Machine Learning Guided Exploration of an Empirical Ribozyme Fitness Landscape

Okinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyFitness landscape of a biomolecule is a representation of its activity as a function of its sequence. Properties of a fitness landscape determine how evolution proceeds. Therefore, the distribution of functional variants and more importantly, the connectivity of these variants within the sequence space are important scientific questions. Exploration of these spaces, however, is impeded by the combinatorial explosion of the sequence space. High-throughput experimental methods have recently reduced this impediment but only modestly. Better computational methods are needed to fully utilize the rich information from these experimental data to better understand the properties of the fitness landscape. In this work, I seek to improve this exploration process by combining data from massively parallel experimental assay with smart library design using advanced computational techniques. I focus on an artificial RNA enzyme or ribozyme that can catalyze a ligation reaction between two RNA fragments. This chemistry is analogous to that of the modern RNA polymeraseenzymes, therefore, represents an important reaction in the origin of life. In the first chapter, I discuss the background to this work in the context of evolutionary theory of fitness landscape and its implications in biotechnology. In chapter 2, I explore the use of processes borrowed from the field of evolutionary computation to solve optimization problems using real experimental sequence-activity data. In chapter 3, I investigate the use of supervised machine learning models to extract information on epistatic interactions from the dataset collected during multiple rounds of directed evolution. I investigate and experimentally validate the extent to which a deep learning model can be used to guide a completely computational evolutionary algorithm towards distant regions of the fitness landscape. In the final chapter, I perform a comprehensive experimental assay of the combinatorial region explored by the deep learning-guided evolutionary algorithm. Using this dataset, I analyze higher-order epistasis and attempt to explain the increased predictability of the region sampled by the algorithm. Finally, I provide the first experimental evidence of a large RNA ‘neutral network’. Altogether, this work represents the most comprehensive experimental and computational study of the RNA ligase ribozyme fitness landscape to date, providing important insights into the evolutionary search space possibly explored during the earliest stages of life.doctoral thesi

A modular data analysis pipeline for the discovery of novel RNA motifs

This dissertation presents a modular software pipeline that searches collections of RNA sequences for novel RNA motifs. In this case the motifs incorporate elements of primary and secondary structure. The motif search pipeline breaks up sets of RNA sequences into shortened segments of RNA primary sequence. The shortened segments are then folded to obtain low energy secondary structures. The distance estimation module of the pipeline then calculates distances between the folded bricks, and then analyzes the resulting distance matrices for patterns;An initial implementation of the pipeline is applied to synthetic and biological data sets. This implementation introduces a new distance measure for comparing RNA sequences based on structural annotation of the folded sequence as well as a new data analysis technique called non-linear projection. The modular nature of the pipeline is then used to explore the relationships between several different distance measures on random data, synthetic data, and a biological data set consisting of iron response elements. It is shown that the different distance measures capture different relationships between the RNA sequences. The non-linear projection algorithm is used to produce 2-dimensional projections of the distance matrices which are examined via inspection and k-means multiclustering. The pipeline is able to successfully cluster synthetic RNA sequences based only on primary sequence data as well as the iron response elements data set. The dissertation also presents a preliminary analysis of a large biological data set of HIV sequences

An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm

One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). The basic of multiple sequence alignment problems is to determine the most biologically plausible alignments of protein or DNA sequences. In this paper, an alignment method using genetic algorithm for multiple sequence alignment has been proposed. Two different genetic operators mainly crossover and mutation were defined and implemented with the proposed method in order to know the population evolution and quality of the sequence aligned. The proposed method is assessed with protein benchmark dataset, e.g., BALIBASE, by comparing the obtained results to those obtained with other alignment algorithms, e.g., SAGA, RBT-GA, PRRP, HMMT, SBPIMA, CLUSTALX, CLUSTAL W, DIALIGN and PILEUP8 etc. Experiments on a wide range of data have shown that the proposed algorithm is much better (it terms of score) than previously proposed algorithms in its ability to achieve high alignment quality

Operator Sequence Alters Gene Expression Independently of Transcription Factor Occupancy in Bacteria

A canonical quantitative view of transcriptional regulation holds that the only role of operator sequence is to set the probability of transcription factor binding, with operator occupancy determining the level of gene expression. In this work, we test this idea by characterizing repression in vivo and the binding of RNA polymerase in vitro in experiments where operators of various sequences were placed either upstream or downstream from the promoter in Escherichia coli. Surprisingly, we find that operators with a weaker binding affinity can yield higher repression levels than stronger operators. Repressor bound to upstream operators modulates promoter escape, and the magnitude of this modulation is not correlated with the repressor-operator binding affinity. This suggests that operator sequences may modulate transcription by altering the nature of the interaction of the bound transcription factor with the transcriptional machinery, implying a new layer of sequence dependence that must be confronted in the quantitative understanding of gene expression

RNA SECONDARY STRUCTURE PREDICTION TOOL

Ribonucleic Acid (RNA) is one of the major macromolecules essential to all forms of life. Apart from the important role played in protein synthesis, it performs several important functions such as gene regulation, catalyst of biochemical reactions and modification of other RNAs. In some viruses, instead of DNA, RNA serves as the carrier of genetic information. RNA is an interesting subject of research in the scientific community. It has lead to important biological discoveries. One of the major problems researchers are trying to solve is the RNA structure prediction problem. It has been found that the structure of RNA is evolutionary conserved and it can help to determine the functions served by them. In this project, I will be developing a tool to predict the secondary structure of RNA using simulated annealing. The aim of this project is to understand in detail the simulated annealing algorithm and implement it to find solutions to RNA secondary structure. The results will be compared with the very famous tool Mfold, developed by Michael Zuker, using the minimum free energy approach

MODENA: a multi-objective RNA inverse folding

Artificially synthesized RNA molecules have recently come under study since such molecules have a potential for creating a variety of novel functional molecules. When designing artificial RNA sequences, secondary structure should be taken into account since functions of noncoding RNAs strongly depend on their structure. RNA inverse folding is a methodology for computationally exploring the RNA sequences folding into a user-given target structure. In the present study, we developed a multi-objective genetic algorithm, MODENA (Multi-Objective DEsign of Nucleic Acids), for RNA inverse folding. MODENA explores the approximate set of weak Pareto optimal solutions in the objective function space of 2 objective functions, a structure stability score and structure similarity score. MODENA can simultaneously design multiple different RNA sequences at 1 run, whose lowest free energies range from a very stable value to a higher value near those of natural counterparts. MODENA and previous RNA inverse folding programs were benchmarked with 29 target structures taken from the Rfam database, and we found that MODENA can successfully design 23 RNA sequences folding into the target structures; this result is better than those of the other benchmarked RNA inverse folding programs. The multi-objective genetic algorithm gives a useful framework for a functional biomolecular design. Executable files of MODENA can be obtained at http://rna.eit.hirosaki-u.ac.jp/modena/

From RNA folding to inverse folding: a computational study: Folding and design of RNA molecules

Since the discovery of the structure of DNA in the early 1953s and its double-chained complement of information hinting at its means of replication, biologists have recognized the strong connection between molecular structure and function. In the past two decades, there has been a surge of research on an ever-growing class of RNA molecules that are non-coding but whose various folded structures allow a diverse array of vital functions. From the well-known splicing and modification of ribosomal RNA, non-coding RNAs (ncRNAs) are now known to be intimately involved in possibly every stage of DNA translation and protein transcription, as well as RNA signalling and gene regulation processes. Despite the rapid development and declining cost of modern molecular methods, they typically can only describe ncRNA's structural conformations in vitro, which differ from their in vivo counterparts. Moreover, it is estimated that only a tiny fraction of known ncRNAs has been documented experimentally, often at a high cost. There is thus a growing realization that computational methods must play a central role in the analysis of ncRNAs. Not only do computational approaches hold the promise of rapidly characterizing many ncRNAs yet to be described, but there is also the hope that by understanding the rules that determine their structure, we will gain better insight into their function and design. Many studies revealed that the ncRNA functions are performed by high-level structures that often depend on their low-level structures, such as the secondary structure. This thesis studies the computational folding mechanism and inverse folding of ncRNAs at the secondary level. In this thesis, we describe the development of two bioinformatic tools that have the potential to improve our understanding of RNA secondary structure. These tools are as follows: (1) RAFFT for efficient prediction of pseudoknot-free RNA folding pathways using the fast Fourier transform (FFT)}; (2) aRNAque, an evolutionary algorithm inspired by Lévy flights for RNA inverse folding with or without pseudoknot (A secondary structure that often poses difficulties for bio-computational detection). The first tool, RAFFT, implements a novel heuristic to predict RNA secondary structure formation pathways that has two components: (i) a folding algorithm and (ii) a kinetic ansatz. When considering the best prediction in the ensemble of 50 secondary structures predicted by RAFFT, its performance matches the recent deep-learning-based structure prediction methods. RAFFT also acts as a folding kinetic ansatz, which we tested on two RNAs: the CFSE and a classic bi-stable sequence. In both test cases, fewer structures were required to reproduce the full kinetics, whereas known methods (such as Treekin) required a sample of 20,000 structures and more. The second tool, aRNAque, implements an evolutionary algorithm (EA) inspired by the Lévy flight, allowing both local global search and which supports pseudoknotted target structures. The number of point mutations at every step of aRNAque's EA is drawn from a Zipf distribution. Therefore, our proposed method increases the diversity of designed RNA sequences and reduces the average number of evaluations of the evolutionary algorithm. The overall performance showed improved empirical results compared to existing tools through intensive benchmarks on both pseudoknotted and pseudoknot-free datasets. In conclusion, we highlight some promising extensions of the versatile RAFFT method to RNA-RNA interaction studies. We also provide an outlook on both tools' implications in studying evolutionary dynamics

The Genetic Code as a Periodic Table: Algebraic Aspects

The systematics of indices of physico-chemical properties of codons and amino acids across the genetic code are examined. Using a simple numerical labelling scheme for nucleic acid bases, data can be fitted as low-order polynomials of the 6 coordinates in the 64-dimensional codon weight space. The work confirms and extends recent studies by Siemion of amino acid conformational parameters. The connections between the present work, and recent studies of the genetic code structure using dynamical symmetry algebras, are pointed out.Comment: 26 pages Latex, 10 figures (4 ps, 6 Tex). Refereed version, small changes to discussion (conclusion unaltered). Minor alterations to format of figures and tables. To appear in BioSystem