Bujnicki lab - SOFTWARE
  • RNA has recently emerged as an attractive target for new drug development. Our team is developing new methods to study the interactions between RNA and ligands. Recently, we have developed a new machine learning method called AnnapuRNA to predict how small chemical molecules interact with structured RNA molecules. Research published in PLoS Comput Biol. 2021 Feb 1;17(2):e1008309. doi: 10.1371/journal.pcbi.1008309. Read More
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About Laboratory Of Bioinformatics And Protein Engineering

Our group is involved in theoretical and experimental research on nucleic acids and proteins. The current focus is on RNA sequence-structure-function relationships (in particular 3D modeling), RNA-protein complexes, and enzymes acting on RNA.
 
We study the rules that govern the sequence-structure-function relationships in proteins and nucleic acids and use the acquired knowledge to predict structures and functions for uncharacterized gene products, to alter the known structures and functions of proteins and RNAs and to engineer molecules with new properties.
 
Our key strength is in the integration of various types of theoretical and experimental analyses. We develop and use computer programs for modeling of protein three-dimensional structures based on heterogenous, low-resolution, noisy and ambivalent experimental data. We are also involved in genome-scale phylogenetic analyses, with the focus on identification of proteins that belong to particular families. Subsequently, we characterize experimentally the function of the most interesting new genes/proteins identified by bioinformatics. We also use theoretical predictions to guide protein engineering, using rational and random approaches. Our ultimate goal is to identify complete sets of enzymes involved in particular metabolic pathways (e.g. RNA modification, DNA repair) and to design proteins with new properties, in particular enzymes with new useful functions, which have not been observed in the nature.
 
We are well-equipped with respect to both theoretical and experimental analyses. Our lab offers excellent environment for training of young researchers in both bioinformatics and molecular biology/biochemistry of protein-nucleic acid interactions.


More Good Science

PROTMAP2D
Two-dimensional maps of contacts summarize interactions between amino acids in the structure. They reveal characteristic patterns of interactions between secondary and super-secondary structures and are very attractive for visual analysis. The overlap of the residue contact maps of two structures can be easily calculated, providing a sensitive measure of protein structure similarity. 
 

ModeRNA 
We developed a method for 3D homology modeling of RNA structures. It requires a pairwise sequence alignment and a structural template to generate a 3D structural model of the target RNA sequence via either a fully automated or script-based approaches. ModeRNA is capable of handling 115 different nucleotide modifications and bridging gaps using fragments derived from an extensive fragment library.
 

RNAmap2D
RNAmap2D is a software tool for calculation of contact and distance maps based on user-defined criteria, and to some extent, quantitative comparison of pairs or series of contact maps and visualization of the results.
 

FILTREST3D
Filtrest3D is a program for discrimination of a large number of alternative models of protein structure or protein-ligand structure against a set of restraints derived from low-resolution experimental analyses (such as cross-linking, mutagenesis, circular dichrosm etc.) as well as from computational predictions (e.g. solvent accessibility, amino acid contact maps).
 

PyRy3D
PyRy3D is a software tool for modeling of structures for large macromolecular complexes. It uses Monte Carlo simulation to sample conformational space and to identify the best fit of complex components structures into a density map. Complex building process is based on distance restraints derived from experiments. 
 

Statistical geometry algorithm implementation in Python 
The implementation of the statistical geometry in sequence (binary and quaternary) space algorithm written in Python.It is mainly applied in biology and sequence analysis in the context of evolution, e.g. for evaluating evolutionary models. The algorithm allows for checking divergence of a given sequence alignment. It allows you to check whether your sequences (RNA, DNA, protein) follow a tree-like pattern of divergence or a bundle-like pattern.
 

DARS-RNP and QUASI-RNP, potentials for protein-RNA docking
We developed two medium-resolution, knowledge-based potentials for scoring protein-RNA models obtained by docking: the quasi-chemical potential (QUASI-RNP) and the Decoys As the Reference State potential (DARS-RNP). Both potentials use a coarse-grained representation for both RNA and protein molecules and are capable of dealing with RNA structures with posttranscriptionally modified residues. In our tests that compared these methods to other published potentials, DARS-RNP showed the highest ability to identify native-like structures.


QRNAS
QRNAS is an extension of the AMBER simulation method with additional terms associated with explicit hydrogen bonds, co-planarity base pairs, backbone regularization, and custom restraints. QRNAS is capable of handling RNA, DNA, chimeras and hybrids thereof, and enables modeling of nucleic acids containing modified residues.