fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions
Natalia A. Szulc, Zuzanna Mackiewicz, Janusz M. Bujnicki, Filip Stefaniak
Abstract
Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.
Introduction
Nucleic acids are essential bioorganic molecules present in every living organism. Although deoxyribonucleic acid (DNA) is traditionally viewed as a mere genetic information carrier and ribonucleic acid (RNA) as a scaffold in the protein synthesis process, their functions go far beyond that [1–3]. Both DNAs and RNAs regulate diverse biological pathways and thus have a central role in cellular metabolism. Non-coding DNAs constitute the majority of the human genome and regulate protein-coding sequences by acting as a binding site for other transcriptional regulatory factors, an origin of replication site, a centromere, or a telomere [4,5]. Moreover, some non-coding DNAs can be transcribed into non-coding RNAs, which play a fundamental role in the cell, as they build large macromolecular machines, deliver amino acids to ribosomes, or regulate different molecular processes, e.g., by silencing genes or driving catalytic reactions.
Materials and methods
The fingeRNAt method
fingeRNAt is a set of Python 3 programs for detection, classification, and analysis of interactions formed within nucleic acid-ligand interactions. It consists of three main tools, each serving a different purpose.
fingeRNAt.py
fingeRNAt.py is a program for the detection and classification of non-covalent nucleic acid-ligand interactions. It can be run from the command line or via the graphical user interface. As an input, it takes a 3D structure of a receptor (RNA or DNA) and a file containing ligands, which form a complex with this macromolecule. fingeRNAt.py accepts six types of ligand molecules: small molecules, proteins, metal cations, DNAs, RNAs, and LNAs (locked nucleic acids) (Fig 9). The output is a fingerprint—a bit vector containing information on the declared interactions detected between the receptor and the ligand.
Results and discussion
fingeRNAt is a program for detecting and classifying non-covalent interactions between a nucleic acid (RNA or DNA; called a receptor) and ligands (metal cations, small molecules, nucleic acids, or proteins). These data are encoded in the form of Structural Interaction Fingerprints (SIFts)—a 1D bit vector indicating the presence or absence of a given type of interaction, as well as in the form of a detailed listing of all detected interactions, spatial coordinates of the interacting partners, and distances between interacting atoms or aromatic rings.
Here we present three analyses performed for RNA-ligand complexes. In all the cases, the fingeRNAt played a pivotal role in data gathering and analysis.
Acknowledgments
We thank Dr.Marcin Magnus and Dr.Zhichao Miao for providing RNA-Puzzles’ datasets. We thank Dr. Eugene Baulin for his constructive comments on the manuscript. This research was carried out in part with the support of the Interdisciplinary Centre for Mathematical and Computational Modelling (ICM), University of Warsaw under computational allocation no GB76-20 to F.S.
Citation: Szulc NA, Mackiewicz Z, Bujnicki JM, Stefaniak F (2022) fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions. PLoSComputBiol 18(6): e1009783. https://doi.org/10.1371/journal.pcbi.1009783
Editor: Shi-Jie Chen, University of Missouri, UNITED STATES
Received: December 23, 2021; Accepted: May 6, 2022; Published: June 2, 2022
Copyright: © 2022 Szulc et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The fingeRNAt program is freely available and distributed under the open-source GPL-3.0 License. It can be downloaded, along with a manual, collection of helper utilities, and sample data from https://github.com/n-szulc/fingeRNAt. The program was extensively tested on Python 3.6, 3.7, 3.8, and 3.9 under Ubuntu Linux (18.04, 20.04, and 21.10) and macOS (macOS Catalina 10.15). The supporting data presented in the manuscript along with the code used for the analysis can be found at https://github.com/n-szulc/fingeRNAt-supplementary.
Funding: This research was supported by the Foundation for Polish Science and the EU European Regional Development Fund https://www.fnp.org.pl/ (grant number POIR.04.04.00-00-3CF0/16 to J.M.B.) and National Science Centre, Poland https://www.ncn.gov.pl/ (grant number 2020/39/B/NZ2/03127 to F.S.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009783#ack
