Bujnicki lab - FNP (TEAM)

FNP (TEAM): Modeling of dynamic interactions between RNA and small molecules and its practical applications (POIR.04.04.00-00-3CF0/16-00); 3 449 541 PLN; 2017-2020. PI: J.M.Bujnicki, vice-PI: F.Stefaniak

Ribonucleic acid (RNA) molecules play pivotal roles in living organisms. They are involved in a variety of biological processes: they transmit genetic information, they sense and communicate responses to cellular signals, and even catalyze chemical reactions. The cellular and molecular functions of RNAs depend on the structure of the ribonucleotide chain and on interactions with other molecules, which are defined by the ribonucleotide sequence. Structures and functions of RNAs are often modulated by chemical compounds, including naturally occurring molecules as well as compounds obtained by synthetic organic chemistry. Many RNA molecules are known or predicted targets of small molecule drugs, and the continuous discovery of new functional RNAs involved in various biomedically important processes increases the demand on the development of new small molecules targeting RNA, and on methods for analyzing RNA-small molecule ligand interactions.

Unfortunately, the advancement of computational methods for predicting RNA-ligand interactions lags behind the analogous methods for analyzing protein-ligand interactions. In particular, there is a dearth of computational methods for modeling the 3D structure and dynamics of RNA-ligand complexes. Currently, it is almost impossible to computationally predict structures of RNA-ligand complexes that involve large conformational changes of the RNA upon ligand binding, or that are stable only in the presence of the ligand, unless very similar structures are already known. This situation hampers equally basic studies of RNA sequence-structure-function relationships, and applied research on the development of small molecule regulators of biomedically important RNAs.

In this research project, we develop and experimentally validate a general-purpose computational method for predicting RNA-ligand interactions that can model conformational changes. The new method enables simulations of conformational changes in RNA in response to ligand binding, such as those in riboswitches, which are currently out of reach for existing programs. It also extends the range of applications involving the prediction of potential ligands for target RNAs in the context of virtual screening.

We also test our computational approach in practice. It is applied to study the basic mechanism of action of RNAs known to be regulated by small molecules, e.g., riboswitches. We look for novel inhibitors for RNAs from bacterial and viral pathogens, like RNA promoter of influenza A or hepatitis C virus (HCV) and internal ribosome entry site (IRES). Such holistic and interdisciplinary approach enables us not only to verify the developed computational methods but also significantly expands the knowledge of the nature of RNA, with possible practical applications in many areas of science and industry.


Publications resulting from and supported by the project:

Ponce-Salvatierra A, Astha, Merdas K, Nithin C, Ghosh P, Mukherjee S, Bujnicki JM
Computational modeling of RNA 3D structure based on experimental data.
Biosci Rep. 2019 Feb 8;39(2).

Nithin C, Ghosh P, Bujnicki JM
Bioinformatics Tools and Benchmarks for Computational Docking and 3D structure prediction of RNA-protein complexes
Genes (Basel). 2018 Aug 25;9(9). pii: E432. doi: 10.3390/genes9090432.

Kumari P, Aeschimann F, Gaidatzis D, Keusch J, Ghosh P, Neagu A, Pachulska-Wieczorek K, Bujnicki JM, Gut H, Grosshans H, Ciosk R
Evolutionary plasticity of the NHL domain underlies distinct solutions to RNA recognition.
Nat Commun. 2018 Apr 19;9(1):1549.