Seminar : In silico molecular evolution and Boltzmann sampling: implications in origin of life and molecular design

Abstract: RNA is a class of molecules present in all living organisms that counts on two important properties: information storage and catalytic activity. This duality makes RNA an interesting molecule to study as potentially the first molecule to support life, as well as a key component of many cellular processes. One of the most important factors in determining RNA function is the shape, or structure that the molecule adopts. This shape is determined by a specific set of interactions encoded in the RNA sequence that give rise to a 2D and 3D shape.  To this end, RNA structure prediction algorithms have made substantial progress, and serve as a notable example of computer science being applied to answer fundamental biological questions. While the algorithmics of mapping a sequence to a structure are well established, there still remain many questions about how the composition and exploration of sequence space affects this mapping. In this work, we use structure prediction algorithms, combined with Boltzmann sampling, and evolutionary algorithms to study the energy landscapes of RNA populations under various sequence-space constraints. We show that restricting sequence space has a strong influence on the stability of structure-sequence pairs, the emergence of structural complexity, and the evolutionary dynamics of populations. All of these insights can be used to further our understanding of how sequence-structure space affects the diversity of molecules we observe today, as well as provides useful tools for controlling molecular function.