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
Selected publications
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.
Szczupak, Patrycja; Radzikowska-Cieciura, Ewa; Kulik, Katarzyna; Madaj, Rafał; Sierant, Małgorzata; Krakowiak, Agnieszka; Nawrot, Barbara
Escherichia coli tRNA 2-selenouridine synthase SelU selects its prenyl substrate to accomplish its enzymatic function Journal Article
In: Bioorganic Chemistry, vol. 122, pp. 105739, 2022, ISSN: 00452068.
@article{RM1,
title = {Escherichia coli tRNA 2-selenouridine synthase SelU selects its prenyl substrate to accomplish its enzymatic function},
author = {Patrycja Szczupak and Ewa Radzikowska-Cieciura and Katarzyna Kulik and Rafał Madaj and Małgorzata Sierant and Agnieszka Krakowiak and Barbara Nawrot},
doi = {10.1016/j.bioorg.2022.105739},
issn = {00452068},
year = {2022},
date = {2022-01-01},
journal = {Bioorganic Chemistry},
volume = {122},
pages = {105739},
abstract = {Bacterial tRNA 2-selenouridine synthase (SelU) in vitro converts S2U-RNA to its selenium analog (Se2U-RNA) in a two-step process: (i) geranylation of S2U-RNA (with geranyl pyrophosphate, gePP), and (ii) selenation of the resulting geS2U-RNA (with the selenophosphate anion, SePO33−). Using an S2U-containing anticodon stem-loop fragment derived from tRNALys (S2U-RNA) and recombinant SelU with an MBP tag, we found that only geranyl (C10) pyrophosphate is the substrate for this enzyme, while other pyrophosphates such as isopentenyl (C5), dimethylallyl (C5), farnesyl (C15) and geranylgeranyl (C20) are not. Interestingly, methyl (C1)− and C5−, C10−, and C15-prenyl-containing S2U-RNAs (which were chemically obtained) underwent the selenation reaction promoted by SelU, although the Se2U-RNA product was obtained in decreasing yields in the following order: geranyl ≥ farnesyl > dimethylallyl ≫ methyl. Microscale thermophoresis showed an affinity between gePP and SelU in the micromolar range, while the other pyrophosphates tested, such as isopentenyl, dimethylallyl, farnesyl and geranylgeranyl, either did not bind to the protein or their binding affinity was above 1 mM. These results agree well with the in silico analysis, with gePP being the best binding substrate (the lowest relative free energy of binding (ΔG) and a small solvent-accessible surface area (SASA)). These results suggest that SelU has high substrate specificity for the prenylation reaction (only gePP is accepted), whereas there is little discrimination for the selenation reaction. We therefore suggest that only gePP and the geranylated tRNA serve as substrates for the conversion of 2-thio-tRNAs to 2-seleno-tRNAs, as it is found in the bacterial system.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bacterial tRNA 2-selenouridine synthase (SelU) in vitro converts S2U-RNA to its selenium analog (Se2U-RNA) in a two-step process: (i) geranylation of S2U-RNA (with geranyl pyrophosphate, gePP), and (ii) selenation of the resulting geS2U-RNA (with the selenophosphate anion, SePO33−). Using an S2U-containing anticodon stem-loop fragment derived from tRNALys (S2U-RNA) and recombinant SelU with an MBP tag, we found that only geranyl (C10) pyrophosphate is the substrate for this enzyme, while other pyrophosphates such as isopentenyl (C5), dimethylallyl (C5), farnesyl (C15) and geranylgeranyl (C20) are not. Interestingly, methyl (C1)− and C5−, C10−, and C15-prenyl-containing S2U-RNAs (which were chemically obtained) underwent the selenation reaction promoted by SelU, although the Se2U-RNA product was obtained in decreasing yields in the following order: geranyl ≥ farnesyl > dimethylallyl ≫ methyl. Microscale thermophoresis showed an affinity between gePP and SelU in the micromolar range, while the other pyrophosphates tested, such as isopentenyl, dimethylallyl, farnesyl and geranylgeranyl, either did not bind to the protein or their binding affinity was above 1 mM. These results agree well with the in silico analysis, with gePP being the best binding substrate (the lowest relative free energy of binding (ΔG) and a small solvent-accessible surface area (SASA)). These results suggest that SelU has high substrate specificity for the prenylation reaction (only gePP is accepted), whereas there is little discrimination for the selenation reaction. We therefore suggest that only gePP and the geranylated tRNA serve as substrates for the conversion of 2-thio-tRNAs to 2-seleno-tRNAs, as it is found in the bacterial system.
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.