Predicting RNA 3D Structures with Motivus
Rodrigo Inostroza, 2020-12-29
Predicting and building RNA 3D structures is key for understanding its function and behaviors. With a 3D model, scientists can plan more precise experiments in the lab, which can lead to more effective medicines. Keep reading to learn how Simon Poblete, a Chilean researcher, was able to use the Motivus framework to obtain the full structure of an RNA motif based uniquely on its sequence.
What is RNA?
Ribonucleic acid (RNA) is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes.
For years RNA was thought of as a simple messenger and a path between DNA and proteins. During the last decades, RNA has been found to be much more than a mere messenger and translator of the genetic information in the cell, nowadays it is known that it can regulate functions of different proteins or it can even perform functions by itself. Its enzymatic and regulatory function have been observed in a variety of cellular processes, conferring it a major role in evolution and cellular metabolism.
For the thorough understanding of these functions, an insight on the three-dimensional structure of RNA molecules is of crucial importance. Nevertheless, the reliable prediction of the full structure of a RNA motif based uniquely on its sequence is still a challenging aim. More than 100,000 structures are currently available in the Protein Data Bank; however, RNA-containing structures take up less than 6% of these depositions, including RNA structures complexed with other molecules.
Despite the new technological advances of the 21st century, determining and analyzing RNA structures are both still very difficult and time-consuming tasks. The size, complexity, and specific detail of RNA 3D structures have been studied using nuclear magnetic resonance, electron microscopy, and crystallography. These techniques require multiple stages to perform in a laboratory.
This is why a program that allows us to obtain the full structure of a RNA motif, based uniquely on its sequence, is so significant. It will allow to predict accurately and refine 3D Structures that will allow better and more efficient laboratory work with RNA.
How does RNA 3D prediction work?
For the RNA model, the atoms that compound the RNA are grouped by their respective components (nitrogen base, sugar group or phosphate group) and they are represented by a common figure, for example, all the atoms that compound the phosphate group of a certain nucleotide are represented by a sphere. This is what is known as a Coarse-Grained model, which is a good option for representing complex molecular systems like RNA sequences due to their focus on the atom compound.
Modelling RNA sequences could be very time-consuming if not-proper algorithms are used on the simulation, and here is where it come the solution proposed by Dr. Simon Poblete comes in. An algorithm that uses split and conquer and Montecarlo techniques for the implementation in the simulation (SPQR-MC simulation) that works well in this type of problems where the ARN structure simulation start with a sequence of nitrogen bases.
"SPQR is code to represent RNA through its nitrogen base, sugar group or phosphate group. What Motivus does is that it implements this code to explore what happens with a certain RNA sequence, when it, for examples, is thrown into a cup of water. Motivus will then give you a 3D structure in a file, with the different positions of all the elements", says Poblete.
For example, the sequence "GGGCGCAAGCCU" is initialized as a disordered 3D structure, the simulation in Motivus then iterates over it until it reaches the minimal state of energy.
The role of distributed computing through Motivus
The way SPQR works through the Motivus framework is through simultaneous simulations. Let's say that you want to do a calculation that implies 4 different conformations of an RNA, they will have to run simultaneously to reach an accurate result. For example, one simulation connects the different components, another removes nots and errors, another one remodels, and another one minimizes the structure. The algorithm that uses SPQR through Motivus then works as a black box, where different calculations that inform each other happen simultaneously, and these simulations are sent through the Motivus framework to different users all over the world. The users have the function of nodes.
In the predicting RNA 3D structures through SPQR, parallel data processing is essential. You can have a workstation with 32 processors, where each one takes 2 hours to process, but if your structures requires 700 simulations, then the computer would take 2 days to finalize the calculations. On the other hand, if you have 700 computers, or nodes, and even if they are significantly slower than the workstation, the calculations could be run simultaneously and achieve results in even 10 hours.
"There are no limits to what can be achieved. What Motivus did is that they took my code, and they implemented it in a different way so that it rests on their server. Then, if I or any scientist in the world has a calculation it wants to do, they can enter the sequence in the Motivus framework and then distribute it all over the world for it to be calculated."
"There are very interesting things that my model can offer, and that is why I want people to be able to use it. I'm not satisfied with only publishing things, I want them to be utilized. Hopefully it will inspire other scientists to solve different problems".
Dr. Simon Poblete is part of the Physics Sciences and Mathematics Institute of Universisdad Austral de Chile.