Our group is formed by researchers from the Faculty of Mathematics and Physics and Faculty of Science of the Charles University. Being at the interface of biology and computer science allows us to efficiently combine the expertise of our respective research fields in order to understand cellular processes on the molecular level and to develop biologicaly-plausible, high-quality software solutions.
The main focus of our group is in the following areas:
- Understanding mechanisms of cellular processes using 3D structure information
- Predictive modeling focused on the detection of macromolecular interaction sites such as protein-ligand, protein-protein, protein-dna or phosphorylation sites.
- 3D structure prediction
- Visualization of macromolecular structures
State-of-the-art machine learning-based method (P2Rank) and web server (PrankWeb) for ligand binding sites prediction based from protein structure.
Traveler is an RNA sescondary structure visualization tool implementing a template-based approach enabling to lay out even the largest RNA structures in the standard orientation. It is used by RNACentral for visualization of all the secondary structures available in RNACentral via the R2DT project.
AHoJ (Apo-Holo Juxtaposition) is a webserver and a command-line tool for search of Apo (unbound) protein structures from Holo (bound) forms and vice versa.
INSPiRE is a state-of-the-art knowledge-based protein-protein INteraction Sites PREdictor.
SETTER (SEcondary sTructure-based TERtiary Structure Similarity Algorithm) is a set of tools for fast pairwise and multiple RNA 3D structure superposition.
Molpher (Molecular morphing) aims to be scalable interactive software framework to aid the exploration of the chemical space.
Current members of CUSBG
Marian NovotnýFaculty of Science, Assistant Professor
Marian is the head of the biology branch of the group. Marian is interested in using 3D structural information to understand cellular processes and their evolution. Marian was involved in a 3D structural prediction, a development of methods for structure validation (accessible surface area-calculation precision) and a development of methods for structure-based function prediction (left-handed helices) .
David HokszaFaculty of Mathematics and Physics, Associate Professor
David is the head of the computer science branch of the group. He is interested in the development of efficient algorithms in the area of structural bioinformatics and data visualization. He has been involved in projects dealing mostly with protein and RNA structure with occasional excursions to the fields of cheminformatics (ligand-based virtual screening, exploration of chemical space), computational genomics (analysis of MinION data) and systems biology (visualization and analysis of molecular networks).
Petr ŠkodaFaculty of Mathematics and Physics, Assistant Professor
Petr, an assistant professor at Charles University, is a researcher and a developer. He focuses mainly on fields of similarity modelling, ligand-based virtual screening, data transformation, and linked data. Petr is an open-source contributor to projects such as LinkedPipes ETL, DCAT-AP Viewer, and p2rank web interface.
Kamila RiedlováFaculty of Mathematics and Physics, Postdoc
Kamila, a postdoctoral researcher, has an education in molecular dynamics simulations. She has performed both all-atom and coarse-grained simulations of potential new drugs with model lipid membranes and monolayers. Her expertise is interdisciplinary - she has studied molecular biology and biochemistry of organisms along with general chemistry. Her PhD is in the modeling of chemical nano- and biostructures, a field that falls under physical chemistry. Currently, she is focusing on docking and the integration of MD simulations in relation to machine learning for bioinformatics purposes.
Radoslav KrivákFaculty of Mathematics and Physics, PhD student
Radoslav has a background in theoretical computer science and machine learning. His research interests include applying machine learning to analyze and map protein surfaces and exploring protein and chemical structural spaces. Radoslav is the main author of P2Rank, ligand-binding site prediction software.
Christos FeidakisFaculty of Science, PhD student
Using informatics in order to make sense out of biological data, specifically structure-related data
and processes, such as the interactions of proteins with other proteins and smaller molecules.
Interests: Structural bioinformatics, comparative modeling, evolution, in-silico drug design, Python
Jan JelínekFaculty of Mathematics and Physics, PhD student
Jan is a PhD. student in the computer science branch of the group. He focuses on machine learning and structural bioinformatics. His main research area is a protein structure prediction, however he is also involved in a projects dealing with protein structure identification using mass spectroscopy, RNA structure prediction, and statistical analysis of Ribo-Seq data.
Hamza GamouhFaculty of Mathematics and Physics, PhD student
A PhD student in computer science with a background in artificial intelligence and machine learning. Main focus is on the applying machine learning for detecting protein interaction sites. Main research areas include protein-ligand binding sites prediction, cryptic binding sites, protein-protein interaction sites and protein function prediction.
Vít ŠkrhákFaculty of Mathematics and Physics, Researcher
Vít is a researcheer with a background in software engineering. His current research focus revolves around the development of innovative tools for exploring conformation changes in protein structures. Moreover, he also finds interest in exploiting gene mutation data to estimate potential functionality changes in proteins.
Dávid JakubecFaculty of Mathematics and Physics, Postdoc
David is a postdoctoral researcher in the computer science branch of the group. His main expertise lies in molecular modelling and computational chemistry, with additional background in molecular biology and biochemistry. David is primarily interested in the nature of forces driving molecular interactions and recognition. He is further interested in molecular evolution and has been developing tools for its computational study.
Andrea ŠoltésováFaculty of Mathematics and Physics, Postdoc
Andrea is a postdoctoral researcher in the computer science branch of the group. Main expertise of Andrea Šoltésová lies in the field of computational biology, machine learning and data mining in general. Her main research area is in structural bioinformatics. Her research interest focuses on predictive modelling of protein-DNA interaction through machine learning.
Opened positions in our group
If you are interested in pursuing PhD in the area of structural bioinformatics, visualization of molecular data, integration of bioinformatics data, or a related field, get in touch with us and we can discuss how to align your ideas and interests with our preferences.
Malostranské nám. 25
118 00 Prague
+420 95155 4406
128 00 Prague
+420 22195 1076