The Roles of Gene Duplication, Gene Conversion and Positive Selection in Rodent \u3ci\u3eEsp\u3c/i\u3e and \u3ci\u3eMup\u3c/i\u3e Pheromone Gene Families with Comparison to the \u3ci\u3eAbp\u3c/i\u3e Family

Three proteinaceous pheromone families, the androgen-binding proteins (ABPs), the exocrine-gland secreting peptides (ESPs) and the major urinary proteins (MUPs) are encoded by large gene families in the genomes of Mus musculus and Rattus norvegicus. We studied the evolutionary histories of the Mup and Esp genes and compared them with what is known about the Abp genes. Apparently gene conversion has played little if any role in the expansion of the mouse Class A and Class B Mup genes and pseudogenes, and the rat Mups. By contrast, we found evidence of extensive gene conversion in many Esp genes although not in all of them. Our studies of selection identified at least two amino acid sites in β-sheets as having evolved under positive selection in the mouse Class A and Class B MUPs and in rat MUPs. We show that selection may have acted on the ESPs by determining Ka/Ks for Exon 3 sequences with and without the converted sequence segment. While it appears that purifying selection acted on the ESP signal peptides, the secreted portions of the ESPs probably have undergone much more rapid evolution. When the inner gene converted fragment sequences were removed, eleven Esp paralogs were present in two or more pairs with Ka/Ks \u3e1.0 and thus we propose that positive selection is detectable by this means in at least some mouse Esp paralogs. We compare and contrast the evolutionary histories of all three mouse pheromone gene families in light of their proposed functions in mouse communication

Toward a Molecular Mechanism of Phase Separation in Disordered Elastin-Like Proteins

Since the last decade, an increasing number of proteins have been shown to be capable of undergoing reversible liquid-liquid phase separation (LLPS) in response to an external stimulus, and the resulting protein-rich phase (coacervate) is considered as one of the main components of membrane-less organelles. Most of these proteins are intrinsically disordered proteins (IDPs) or contain intrinsically disordered regions. More importantly, LLPS often plays an important role in cellular signaling and development of cells and tissues. However, the molecular mechanisms underlying LLPS of proteins remain poorly understood. Elastin-like proteins (ELPs), a class of IDPs derived from the hydrophobic domains of tropoelastin, are known to undergo LLPS reversibly above a concentration-dependent transition temperature (TT), allowing ELPs to be a promising thermo-responsive drug delivery vector for treating cancer. Previous studies have suggested that, as temperature increases, ELPs experience an increased propensity for type II beta-turns. Our hypothesis is that the interaction is initiated at the beta-turn positions. In this work, integrative approaches including experimental and computational methods were employed to study the early stages of ELP phase separation. Using nuclear magnetic resonance spectroscopy (NMR), and paramagnetic relaxation enhancement (PRE), we have characterized structural properties of self-association in several ELPs. NMR chemical shifts suggest that ELPs adopt a beta-turn conformation even at temperatures below the TT. The intermolecular PRE reveals there is a stronger interaction between the higher beta-turn propensity regions. Building on this observation, a series of structural ensembles were generated for ELP incorporating differing amounts of beta-turn bias, from 1% to 90%. To mimic the early stages of the phase change, two monomers were paired, assuming preferential interaction at beta-turn regions. Following dimerization, the ensemble-averaged hydrodynamic properties were calculated for each degree of beta-turn bias, and results were compared with analytical ultracentrifugation (AUC) experiments at various temperatures. The ensemble calculation reveals that accessible surface area changes dramatically as oligomers are formed from monomers with a high beta-turn content. Together, these observations suggest a model where ELP self-association is initiated at beta-turn positions, where the driving force of phase separation is solvent exclusion due to changes in the hydrophobic accessible surface area

Predicting the triple beta-spiral fold from primary sequence data

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and, (M.B.A.)--Massachusetts Institute of Technology Sloan School of Management, 2004.Includes bibliographical references (leaves 118-125).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.The Triple β-Spiral is a novel protein structure that plays a role in viral attachment and pathogenesis. At present, there are two Triple β-Spiral structures with solved crystallographic coordinates - one from Adenovirus and the other from Reovirus. There is evidence that the fold also occurs in Bacteriophage SF6. In this thesis, we present a computational analysis of the Triple β-Spiral fold. Our goal is to discover new instances of the fold in protein sequence databases. In Chapter 2, we present a series of sequence-based methods for the discovery of the fold. The final method in this Chapter is an iterative profile-based search that outperforms existing sequence-based algorithms. In Chapter 3, we introduce specific knowledge of the protein's structure into our prediction algorithms. Although this additional information does not improve the profile-based methods in Chapter 2, it does provide insight into the important forces that drive the Triple β-Spiral folding process. In Chapter 4, we employ logistic regression to integrate the score information from the previous Chapter into a single unified framework. This framework outperforms all previous methods in cross-validation tests. We do not discover a great number of additional instances of the Triple β-Spiral fold outside of the Adenovirus and Reovirus families. The results of our profile based templates and score integration tools, however, suggest that these methods might well succeed for other protein structures.by Eben Louis Scanlon.M.B.A.S.M

High Performance Computing Techniques to Better Understand Protein Conformational Space

This thesis presents an amalgamation of high performance computing techniques to get better insight into protein molecular dynamics. Key aspects of protein function and dynamics can be learned from their conformational space. Datasets that represent the complex nuances of a protein molecule are high dimensional. Efficient dimensionality reduction becomes indispensable for the analysis of such exorbitant datasets. Dimensionality reduction forms a formidable portion of this work and its application has been explored for other datasets as well. It begins with the parallelization of a known non-liner feature reduction algorithm called Isomap. The code for the algorithm was re-written in C with portions of it parallelized using OpenMP. Next, a novel data instance reduction method was devised which evaluates the information content offered by each data point, which ultimately helps in truncation of the dataset with much fewer data points to evaluate. Once a framework has been established to reduce the number of variables representing a dataset, the work is extended to explore algebraic topology techniques to extract meaningful information from these datasets. This step is the one that helps in sampling the conformations of interest of a protein molecule. The method employs the notion of hierarchical clustering to identify classes within a molecule, thereafter, algebraic topology is used to analyze these classes. Finally, the work is concluded by presenting an approach to solve the open problem of protein folding. A Monte-Carlo based tree search algorithm is put forth to simulate the pathway that a certain protein conformation undertakes to reach another conformation. The dissertation, in its entirety, offers solutions to a few problems that hinder the progress of solution for the vast problem of understanding protein dynamics. The motion of a protein molecule is guided by changes in its energy profile. In this course the molecule gradually slips from one energy class to another. Structurally, this switch is transient spanning over milliseconds or less and hence is difficult to be captured solely by the work in wet laboratories

Integrating protein structural information

Dissertação apresentada para obtenção de Grau de Doutor em Bioquímica,Bioquímica Estrutural, pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaThe central theme of this work is the application of constraint programming and other artificial intelligence techniques to protein structure problems, with the goal of better combining experimental data with structure prediction methods. Part one of the dissertation introduces the main subjects of protein structure and constraint programming, summarises the state of the art in the modelling of protein structures and complexes, sets the context for the techniques described later on, and outlines the main points of the thesis: the integration of experimental data in modelling. The first chapter, Protein Structure, introduces the reader to the basic notions of amino acid structure, protein chains, and protein folding and interaction. These are important concepts to understand the work described in parts two and three. Chapter two, Protein Modelling, gives a brief overview of experimental and theoretical techniques to model protein structures. The information in this chapter provides the context of the investigations described in parts two and three, but is not essential to understanding the methods developed. Chapter three, Constraint Programming, outlines the main concepts of this programming technique. Understanding variable modelling, the notions of consistency and propagation, and search methods should greatly help the reader interested in the details of the algorithms, as described in part two of this book. The fourth chapter, Integrating Structural Information, is a summary of the thesis proposed here. This chapter is an overview of the objectives of this work, and gives an idea of how the algorithms developed here could help in modelling protein structures. The main goal is to provide a flexible and continuously evolving framework for the integration of structural information from a diversity of experimental techniques and theoretical predictions. Part two describes the algorithms developed, which make up the main original contribution of this work. This part is aimed especially at developers interested in the details of the algorithms, in replicating the results, in improving the method or in integrating them in other applications. Biochemical aspects are dealt with briefly and as necessary, and the emphasis is on the algorithms and the code

Comparison of Methods Used for Aligning Protein Sequences

Comparing protein sequences is an essential procedure that has many applications in the field of bioinformatics. The recent advances in computational capabilities and algorithm design, simplified the comparison procedure of protein sequences from several databases. Various algorithms have emerged using state of the art approaches to match protein sequences based on structural and functional properties of the amino acids. The matching involves structural alignment, and this alignment may be global; comprising of the whole length of the protein, or local; comprising of the sub-sequences of the proteins. Families of related proteins are found by clustering sequence alignments. The frequency distributions of the amino acids within these different clusters define the sequence profile. The best alignment algorithm uses these profiles. In this thesis, we have studied different profile alignment algorithms where the cost function for comparing two profiles is changed. These are compared to the FFAS3 (Fold and Function Assignment) algorithm

The Dam1 ring binds to the E-hook of tubulin and diffuses along the microtubule.

There has been much effort in recent years aimed at understanding the molecular mechanism by which the Dam1 kinetochore complex is able to couple microtubule depolymerization to poleward movement. Both a biased diffusion and a forced walk model have been proposed, and several key functional aspects of Dam1-microtubule binding are disputed. Here, we investigate the elements involved in tubulin-Dam1 complex interactions and directly visualize Dam1 rings on microtubules in order to infer their dynamic behavior on the microtubule lattice and its likely relevance at the kinetochore. We find that the Dam1 complex has a preference for native tubulin over tubulin that is lacking its acidic C-terminal tail. Statistical mechanical analysis of images of Dam1 rings on microtubules, applied to both the distance between rings and the tilt angle of the rings with respect to the microtubule axis, supports a diffusive ring model. We also present a cryo-EM reconstruction of the Dam1 ring, likely the relevant assembly form of the complex for energy coupling during microtubule depolymerization in budding yeast. The present studies constitute a significant step forward by linking structural and biochemical observations toward a comprehensive understanding of the Dam1 complex

Magnetism, FeS colloids, and Origins of Life

A number of features of living systems: reversible interactions and weak bonds underlying motor-dynamics; gel-sol transitions; cellular connected fractal organization; asymmetry in interactions and organization; quantum coherent phenomena; to name some, can have a natural accounting via $physical$ interactions, which we therefore seek to incorporate by expanding the horizons of `chemistry-only' approaches to the origins of life. It is suggested that the magnetic 'face' of the minerals from the inorganic world, recognized to have played a pivotal role in initiating Life, may throw light on some of these issues. A magnetic environment in the form of rocks in the Hadean Ocean could have enabled the accretion and therefore an ordered confinement of super-paramagnetic colloids within a structured phase. A moderate H-field can help magnetic nano-particles to not only overcome thermal fluctuations but also harness them. Such controlled dynamics brings in the possibility of accessing quantum effects, which together with frustrations in magnetic ordering and hysteresis (a natural mechanism for a primitive memory) could throw light on the birth of biological information which, as Abel argues, requires a combination of order and complexity. This scenario gains strength from observations of scale-free framboidal forms of the greigite mineral, with a magnetic basis of assembly. And greigite's metabolic potential plays a key role in the mound scenario of Russell and coworkers-an expansion of which is suggested for including magnetism.Comment: 42 pages, 5 figures, to be published in A.R. Memorial volume, Ed Krishnaswami Alladi, Springer 201