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Computational Molecular Coevolution Western University Scholarship@Western Electronic Thesis and Dissertation Repository 12-13-2013 12:00 AM Computational Molecular Coevolution Russell J. Dickson The University of Western Ontario Supervisor Dr. Gregory B. Gloor The University of Western Ontario Graduate Program in Biochemistry A thesis submitted in partial fulfillment of the equirr ements for the degree in Doctor of Philosophy © Russell J. Dickson 2013 Follow this and additional works at: https://ir.lib.uwo.ca/etd Part of the Bioinformatics Commons Recommended Citation Dickson, Russell J., "Computational Molecular Coevolution" (2013). Electronic Thesis and Dissertation Repository. 1798. https://ir.lib.uwo.ca/etd/1798 This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been accepted for inclusion in Electronic Thesis and Dissertation Repository by an authorized administrator of Scholarship@Western. For more information, please contact [email protected]. COMPUTATIONAL MOLECULAR COEVOLUTION (Thesis format: Integrated Article) by Russell Dickson Graduate Program in Biochemistry A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy The School of Graduate and Postdoctoral Studies The University of Western Ontario London, Ontario, Canada c Russell J Dickson 2013 Abstract A major goal in computational biochemistry is to obtain three-dimensional structure informa- tion from protein sequence. Coevolution represents a biological mechanism through which structural information can be obtained from a family of protein sequences. Evolutionary rela- tionships within a family of protein sequences are revealed through sequence alignment. Statis- tical analyses of these sequence alignments reveals positions in the protein family that covary, and thus appear to be dependent on one another throughout the evolution of the protein family. These covarying positions are inferred to be coevolving via one of two biological mechanisms, both of which imply that coevolution is facilitated by inter-residue contact. Thus, high-quality multiple sequence alignments and robust coevolution-inferring statistics can produce structural information from sequence alone. This work characterizes the relationship between coevo- lution statistics and sequence alignments and highlights the implicit assumptions and caveats associated with coevolutionary inference. An investigation of sequence alignment quality and coevolutionary-inference methods revealed that such methods are very sensitive to the system- atic misalignments discovered in public databases. However, repairing the misalignments in such alignments restores the predictive power of coevolution statistics. To overcome the sen- sitivity to misalignments, two novel coevolution-inferring statistics were developed that show increased contact prediction accuracy, especially in alignments that contain misalignments. These new statistics were developed into a suite of coevolution tools, the MIpToolset. Because systematic misalignments produce a distinctive pattern when analyzed by coevolution-inferring statistics, a new method for detecting systematic misalignments was created to exploit this phe- nomenon. This new method called “local covariation” was used to analyze publicly-available multiple sequence alignment databases. Local covariation detected putative misalignments in a database designed to benchmark sequence alignment software accuracy. Local covariation was incorporated into a new software tool, LoCo, which displays regions of potential misalignment during alignment editing assists in their correction. This work represents advances in multiple sequence alignment creation and coevolutionary inference. ii Keywords: Coevolution, multiple sequence alignment, protein structure prediction, local covariation, protein family curation, Mutual Information iii Co-Authorship Statement Chapter 1 was written with helpful editorial comments from GBG. Chapter 2 is presently in-press to be published in the journal Methods in Molecular Biol- ogy as “Bioinformatics Identification of Coevolving Residues” with my supervisor Gregory B. Gloor (GBG). The workflow was designed with contributions from GBG. GBG provided editorial comments. Chapter 3 is publicly available on arxiv.org as “The MIp Toolset: an efficient algorithm for calculating Mutual Information in protein alignments” co-authored by GBG, who provided helpful editorial comments. Chapter 4 is under review at the journal Bioinformatics as “Gambling on Gaps: An Expla- nation of the Alignment-Coevolution Relationship”. It is co-authored by GBG who provided helpful editorial comments and assisted with experimental design for Figure 3. Chapter 5 is published in the journal PLoS One as “Identifying and Seeing beyond Multiple Sequence Alignment Errors Using Intro-Molecular Protein Covariation”. I am the first author of this manuscript with Lindi M. Wahl (LMW), Andrew D. Fernandes (ADF), and GBG as co- authors. The experiment that is featured in Figure 5.8 was designed and conducted by LMW. ADF provided a more formal reformulation of an earlier version of the ∆Zp statistic. The new coevolution statistics were created with GBG. Software that was used to conduct experiments for this chapter was co-written by GBG. The manuscript was written predominantly by me with GBG. Supplementary figures and data for Chapter 5 is available at plosone.org. Chapter 6 is published in the journal PLoS One as “Protein Sequence Alignment Analysis by Local Covariation: Coevolution Statistics Detect Benchmark Alignment Errors”. It is co- authored by GBG who provided helpful editorial commentary. Chapter 7 was written with helpful editorial comments from GBG. iv Acknowlegements Foremost, I would like to thank my supervisor, Dr. Gregory Gloor for his guidance, support, patience, and help throughout my studies. I would not be where I am today if it was not for his generous investment of time and effort. I learned a great deal about what it means to be a Professor from him. Being his teaching assistant was an eye opening experience for how to provide an ideal learning environment for students. I also learned the importance scientific and intellectual integrity. I truly appreciate the time I had to learn from him and was a part of his group. I also wish to thank my thesis advisors Dr. Stan Dunn and Dr. Chris Brandl. During my time at Western I had the pleasure of taking classes with both of these esteemed professors and learned a great deal from both. Notably, a fateful conversation with Dr. Dunn after a 3rd-year biochemistry class led me to the topic of this PhD thesis and Dr. Gloor’s lab. I appreciate them taking the time to contribute so profoundly to my graduate education. Also I must thank members of the Gloor lab, past and present. Especially (future Drs.) Jean Macklaim, Tom McMurrough, and Dr. Andrew Fernandes, whose hard work and friendship are very much appreciated. As well, thanks to Dr. Ardeshir Goliaei from Dr. Dunn’s lab. It has been a pleasure working with all of you. Thanks also to Dr. David Edgell for his mentoring, guidance and support, Dr. Lindi Wahl for her thought provoking insights, and Dr. Mark Daley for his motivating conversations and advice. I deeply appreciate the time that these mentors invested in me. I would also like to thank Dr. Heather Gordon at Brock University for my first exposure to Bioinformatics in high school that started me on a path to this PhD thesis. I want to thank my family, especially my parents for their love and support, and for fostering in me a love of science. And to my friends, who endured my attempts to explain coevolution at social gatherings. Most importantly, I want to thank my loving wife, Jackie. Her love, support, and sense of v teamwork is what kept me going. I couldn’t ask for a better, more patient, more understanding, or more supportive partner than her. I have been supported by an NSERC PGS-M and an NSERC CGS-D scholarships, as well as from The University of Western Ontario, and an NSERC Discovery Grant to Gregory Gloor. vi Contents Abstract ii Co-Authorship Statement iv Acknowlegements v List of Figures xiii List of Appendices xv List of Abbreviations, Symbols, and Nomenclature xvi 1 General Introduction 1 1.1 Open Problems in Bioinformatics . 1 1.2 Protein Molecular Evolution . 2 1.2.1 Homology . 2 1.2.2 Orthology and paralogy . 3 1.2.3 Protein families and superfamilies . 5 1.3 Multiple Sequence Alignments . 5 1.3.1 Sequence alignment overview . 5 1.3.2 Challenges in multiple sequence alignment evaluation . 6 1.3.3 Alignment scoring matrices . 8 1.3.4 Local and global alignment . 11 1.3.5 Global multiple sequence alignment methods . 12 vii 1.3.6 Structure-guided multiple sequence alignment . 13 1.3.7 The role of gaps in an alignment . 16 1.3.8 Assessing alignment quality . 18 1.4 Intra-Molecular Protein Coevolution . 19 1.4.1 Positional non-independence . 19 1.4.2 Coevolution . 21 1.4.3 Coevolution inference statistics . 22 1.4.4 Mutual Information-based covariation . 23 1.5 Summary . 28 2 Methods 41 2.1 Introduction . 42 2.2 Materials . 44 2.2.1 Computer and general-purpose software . 44 2.2.2 Sequence collection tools and databases . 45 2.2.3 Sequence alignment tools . 45 2.2.4 Alignment curation tools . 46 2.2.5 Coevolution analysis . 46 2.3 Methods . 46 2.3.1 Building and installing bioinformatics tools . 46 2.3.2 Collecting protein sequences and structures . 47 2.3.3 Building a structure-guided sequence alignment (Alternately, use 2.3.4 for a sequence-only alignment if no structure is available.) . 48 2.3.4 Creating a sequence-only alignment (Alternately, use 2.3.3 for a structure- guided alignment.) . 52 2.3.5 Curating and validating an alignment . 53 2.3.6 Coevolution analysis . 55 2.3.7 Visualizing and interpreting results . 56 viii 2.4 Notes . 57 3 The MIp Toolset Algorithm 72 3.1 Abstract . 72 3.2 Introduction . 73 3.3 Algorithm . 74 3.3.1 Mutual Information . 74 3.3.2 Storage of sparse matrix in linked list . 75 3.3.3 Direct access to linked list improves speed .
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