Subtype-specific structural constraints in the evolution of influenza A virus hemagglutinin genes

Computer Systems, Imagery and Medi

RNA structural constraints in the evolution of the influenza A virus genome NP segment

Supramolecular & Biomaterials Chemistr

A compensatory mutagenesis study of a conserved hairpin in the M gene segment of influenza A virus shows its role in virus replication

RNA structures are increasingly recognized to be of importance during influenza A virus replication. Here, we investigated a predicted conserved hairpin in the M gene segment (nt 967-994) within the region of the vRNA 5′ packaging signal. The existence of this RNA structure and its possible role in virus replication was investigated using a compensatory mutagenesis approach. Mutations were introduced in the hairpin stem, based on natural variation. Virus replication properties were studied for the mutant viruses with disrupted and restored RNA structures. Viruses with structure-disrupting mutations had lower virus titers and a significantly reduced median plaque size when compared with the wild-type (WT) virus, while viruses with structure restoring-mutations replicated comparable to WT. Moreover, virus replication was also reduced when mutations were introduced in the hairpin loop, suggesting its involvement in RNA interactions. Northern blot and FACS experiments were performed to study differences in RNA levels as well as production of M1 and M2 proteins, expressed via alternative splicing. Stem-disruptive mutants caused lower vRNA and M2 mRNA levels and reduced M2 protein production at early time-points. When the RNA structure was restored, vRNA, M2 mRNA and M2 protein levels were increased, demonstrating a compensatory effect. Thus, this study provides evidence for functional importance of the predicted M RNA structure and suggests its role in splicing regulation

Functionally Relevant Macromolecular Interactions of Disordered Proteins

Disordered proteins are relatively recent newcomers in protein science. They were first described in detail by Wright and Dyson, in their J. Mol. Biol. paper in 1999. First, it was generally thought for more than a decade that disordered proteins or disordered parts of proteins have different amino acid compositions than folded proteins, and various prediction methods were developed based on this principle. These methods were suitable for distinguishing between the disordered (unstructured) and structured proteins known at that time. In addition, they could predict the site where a folded protein binds to the disordered part of a protein, shaping the latter into a well-defined 3D structure. Recently, however, evidence has emerged for a new type of disordered protein family whose members can undergo coupled folding and binding without the involvement of any folded proteins. Instead, they interact with each other, stabilizing their structure via “mutual synergistic folding” and, surprisingly, they exhibit the same residue composition as the folded protein. Increasingly more examples have been found where disordered proteins interact with non-protein macromolecules, adding to the already large variety of protein–protein interactions. There is also a very new phenomenon when proteins are involved in phase separation, which can represent a weak but functionally important macromolecular interaction. These phenomena are presented and discussed in the chapters of this book

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Understanding and Exploiting Protein Allostery and Dynamics Using Molecular Simulations

Protein conformational landscapes contain much of the functionally relevant information that is useful for understanding biological processes at the chemical scale. Understanding and mapping out these conformational landscapescan provide valuable insight into protein behaviors and biological phenomena, and has relevance to the process of therapeutic design. While structural biology methods have been transformative in studying protein dynamics, they are limited by technicallimitations and have inherent resolution limits. Molecular dynamics (MD) simulations are a powerful tool for exploring conformational landscapes, and provide atomic-scale information that is useful in understanding protein behaviors. With recent advances in generating datasets of large timescale simulations (using Folding@home) and powerful methods to interpret conformational landscapes such as Markov State Models (MSMs), it is now possible to study complex biological phenomena and long-timescale processes. However, inferring communication between residues across long distances, referred to as allosteric communication, remains a challenge. Allostery is a ubiquitious biological phenomena by which two distant regions of a protein are coupled to one anotherover large distances. Allosteric coupling is the mechanism through which events in one region (such as ligand binding) alter the conformation or dynamics of another region (ie. large conformational domain motions). For example, allostery plays a critical role in cellular signaling, such as in the transfer of a signal from outside the cell to cytosolic proteins for generating a cellular response. While many methods have made tremendous progress in inferring and measuring allosteric communication usingstructures or molecular simulations, they rely on a structural view of allostery and do not account for the role of conformational entropy. Furthermore, it remains a challenge to interpret allosteric coupling in large, complex biomolecules relevant to physiology and disease. In this thesis, I present a method to measure the Correlation of All Rotameric and Dynamical States (CARDS) whichis used to construct and interpret allosteric networks in biological systems. CARDS allows us to infer allostery both via concerted changes in protein structure and in correlated changes in conformational entropy (dynamic allostery). CARDS does so by parsing trajectories into dynamical states which reflect whether a residue is locally ordered (ie. stable in a single rotameric basin) or disordered (ie. rapidly hopping between rotamers). Here I explain the CARDS methodology (chapter 2) and demonstrate applications to a variety of disease-relevantsystems. In particular, I apply CARDS and other sophisticated computational methods to understand the process of G protein activation (chapter 3), a protein whose mutations are linked to cancers such as uveal melanoma. I further demonstrate the utility of CARDS in the study a potentially druggable pocket in the ebolavirus protein VP35 (chapter 4). The analyses and models constructed in this work are supported by experimental testing. Lastly, I demonstrate how integrating MD with experiments, sometimes with the help of citizen-scientists around the world, can provide unique insight into biological systems and identify potentially useful targets. In particular, I highlight our recent effort converting Folding@home into an exascale computer platform to hunt for potentially druggable pockets in the proteome of SARS-CoV-2 (chapter 7) (the cause of the COVID19 pandemic)

Exploring the phylodynamics, genetic reassortment and RNA secondary structure formation patterns of orthomyxoviruses by comparative sequence analysis

RNA viruses are among the most virulent microorganisms that threaten the health of humans and livestock. Among the most socio-economically important of the known RNA viruses are those found in the family Orthomyxovirus. In this era of rapid low-cost genome sequencing and advancements in computational biology techniques, many previously difficult research questions relating to the molecular epidemiology and evolutionary dynamics of these viruses can now be answered with ease. Using sequence data together with associated meta-data, in chapter two of this dissertation I tested the hypothesis that the Influenza A/H1N1 2009 pandemic virus was introduced multiple times into Africa, and subsequently dispersed heterogeneously across the continent. I further tested to what degree factors such as road distances and air travel distances impacted the observed pattern of spread of this virus in Africa using a generalised linear modelbased approach. The results suggested that their were multiple simultaneous introductions of 2009 pandemic A/H1N1 into Africa, and geographical distance and human mobility through air travel played an important role towards dissemination. In chapter three, I set out to test two hypotheses: (1) that there is no difference in the frequency of reassortments among the segments that constitute influenza virus genomes; and (2) that there is epochal temporal reassortment among influenza viruses and that all geographical regions are equally likely sources of epidemiologically important influenza virus reassortant lineages. The findings suggested that surface segments are more frequently exchanges than internal genes and that North America/Asia, Oceania, and Asia could be the most likely source locations for reassortant Influenza A, B and C virus lineages respectively. In chapter four of this thesis, I explored the formation of RNA secondary structures within the genomes of orthomyxoviruses belonging to five genera: Influenza A, B and C, Infectious Salmon Anaemia Virus and Thogotovirus using in silico RNA folding predictions and additional molecular evolution and phylogenetic tests to show that structured regions may be biologically functional. The presence of some conserved structures across the five genera is likely a reflection of the biological importance of these structures, warranting further investigation regarding their role in the evolution and possible development of antiviral resistance. The studies herein demonstrate that pathogen genomics-based analytical approaches are useful both for understanding the mechanisms that drive the evolution and spread of rapidly evolving viral pathogens such as orthomyxoviruses, and for illuminating how these approaches could be leveraged to improve the management of these pathogens

Structure-based mechanism of proton transport through the influenza A M2 protein

Influenza A/M2 is a minimalistic integral membrane protein that mediates proton transport across the viral membrane and is of interest as an antiviral drug target. This protein has been extensively studied by electrophysiologists, biophysicists, structural biologists, and medicinal chemists, but a synthesis and unified extension of the knowledge bases from these fields has not been undertaken. The principal aim of this thesis is to develop a comprehensive, quantitative, structure-based mechanism that accounts for the key functional and biophysical properties of A/M2. To demonstrate the electrophysiological equivalence of the protein’s transmembrane domain (M2TM) and full length A/M2, proteoliposome flux experiments are conducted as a first step. Next, high-resolution crystals of an M2TM variant are obtained; the resulting structure and computational simulations provide a structural basis for the unusually high degree of charge stabilization inside the M2TM helical bundle, suggest a previously unseen mechanism used by Nature to stabilize charge in a membrane, and shed light on the likely pH-dependent structural transitions that the protein undergoes. Fluorescence quenching and EPR spectroscopy experiments confirm that M2 reconstituted in bilayers undergoes pH-driven changes in its conformational equilibrium that are consistent with available structures and governed by previously reported pKa values. Mechanistic models of this process are constructed and successfully fit to functional data. The fitting results show that proton transport and rectification are mediated by conformational transitions between structural ensembles with different proton affinities. Finally, the functional implications of targeted changes to the geometric and electronic properties of the key His 37 sidechain are observed, indicating that the shape of the His 37 imidazole rings is exquisitely tuned to mediate proton transport

Ecological and Evolutionary Dynamics of Influenza Viruses.

Host-pathogen interactions, especially those involving RNA viruses and bacteria, are often characterized by a convergence of ecological and evolutionary time scales. This work explores how such convergence affects the diversity of a fast-evolving RNA virus, influenza, in different host populations. The first study evaluates molecular evidence for a theory of H3N2 dynamics in humans. There is support for episodically strong, continuous positive selection on the hemagglutinin protein, and previously described punctuated changes in antigenicity are not driven by the addition of glycosylation sites. The neuraminidase, nucleoprotein, and matrix 2 proteins also show evidence of positive selection. The second study analyzes time series of serologically confirmed cases of H3N2, H1N1, and influenza B in patients in present-day St. Petersburg, Russia, from 1969 to 1991 to determine whether there is cross-immunity between heterologous strains. Results suggest a role for cross-immunity, but further investigation is necessary. Differences in intrinsic growth rates and rates of antigenic evolution might explain age-related patterns in incidence by virus type and subtype. The third study investigates the effects of heterogeneity in hosts’ immune responses on the outcome of strain competition. When immunodominance is skewed toward a single epitope, coexistence inevitably results. When multiple epitopes can be immunodominant, coexistence, limit cycling, chaotic dynamics, and competitive exclusion can occur. Increasing the diversity and breadth of host responses increases the range of cyclic, chaotic, and exclusive dynamics. The last study considers how host ecology affects the long term evolution of influenza’s host range, assuming a tradeoff in the virus’s preference for certain forms of host sialic acid receptor. A common outcome is the coexistence of specialists, and this outcome is more sensitive to interspecific transmission rates and host population densities than the strength of the tradeoff. Finally, I map three areas of future inquiry: the ability of spatial dynamics and constant antigenic evolution alone to restrict influenza virus diversity, implications of antibody affinity versus neutralization ability for vaccine development, and long-term strategies to manage influenza virus evolution. These studies show that a phylodynamic perspective will be invaluable in developing better predictive models of influenza.Ph.D.Ecology and Evolutionary BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64670/1/cobey_1.pd