is available as a web server (https://lbs.cent.uw.edu.pl/samcc_turbo) and as a Python library (https://github.com/labstructbioinf/samcc_turbo), whereas the results of the Protein Data Bank scan can be browsed and downloaded at https://lbs.cent.uw.edu.pl/ccdb.
Banaś, Anna M; Bocian-Ostrzycka, Katarzyna M; Dunin-Horkawicz, Stanisław; Ludwiczak, Jan; Wilk, Piotr; Orlikowska, Marta; Wyszyńska, Agnieszka; Dąbrowska, Maria; Plichta, Maciej; Spodzieja, Marta; Polańska, Marta A; Malinowska, Agata; Jagusztyn-Krynicka, Elżbieta Katarzyna
In: International Journal of Molecular Sciences, vol. 22, pp. 13451, 2021, ISSN: 1422-0067.
@article{SDH3,
title = {Interplay between DsbA1, DsbA2 and C8J_1298 Periplasmic Oxidoreductases of Campylobacter jejuni and Their Impact on Bacterial Physiology and Pathogenesis},
author = {Anna M Banaś and Katarzyna M Bocian-Ostrzycka and Stanisław Dunin-Horkawicz and Jan Ludwiczak and Piotr Wilk and Marta Orlikowska and Agnieszka Wyszyńska and Maria Dąbrowska and Maciej Plichta and Marta Spodzieja and Marta A Polańska and Agata Malinowska and Elżbieta Katarzyna Jagusztyn-Krynicka},
doi = {10.3390/ijms222413451},
issn = {1422-0067},
year = {2021},
date = {2021-01-01},
journal = {International Journal of Molecular Sciences},
volume = {22},
pages = {13451},
abstract = {The bacterial proteins of the Dsb family catalyze the formation of disulfide bridges between cysteine residues that stabilize protein structures and ensure their proper functioning. Here, we report the detailed analysis of the Dsb pathway of Campylobacter jejuni. The oxidizing Dsb system of this pathogen is unique because it consists of two monomeric DsbAs (DsbA1 and DsbA2) and one dimeric bifunctional protein (C8J_1298). Previously, we showed that DsbA1 and C8J_1298 are redundant. Here, we unraveled the interaction between the two monomeric DsbAs by in vitro and in vivo experiments and by solving their structures and found that both monomeric DsbAs are dispensable proteins. Their structures confirmed that they are homologs of EcDsbL. The slight differences seen in the surface charge of the proteins do not affect the interaction with their redox partner. Comparative proteomics showed that several respiratory proteins, as well as periplasmic transport proteins, are targets of the Dsb system. Some of these, both donors and electron acceptors, are essential elements of the C. jejuni respiratory process under oxygen-limiting conditions in the host intestine. The data presented provide detailed information on the function of the C. jejuni Dsb system, identifying it as a potential target for novel antibacterial molecules.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The bacterial proteins of the Dsb family catalyze the formation of disulfide bridges between cysteine residues that stabilize protein structures and ensure their proper functioning. Here, we report the detailed analysis of the Dsb pathway of Campylobacter jejuni. The oxidizing Dsb system of this pathogen is unique because it consists of two monomeric DsbAs (DsbA1 and DsbA2) and one dimeric bifunctional protein (C8J_1298). Previously, we showed that DsbA1 and C8J_1298 are redundant. Here, we unraveled the interaction between the two monomeric DsbAs by in vitro and in vivo experiments and by solving their structures and found that both monomeric DsbAs are dispensable proteins. Their structures confirmed that they are homologs of EcDsbL. The slight differences seen in the surface charge of the proteins do not affect the interaction with their redox partner. Comparative proteomics showed that several respiratory proteins, as well as periplasmic transport proteins, are targets of the Dsb system. Some of these, both donors and electron acceptors, are essential elements of the C. jejuni respiratory process under oxygen-limiting conditions in the host intestine. The data presented provide detailed information on the function of the C. jejuni Dsb system, identifying it as a potential target for novel antibacterial molecules.
Adamczyk, M; Lewicka, E; Szatkowska, R; Nieznanska, H; Ludwiczak, J; Jasiński, M; Dunin-Horkawicz, S; Sitkiewicz, E; Swiderska, B; Goch, G; Jagura-Burdzy, G
Revealing biophysical properties of KfrA-type proteins as a novel class of cytoskeletal, coiled-coil plasmid-encoded proteins Journal Article
In: BMC Microbiology, vol. 21, pp. 32, 2021, ISSN: 1471-2180.
@article{SDH6,
title = {Revealing biophysical properties of KfrA-type proteins as a novel class of cytoskeletal, coiled-coil plasmid-encoded proteins},
author = {M Adamczyk and E Lewicka and R Szatkowska and H Nieznanska and J Ludwiczak and M Jasiński and S Dunin-Horkawicz and E Sitkiewicz and B Swiderska and G Goch and G Jagura-Burdzy},
doi = {10.1186/s12866-020-02079-w},
issn = {1471-2180},
year = {2021},
date = {2021-01-01},
journal = {BMC Microbiology},
volume = {21},
pages = {32},
abstract = {Background
DNA binding KfrA-type proteins of broad-host-range bacterial plasmids belonging to IncP-1 and IncU incompatibility groups are characterized by globular N-terminal head domains and long alpha-helical coiled-coil tails. They have been shown to act as transcriptional auto-regulators.
Results
This study was focused on two members of the growing family of KfrA-type proteins encoded by the broad-host-range plasmids, R751 of IncP-1β and RA3 of IncU groups. Comparative in vitro and in silico studies on KfrAR751 and KfrARA3 confirmed their similar biophysical properties despite low conservation of the amino acid sequences. They form a wide range of oligomeric forms in vitro and, in the presence of their cognate DNA binding sites, they polymerize into the higher order filaments visualized as “threads” by negative staining electron microscopy. The studies revealed also temperature-dependent changes in the coiled-coil segment of KfrA proteins that is involved in the stabilization of dimers required for DNA interactions.
Conclusion
KfrAR751 and KfrARA3 are structural homologues. We postulate that KfrA type proteins have moonlighting activity. They not only act as transcriptional auto-regulators but form cytoskeletal structures, which might facilitate plasmid DNA delivery and positioning in the cells before cell division, involving thermal energy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
DNA binding KfrA-type proteins of broad-host-range bacterial plasmids belonging to IncP-1 and IncU incompatibility groups are characterized by globular N-terminal head domains and long alpha-helical coiled-coil tails. They have been shown to act as transcriptional auto-regulators.
Results
This study was focused on two members of the growing family of KfrA-type proteins encoded by the broad-host-range plasmids, R751 of IncP-1β and RA3 of IncU groups. Comparative in vitro and in silico studies on KfrAR751 and KfrARA3 confirmed their similar biophysical properties despite low conservation of the amino acid sequences. They form a wide range of oligomeric forms in vitro and, in the presence of their cognate DNA binding sites, they polymerize into the higher order filaments visualized as “threads” by negative staining electron microscopy. The studies revealed also temperature-dependent changes in the coiled-coil segment of KfrA proteins that is involved in the stabilization of dimers required for DNA interactions.
Conclusion
KfrAR751 and KfrARA3 are structural homologues. We postulate that KfrA type proteins have moonlighting activity. They not only act as transcriptional auto-regulators but form cytoskeletal structures, which might facilitate plasmid DNA delivery and positioning in the cells before cell division, involving thermal energy.
DNA binding KfrA-type proteins of broad-host-range bacterial plasmids belonging to IncP-1 and IncU incompatibility groups are characterized by globular N-terminal head domains and long alpha-helical coiled-coil tails. They have been shown to act as transcriptional auto-regulators.
Results
This study was focused on two members of the growing family of KfrA-type proteins encoded by the broad-host-range plasmids, R751 of IncP-1β and RA3 of IncU groups. Comparative in vitro and in silico studies on KfrAR751 and KfrARA3 confirmed their similar biophysical properties despite low conservation of the amino acid sequences. They form a wide range of oligomeric forms in vitro and, in the presence of their cognate DNA binding sites, they polymerize into the higher order filaments visualized as “threads” by negative staining electron microscopy. The studies revealed also temperature-dependent changes in the coiled-coil segment of KfrA proteins that is involved in the stabilization of dimers required for DNA interactions.
Conclusion
KfrAR751 and KfrARA3 are structural homologues. We postulate that KfrA type proteins have moonlighting activity. They not only act as transcriptional auto-regulators but form cytoskeletal structures, which might facilitate plasmid DNA delivery and positioning in the cells before cell division, involving thermal energy.
Ludwiczak, Jan; Winski, Aleksander; Szczepaniak, Krzysztof; Alva, Vikram; Dunin-Horkawicz, Stanislaw
DeepCoil—a fast and accurate prediction of coiled-coil domains in protein sequences Journal Article
In: Bioinformatics, vol. 35, pp. 2790-2795, 2019, ISSN: 1367-4803.
@article{SDH11,
title = {DeepCoil—a fast and accurate prediction of coiled-coil domains in protein sequences},
author = {Jan Ludwiczak and Aleksander Winski and Krzysztof Szczepaniak and Vikram Alva and Stanislaw Dunin-Horkawicz},
doi = {10.1093/bioinformatics/bty1062},
issn = {1367-4803},
year = {2019},
date = {2019-01-01},
journal = {Bioinformatics},
volume = {35},
pages = {2790-2795},
abstract = {Motivation
Coiled coils are protein structural domains that mediate a plethora of biological interactions, and thus their reliable annotation is crucial for studies of protein structure and function.
Results
Here, we report DeepCoil, a new neural network-based tool for the detection of coiled-coil domains in protein sequences. In our benchmarks, DeepCoil significantly outperformed current state-of-the-art tools, such as PCOILS and Marcoil, both in the prediction of canonical and non-canonical coiled coils. Furthermore, in a scan of the human genome with DeepCoil, we detected many coiled-coil domains that remained undetected by other methods. This higher sensitivity of DeepCoil should make it a method of choice for accurate genome-wide detection of coiled-coil domains.
Availability and implementation
DeepCoil is written in Python and utilizes the Keras machine learning library. A web server is freely available at https://toolkit.tuebingen.mpg.de/#/tools/deepcoil and a standalone version can be downloaded at https://github.com/labstructbioinf/DeepCoil.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Motivation
Coiled coils are protein structural domains that mediate a plethora of biological interactions, and thus their reliable annotation is crucial for studies of protein structure and function.
Results
Here, we report DeepCoil, a new neural network-based tool for the detection of coiled-coil domains in protein sequences. In our benchmarks, DeepCoil significantly outperformed current state-of-the-art tools, such as PCOILS and Marcoil, both in the prediction of canonical and non-canonical coiled coils. Furthermore, in a scan of the human genome with DeepCoil, we detected many coiled-coil domains that remained undetected by other methods. This higher sensitivity of DeepCoil should make it a method of choice for accurate genome-wide detection of coiled-coil domains.
Availability and implementation
DeepCoil is written in Python and utilizes the Keras machine learning library. A web server is freely available at https://toolkit.tuebingen.mpg.de/#/tools/deepcoil and a standalone version can be downloaded at https://github.com/labstructbioinf/DeepCoil.
Coiled coils are protein structural domains that mediate a plethora of biological interactions, and thus their reliable annotation is crucial for studies of protein structure and function.
Results
Here, we report DeepCoil, a new neural network-based tool for the detection of coiled-coil domains in protein sequences. In our benchmarks, DeepCoil significantly outperformed current state-of-the-art tools, such as PCOILS and Marcoil, both in the prediction of canonical and non-canonical coiled coils. Furthermore, in a scan of the human genome with DeepCoil, we detected many coiled-coil domains that remained undetected by other methods. This higher sensitivity of DeepCoil should make it a method of choice for accurate genome-wide detection of coiled-coil domains.
Availability and implementation
DeepCoil is written in Python and utilizes the Keras machine learning library. A web server is freely available at https://toolkit.tuebingen.mpg.de/#/tools/deepcoil and a standalone version can be downloaded at https://github.com/labstructbioinf/DeepCoil.
Nowacka, Martyna; Boccaletto, Pietro; Jankowska, Elzbieta; Jarzynka, Tomasz; Bujnicki, Janusz M; Dunin-Horkawicz, Stanislaw
RRMdb—an evolutionary-oriented database of RNA recognition motif sequences Journal Article
In: Database, vol. 2019, 2019, ISSN: 1758-0463.
@article{SDH10,
title = {RRMdb—an evolutionary-oriented database of RNA recognition motif sequences},
author = {Martyna Nowacka and Pietro Boccaletto and Elzbieta Jankowska and Tomasz Jarzynka and Janusz M Bujnicki and Stanislaw Dunin-Horkawicz},
doi = {10.1093/database/bay148},
issn = {1758-0463},
year = {2019},
date = {2019-01-01},
journal = {Database},
volume = {2019},
abstract = {RNA-recognition motif (RRM) is an RNA-interacting protein domain that plays an important role in the processes of RNA metabolism such as the splicing, editing, export, degradation, and regulation of translation. Here, we present the RNA-recognition motif database (RRMdb), which affords rapid identification and annotation of RRM domains in a given protein sequence. The RRMdb database is compiled from ~57 000 collected representative RRM domain sequences, classified into 415 families. Whenever possible, the families are associated with the available literature and structural data. Moreover, the RRM families are organized into a network of sequence similarities that allows for the assessment of the evolutionary relationships between them.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
RNA-recognition motif (RRM) is an RNA-interacting protein domain that plays an important role in the processes of RNA metabolism such as the splicing, editing, export, degradation, and regulation of translation. Here, we present the RNA-recognition motif database (RRMdb), which affords rapid identification and annotation of RRM domains in a given protein sequence. The RRMdb database is compiled from ~57 000 collected representative RRM domain sequences, classified into 415 families. Whenever possible, the families are associated with the available literature and structural data. Moreover, the RRM families are organized into a network of sequence similarities that allows for the assessment of the evolutionary relationships between them.
Ludwiczak, Jan; Jarmula, Adam; Dunin-Horkawicz, Stanislaw
Combining Rosetta with molecular dynamics (MD): A benchmark of the MD-based ensemble protein design Journal Article
In: Journal of Structural Biology, vol. 203, pp. 54-61, 2018, ISSN: 10478477.
@article{SDH13,
title = {Combining Rosetta with molecular dynamics (MD): A benchmark of the MD-based ensemble protein design},
author = {Jan Ludwiczak and Adam Jarmula and Stanislaw Dunin-Horkawicz},
doi = {10.1016/j.jsb.2018.02.004},
issn = {10478477},
year = {2018},
date = {2018-01-01},
journal = {Journal of Structural Biology},
volume = {203},
pages = {54-61},
abstract = {Computational protein design is a set of procedures for computing amino acid sequences that will fold into a specified structure. Rosetta Design, a commonly used software for protein design, allows for the effective identification of sequences compatible with a given backbone structure, while molecular dynamics (MD) simulations can thoroughly sample near-native conformations. We benchmarked a procedure in which Rosetta design is started on MD-derived structural ensembles and showed that such a combined approach generates 20–30% more diverse sequences than currently available methods with only a slight increase in computation time. Importantly, the increase in diversity is achieved without a loss in the quality of the designed sequences assessed by their resemblance to natural sequences. We demonstrate that the MD-based procedure is also applicable to de novo design tasks started from backbone structures without any sequence information. In addition, we implemented a protocol that can be used to assess the stability of designed models and to select the best candidates for experimental validation. In sum our results demonstrate that the MD ensemble-based flexible backbone design can be a viable method for protein design, especially for tasks that require a large pool of diverse sequences.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Computational protein design is a set of procedures for computing amino acid sequences that will fold into a specified structure. Rosetta Design, a commonly used software for protein design, allows for the effective identification of sequences compatible with a given backbone structure, while molecular dynamics (MD) simulations can thoroughly sample near-native conformations. We benchmarked a procedure in which Rosetta design is started on MD-derived structural ensembles and showed that such a combined approach generates 20–30% more diverse sequences than currently available methods with only a slight increase in computation time. Importantly, the increase in diversity is achieved without a loss in the quality of the designed sequences assessed by their resemblance to natural sequences. We demonstrate that the MD-based procedure is also applicable to de novo design tasks started from backbone structures without any sequence information. In addition, we implemented a protocol that can be used to assess the stability of designed models and to select the best candidates for experimental validation. In sum our results demonstrate that the MD ensemble-based flexible backbone design can be a viable method for protein design, especially for tasks that require a large pool of diverse sequences.
Szustak, Marcin; Korkus, Eliza; Madaj, Rafal; Chworos, Arkadiusz; Dąbrowski, Grzegorz; Czaplicki, Sylwester; Tabandeh, Erfan; Maciejewska, Gabriela; Koziołkiewicz, Maria; Konopka, Iwona; Gliszczyńska, Anna; Gendaszewska-Darmach, Edyta
Lysophosphatidylcholines Enriched with cis and trans Palmitoleic Acid Regulate Insulin Secretion via GPR119 Receptor Journal Article
In: ACS Medicinal Chemistry Letters, vol. 0, no. 0, pp. null, 0000.
@article{doi:10.1021/acsmedchemlett.3c00263,
title = {Lysophosphatidylcholines Enriched with cis and trans Palmitoleic Acid Regulate Insulin Secretion via GPR119 Receptor},
author = {Marcin Szustak and Eliza Korkus and Rafal Madaj and Arkadiusz Chworos and Grzegorz Dąbrowski and Sylwester Czaplicki and Erfan Tabandeh and Gabriela Maciejewska and Maria Koziołkiewicz and Iwona Konopka and Anna Gliszczyńska and Edyta Gendaszewska-Darmach},
url = {https://doi.org/10.1021/acsmedchemlett.3c00263},
doi = {10.1021/acsmedchemlett.3c00263},
journal = {ACS Medicinal Chemistry Letters},
volume = {0},
number = {0},
pages = {null},
keywords = {},
pubstate = {published},
tppubtype = {article}
}