Laboratory of Structural Bioinformatics

About us


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. For details see projects tab.


President of Polish Academy of Sciences award for Jan Ludwiczak

Dec. 3, 2019
We are happy to inform that our colleague, Jan Ludwiczak, has obtained an award for PhD students founded by the President of Polish Academy of Sciences. More details (in Polish) can be found here. Congratulations!

Molecular dynamics study of Ncd motor domain published

Aug. 28, 2019
The last ~30 amino acids of kinesin-14 Ncd head play a crucial but still poorly understood role in a lever-like rotation of a long superhelical 'stalk' segment while one of the kinesin's heads is bound to the microtubule. The members of our lab Jan Ludwiczak and Antonio Marinho published a paper describing how a new conformational state can be accessed and stabilized with several hydrogen bonds mediated by residues located on the border or within the C-terminal linker. Such stable compact conformation of Ncd may represent an initial state of the working stroke.

Diamond grant and Etiuda awarded to our students

Aug. 2, 2019
It is our pleasure to inform that Aleksander Wiński was awarded Diamond Grant that will allow him to start his PhD studies in the fall. Moreover, Jan Ludwiczak obtained Etiuda grant during which he will visit our collaborators in Max Planck Institute for Developmental Biology in Tübingen.

Polish Children's Fund students

July 5, 2019
Jun 10-14 we were honored to host two young students of Polish Children's Fund. During the stay, they had an opportunity to participate in ever-day lab life and work on their own project. The students, Emilia and Krzysztof, have developed a deep-learning based predictor of protein secondary structure, which can be accessed online. In the last day, we got a beautiful hand-made postcard. Thus, we believe that the students enjoyed the stay as much as we enjoyed hosting them :-).

PiPred saw the light of day!

May 6, 2019
As you may have heard, protein structures consist of secondary structure elements. The most abundant are alpha-helices and beta-sheets; however, there are more types of secondary structure elements, for example, pi-helices. Pi-helices are similar to alpha-helices but are less stable. In our recent work we showed that these rare secondary structure elements are frequently associated with functionalities such as ligand- or ion-binding and, most importantly, we presented a tool - PiPred - that can predict pi-helices directly from a sequence. PiPred is available as a web service and can be downloaded as a standalone tool.

FleXgeo published!

Dec. 29, 2018
One of the main challenges of protein flexibility analyses is the mathematical representation of the protein conformational space. Our team member Antonio Marinho recently published a paper describing a new protein dynamic analysis method based on differential geometry representation of protein backbone. Presented method avoids most pitfalls of popular mathematical representation, such as phi-psi, atomic coordinates and collective variables. The FleXgeo software to compute such analyses is available for download.

RRMdb paper was published in Database!

Dec. 29, 2018
RNA-recognition motif (RRM) is an RNA-interacting protein domain that plays an important role in the processes of RNA metabolism such as splicing, editing, export, degradation, and regulation of translation. We developed RRMdb database, which allows for a quick identification and annotation of RRM domains in a given protein sequence. We provide the RRMdb database as a tool both for the experimentalists searching for basic knowledge about RNA-binding proteins with RRM domains as well as those studying the evolution of RRM domains. RRMdb can be accessed here and the paper is available here.

DeepCoil paper was published in Bioinformatics!

Dec. 29, 2018
That's one small step for man, one giant leap for coiled coils! By combining our expertise on coiled-coil domains with the modern deep learning approaches, we have built DeepCoil, a fast and sensitive method for the detection of coiled coils based on sequence data. DeepCoil can be accessed via MPI Toolkit or downloaded as a stand-alone software. We plan to further develop DeepCoil, thus all suggestions on how to improve it will be more than welcome. The paper is available here.

Aleksander Wiński has obtained a BSc degree!

Sept. 3, 2018
We are happy to announce that Aleksander Wiński obtained BSc degree from the Faculty of Physics, University of Warsaw. The title of his work was "The use of deep learning methods to predict protein π-helix secondary structure based on sequence and evolutionary information" (Polish title: "Zastosowanie metod uczenia głębokiego do przewidywania białkowych struktur drugorzędowych typu helisa π na podstawie sekwencji i informacji ewolucyjnej"). The work (only in Polish) can be downloaded here.

First Team grant awarded to Stanisław Dunin-Horkawicz

Aug. 8, 2018
We are very happy to announce that Foundation for Polish Science awarded Stanisław Dunin-Horkawicz with First Team grant for the project entitled "An evolutionary-based approach to design the substrate specificity of the Rossmann fold enzymes".

CCpred published!

July 3, 2018
Sequence design combined with machine learning techniques comprise a powerful tool for aiding sequence-structure relationship studies. In our work "Variability of the core geometry in parallel coiled-coil bundles" recently published in Journal of Structural Biology, we applied this approach to study determinants of hydrophobic core packing geometry in parallel homotetrameric coiled coils.

PiPred: a tool for predicting π-helices in protein sequences

June 1, 2018
π-helices are short, unstable secondary structure elements present in 25% of all protein structures, often in functionally important regions. Given their similarity to α-helices, prediction of π-helices is a challenging task and none of the currently available secondary structure prediction methods tackle it. To fill this void, we have developed PiPred, a publicly available tool for predicting π-helices in protein sequences. PiPred is freely available on the web (as a part of the Quick2D tool). A standalone version is available for download at GitHub.

New publication with our collaborators from University of Warsaw

April 21, 2018
The collaboration with Prof. Katarzyna Jagusztyn-Krynicka from Faculty of Biology (University of Warsaw) resulted in a new publication that just has appeared in PLoS ONE (link). Our phylogenetic analysis helped to put the experimental work of Prof. Jagusztyn-Krynicka and her co-workers into an evolutionary context.

Publication in Journal of Structural Biology

Feb. 14, 2018
We are happy to announce that our paper "Combining Rosetta with molecular dynamics (MD): a benchmark of the MD-based ensemble protein design" has been published in Journal of Structural Biology. The work has been done in collaboration with Dr. Adam Jarmuła from Nencki Institute of Experimental Biology. The paper is available online and more information can be found on the project's website at GitHub.

CCpred server for parallel helices available.

Jan. 3, 2018
CCpred is a server for prediction of helix axial rotation from sequence. You can acess it from Software tab on our webpage. Documentation will be available soon.

Our webpage is up and running!

Nov. 28, 2017
There are still many sections that demand more content but we will be filling them soon. Fresh news about our group will be placed in Home section.

Lab members (in random order)

SDH Jan Ludwiczak, MSc, PhD Student
Favorite research & tools: My scientific interests revolve around proteins, their sequences, structures and dynamics. Favorite techniques I use are machine learning, molecular dynamics and protein design.
Alma mater: Łódź University of Technology
Other interests: brewing, computer games and music

SDH Natalia Szulc, MSc, MSc Student
Favorite research & tools: Application of machine learning algorithms in bioinformatics and cheminformatics, structural biology, BioShell package. Laboratory of Protein Metabolism in Development and Aging (IIMCB) collaborator
Alma mater: Interdisciplinary Centre for Mathematical and Computational Modelling University of Warsaw (ICM)
Other interests: Books, movies, history of art

SDH Adriana Bukała, -, BSc Student
Favorite research & tools: Various subcategories of bioinformatics and biology of systems. Right know, I am exploring the usage of machine and deep learning techniques in the field.
Alma mater: University of Warsaw, Faculty of Mathematics, Informatics and Mechanics
Other interests: board and computer games, involvement in faculty's social life and fantasy literature

SDH Antonio Marinho da Silva Neto, PhD, Postdoc
Favorite research & tools: Application of mathematical and computational tools to soliving biotechnology and biomedicine problems; thermodynamics, structural biology, data science, differential geometry, information theory, molecular dynamics
Alma mater: Federal University of Pernambuco and University of São Paulo
Other interests: Science popularization projects.

SDH Maciej Jasiński, PhD, Postdoc
Favorite research & tools: Application of physics and computer science tools to understand biological processes. I'd like to find a machine learning-based approach that could replace molecular dynamics simulations.
Alma mater: Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, Poland
Other interests: cars, carpentry, science communication

SDH Krzysztof Szczepaniak, PhD, Postdoc
Favorite research & tools: I study relationships between protein sequence, structure and function. My favorite tools include protein design and machine learning.
Alma mater: International Institute of Molecular and Cell Biology in Warsaw / IBB PAS
Other interests: RPG, biking, scouting

SDH Johanna Haffner, -, Intern
Favorite research & tools: Computational systems biology & bioinformatics, using mathematics to probe the behavior of complex systems & exploring how machine learning can help with that
Alma mater: Humboldt Universität zu Berlin
Other interests: Sailing, hiking & learning languages

SDH Kamil Kamiński, BSc, MSc Student
Favorite research & tools: Data Science applications to biological and medical data, programming and statistical modeling
Alma mater: Faculty of Physics, University of Warsaw
Other interests: historical books, brazilian ju jitsu and music

SDH Aleksander Wiński, BSc, MSc Student
Favorite research & tools: I am interested in bioinformatics, especially in application of machine learning to protein sequence and structure analysis
Alma mater: University of Warsaw, Faculty of Physics
Other interests: Movies, table tennis, chess

SDH Stanisław Dunin-Horkawicz, PhD, Lab Leader
Favorite research & tools: My main research interest is the application of computational tools to understand biological complexity. Our team focuses on deciphering the sequence-structure-function relationships of proteins.
Alma mater: Max Planck Institute for Developmental Biology / The University of Tübingen
Other interests: Biking, books and cats.

SDH Maria Domańska, MSc, MSc Student
Favorite research & tools: My previous research was focused on neurobiology, more specifically on finding out the impact of increased production of matrix metalloproteinases and COX-2 on brain metaplasticity after stroke. At the moment I am learning Python and bioinformatics.
Alma mater: Inter-faculty Studies in Mathematics and Natural Sciences, University of Warsaw
Other interests: sociology, books, long walks with my dog